[Issue]: RuntimeError with ATOM docker of rocm/atom:rocm7.2.1-ubuntu24.04-pytorch2.9.1-atom0.1.2 for ISL/OSL of 1k/1k and conc64 due to dtype mismatch of two matrices in one gemm kernel call.
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Description
Problem Description
RuntimeError with ATOM docker of rocm/atom:rocm7.2.1-ubuntu24.04-pytorch2.9.1-atom0.1.2 for ISL/OSL of 1k/1k and conc64 due to dtype mismatch of two matrices in one gemm kernel call.
Model: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
We observed the following RuntimeError with ATOM docker of rocm/atom:rocm7.2.1-ubuntu24.04-pytorch2.9.1-atom0.1.2 for ISL/OSL of 1k/1k and conc64 due to dtype mismatch of two matrices in one gemm kernel call.
Docker: rocm/atom:rocm7.2.1-ubuntu24.04-pytorch2.9.1-atom0.1.2
RuntimeError: expected mat1 and mat2 to have the same dtype, but got: c10::BFloat16 != c10::Float8_e4m3fn
How to reproduce this issue?
The link of the CI/CD run is here: Benchmark | OOB | smci355-ccs-aus-n10-25 | rocm/atom-dev:vllm-latest | Llama-3-1-8B-Instruct-FP8-KV | atom | warmup · AMD-MLPerf/workloads-inference@1153c26
Operating System
ubuntu24.04
CPU
AMD EPYC 9575F 64-Core Processor x 2
GPU
AMD Instinct MI355x
ROCm Version
ROCm7.2.1
ROCm Component
No response
Steps to Reproduce
How to reproduce this issue?
The link of the CI/CD run is here: Benchmark | OOB | smci355-ccs-aus-n10-25 | rocm/atom-dev:vllm-latest | Llama-3-1-8B-Instruct-FP8-KV | atom | warmup · AMD-MLPerf/workloads-inference@1153c26
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
No response
Additional Information
No response
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First steps
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Research direction
Start with the linked CI/CD run and the rocm/atom:rocm7.2.1-ubuntu24.04-pytorch2.9.1-atom0.1.2 environment, using the amd/Llama-3.1-8B-Instruct-FP8-KV model with ISL/OSL 1k/1k and concurrency 64. Trace the GEMM kernel call that reports c10::BFloat16 versus c10::Float8_e4m3fn, and consider the issue complete when the workload runs without the dtype mismatch.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- docker, python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100