Install & Compatibility
Where this runs
tested against v0.5.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
py 3.10
✕ build_error
✓ 71.23s
py 3.11
✕ build_error
✓ 69.13s
py 3.12
✕ build_error
✓ 65.83s
py 3.13
✕ build_error
✓ 64.18s
py 3.9
✕ build_error
✕ build_error
4787MB installed
● package 4787MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
extend_path
✓ from fla import extend_path
✗ from fla.modules import FusedRMSNormGated
importlib
✓ from fla import importlib
✗ from fla.modules import FusedRMSNormGated
This quickstart demonstrates the use of a fused normalization module from `fla-core`. It initializes `FusedRMSNormGated` and applies it to a dummy tensor on a CUDA-enabled GPU. This illustrates how to integrate low-level, optimized operations provided by `fla-core`.
import torch
from fla.modules import FusedRMSNormGated
import os
# fla-core operations require a CUDA-enabled GPU
if not torch.cuda.is_available():
raise RuntimeError("CUDA not available. fla-core requires a CUDA-enabled GPU.")
device = torch.device("cuda")
# Define model parameters
hidden_size = 768
batch_size = 4
sequence_length = 512
# Initialize FusedRMSNormGated module from fla-core
norm_layer = FusedRMSNormGated(hidden_size).to(device)
# Create a dummy input tensor
input_tensor = torch.randn(batch_size, sequence_length, hidden_size, device=device, dtype=torch.float16)
# Perform a forward pass
output_tensor = norm_layer(input_tensor)
print(f"Input tensor shape: {input_tensor.shape}")
print(f"Output tensor shape: {output_tensor.shape}")
print("FusedRMSNormGated operation successful, demonstrating fla-core usage.")
Debug
Known issues
gotchaThe `fla-core` package is a minimal subset of `flash-linear-attention`. It contains core kernels and modules (e.g., in `fla.ops` and `fla.modules`) but lacks higher-level layers and models (e.g., `fla.layers`, `fla.models`). Attempting to import these high-level components with only `fla-core` installed will result in an `ImportError`.fixIf you need layers or models, install the full `flash-linear-attention` package: `pip install flash-linear-attention`.
affects: >=0.3.2
breakingThe input tensor format for some kernels switched from 'head-first' to 'sequence-first'. This change affects how dimensions are ordered for input tensors (e.g., `(batch, heads, sequence, dim)` might become `(batch, sequence, heads, dim)`).fixReview the documentation or source code for the specific kernel being used to ensure correct input tensor dimension ordering (e.g., (B, T, H, K) or (B, H, T, K)).
affects: Between v0.2.0 and v0.4.0 (specifically around v0.2.0-v0.3.0 releases).
gotchafla-core heavily relies on NVIDIA Triton for its optimized kernels. Specific Triton versions (>=3.0 or nightly) and correct backend installations are required, especially for AMD ROCm or Intel XPU GPUs.fixEnsure Triton is installed correctly (`pip install triton`) and that the appropriate backend is configured for your GPU hardware. Check Triton FAQs for platform-specific instructions.
affects: All versions
gotchaRequires Python 3.10 or higher. Running with older Python versions will lead to installation or runtime errors.fixUpgrade your Python environment to version 3.10 or newer.
affects: <=0.4.2
gotchaUsers on H100 GPUs may encounter 'MMA Assertion Error' or 'LinearLayout Assertion Error' due to known Triton issues.fixInstall the latest nightly version of Triton, which often includes fixes for such hardware-specific issues. Refer to Triton's GitHub issues or FAQs for the most current solutions.
affects: All versions, depending on Triton compatibility.
gotchaPyTorch version requirement: fla-core expects PyTorch >= 2.5. Older versions may cause compatibility issues or runtime errors.fixEnsure your PyTorch installation is version 2.5 or newer: `pip install 'torch>=2.5'`.
affects: All versions
Upgrade
Version history
0.5.0latest on PyPI · released Apr 21, 2026
Audit
Dependencies
torchrequiredCore deep learning framework for tensor operations and GPU acceleration.
tritonrequiredRequired for high-performance GPU kernels.
einopsrequiredFor flexible tensor manipulations.