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ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA devices.
请问这个错误怎么解决？
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Loaded cached embeddings from file.
Checking if the server is listening on port 8890...
Server not ready, waiting 4 seconds...
Traceback (most recent call last):
File "D:\LivePortrait-Windows-v2…
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Half precision floats are commonly used in rendering for numbers that don't need the full precision of 32 bit floats. (example: terrain heights, look up tables, simple meshes). There is no way to do t…
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There are a number of one off issues from people complaining about some half precision operator not having enough precision, and us fixing it by increasing the internal precision in the operator. For …
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I get a very long and complicated error when I do:
```julia
using Zygote: gradient
import Metal
using LinearAlgebra: norm
gradient(p -> norm(tanh.(p)), Metal.rand(Float16, 10))[1]
```
This…
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While comparing APFloat against [berkeley-softfloat-3e](https://github.com/ucb-bar/berkeley-softfloat-3) I found a discrepancy in `fusedMultiplyAdd` in a particular corner-case:
```cpp
#include
…
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Either produces nans, or doesn't train at all, all the sample images are the same.
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May I ask if you train the five subtasks (RNNs) separately during the training process, or if they are mixed together for training? Do we need to use half-precision training method?
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```bash
# env CMAKE_BUILD_PARALLEL_LEVEL="" pip install . -v
```
Includes in the output:
```
/Users/user/Documents/AI/mlx/mlx/mlx/backend/accelerate/matmul.cpp:109:9: warning: 'BNNSLayerParame…
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### 🐛 Describe the bug
The way it's written right now, it only tests float32 and float64, even though accelerator (and sometimes CPU) implementation exists for half precision types
### Versions
CI
…