Why force_fp32 is set to true for conv weight and biases on XNNPack backend
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Description
Is there a specific reason force_fp32 set to true for convolution weight and biases for the XNNPack backend? I would like to use fp16 operations but default does not allow me.
self.define_tensor(
kernel_node,
xnn_graph,
vals_to_ids,
...,
force_fp32=True,
)
...
self.define_tensor(
get_input_node(node, 2),
xnn_graph,
vals_to_ids,
...,
quant_params=bias_quant_params,
force_fp32=True,
)
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Research direction
Review the XNNPack backend's define_tensor calls for convolution weights and biases, focusing on the force_fp32 and quant_params arguments shown in the issue. Trace the backend's supported weight and bias types, then check existing coverage for fp16 operations. Done means establishing whether fp16 can be enabled safely and documenting or testing the resulting behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- embedded-iot, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100