Add FP8 support for the ONNX backend#4072
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andrey-churkin wants to merge 2 commits into
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daniil-lyakhov
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No major comments, please add some tests
| get_weight_quantization_axis(node, target_point.port_id) if target_point.is_weight_target_point() else 1 | ||
| ) | ||
| onnx_parameters = convert_fc_params_to_onnx_params(parameters, axis) | ||
| nncf_input_node_next_nodes = ONNXMinMaxAlgoBackend._get_input_edges_mapping(nncf_graph) |
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Potential point for an optimization in future. Maybe we could add a comment to highligt it
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| if weight_dtype == onnx.TensorProto.FLOAT8E4M3FN: | ||
| np_dtype = helper.tensor_dtype_to_np_dtype(weight_dtype) | ||
| vals = onnx.numpy_helper.saturate_cast(np.asarray(quantized_weights), np_dtype).flatten() | ||
| else: | ||
| vals = quantized_weights |
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Two similar code blocks, maybe worth a private method?
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Changes
nncf.CompressWeightsMode.FP8_E4M3mode in thenncf.compress_weights()method for the ONNX backend.nncf.QuantizationMode.FP8_E4M3andnncf.QuantizationMode.FP8_E5M2modes in thenncf.quantize()method for the ONNX backend.Reason for changes
Add support for FP8 quantization and weight compression in the ONNX backend.
Related tickets
Tests
TBD
Weight compression - success