Assertion error in tutorial part4.1_HG_quantization

[DrBwts]

in tutorial part4.1_HG_quantization cell 11,

proxy = to_proxy_model(model)

The following error occurs,

AssertionError: Layer h_quantize: FixedPointQuantizer is not supported by hls4ml.

Anyone know why?

Full traceback,

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Cell In[11], line 1
----> 1 proxy = to_proxy_model(model)

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:368, in to_proxy_model(model, aggressive, accum_fp_max_offset, unary_lut_max_table_size)
    366 if accum_fp_max_offset is not None and accum_fp_max_offset < 0:
    367     warn('You are using a negative value for bias_accum_bits. Please make sure you know what you are doing.')
--> 368 proxy = convert_model(model, layer_xformer=ProxyLayerXFormer('WRAP' if aggressive else 'SAT').__call__)
    369 if unary_lut_max_table_size > 0:
    370     proxy = convert_model(proxy, layer_xformer=partial(xfr_to_unary_lut, max_table_size=unary_lut_max_table_size))

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:214, in convert_model(model, layer_xformer)
    212 if len(inputs) == 1:
    213     inputs = inputs[0]
--> 214 outs = apply_layer(model, inputs, layer_xformer=layer_xformer)
    215 return keras.Model(inputs, outs)

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/functools.py:889, in singledispatch.<locals>.wrapper(*args, **kw)
    885 if not args:
    886     raise TypeError(f'{funcname} requires at least '
    887                     '1 positional argument')
--> 889 return dispatch(args[0].__class__)(*args, **kw)

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:192, in _(layer, inp_tensors, namer, layer_xformer)
    190 satisfied = {node: tensor for node, tensor in zip(input_nodes, inp_tensors)}
    191 get_out_tensor = partial(_get_out_tensor, dependencies_dict, satisfied, namer=namer, layer_xformer=layer_xformer)
--> 192 outs = [get_out_tensor(output_node) for output_node in output_nodes]
    193 return outs[0] if len(outs) == 1 else outs

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:192, in <listcomp>(.0)
    190 satisfied = {node: tensor for node, tensor in zip(input_nodes, inp_tensors)}
    191 get_out_tensor = partial(_get_out_tensor, dependencies_dict, satisfied, namer=namer, layer_xformer=layer_xformer)
--> 192 outs = [get_out_tensor(output_node) for output_node in output_nodes]
    193 return outs[0] if len(outs) == 1 else outs

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:125, in _get_out_tensor(dependencies_dict, satisfied, node, namer, layer_xformer)
    123         inps.append(satisfied[req])
    124     else:
--> 125         inp = _get_out_tensor(dependencies_dict, satisfied, req, namer, layer_xformer)
    126         inps.append(inp)
    127 if len(inps) == 1:

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:125, in _get_out_tensor(dependencies_dict, satisfied, node, namer, layer_xformer)
    123         inps.append(satisfied[req])
    124     else:
--> 125         inp = _get_out_tensor(dependencies_dict, satisfied, req, namer, layer_xformer)
    126         inps.append(inp)
    127 if len(inps) == 1:

    [... skipping similar frames: _get_out_tensor at line 125 (1 times)]

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:125, in _get_out_tensor(dependencies_dict, satisfied, node, namer, layer_xformer)
    123         inps.append(satisfied[req])
    124     else:
--> 125         inp = _get_out_tensor(dependencies_dict, satisfied, req, namer, layer_xformer)
    126         inps.append(inp)
    127 if len(inps) == 1:

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:130, in _get_out_tensor(dependencies_dict, satisfied, node, namer, layer_xformer)
    127 if len(inps) == 1:
    128     inps = inps[0]
--> 130 satisfied[node] = apply_layer(layer, inps, namer=namer, layer_xformer=layer_xformer)
    131 return satisfied[node]

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/functools.py:889, in singledispatch.<locals>.wrapper(*args, **kw)
    885 if not args:
    886     raise TypeError(f'{funcname} requires at least '
    887                     '1 positional argument')
--> 889 return dispatch(args[0].__class__)(*args, **kw)

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:157, in apply_layer(layer, inp_tensors, namer, layer_xformer)
    155     assert n < 1024, f'layer_transformer does not converge for layer (name: {layer.name if layer is not None else None}).'
    156     layer = layer_xf
--> 157     layer_xf = layer_xformer(layer)
    158     n += 1
    160 if layer_xf is not None:

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/functools.py:926, in singledispatchmethod.__get__.<locals>._method(*args, **kwargs)
    924 def _method(*args, **kwargs):
    925     method = self.dispatcher.dispatch(args[0].__class__)
--> 926     return method.__get__(obj, cls)(*args, **kwargs)

File ~/anaconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/HGQ/proxy/convert.py:286, in ProxyLayerXFormer.__call__(self, layer)
    284     return layer
    285 cls_name = layer.__class__.__name__
--> 286 assert cls_name in layer_handlers, f'Layer {layer.name}: {cls_name} is not supported by hls4ml.'
    287 return layer

AssertionError: Layer h_quantize: FixedPointQuantizer is not supported by hls4ml.

[vloncar]

Only possible if your hls4ml installation is outdated

[DrBwts]

this might be the issue as I'm getting type errors in the CNN example notebook too - will update now & let you know

[DrBwts]

yep that was it