Add hint on import failure

hls4ml/converters/__init__.py

@@ -1,6 +1,5 @@
 import importlib
 import os
-import warnings
 
 import yaml
 
@@ -10,33 +9,19 @@
 from hls4ml.converters.keras_to_hls import get_supported_keras_layers  # noqa: F401
 from hls4ml.converters.keras_to_hls import parse_keras_model  # noqa: F401
 from hls4ml.converters.keras_to_hls import keras_to_hls, register_keras_layer_handler
+from hls4ml.converters.onnx_to_hls import get_supported_onnx_layers  # noqa: F401
 from hls4ml.converters.onnx_to_hls import parse_onnx_model  # noqa: F401
+from hls4ml.converters.onnx_to_hls import onnx_to_hls, register_onnx_layer_handler
+from hls4ml.converters.pytorch_to_hls import (  # noqa: F401
+    get_supported_pytorch_layers,
+    pytorch_to_hls,
+    register_pytorch_layer_handler,
+)
 from hls4ml.model import ModelGraph
 from hls4ml.utils.config import create_config
+from hls4ml.utils.dependency import requires
 from hls4ml.utils.symbolic_utils import LUTFunction
 
-# ----------Make converters available if the libraries can be imported----------#
-try:
-    from hls4ml.converters.pytorch_to_hls import (  # noqa: F401
-        get_supported_pytorch_layers,
-        pytorch_to_hls,
-        register_pytorch_layer_handler,
-    )
-
-    __pytorch_enabled__ = True
-except ImportError:
-    warnings.warn("WARNING: Pytorch converter is not enabled!", stacklevel=1)
-    __pytorch_enabled__ = False
-
-try:
-    from hls4ml.converters.onnx_to_hls import get_supported_onnx_layers  # noqa: F401
-    from hls4ml.converters.onnx_to_hls import onnx_to_hls, register_onnx_layer_handler
-
-    __onnx_enabled__ = True
-except ImportError:
-    warnings.warn("WARNING: ONNX converter is not enabled!", stacklevel=1)
-    __onnx_enabled__ = False
-
 # ----------Layer handling register----------#
 model_types = ['keras', 'pytorch', 'onnx']
 
@@ -51,7 +36,7 @@
                 # and has 'handles' attribute
                 # and is defined in this module (i.e., not imported)
                 if callable(func) and hasattr(func, 'handles') and func.__module__ == lib.__name__:
-                    for layer in func.handles:
+                    for layer in func.handles:  # type: ignore
                         if model_type == 'keras':
                             register_keras_layer_handler(layer, func)
                         elif model_type == 'pytorch':
@@ -124,15 +109,9 @@ def convert_from_config(config):
 
     model = None
     if 'OnnxModel' in yamlConfig:
-        if __onnx_enabled__:
-            model = onnx_to_hls(yamlConfig)
-        else:
-            raise Exception("ONNX not found. Please install ONNX.")
+        model = onnx_to_hls(yamlConfig)
     elif 'PytorchModel' in yamlConfig:
-        if __pytorch_enabled__:
-            model = pytorch_to_hls(yamlConfig)
-        else:
-            raise Exception("PyTorch not found. Please install PyTorch.")
+        model = pytorch_to_hls(yamlConfig)
     else:
         model = keras_to_hls(yamlConfig)
 
@@ -174,6 +153,7 @@ def _check_model_config(model_config):
     return model_config
 
 
+@requires('_keras')
 def convert_from_keras_model(
     model,
     output_dir='my-hls-test',
@@ -237,6 +217,7 @@ def convert_from_keras_model(
     return keras_to_hls(config)
 
 
+@requires('_torch')
 def convert_from_pytorch_model(
     model,
     output_dir='my-hls-test',
@@ -308,6 +289,7 @@ def convert_from_pytorch_model(
     return pytorch_to_hls(config)
 
 
+@requires('onnx')
 def convert_from_onnx_model(
     model,
     output_dir='my-hls-test',
@@ -371,6 +353,7 @@ def convert_from_onnx_model(
     return onnx_to_hls(config)
 
 
+@requires('sr')
 def convert_from_symbolic_expression(
     expr,
     n_symbols=None,

hls4ml/converters/onnx_to_hls.py

@@ -1,4 +1,5 @@
 from hls4ml.model import ModelGraph
+from hls4ml.utils.dependency import requires
 
 
 # ----------------------Helpers---------------------
@@ -17,6 +18,7 @@ def replace_char_inconsitency(name):
     return name.replace('.', '_')
 
 
+@requires('onnx')
 def get_onnx_attribute(operation, name, default=None):
     from onnx import helper
 
@@ -73,6 +75,7 @@ def get_input_shape(graph, node):
     return rv
 
 
+@requires('onnx')
 def get_constant_value(graph, constant_name):
     tensor = next((x for x in graph.initializer if x.name == constant_name), None)
     from onnx import numpy_helper
@@ -258,6 +261,7 @@ def parse_onnx_model(onnx_model):
     return layer_list, input_layers, output_layers
 
 
+@requires('onnx')
 def onnx_to_hls(config):
     """Convert onnx model to hls model from configuration.
 

hls4ml/converters/pytorch_to_hls.py

@@ -1,4 +1,5 @@
 from hls4ml.model import ModelGraph
+from hls4ml.utils.dependency import requires
 
 
 class PyTorchModelReader:
@@ -22,6 +23,7 @@ def get_weights_data(self, layer_name, var_name):
         return data
 
 
+@requires('_torch')
 class PyTorchFileReader(PyTorchModelReader):  # Inherit get_weights_data method
     def __init__(self, config):
         import torch
@@ -103,6 +105,7 @@ def decorator(function):
 # ----------------------------------------------------------------
 
 
+@requires('_torch')
 def parse_pytorch_model(config, verbose=True):
     """Convert PyTorch model to hls4ml ModelGraph.
 
@@ -368,6 +371,7 @@ def parse_pytorch_model(config, verbose=True):
     return layer_list, input_layers
 
 
+@requires('_torch')
 def pytorch_to_hls(config):
     layer_list, input_layers = parse_pytorch_model(config)
     print('Creating HLS model')

hls4ml/model/__init__.py

@@ -1,8 +1 @@
 from hls4ml.model.graph import HLSConfig, ModelGraph  # noqa: F401
-
-try:
-    from hls4ml.model import profiling  # noqa: F401
-
-    __profiling_enabled__ = True
-except ImportError:
-    __profiling_enabled__ = False

hls4ml/model/quantizers.py

@@ -14,6 +14,7 @@
     SaturationMode,
     XnorPrecisionType,
 )
+from hls4ml.utils.dependency import requires
 
 
 class Quantizer:
@@ -84,6 +85,7 @@ class QKerasQuantizer(Quantizer):
         config (dict): Config of the QKeras quantizer to wrap.
     """
 
+    @requires('qkeras')
     def __init__(self, config):
         from qkeras.quantizers import get_quantizer
 
@@ -131,6 +133,7 @@ class QKerasBinaryQuantizer(Quantizer):
         config (dict): Config of the QKeras quantizer to wrap.
     """
 
+    @requires('qkeras')
     def __init__(self, config, xnor=False):
         from qkeras.quantizers import get_quantizer
 
@@ -155,6 +158,7 @@ class QKerasPO2Quantizer(Quantizer):
         config (dict): Config of the QKeras quantizer to wrap.
     """
 
+    @requires('qkeras')
     def __init__(self, config):
         from qkeras.quantizers import get_quantizer
 

hls4ml/report/quartus_report.py

@@ -2,6 +2,8 @@
 import webbrowser
 from ast import literal_eval
 
+from hls4ml.utils.dependency import requires
+
 
 def parse_quartus_report(hls_dir, write_to_file=True):
     '''
@@ -39,6 +41,7 @@ def parse_quartus_report(hls_dir, write_to_file=True):
     return results
 
 
+@requires('quantus-report')
 def read_quartus_report(hls_dir, open_browser=False):
     '''
     Parse and print the Quartus report to print the report. Optionally open a browser.
@@ -89,6 +92,7 @@ def _find_project_dir(hls_dir):
     return top_func_name + '-fpga.prj'
 
 
+@requires('quantus-report')
 def read_js_object(js_script):
     '''
     Reads the JavaScript file and return a dictionary of variables definded in the script.

hls4ml/utils/config.py

@@ -1,6 +1,7 @@
 import json
 
 import hls4ml
+from hls4ml.utils.dependency import requires
 
 
 def create_config(output_dir='my-hls-test', project_name='myproject', backend='Vivado', version='1.0.0', **kwargs):
@@ -44,6 +45,7 @@ def create_config(output_dir='my-hls-test', project_name='myproject', backend='V
     return config
 
 
+@requires('qkeras')
 def _get_precision_from_quantizer(quantizer):
     if isinstance(quantizer, str):
         import qkeras

pyproject.toml

@@ -34,6 +34,7 @@ optional-dependencies.doc = [
   "sphinx-rtd-theme",
 ]
 optional-dependencies.HGQ = [ "hgq~=0.2.0" ]
+optional-dependencies.onnx = [ "onnx>=1.4" ]
 optional-dependencies.optimization = [
   "keras-tuner==1.1.3",
   "ortools==9.4.1874",