Lazy converter imports and migrate to pyproject.toml

.pre-commit-config.yaml

@@ -9,13 +9,19 @@ repos:
     args: ['--line-length=125',
            '--skip-string-normalization']
 
+- repo: https://github.com/tox-dev/pyproject-fmt
+  rev: v2.5.0
+  hooks:
+    - id: pyproject-fmt
+
 - repo: https://github.com/pre-commit/pre-commit-hooks
   rev: v5.0.0
   hooks:
   - id: check-added-large-files
   - id: check-case-conflict
   - id: check-merge-conflict
   - id: check-symlinks
+  - id: check-toml
   - id: check-yaml
   - id: debug-statements
   - id: end-of-file-fixer
@@ -27,19 +33,13 @@ repos:
   rev: 5.13.2
   hooks:
   - id: isort
-    args: ["--profile", "black", --line-length=125]
 
 - repo: https://github.com/asottile/pyupgrade
   rev: v3.19.1
   hooks:
   - id: pyupgrade
     args: ["--py36-plus"]
 
-- repo: https://github.com/asottile/setup-cfg-fmt
-  rev: v2.7.0
-  hooks:
-  - id: setup-cfg-fmt
-
 - repo: https://github.com/pycqa/flake8
   rev: 7.1.1
   hooks:

MANIFEST.in

@@ -1,7 +1,9 @@
-include LICENSE README.md CONTRIBUTING.md CITATION.cff pyproject.toml setup.py setup.cfg .clang-format
+include LICENSE README.md CONTRIBUTING.md CITATION.cff pyproject.toml .clang-format
 graft example-models
 graft test
 graft contrib
 recursive-include hls4ml/templates *
-global-exclude .git .gitmodules .gitlab-ci.yml
+recursive-include hls4ml *.py
+recursive-include hls4ml/contrib *
+global-exclude .git .gitmodules .gitlab-ci.yml *.pyc
 include hls4ml/backends/vivado_accelerator/supported_boards.json

hls4ml/__init__.py

@@ -1,33 +1,3 @@
-# Temporary workaround for QKeras installation requirement, will be removed after 1.0.0
-def maybe_install_qkeras():
-    import subprocess
-    import sys
-
-    QKERAS_PKG_NAME = 'QKeras'
-    # QKERAS_PKG_SOURCE = QKERAS_PKG_NAME
-    QKERAS_PKG_SOURCE = 'qkeras@git+https://github.com/fastmachinelearning/qkeras.git'
-
-    def pip_list():
-        p = subprocess.run([sys.executable, '-m', 'pip', 'list'], check=True, capture_output=True)
-        return p.stdout.decode()
-
-    def pip_install(package):
-        subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])
-
-    all_pkgs = pip_list()
-    if QKERAS_PKG_NAME not in all_pkgs:
-        print('QKeras installation not found, installing one...')
-        pip_install(QKERAS_PKG_SOURCE)
-        print('QKeras installed.')
-
-
-try:
-    maybe_install_qkeras()
-except Exception:
-    print('Could not find QKeras installation, make sure you have QKeras installed.')
-
-# End of workaround
-
 from hls4ml import converters, report, utils  # noqa: F401, E402
 
 try:

hls4ml/cli/__main__.py

@@ -0,0 +1,3 @@
+from . import main
+
+main()

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':
@@ -93,10 +78,10 @@ def parse_yaml_config(config_file):
     """
 
     def construct_keras_model(loader, node):
-        from tensorflow.keras.models import load_model
-
         model_str = loader.construct_scalar(node)
-        return load_model(model_str)
+        import keras
+
+        return keras.models.load_model(model_str)
 
     yaml.add_constructor('!keras_model', construct_keras_model, Loader=yaml.SafeLoader)
 
@@ -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/keras/qkeras.py

@@ -1,5 +1,3 @@
-from qkeras.quantizers import get_quantizer
-
 from hls4ml.converters.keras.convolution import parse_conv1d_layer, parse_conv2d_layer
 from hls4ml.converters.keras.core import parse_batchnorm_layer, parse_dense_layer
 from hls4ml.converters.keras.recurrent import parse_rnn_layer
@@ -88,6 +86,8 @@ def parse_qrnn_layer(keras_layer, input_names, input_shapes, data_reader):
 
 @keras_handler('QActivation')
 def parse_qactivation_layer(keras_layer, input_names, input_shapes, data_reader):
+    from qkeras.quantizers import get_quantizer
+
     assert keras_layer['class_name'] == 'QActivation'
     supported_activations = [
         'quantized_relu',

hls4ml/converters/keras_to_hls.py

@@ -160,9 +160,9 @@ def get_model_arch(config):
         # Model instance passed in config from API
         keras_model = config['KerasModel']
         if isinstance(keras_model, str):
-            from tensorflow.keras.models import load_model
+            import keras
 
-            keras_model = load_model(keras_model)
+            keras_model = keras.models.load_model(keras_model)
         model_arch = json.loads(keras_model.to_json())
         reader = KerasModelReader(keras_model)
     elif 'KerasJson' in config:

hls4ml/converters/onnx_to_hls.py

@@ -1,7 +1,5 @@
-import onnx
-from onnx import helper, numpy_helper
-
 from hls4ml.model import ModelGraph
+from hls4ml.utils.dependency import requires
 
 
 # ----------------------Helpers---------------------
@@ -21,6 +19,8 @@ def replace_char_inconsitency(name):
 
 
 def get_onnx_attribute(operation, name, default=None):
+    from onnx import helper
+
     attr = next((x for x in operation.attribute if x.name == name), None)
     if attr is None:
         value = default
@@ -76,6 +76,8 @@ def get_input_shape(graph, node):
 
 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
+
     return numpy_helper.to_array(tensor)
 
 
@@ -257,6 +259,7 @@ 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.
 
@@ -273,6 +276,8 @@ def onnx_to_hls(config):
     # Extract model architecture
     print('Interpreting Model ...')
 
+    import onnx
+
     onnx_model = onnx.load(config['OnnxModel']) if isinstance(config['OnnxModel'], str) else config['OnnxModel']
 
     layer_list, input_layers, output_layers = parse_onnx_model(onnx_model)

hls4ml/converters/pytorch_to_hls.py

@@ -1,7 +1,7 @@
 import numpy as np
-import torch
 
 from hls4ml.model import ModelGraph
+from hls4ml.utils.dependency import requires
 
 
 class PyTorchModelReader:
@@ -27,6 +27,8 @@ def get_weights_data(self, layer_name, var_name):
 
 class PyTorchFileReader(PyTorchModelReader):  # Inherit get_weights_data method
     def __init__(self, config):
+        import torch
+
         self.config = config
 
         if not torch.cuda.is_available():
@@ -116,6 +118,8 @@ def parse_pytorch_model(config, verbose=True):
     Returns:
         ModelGraph: hls4ml model object.
     """
+    import torch
+    from torch.fx import symbolic_trace
 
     # This is a list of dictionaries to hold all the layer info we need to generate HLS
     layer_list = []
@@ -135,7 +139,6 @@ def parse_pytorch_model(config, verbose=True):
     # dict of layer objects in non-traced form for access lateron
     children = {c[0]: c[1] for c in model.named_children()}
     # use symbolic_trace to get a full graph of the model
-    from torch.fx import symbolic_trace
 
     traced_model = symbolic_trace(model)
     # Define layers to skip for conversion to HLS
@@ -399,6 +402,7 @@ 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/optimizer/passes/qkeras.py

@@ -1,5 +1,4 @@
 import numpy as np
-import tensorflow as tf
 
 from hls4ml.model.layers import ApplyAlpha
 from hls4ml.model.optimizer import ConfigurableOptimizerPass, OptimizerPass, register_pass
@@ -113,6 +112,8 @@ def match(self, node):
     def transform(self, model, node):
         # The quantizer has to be applied to set the scale attribute
         # This must be applied to the _unquantized_ weights to obtain the correct scale
+        import tensorflow as tf
+
         quantizer = node.weights['weight'].quantizer.quantizer_fn  # get QKeras quantizer
         weights = node.weights['weight'].data_unquantized  # get weights
         qweights = quantizer(tf.convert_to_tensor(weights))