Fix partitioning for unsupported operation inside supported resnet/in…

…ception like block & add test

python/pyxir/_libpyxir.py

@@ -0,0 +1,15 @@
+# Copyright 2021 Xilinx Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from libpyxir import *

python/pyxir/frontend/tvm/relay_tools/relay_2_xgraph_converter.py

@@ -123,7 +123,6 @@ def from_relay_to_xgraph(self,
 
         # Possibly replace Input layers with CvxInput layers
         xlayers = [net[op_id] for op_id in schedule]
-
         xgraph = self.xgraph_factory.build_from_xlayer(
             net=xlayers,
             name='relay_xgraph',

python/pyxir/graph/ops/l1_basic_nn.py

@@ -164,7 +164,7 @@ def concat(op_name: str, input_layers: List[XLayer], axis: int, **kwargs):
         # TODO workaround for concatenating when batch is -1 and some other constant
         if len(check) > 1 and -1 in check:
             check.remove(-1)
-        assert i == axis or len(check) == 1
+        assert i == axis or len(check) == 1, "i: {0}, axis: {1}, check: {2}".format(i, axis, check)
 
     shape = input_layers[0].shapes[:]
     shape[axis] = sum([il.shapes[axis] for il in input_layers])

python/pyxir/graph/ops/l2_convolution.py

@@ -69,7 +69,7 @@ def batch_norm(
     gamma_layer: XLayer,
     beta_layer: XLayer,
     axis: int,
-    epsilon: float,
+    epsilon: float = 1e-5,
     **kwargs
 ) -> XLayer:
     """
@@ -136,13 +136,13 @@ def conv2d(
     input_layer: XLayer,
     weights_layer: XLayer,
     kernel_size: List[int],
-    strides: List[int],
-    padding_hw: List[int],
-    dilation: List[int],
-    groups: int,
-    channels: int,
-    data_layout: str,
-    kernel_layout: str,
+    strides: List[int] = [1, 1],
+    padding_hw: List[int] = [0, 0, 0, 0],
+    dilation: List[int] = [1, 1],
+    groups: int = 1,
+    channels: int = None,
+    data_layout: str = "NCHW",
+    kernel_layout: str = "OIHW",
     target_kernel_layout: str = "OIHW",
     **kwargs
 ) -> XLayer:
@@ -222,6 +222,7 @@ def conv2d(
     logger.debug("-- channels: {}".format(channels))
 
     assert channels is None or out_ch == channels
+    channels = out_ch if channels is None else channels
 
     B = np.zeros([out_ch], dtype=np.float32)
     data = ConvData(W, B)
@@ -230,7 +231,11 @@ def conv2d(
     insize = [input_layer.shapes[H_idx], input_layer.shapes[W_idx]]
     batches = input_layer.shapes[0]
     logger.debug("-- in shape: {}".format(input_layer.shapes))
-    assert input_layer.shapes[C_idx] == in_ch
+    # assert (
+    #     input_layer.shapes[C_idx] == in_ch,
+    #     "Expected number of input channels (in_ch) was {0} but got {1}"\
+    #     .format(in_ch, input_layer.shapes[C_idx])
+    # )
 
     logger.debug("-- padding (t,b,l,r): {}".format((pad_ht, pad_hb, pad_wl, pad_wr)))
 
@@ -316,13 +321,13 @@ def conv2d_transpose(
     input_layer: XLayer,
     weights_layer: XLayer,
     kernel_size: List[int],
-    strides: List[int],
-    padding_hw: List[int],
-    dilation: List[int],
-    groups: int,
-    channels: int,
-    data_layout: str,
-    kernel_layout: str,
+    strides: List[int] = [1, 1],
+    padding_hw: List[int] = [0, 0, 0, 0],
+    dilation: List[int] = [1, 1],
+    groups: int = 1,
+    channels: int = None,
+    data_layout: str = "NCHW",
+    kernel_layout: str = "OIHW",
     target_kernel_layout: str = "OIHW",
     **kwargs
 ) -> XLayer:
@@ -391,6 +396,7 @@ def conv2d_transpose(
     logger.debug("-- channels: {}".format(channels))
 
     assert channels is None or out_ch == channels
+    channels = out_ch if channels is None else channels
 
     B = np.zeros([out_ch], dtype=np.float32)
     data = ConvData(W, B)
@@ -638,9 +644,9 @@ def pool2d(
     input_layer: XLayer,
     pool_type: str,
     pool_size: List[int],
-    strides: List[int],
-    padding: List[int],
-    layout: str,
+    strides: List[int] = [1, 1],
+    padding: List[int] = [0, 0, 0, 0],
+    layout: str = "NCHW",
     ceil_mode: bool = False,
     count_include_pad: bool = False,
     **kwargs

python/pyxir/graph/ops/l3_math_and_transformations.py

@@ -419,7 +419,7 @@ def take(attrs: Dict[str, Any], in_xlayers: List[XLayer]) -> Dict[str, List[int]
 #############
 
 @xop_register_factory('Transpose')
-def transpose(op_name: str, input_layer: XLayer, axes: List[int], **kwargs):
+def transpose(op_name: str, input_layer: XLayer, axes: List[int], internal=0, **kwargs):
     """
     Create a Transpose XLayer
 
@@ -432,31 +432,26 @@ def transpose(op_name: str, input_layer: XLayer, axes: List[int], **kwargs):
     input_layer: XLayer
         The input layer to this scaling layer
     """
-    if 'Constant' in input_layer.type:
-        # precompute
-        X = input_layer._replace(
-            data=np.transpose(input_layer.data, tuple(axes))
-        )
-    else:
-        bottoms = [input_layer.name]
+    bottoms = [input_layer.name]
 
-        new_shape = TensorShape([input_layer.shapes[i] for i in axes])
+    new_shape = TensorShape([input_layer.shapes[i] for i in axes])
 
-        attrs = kwargs
-        attrs.update({
-            'axes': axes
-        })
+    attrs = kwargs
+    attrs.update({
+        'axes': axes
+    })
 
-        X = XLayer()
-        X = X._replace(
-            name=op_name,
-            type=['Transpose'],
-            shapes=new_shape,
-            sizes=new_shape.get_size(),
-            layer=[op_name],
-            tops=[],
-            bottoms=bottoms,
-            attrs=attrs,
-            targets=[]
-        )
+    X = XLayer()
+    X = X._replace(
+        name=op_name,
+        type=['Transpose'],
+        shapes=new_shape,
+        sizes=new_shape.get_size(),
+        layer=[op_name],
+        tops=[],
+        bottoms=bottoms,
+        attrs=attrs,
+        internal=internal,
+        targets=[]
+    )
     return X

python/pyxir/graph/optimization/conditions.py

@@ -15,8 +15,6 @@
 """
 Module containing XLayer condition functions for graph optimization
 passes
-
-
 """
 
 import numpy as np

python/pyxir/graph/optimization/optimizers/basic_optimizer.py

@@ -32,63 +32,12 @@ class XGraphBasicOptimizer(XGraphBaseOptimizer):
     def __init__(self, xgraph,  copy=False):
         super(XGraphBasicOptimizer, self).__init__(xgraph, copy)
 
-        # 1. Merge transposes
-        # opt_pass = XGraphOptimizationPass(
-        #     name='BasicOptimizationPass-1',
-        #     output_png='after_basic_merge_transposes.png',
-        #     repeat_until_stable=True
-        # )
-
-        # logger.info("Add MergeTransposes pass")
-        # opt_pass.add_optimization(
-        #     condition_func=lambda bXs, X, tXs:
-        #         all([tX.type[0] == 'Transpose' for tX in tXs]),
-        #     opt_func=optimizations.merge_transposes,
-        #     name='MergeTransposes'
-        # )
-
-        # self.add_optimization_pass(10, opt_pass)
-
-        # 2. Expand transposes
-        # opt_pass = XGraphOptimizationPass(
-        #     name='BasicOptimizationPass-2',
-        #     output_png='after_expand_transposes.png',
-        #     repeat_until_stable=True
-        # )
-
-        # logger.info("Add ExpandTransposes pass")
-        # opt_pass.add_optimization(
-        #     condition_func=lambda bXs, X, tXs:
-        #         X.type[0] == 'Transpose',
-        #     opt_func=optimizations.expand_transposes,
-        #     name='ExpandTransposes'
-        # )
-
-        # self.add_optimization_pass(20, opt_pass)
-
-        # 2. Sweep transposes
-        # opt_pass = XGraphOptimizationPass(
-        #     name='BasicOptimizationPass-2',
-        #     output_png='after_basic_sweep_transposes.png',
-        #     repeat_until_stable=True
-        # )
-
-        # logger.info("Add SweepTransposesFlow pass")
-        # opt_pass.add_optimization(
-        #     condition_func=lambda bXs, X, tXs:
-        #         all([bX.type[0] == 'Transpose' for bX in bXs]),
-        #     opt_func=optimizations.sweep_transposes_flow,
-        #     name='SweepTransposesFlowDirection',
-        #     target=target
-        # )
-
-        # self.add_optimization_pass(20, opt_pass)
-
         # 1.
         opt_pass = XGraphOptimizationPass(
             name='BasicOptimizationPass-1',
             output_png='after_basic_optimizations.png'
         )
+
         logger.info("Add RemoveScalingBy1Layers pass")
         opt_pass.add_optimization(
             condition_func=lambda bXs, X, tXs:
@@ -129,11 +78,4 @@ def __init__(self, xgraph,  copy=False):
             opt_func=optimizations.merge_bias,
             name='MergeBiasIntoConvDense'
         )
-        # import pdb; pdb.set_trace()
-        # logger.info("Add TransformMulAndMaxIntoLeakyRelu pass")
-        # opt_pass.add_optimization(
-        #     condition_func=lambda bXs, X, tXs: 'Maximum' in X.type,
-        #     opt_func=optimizations.transform_mul_and_max_into_lrelu,
-        #     name='TransformMulAndMaxIntoLeakyRelu'
-        # )
         self.add_optimization_pass(10, opt_pass)