[tests]: added test case for 1D convolution and modified existing test case

tests/fpgadataflow/test_convert_to_hls_1d_conv_layer.py

+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from onnx import TensorProto, helper
+import numpy as np
+import pytest
+
+from finn.core.datatype import DataType
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.util.basic import gen_finn_dt_tensor
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.custom_op.general.im2col import compute_conv_output_dim
+from finn.custom_op.registry import getCustomOp
+from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
+
+
+# conv_config:
+# [pad_h_begin, pad_w_begin, pad_h_end, pad_w_end]
+# [kernel_size_h, kernel_size_w]
+# [stride_h, stride_w]
+# [dilation_h, dilation_w]
+@pytest.mark.parametrize(
+    "conv_config",
+    [
+        [[0, 0, 0, 0], [4, 1], [1, 1], [1, 1]],
+        [[1, 0, 1, 0], [4, 1], [1, 1], [1, 1]],
+        [[1, 0, 1, 0], [4, 1], [2, 1], [1, 1]],
+        # [[1, 0, 1, 0], [4, 1], [1, 1], [2, 1]]
+    ],
+)
+@pytest.mark.parametrize("depthwise", [False, True])
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_convert_to_hls_1d_conv_layer(conv_config, depthwise, exec_mode):
+    pad, kernel_size, stride, dilation = conv_config
+    np.random.seed(0)
+    idt = DataType.UINT4
+
+    in_feature_dim_h, in_feature_dim_w = [10, 1]
+    in_chn = 16
+
+    k_h, k_w = kernel_size
+    stride_h, stride_w = stride
+    dilation_h, dilation_w = dilation
+    pad_h = pad[0] + pad[2]
+    pad_w = pad[1] + pad[3]
+
+    if depthwise is True:
+        group = out_chn = in_chn
+        conv_param_shape = [out_chn, 1, k_h, k_w]
+    else:
+        group = 1
+        out_chn = 20
+        conv_param_shape = [out_chn, in_chn, k_h, k_w]
+
+    out_feature_dim_h = compute_conv_output_dim(
+        in_feature_dim_h, k_h, stride_h, pad_h, dilation_h
+    )
+    out_feature_dim_w = compute_conv_output_dim(
+        in_feature_dim_w, k_w, stride_w, pad_w, dilation_w
+    )
+
+    input_shape = [1, in_chn, in_feature_dim_h, in_feature_dim_w]
+    output_shape = [1, out_chn, out_feature_dim_h, out_feature_dim_w]
+
+    conv_weight_dt = DataType.UINT4
+
+    conv_config = {}
+    conv_config["dilations"] = [dilation_h, dilation_w]
+    conv_config["group"] = group
+    conv_config["kernel_shape"] = [k_h, k_w]
+    conv_config["pads"] = pad
+    conv_config["strides"] = [stride_h, stride_w]
+
+    top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+    top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+    value_info = [
+        helper.make_tensor_value_info("p1", TensorProto.FLOAT, conv_param_shape)
+    ]
+
+    modelproto = helper.make_model(
+        helper.make_graph(
+            name="conv_test",
+            inputs=[top_in],
+            outputs=[top_out],
+            value_info=value_info,
+            nodes=[
+                helper.make_node("Conv", ["top_in", "p1"], ["top_out"], **conv_config)
+            ],
+        )
+    )
+
+    model = ModelWrapper(modelproto)
+    model.set_tensor_datatype("top_in", idt)
+    model.set_tensor_datatype("top_out", idt)
+    model.set_tensor_datatype("p1", conv_weight_dt)
+    model.set_initializer("p1", gen_finn_dt_tensor(conv_weight_dt, conv_param_shape))
+
+    model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
+
+    new_model = model.transform(LowerConvsToMatMul())
+    new_model = new_model.transform(to_hls.InferConvInpGen())
+    if depthwise is True:
+        new_model = new_model.transform(to_hls.InferVVAU())
+    else:
+        new_model = new_model.transform(to_hls.InferQuantizedStreamingFCLayer())
+        fc_node = new_model.get_nodes_by_op_type("StreamingFCLayer_Batch")[0]
+        fc_inst = getCustomOp(fc_node)
+        mw = fc_inst.get_nodeattr("MW")
+        mh = fc_inst.get_nodeattr("MH")
+        pe_cands = list(filter(lambda x: mh % x == 0, range(2, mh + 1)))
+        simd_cands = list(filter(lambda x: mw % x == 0, range(2, mw + 1)))
+        fc_inst.set_nodeattr("PE", pe_cands[0])
+        fc_inst.set_nodeattr("SIMD", simd_cands[0])
+
+    new_model = new_model.transform(GiveUniqueNodeNames())
+    new_model = new_model.transform(InferShapes())
+    new_model = new_model.transform(InferDataTypes())
+
+    if exec_mode == "cppsim":
+        new_model = new_model.transform(PrepareCppSim())
+        new_model = new_model.transform(CompileCppSim())
+        new_model = new_model.transform(SetExecMode("cppsim"))
+    elif exec_mode == "rtlsim":
+        new_model = new_model.transform(SetExecMode("rtlsim"))
+        new_model = new_model.transform(GiveUniqueNodeNames())
+        new_model = new_model.transform(PrepareIP("xc7z020clg400-1", 5))
+        new_model = new_model.transform(HLSSynthIP())
+        new_model = new_model.transform(PrepareRTLSim())
+    else:
+        raise Exception("Unknown exec_mode")
+
+    x = gen_finn_dt_tensor(idt, input_shape)
+    inp_dict = {model.graph.input[0].name: x}
+    assert oxe.compare_execution(model, new_model, inp_dict)
+
+    if pad_h == 1 and pad_w == 1:
+        padding_node = new_model.get_nodes_by_op_type("FMPadding_Batch")[0]
+        padding_inst = getCustomOp(padding_node)
+        assert padding_inst.get_nodeattr("SIMD") == in_chn
+
+    if depthwise is True and exec_mode == "rtlsim":
+        node = new_model.get_nodes_by_op_type("Vector_Vector_Activate_Batch")[0]
+        inst = getCustomOp(node)
+        cycles_rtlsim = inst.get_nodeattr("cycles_rtlsim")
+        exp_cycles_dict = new_model.analysis(exp_cycles_per_layer)
+        exp_cycles = exp_cycles_dict[node.name]
+        assert np.isclose(exp_cycles, cycles_rtlsim, atol=11)
+        assert exp_cycles != 0

tests/fpgadataflow/test_depthwise_convolution.py

@@ -98,7 +98,7 @@ def set_up_reference_model(act, idt, wdt, k, ifm_dim, ifm_ch, stride, padding):
         inputs=["inp"],
         outputs=["im2col_out"],
         kernel_size=[k, k],
-        stride=stride,
+        stride=[stride, stride],
         pad_amount=[padding, padding, padding, padding],
         input_shape="(1, {}, {}, {})".format(ifm_dim, ifm_dim, ifm_ch),
         depthwise=1,
@@ -142,7 +142,7 @@ def set_up_reference_model(act, idt, wdt, k, ifm_dim, ifm_ch, stride, padding):
     W_matrix = W_matrix.reshape(ofm_ch, ifm_ch * k * k)
 
     model.set_initializer("W_sparse", W_matrix.T)
-    sparsity = {"dw": {"kernel_shape": k}}
+    sparsity = {"dw": {"kernel_shape": [k, k]}}
     model.set_tensor_sparsity("W_sparse", sparsity)
 
     if act is not None: