Merge pull request #90 from fastmachinelearning/matmul_mac_update

update in the matmul mac calculation

Author: maltanar

src/qonnx/analysis/inference_cost.py

@@ -134,7 +134,7 @@ def inference_cost_matmul(model, node, discount_sparsity):
         if tB is not None and tB.i == 1:
             w_shape = w_shape[::-1]
     # exclude common dim (last axis) from one side to avoid duplication
-    n_macs = np.prod(i_shape[:-1]) * np.prod(w_shape)
+    n_macs = i_shape[-1] * np.prod(o_shape)
     # deal with both dyn,param and dyn,dyn cases for weight memory
     inp0_is_const = model.get_initializer(node.input[0]) is not None
     inp1_is_const = model.get_initializer(node.input[1]) is not None

src/qonnx/data/onnx/matmul_update/sdp.onnx

@@ -0,0 +1,45 @@
+# Copyright (c) 2023 Advanced Micro Devices, Inc.
+# 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 Xilinx 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.
+
+
+import pytest
+import qonnx
+from pkgutil import get_data
+import qonnx.util.inference_cost as infc
+from qonnx.util.cleanup import cleanup_model
+from qonnx.core.modelwrapper import ModelWrapper
+
+
+def test_matmul_mac_cost():
+    raw_model = get_data("qonnx","data/onnx/matmul_update/sdp.onnx")
+    model = ModelWrapper(raw_model)
+    cleaned_model = cleanup_model(model)
+    # Two Matmul layers with shape (i_shape, w_shape, o_shape), L1: ([4, 64, 32], [4, 32, 64], [4, 64, 64]) and L2: ([4, 64, 64], [4, 64, 32], [4, 64, 32])
+    inf_cost_dict = infc.inference_cost(cleaned_model, discount_sparsity=False)
+    mac_cost = inf_cost_dict['op_mac_FLOAT32_FLOAT32'] # Expected mac cost 4*32*64*64 + 4*64*64*32 = 1048576
+    assert mac_cost == 1048576.0, "Error: discrepancy in mac cost."