inference cost breakdown

src/qonnx/analysis/inference_cost.py

@@ -201,10 +201,10 @@ def inference_cost_upsample(model, node, discount_sparsity):
     return ret
 
 
-def inference_cost(model, discount_sparsity=True):
+def inference_cost(model, discount_sparsity=True, cost_breakdown=False):
     "Ensure all nodes have unique names prior to calling this analysis pass."
 
-    node_costs = {}
+    ret, node_costs, nodes_per_optype = {}, {}, {}
     zero_cost_ops = [
         "MaxPool",
         "AveragePool",
@@ -240,13 +240,24 @@ def inference_cost(model, discount_sparsity=True):
         if node.op_type in inference_cost_fxn_map.keys():
             node_cost = inference_cost_fxn_map[node.op_type](model, node, discount_sparsity)
             node_costs[node.name] = node_cost
+            if node.op_type not in nodes_per_optype.keys():
+                new_optype = {}
+                new_optype[node.name] = node_cost
+                nodes_per_optype[node.op_type] = new_optype
+            else:
+                nodes_per_optype[node.op_type][node.name] = node_cost
         elif node.op_type in zero_cost_ops:
             continue
         else:
             unsupported_ops.add(node.op_type)
-
-    ret = aggregate_dict_keys(node_costs)
-    ret["unsupported"] = unsupported_ops
-    ret["discount_sparsity"] = discount_sparsity
-
+    total = aggregate_dict_keys(node_costs)
+    total["unsupported"] = unsupported_ops
+    total["discount_sparsity"] = discount_sparsity
+    ret["total_cost"] = total
+    if cost_breakdown:
+        optype_cost = {}
+        for optype, resources in nodes_per_optype.items():
+            optype_cost[optype] = aggregate_dict_keys(resources)
+        ret["optype_cost"] = optype_cost
+        ret["node_cost"] = node_costs
     return ret

src/qonnx/util/inference_cost.py

@@ -71,7 +71,13 @@ def compute_mem_bits_and_elems(inf_cost_dict, filter_string="mem_w"):
 
 
 def inference_cost(
-    model_filename_or_wrapper, *, output_json=None, output_onnx=None, preprocess=True, discount_sparsity=True
+    model_filename_or_wrapper,
+    *,
+    output_json=None,
+    output_onnx=None,
+    preprocess=True,
+    discount_sparsity=True,
+    cost_breakdown=False
 ):
     """Return the inference cost estimate metric for given ONNX model.
     Supports the Quant op for weight/activation quantization.
@@ -83,8 +89,8 @@ def inference_cost(
     :param preprocess: If set, run preprocessing steps such as shape inference,
         datatype inference and constant folding. Strongly recommended.
     :param discount_sparsity: If set, will discount op cost of MAC ops with a
-        constant zero weight, and the mem cost of constant zero weights.
-    """
+        constant zero weight, and the mem cost of constant zero weights."""
+    combined_results = {}
     if isinstance(model_filename_or_wrapper, ModelWrapper):
         model = model_filename_or_wrapper
     else:
@@ -104,25 +110,51 @@ def inference_cost(
     model = model.transform(GiveReadableTensorNames())
     if output_onnx is not None:
         model.save(output_onnx)
-    ret = model.analysis(lambda x: infca.inference_cost(x, discount_sparsity))
-    bops, macs = compute_bops_and_macs(ret)
-    mem_w_bits, mem_w_elems = compute_mem_bits_and_elems(ret, "mem_w")
-    mem_o_bits, mem_o_elems = compute_mem_bits_and_elems(ret, "mem_o")
-    ret["total_bops"] = bops
-    ret["total_macs"] = macs
-    ret["total_mem_w_bits"] = mem_w_bits
-    ret["total_mem_w_elems"] = mem_w_elems
-    ret["total_mem_o_bits"] = mem_o_bits
-    ret["total_mem_o_elems"] = mem_o_elems
-
-    if "unsupported" in ret:
-        ret["unsupported"] = str(ret["unsupported"])
-
-    if output_json is not None:
-        with open(output_json, "w") as f:
-            json.dump(ret, f, sort_keys=True, indent=2)
-
-    return ret
+    ret = model.analysis(lambda x: infca.inference_cost(x, discount_sparsity, cost_breakdown))
+    for i, res in ret.items():
+        if i == "total_cost":
+            bops, macs = compute_bops_and_macs(res)
+            mem_w_bits, mem_w_elems = compute_mem_bits_and_elems(res, "mem_w")
+            mem_o_bits, mem_o_elems = compute_mem_bits_and_elems(res, "mem_o")
+            res["total_bops"] = bops
+            res["total_macs"] = macs
+            res["total_mem_w_bits"] = mem_w_bits
+            res["total_mem_w_elems"] = mem_w_elems
+            res["total_mem_o_bits"] = mem_o_bits
+            res["total_mem_o_elems"] = mem_o_elems
+            if "unsupported" in res:
+                res["unsupported"] = str(res["unsupported"])
+            if output_json is not None:
+                with open(output_json, "w") as f:
+                    json.dump(res, f, sort_keys=True, indent=2)
+            combined_results[i] = res
+        elif i == "optype_cost":
+            per_optype_breakdown = {}
+            for optype, op_res in res.items():
+                bops, macs = compute_bops_and_macs(op_res)
+                mem_w_bits, mem_w_elems = compute_mem_bits_and_elems(op_res, "mem_w")
+                mem_o_bits, mem_o_elems = compute_mem_bits_and_elems(op_res, "mem_o")
+                op_res["total_bops"] = bops
+                op_res["total_macs"] = macs
+                op_res["total_mem_w_bits"] = mem_w_bits
+                op_res["total_mem_w_elems"] = mem_w_elems
+                op_res["total_mem_o_bits"] = mem_o_bits
+                op_res["total_mem_o_elems"] = mem_o_elems
+                per_optype_breakdown[optype] = op_res
+            combined_results[i] = per_optype_breakdown
+        else:
+            per_node_breakdown = {}
+            for node_name in res.keys():
+                node_cost = res[node_name]
+                mem_w_bits, mem_w_elems = compute_mem_bits_and_elems(node_cost, "mem_w")
+                mem_o_bits, mem_o_elems = compute_mem_bits_and_elems(node_cost, "mem_o")
+                node_cost["total_mem_w_bits"] = mem_w_bits
+                node_cost["total_mem_w_elems"] = mem_w_elems
+                node_cost["total_mem_o_bits"] = mem_o_bits
+                node_cost["total_mem_o_elems"] = mem_o_elems
+                per_node_breakdown[node_name] = node_cost
+            combined_results[i] = per_node_breakdown
+    return combined_results
 
 
 def main():

tests/analysis/test_inference_cost_breakdown.py

@@ -0,0 +1,88 @@
+# Copyright (c) 2024 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 qonnx 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 os
+import urllib.request
+
+from qonnx.analysis.inference_cost import aggregate_dict_keys
+from qonnx.core.modelwrapper import ModelWrapper
+from qonnx.util.cleanup import cleanup
+from qonnx.util.inference_cost import inference_cost as infca
+
+download_url = "https://github.com/onnx/models/raw/main/validated/vision/"
+download_url += "classification/resnet/model/resnet18-v1-7.onnx?download="
+
+model_details = {
+    "resnet18-v1-7": {
+        "description": "Resnet18 Opset version 7.",
+        "url": download_url,
+        "enc": {
+            "a": "op_mac_FLOAT32_FLOAT32",
+            "b": "total_mem_w_bits",
+            "c": "total_mem_w_elems",
+            "d": "total_mem_o_bits",
+            "e": "total_mem_o_elems",
+        },
+    },
+}
+
+
+def download_model(test_model, do_cleanup=False, return_modelwrapper=False):
+    qonnx_url = model_details[test_model]["url"]
+    # download test data
+    dl_dir = "/tmp"
+    dl_file = dl_dir + f"/{test_model}.onnx"
+    ret = dl_file
+    if not os.path.isfile(dl_file):
+        urllib.request.urlretrieve(qonnx_url, dl_file)
+    if do_cleanup:
+        out_file = dl_dir + f"/{test_model}_clean.onnx"
+        cleanup(dl_file, out_file=out_file, override_inpsize=1)
+        ret = out_file
+    if return_modelwrapper:
+        ret = ModelWrapper(ret)
+    return ret
+
+
+@pytest.mark.parametrize("test_model", model_details.keys())
+def test_inference_cost_breakdown(test_model):
+    test_details = model_details[test_model]
+    model = download_model(test_model, do_cleanup=True, return_modelwrapper=True)
+    inf_cost = infca(model, discount_sparsity=False, cost_breakdown=True)
+    print(inf_cost.keys())
+    t_cost = inf_cost["total_cost"]  # total cost
+    op_cost = aggregate_dict_keys(inf_cost["optype_cost"])  # cost per optype
+    n_cost = aggregate_dict_keys(inf_cost["node_cost"])  # cost per node.
+    enc = test_details["enc"]
+    assert t_cost[enc["a"]] == op_cost[enc["a"]] == n_cost[enc["a"]], "inf discrepancy"
+    assert t_cost[enc["b"]] == op_cost[enc["b"]] == n_cost[enc["b"]], "inf discrepancy"
+    assert t_cost[enc["c"]] == op_cost[enc["c"]] == n_cost[enc["c"]], "inf discrepancy"
+    assert t_cost[enc["d"]] == op_cost[enc["d"]] == n_cost[enc["d"]], "inf discrepancy"
+    assert t_cost[enc["e"]] == op_cost[enc["e"]] == n_cost[enc["e"]], "inf discrepancy"