From 7608e7c90df931a8daeababf5239f24003a9dec5 Mon Sep 17 00:00:00 2001 From: Harish Date: Thu, 15 Feb 2024 11:29:36 +0000 Subject: [PATCH 1/4] inference cost breakdown --- src/qonnx/analysis/inference_cost.py | 25 ++++-- src/qonnx/util/inference_cost.py | 76 +++++++++++----- .../analysis/test_inference_cost_breakdown.py | 88 +++++++++++++++++++ 3 files changed, 160 insertions(+), 29 deletions(-) create mode 100644 tests/analysis/test_inference_cost_breakdown.py diff --git a/src/qonnx/analysis/inference_cost.py b/src/qonnx/analysis/inference_cost.py index 98e03428..847058b7 100644 --- a/src/qonnx/analysis/inference_cost.py +++ b/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 diff --git a/src/qonnx/util/inference_cost.py b/src/qonnx/util/inference_cost.py index 86428c76..22ee140d 100644 --- a/src/qonnx/util/inference_cost.py +++ b/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(): diff --git a/tests/analysis/test_inference_cost_breakdown.py b/tests/analysis/test_inference_cost_breakdown.py new file mode 100644 index 00000000..b798eaf0 --- /dev/null +++ b/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" From d1207422626d70084e18c4a526dd43e440ac2825 Mon Sep 17 00:00:00 2001 From: Harish <62412574+Harsh9650@users.noreply.github.com> Date: Thu, 15 Feb 2024 12:06:35 +0000 Subject: [PATCH 2/4] Update inference_cost.py --- src/qonnx/util/inference_cost.py | 34 ++++++++++++-------------------- 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/src/qonnx/util/inference_cost.py b/src/qonnx/util/inference_cost.py index 22ee140d..7a212321 100644 --- a/src/qonnx/util/inference_cost.py +++ b/src/qonnx/util/inference_cost.py @@ -69,6 +69,14 @@ def compute_mem_bits_and_elems(inf_cost_dict, filter_string="mem_w"): total_mem_elems += v return total_mem_bits, total_mem_elems +def assign_mem_bits_and_elems(res_dict): + mem_w_bits, mem_w_elems = compute_mem_bits_and_elems(res_dict, "mem_w") + mem_o_bits, mem_o_elems = compute_mem_bits_and_elems(res_dict, "mem_o") + res_dict["total_mem_w_bits"] = mem_w_bits + res_dict["total_mem_w_elems"] = mem_w_elems + res_dict["total_mem_o_bits"] = mem_o_bits + res_dict["total_mem_o_elems"] = mem_o_elems + return res_dict def inference_cost( model_filename_or_wrapper, @@ -114,14 +122,9 @@ def inference_cost( 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 = assign_mem_bits_and_elems(res) 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: @@ -132,31 +135,20 @@ def inference_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 = assign_mem_bits_and_elems(op_res) 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 + node_res = res[node_name] + node_res = assign_mem_bits_and_elems(node_res) + per_node_breakdown[node_name] = node_res combined_results[i] = per_node_breakdown return combined_results - def main(): clize.run(inference_cost) From be9a9f838ab7bd9a107ab937d453f64b22da22f4 Mon Sep 17 00:00:00 2001 From: Harish <62412574+Harsh9650@users.noreply.github.com> Date: Wed, 21 Feb 2024 13:07:52 +0000 Subject: [PATCH 3/4] Update inference_cost.py --- src/qonnx/util/inference_cost.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/src/qonnx/util/inference_cost.py b/src/qonnx/util/inference_cost.py index 7a212321..30ac677d 100644 --- a/src/qonnx/util/inference_cost.py +++ b/src/qonnx/util/inference_cost.py @@ -44,7 +44,6 @@ from qonnx.transformation.infer_datatypes import InferDataTypes from qonnx.transformation.infer_shapes import InferShapes - def compute_bops_and_macs(inf_cost_dict): total_bops = 0.0 total_macs = 0.0 @@ -57,7 +56,6 @@ def compute_bops_and_macs(inf_cost_dict): total_macs += v return total_bops, total_macs - def compute_mem_bits_and_elems(inf_cost_dict, filter_string="mem_w"): total_mem_bits = 0.0 total_mem_elems = 0.0 @@ -98,6 +96,7 @@ def inference_cost( 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.""" + combined_results = {} if isinstance(model_filename_or_wrapper, ModelWrapper): model = model_filename_or_wrapper @@ -118,7 +117,8 @@ 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, cost_breakdown)) + 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) @@ -148,10 +148,9 @@ def inference_cost( per_node_breakdown[node_name] = node_res combined_results[i] = per_node_breakdown return combined_results - + def main(): clize.run(inference_cost) - if __name__ == "__main__": main() From 7e44fbf1c6f138cd859196d8cd26ce281b04d98d Mon Sep 17 00:00:00 2001 From: Harish Date: Mon, 22 Apr 2024 17:58:07 +0100 Subject: [PATCH 4/4] L1_Estimates --- src/qonnx/analysis/l1_resource_estimates.py | 169 ++++++++++++++++++ .../onnx/l1_resource_estimates/network1.onnx | Bin 0 -> 5457020 bytes tests/analysis/test_l1_resource_estimates.py | 75 ++++++++ 3 files changed, 244 insertions(+) create mode 100644 src/qonnx/analysis/l1_resource_estimates.py create mode 100644 src/qonnx/data/onnx/l1_resource_estimates/network1.onnx create mode 100644 tests/analysis/test_l1_resource_estimates.py diff --git a/src/qonnx/analysis/l1_resource_estimates.py b/src/qonnx/analysis/l1_resource_estimates.py new file mode 100644 index 00000000..85f30d37 --- /dev/null +++ b/src/qonnx/analysis/l1_resource_estimates.py @@ -0,0 +1,169 @@ +# 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 math + +from qonnx.analysis.l0_resource_estimates import core_resources +from qonnx.util.l0_performance_estimate import l0_performance_estimate + +resource_map = { + "res_limit": {"LUT": 0.7, "BRAM": 0.80, "URAM": 0.80, "DSP48": 0.80, "DSP58": 0.80}, + "bits_per_res": {"BRAM": 36864, "URAM": 294912, "LUT": 64}, +} + + +def rounding_down(value, decimal_place): + fact = 10**decimal_place + return math.floor(value * fact) / fact + + +def memory_distribution(resource_budget, total_cost, node_cost, unused_resources): + """This function is used for memory resource allocation to each node. In this function, we + are trying to project either one of the following resources URAM, BRAM, LUTRAM to a particular node. + However, for some nodes a combination of resources could be projected. + Parameters:- + resource_budget (dict):- Resource budget of an FPGA in a dictionary format. + Example (ve2802) :-{"LUT": 520704, "BRAM": 600, "URAM": 264, "DSP58": 1312} + total_cost (dict):- Inference cost dictionary of the full model. + per_node_cost (dict):- Inference cost associated with each layer/node in the neural network. + unused_resources (dict):- Dictionary in the format of resource budget to keep record of resources used, + provided internally. + """ + + res_limit, bits_per_res = resource_map["res_limit"], resource_map["bits_per_res"] + + mem_port = ( + node_cost["total_mem_w_bits"] / total_cost["total_mem_w_bits"] + ) # mem portion of this layer in the model (between 0 and 1). + + available_bram = ( + mem_port * (res_limit["BRAM"] * resource_budget["BRAM"]) + unused_resources["BRAM"] + ) # Approximate Bram available for this node. + available_uram = ( + mem_port * (res_limit["URAM"] * resource_budget["URAM"]) + unused_resources["URAM"] + ) # Approximate Uram available for this node. + available_lut = ( + mem_port * (res_limit["LUT"] * resource_budget["LUT"]) + unused_resources["LUT"] + ) # Approximate LUT available for this node. + + req_bram = node_cost["total_mem_w_bits"] / bits_per_res["BRAM"] # BRAM required for this node. + req_uram = node_cost["total_mem_w_bits"] / bits_per_res["URAM"] # URAM required for this node. + req_lut = node_cost["total_mem_w_bits"] / bits_per_res["LUT"] # LUT required for this node. + + if req_bram < 1 and available_lut > req_lut: + per_node_res_bdgt = {"BRAM": 0, "URAM": 0, "LUT": math.ceil(req_lut)} + else: + if available_uram > math.ceil(req_uram) and available_bram > math.ceil(req_bram): + uram_bit_waste = (math.ceil(req_uram) - req_uram) * bits_per_res["URAM"] + bram_bit_waste = (math.ceil(req_bram) - req_bram) * bits_per_res["BRAM"] + min_bit_waste = min(uram_bit_waste, bram_bit_waste) + if min_bit_waste == bram_bit_waste: + per_node_res_bdgt = {"BRAM": math.ceil(req_bram), "URAM": 0, "LUT": 0} + else: + per_node_res_bdgt = {"BRAM": 0, "URAM": math.ceil(req_uram), "LUT": 0} + elif available_uram > math.ceil(req_uram) and available_bram < math.ceil(req_bram): + per_node_res_bdgt = {"BRAM": 0, "URAM": math.ceil(req_uram), "LUT": 0} + elif available_uram < math.ceil(req_uram) and available_bram > math.ceil(req_bram): + per_node_res_bdgt = {"BRAM": math.ceil(req_bram), "URAM": 0, "LUT": 0} + else: + bits_in_bram, bits_in_uram = available_bram * bits_per_res["BRAM"], available_uram * bits_per_res["URAM"] + total_available_bits = bits_in_bram + bits_in_uram + if total_available_bits >= node_cost["total_mem_w_bits"]: + uram_dist_factor = bits_in_uram / total_available_bits + bram_dist_factor = 1 - uram_dist_factor + bram_dist = bram_dist_factor * node_cost["total_mem_w_bits"] / bits_per_res["BRAM"] + uram_dist = uram_dist_factor * node_cost["total_mem_w_bits"] / bits_per_res["URAM"] + per_node_res_bdgt = {"BRAM": math.ceil(bram_dist), "URAM": math.ceil(uram_dist), "LUT": 0} + else: + per_node_res_bdgt = {"BRAM": math.ceil(available_bram), "URAM": math.ceil(available_uram), "LUT": 0} + + unused_resources = { + "BRAM": (available_bram - per_node_res_bdgt["BRAM"]), # -ve value indicates over budget in unused resources. + "URAM": (available_uram - per_node_res_bdgt["URAM"]), + "LUT": (available_lut - per_node_res_bdgt["LUT"]), + } + + return per_node_res_bdgt, unused_resources + + +def l1_resource_estimates( + resource_budget, + total_cost, + per_node_cost, + bwidth_lower_limit=8, + bwidth_upper_limit=32, + clock_freq=300000000, + decimal_place=4, +): + """ + This function provides an estimate about the resources used by each layer of the neural network + when deployed on a particular FPGA. + + Parameters:- + resource_budget (dict):- Resource budget of an FPGA in a dictionary format. + Example (ve2802) :-{"LUT": 520704, "BRAM": 600, "URAM": 264, "DSP58": 1312} + total_cost (dict):- Inference cost dictionary of the full model. + per_node_cost (dict):- Inference cost associated with each layer/node in the neural network. + bwidth_lower_limit (int):- Lower cut off value to use DSPs instead of LUTs. + bwidth_upper_limit (int):- Upper cut off value to use LUTs instead of DSPs. + clock_freq (int):- Default at 300 million (single pump) + decimal_place (int):- used to round upto "decimal_place" values after decimal. + """ + + # 300 MHZ + all_node_res_bdgt = {} + # rounding upto + + dsp_type = next((key for key in resource_budget if "DSP" in key), None) + + unused_resources = {"LUT": 0, "BRAM": 0, "URAM": 0} # passing unused resources to the following layers. + + resource_performance = l0_performance_estimate( + resource_budget, # Inference per sec (slowdown factor * clock cycle) + total_cost, + bwidth_lower_limit, + bwidth_upper_limit, + clock_freq, + ) + + slowdown_factor = resource_performance[1] / clock_freq + + for node_name, node_cost in per_node_cost.items(): + per_node_res_bdgt, unused_resources = memory_distribution(resource_budget, total_cost, node_cost, unused_resources) + + core_res = core_resources(node_cost, dsp_type, bwidth_lower_limit, bwidth_upper_limit) + + for i, j in core_res.items(): + core_amount = slowdown_factor * j + if i in per_node_res_bdgt.keys(): + per_node_res_bdgt[i] += rounding_down(core_amount, decimal_place) + else: + per_node_res_bdgt[i] = rounding_down(core_amount, decimal_place) + all_node_res_bdgt[node_name] = per_node_res_bdgt + return all_node_res_bdgt diff --git a/src/qonnx/data/onnx/l1_resource_estimates/network1.onnx b/src/qonnx/data/onnx/l1_resource_estimates/network1.onnx new file mode 100644 index 0000000000000000000000000000000000000000..130a4fd60c7c4eaa2a66fe035ef0df5d410c8a13 GIT binary patch literal 5457020 zcmcee2|QHa`}pn4Sdvnt&C;%zdq-t?4wY7=P5UC1q(VjehO~-y?MeG0%)O(9xmTNr 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