|
| 1 | +""" |
| 2 | +THIS IS FILE IS INTENDED TO BE USED BY benchmarking.yaml |
| 3 | +IT GENERATES A SUMMARY OF CROSS-DEVICE PREDICTIONS IN HTML FORMAT |
| 4 | +""" |
| 5 | + |
| 6 | +import argparse |
| 7 | +import dominate |
| 8 | +from dominate.tags import * |
| 9 | +import glob |
| 10 | +import pandas as pd |
| 11 | +import math |
| 12 | +from datetime import datetime |
| 13 | + |
| 14 | +SPECIAL_OPERATIONS = { |
| 15 | + # Convolution |
| 16 | + "conv2d", |
| 17 | + "conv_transpose2d", |
| 18 | + # Matrix multiply operations |
| 19 | + "linear", |
| 20 | + "__matmul__", # calls the same kernel as linear |
| 21 | + "bmm", |
| 22 | + # Recurrent operations |
| 23 | + "lstm", |
| 24 | + "gru", |
| 25 | + "rnn_tanh", |
| 26 | + "rnn_relu", |
| 27 | +} |
| 28 | + |
| 29 | + |
| 30 | +BENCHMARKER_TITLE = "deepview.predict-benchmark" |
| 31 | + |
| 32 | + |
| 33 | +def get_pct_error_color(pct_err): |
| 34 | + pct_err = abs(pct_err) |
| 35 | + if pct_err < 0.2: |
| 36 | + return "#088567" |
| 37 | + elif 0.2 <= pct_err < 0.4: |
| 38 | + return "#ffa500" |
| 39 | + else: |
| 40 | + return "#ff0000" |
| 41 | + |
| 42 | + |
| 43 | +def get_pct_err(predicted, measured): |
| 44 | + return round((predicted - measured) / measured, 3) |
| 45 | + |
| 46 | + |
| 47 | +def generate_summary(e2e_files): |
| 48 | + doc = dominate.document(title=BENCHMARKER_TITLE) |
| 49 | + |
| 50 | + with doc.head: |
| 51 | + style( |
| 52 | + """\ |
| 53 | + body { |
| 54 | + padding-left: 10px; |
| 55 | + margin-bottom: 50px; |
| 56 | + background-color: #F9F8F1; |
| 57 | + color: #2C232A; |
| 58 | + font-family: sans-serif; |
| 59 | + } |
| 60 | + .model-div { |
| 61 | + display: flex; |
| 62 | + flex-direction: row; |
| 63 | + align-items: flex-start; |
| 64 | + gap: 30px; |
| 65 | + } |
| 66 | + """ |
| 67 | + ) |
| 68 | + |
| 69 | + with doc: |
| 70 | + h1(f"Last update: {str(datetime.now())}") |
| 71 | + |
| 72 | + for f in sorted(list(glob.glob(f"{e2e_files}/*.csv"))): |
| 73 | + table_tile = f.split("/")[-1].split("-")[0] |
| 74 | + df = pd.read_csv(f) |
| 75 | + devices_names = list(df["origin_device"].unique()) |
| 76 | + with doc: |
| 77 | + h2(table_tile) |
| 78 | + with div(): |
| 79 | + attr(cls="model-div") |
| 80 | + # end-to-end predictions |
| 81 | + file_table = table() |
| 82 | + table_head = thead() |
| 83 | + header_row = tr() |
| 84 | + header_row += th("org_device") |
| 85 | + header_row += th("dst_device") |
| 86 | + header_row += th("run_time_ms_predicted") |
| 87 | + header_row += th("run_time_ms_measured") |
| 88 | + header_row += th("pct_error") |
| 89 | + table_head += header_row |
| 90 | + file_table.add(table_head) |
| 91 | + |
| 92 | + table_body = tbody() |
| 93 | + for _, item in df.iterrows(): |
| 94 | + row = tr() |
| 95 | + row += td(item["origin_device"]) |
| 96 | + row += td(item["dest_device"]) |
| 97 | + row += td(round(item["run_time_ms_predicted"], 3)) |
| 98 | + row += td(round(item["run_time_ms_measured"], 3)) |
| 99 | + row += td(round(item["pct_error"], 3)) |
| 100 | + table_body += row |
| 101 | + |
| 102 | + file_table.add(table_body) |
| 103 | + |
| 104 | + # cross prediction table |
| 105 | + cross_pred_table = table() |
| 106 | + table_head = thead() |
| 107 | + header_row = tr() |
| 108 | + header_row += th("from \ to") |
| 109 | + for device in devices_names: |
| 110 | + header_row += th(f"{device}") |
| 111 | + table_head += header_row |
| 112 | + cross_pred_table.add(table_head) |
| 113 | + |
| 114 | + # creater NxN table with N = number of devices |
| 115 | + placeholder = [ |
| 116 | + ["x"] * len(devices_names) for _ in range(len(devices_names)) |
| 117 | + ] |
| 118 | + hyperlink_names = [ |
| 119 | + ["x"] * len(devices_names) for _ in range(len(devices_names)) |
| 120 | + ] |
| 121 | + for _, item in df.iterrows(): |
| 122 | + t_row = devices_names.index(item["origin_device"]) |
| 123 | + t_col = devices_names.index(item["dest_device"]) |
| 124 | + model_bs = table_tile.replace("+", "-") |
| 125 | + hyperlink_item_name = f"{model_bs}-{item['origin_device']}-{item['dest_device']}-breakdown-combined.html" |
| 126 | + placeholder[t_row][t_col] = round(item["pct_error"], 3) |
| 127 | + hyperlink_names[t_row][t_col] = hyperlink_item_name |
| 128 | + |
| 129 | + table_body = tbody() |
| 130 | + for i, item in enumerate(placeholder): |
| 131 | + hyperlink_list = hyperlink_names[i] |
| 132 | + row = tr() |
| 133 | + row += td(devices_names[i]) |
| 134 | + for j, cross_pred in enumerate(item): |
| 135 | + if cross_pred == "x": |
| 136 | + row += td(cross_pred) |
| 137 | + else: |
| 138 | + link_name = hyperlink_list[j] |
| 139 | + color = get_pct_error_color(cross_pred) |
| 140 | + row += td( |
| 141 | + a(cross_pred, href=link_name, style=f"color:{color};") |
| 142 | + ) |
| 143 | + table_body += row |
| 144 | + cross_pred_table.add(table_body) |
| 145 | + |
| 146 | + footer() |
| 147 | + |
| 148 | + with open("benchmark_summary.html", "w") as file: |
| 149 | + file.write(doc.render()) |
| 150 | + |
| 151 | + |
| 152 | +def generate_details(ops_files): |
| 153 | + for f in sorted(list(glob.glob(f"{ops_files}/*.csv"))): |
| 154 | + doc = dominate.document(title=BENCHMARKER_TITLE) |
| 155 | + with doc.head: |
| 156 | + style( |
| 157 | + """\ |
| 158 | + body { |
| 159 | + padding-left: 10px; |
| 160 | + margin-bottom: 150px; |
| 161 | + background-color: #F9F8F1; |
| 162 | + color: #2C232A; |
| 163 | + font-family: sans-serif; |
| 164 | + } |
| 165 | + """ |
| 166 | + ) |
| 167 | + |
| 168 | + file_name = f.replace("+", "-").split("/")[-1].replace(".csv", "") |
| 169 | + df = pd.read_csv(f) |
| 170 | + df_special_ops = df[df["operation"].isin(SPECIAL_OPERATIONS)] |
| 171 | + df_no_special_ops = df[~df["operation"].isin(SPECIAL_OPERATIONS)] |
| 172 | + mlp_err = get_pct_err( |
| 173 | + df_special_ops["run_time_ms_predicted"].sum(), |
| 174 | + df_special_ops["run_time_ms_measured"].sum(), |
| 175 | + ) |
| 176 | + wave_scale_err = get_pct_err( |
| 177 | + df_no_special_ops["run_time_ms_predicted"].sum(), |
| 178 | + df_no_special_ops["run_time_ms_measured"].sum(), |
| 179 | + ) |
| 180 | + |
| 181 | + err_tbl = [("mlp err", mlp_err), ("wave scale err", wave_scale_err)] |
| 182 | + |
| 183 | + col_names = df.columns.to_list() |
| 184 | + with doc: |
| 185 | + h1(file_name) |
| 186 | + with div(): |
| 187 | + err_table = table() |
| 188 | + table_head = thead() |
| 189 | + header_row = tr() |
| 190 | + header_row += th("category") |
| 191 | + header_row += th("pct err") |
| 192 | + table_head += header_row |
| 193 | + err_table.add(table_head) |
| 194 | + |
| 195 | + table_body = tbody() |
| 196 | + for name, err in err_tbl: |
| 197 | + row = tr() |
| 198 | + row += td(name) |
| 199 | + row += td(err) |
| 200 | + table_body += row |
| 201 | + err_table.add(table_body) |
| 202 | + |
| 203 | + br() |
| 204 | + |
| 205 | + with div(): |
| 206 | + file_table = table() |
| 207 | + table_head = thead() |
| 208 | + header_row = tr() |
| 209 | + for c in col_names: |
| 210 | + header_row += th(c) |
| 211 | + table_head += header_row |
| 212 | + file_table.add(table_head) |
| 213 | + |
| 214 | + table_body = tbody() |
| 215 | + for _, item in df.iterrows(): |
| 216 | + row = tr() |
| 217 | + row += td(item["operation"]) |
| 218 | + row += td(round(item["run_time_ms_predicted"], 3)) |
| 219 | + row += td(round(item["unscaled_predicted_ms"], 3)) |
| 220 | + row += td(round(item["run_time_ms_measured"], 3)) |
| 221 | + row += td(round(item["wgt_pred_time"], 3)) |
| 222 | + row += td(round(item["pct_error"], 3)) |
| 223 | + row += td( |
| 224 | + item["args"] |
| 225 | + if isinstance(item["args"], str) or not math.isnan(item["args"]) |
| 226 | + else "[]" |
| 227 | + ) |
| 228 | + row += td(round(item["ktime_local_ms"], 3)) |
| 229 | + row += td(round(item["runtime_local_ms"], 3)) |
| 230 | + row += td(round(item["predicted_local_ms"], 3)) |
| 231 | + table_body += row |
| 232 | + |
| 233 | + file_table.add(table_body) |
| 234 | + |
| 235 | + with open(f"{file_name}.html", "w") as f: |
| 236 | + f.write(doc.render()) |
| 237 | + |
| 238 | + |
| 239 | +if __name__ == "__main__": |
| 240 | + parser = argparse.ArgumentParser() |
| 241 | + parser.add_argument("--e2e", required=True, help="Path to e2e folder") |
| 242 | + parser.add_argument("--ops", required=True, help="Path to ops folder") |
| 243 | + args = parser.parse_args() |
| 244 | + |
| 245 | + generate_summary(args.e2e) |
| 246 | + generate_details(args.ops) |
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