colramos-amd
220 строки
7.1 KiB
Python
220 строки
7.1 KiB
Python
################################################################################
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# Copyright (c) 2021 - 2022 Advanced Micro Devices, Inc. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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################################################################################
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from omniperf_cli.utils import roofline_calc
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import numpy as np
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from dash import html, dash_table
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from dash import dcc
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import plotly.graph_objects as go
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def to_int(a):
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if str(type(a)) == "<class 'NoneType'>":
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return np.nan
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else:
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return int(a)
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def generate_plots(roof_info, ai_data, verbose, fig=None):
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if fig is None:
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fig = go.Figure()
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line_data = roofline_calc.empirical_roof(roof_info)
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#######################
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# Plot BW Lines
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#######################
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fig.add_trace(
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go.Scatter(
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x=line_data["hbm"][0],
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y=line_data["hbm"][1],
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name="HBM-{}".format(roof_info["dtype"]),
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mode="lines",
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hovertemplate="<b>%{text}</b>",
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text=[
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"{} GB/s".format(to_int(line_data["hbm"][2])),
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"{} GFLOP/s".format(to_int(line_data["hbm"][2])),
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],
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)
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)
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fig.add_trace(
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go.Scatter(
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x=line_data["l2"][0],
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y=line_data["l2"][1],
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name="L2-{}".format(roof_info["dtype"]),
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mode="lines",
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hovertemplate="<b>%{text}</b>",
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text=[
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"{} GB/s".format(to_int(line_data["l2"][2])),
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"{} GFLOP/s".format(to_int(line_data["l2"][2])),
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],
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)
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)
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fig.add_trace(
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go.Scatter(
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x=line_data["l1"][0],
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y=line_data["l1"][1],
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name="L1-{}".format(roof_info["dtype"]),
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mode="lines",
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hovertemplate="<b>%{text}</b>",
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text=[
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"{} GB/s".format(to_int(line_data["l1"][2])),
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"{} GFLOP/s".format(to_int(line_data["l1"][2])),
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],
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)
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)
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fig.add_trace(
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go.Scatter(
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x=line_data["lds"][0],
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y=line_data["lds"][1],
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name="LDS-{}".format(roof_info["dtype"]),
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mode="lines",
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hovertemplate="<b>%{text}</b>",
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text=[
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"{} GB/s".format(to_int(line_data["lds"][2])),
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"{} GFLOP/s".format(to_int(line_data["lds"][2])),
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],
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)
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)
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if roof_info["dtype"] != "FP16" and roof_info["dtype"] != "I8":
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fig.add_trace(
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go.Scatter(
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x=line_data["valu"][0],
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y=line_data["valu"][1],
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name="Peak VALU-{}".format(roof_info["dtype"]),
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mode="lines",
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hovertemplate="<b>%{text}</b>",
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text=[
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"{} GFLOP/s".format(to_int(line_data["valu"][2])),
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"{} GFLOP/s".format(to_int(line_data["valu"][2])),
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],
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)
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)
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fig.add_trace(
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go.Scatter(
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x=line_data["mfma"][0],
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y=line_data["mfma"][1],
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name="Peak MFMA-{}".format(roof_info["dtype"]),
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mode="lines",
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hovertemplate="<b>%{text}</b>",
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text=[
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"{} GFLOP/s".format(to_int(line_data["mfma"][2])),
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"{} GFLOP/s".format(to_int(line_data["mfma"][2])),
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],
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)
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)
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#######################
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# Plot Application AI
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#######################
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if roof_info["dtype"] != "I8":
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fig.add_trace(
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go.Scatter(
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x=ai_data["ai_l1"][0],
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y=ai_data["ai_l1"][1],
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name="ai_l1",
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mode="markers",
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)
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)
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fig.add_trace(
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go.Scatter(
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x=ai_data["ai_l2"][0],
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y=ai_data["ai_l2"][1],
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name="ai_l2",
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mode="markers",
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)
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)
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fig.add_trace(
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go.Scatter(
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x=ai_data["ai_hbm"][0],
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y=ai_data["ai_hbm"][1],
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name="ai_hbm",
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mode="markers",
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)
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)
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fig.update_layout(
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xaxis_title="Arithmetic Intensity (FLOPs/Byte)",
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yaxis_title="Performance (GFLOP/sec)",
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hovermode="x unified",
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margin=dict(l=50, r=50, b=50, t=50, pad=4),
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)
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fig.update_xaxes(type="log", autorange=True)
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fig.update_yaxes(type="log", autorange=True)
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return fig
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def get_roofline(path_to_dir, ret_df, verbose):
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# Roofline settings
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fp32_details = {
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"path": path_to_dir,
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"sort": "kernels",
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"device": 0,
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"dtype": "FP32",
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}
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fp16_details = {
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"path": path_to_dir,
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"sort": "kernels",
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"device": 0,
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"dtype": "FP16",
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}
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int8_details = {"path": path_to_dir, "sort": "kernels", "device": 0, "dtype": "I8"}
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# Generate roofline plots
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print("Path: ", path_to_dir)
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ai_data = roofline_calc.plot_application("kernels", ret_df, verbose)
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if verbose >= 1:
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# print AI data for each mem level
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for i in ai_data:
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print(i, "->", ai_data[i])
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print("\n")
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fp32_fig = generate_plots(fp32_details, ai_data, verbose)
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fp16_fig = generate_plots(fp16_details, ai_data, verbose)
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ml_combo_fig = generate_plots(int8_details, ai_data, verbose, fp16_fig)
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return html.Section(
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id="roofline",
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children=[
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html.Div(
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className="float-container",
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children=[
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html.Div(
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className="float-child",
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children=[
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html.H3(children="Empirical Roofline Analysis (FP32/FP64)"),
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dcc.Graph(figure=fp32_fig),
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],
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),
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html.Div(
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className="float-child",
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children=[
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html.H3(children="Empirical Roofline Analysis (FP16/INT8)"),
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dcc.Graph(figure=ml_combo_fig),
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],
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),
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],
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)
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],
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)
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