Add single kernel filtering to roofline plots (#757)
* Add single kernel filtering for roofline * Add --kernel to documentation * Add kernel labels to roofline pdfs Signed-off-by: Carrie Fallows <Carrie.Fallows@amd.com> * Add test cases Signed-off-by: Carrie Fallows <Carrie.Fallows@amd.com> * Add autodetect for mode (profile or analyze) during roof validate and filter Prevent --kernel from affecting roofline in gui mode- although this may be broken in develop branch anyways Signed-off-by: Carrie Fallows <Carrie.Fallows@amd.com> * Add note about roof-only usage checking for existing profiling files in the dir. If roof-only is not provided, rocprof-compute currently assumes it has to profile in full regardless. Will look into this another day. Signed-off-by: Carrie Fallows <Carrie.Fallows@amd.com> * Update CHANGELOG.md Add line in resolved issues section to highlight that kernel filtering is now working for roofline plots * Apply changes suggested by docs team Signed-off-by: Carrie Fallows <Carrie.Fallows@amd.com> * Update projects/rocprofiler-compute/CHANGELOG.md Co-authored-by: Pratik Basyal <pratik.basyal@amd.com> --------- Signed-off-by: Carrie Fallows <Carrie.Fallows@amd.com> Co-authored-by: Pratik Basyal <pratik.basyal@amd.com>
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cfallows-amdandPratik Basyal authored and 
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@@ -193,6 +193,7 @@ class webui_analysis(OmniAnalyze_Base):
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"include_kernel_names": False,
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"is_standalone": False,
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"roofline_data_type": self.__roofline_data_type,
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"kernel_filter": False,
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}
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)
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roof_obj = self.get_socs()[self.arch].roofline_obj
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@@ -85,6 +85,7 @@ class Roofline:
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"include_kernel_names": False,
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"is_standalone": False,
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"roofline_data_type": ["FP32"], # default to FP32
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"kernel_filter": False,
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}
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)
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self.__ai_data = None
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@@ -102,13 +103,19 @@ class Roofline:
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if hasattr(self.__args, "sort") and self.__args.sort != "ALL":
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self.__run_parameters["sort_type"] = self.__args.sort
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self.__run_parameters["roofline_data_type"] = self.__args.roofline_data_type
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if (hasattr(self.__args, "kernel") and self.__args.kernel) or (
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hasattr(self.__args, "gpu_kernel") and self.__args.gpu_kernel
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):
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self.__run_parameters["kernel_filter"] = True
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self.validate_parameters()
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def validate_parameters(self):
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if self.__run_parameters["include_kernel_names"] and (
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not self.__run_parameters["is_standalone"]
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):
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console_error("--kernel-names cannot be used with --no-roof option")
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console_warning(
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"--kernel-names is nonactionable when used with --no-roof option"
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)
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def roof_setup(self):
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# Setup the workload directory for roofline profiling.
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@@ -165,6 +172,48 @@ class Roofline:
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# Create the directory
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Path(final_dir).mkdir(parents=True, exist_ok=True)
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def validate_apply_kernel_filter(self, df, path=None):
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if self.__run_parameters["kernel_filter"] is True:
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if self.__args.mode == "profile":
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df_pmc = df["pmc_perf"]
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df_filtered = df_pmc.copy()
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df_list = (df_pmc.loc[:, "Kernel_Name"]).to_list()
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for idx in range(0, len(df_list)):
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if df_list[idx].split("(")[0] not in self.__args.kernel:
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# Drop row from dataframe if kernel has not been requested
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df_filtered.drop(index=idx, inplace=True)
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# Verify that final filtered kernel df matches the kernel list requested
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if len(df_filtered.drop_duplicates(subset=["Kernel_Name"])) != len(
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self.__args.kernel
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):
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console_debug(
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"Profiled kernels: {}\n`--kernel`: {}".format(
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df_list, self.__args.kernel
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)
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)
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console_error(
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"Roofline cannot profile - kernels requested with `--kernel` missing from profiling data!" # noqa: E501
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"\n\tRe-profile workload in full or specify subset of available kernels using `--kernel` option." # noqa: E501
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"\n\tComplete profiled kernels list can be found in pmc_perf file.", # noqa: E501
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exit=True,
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)
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# Fix df structure to resemble same df arg passed in
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df["pmc_perf"] = df_filtered
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elif self.__args.mode == "analyze":
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top_kernels_csv = Path(path).joinpath("pmc_kernel_top.csv")
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if not top_kernels_csv.is_file():
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console_error(
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"roofline", "{} does not exist".format(top_kernels_csv)
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)
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k_df = pd.read_csv(top_kernels_csv)
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k_df = k_df.loc[self.__args.gpu_kernel[0], "Kernel_Name"]
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df["pmc_perf"] = df["pmc_perf"][
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df["pmc_perf"]["Kernel_Name"].isin(k_df)
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]
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return df
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@demarcate
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def empirical_roofline(
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self,
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@@ -183,6 +232,10 @@ class Roofline:
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console_debug(
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"roofline", "Path: %s" % self.__run_parameters.get("workload_dir")
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)
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# Verify kernels have been profiled and filter the df
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ret_df = self.validate_apply_kernel_filter(
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df=ret_df, path=self.__run_parameters.get("workload_dir")
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)
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self.__ai_data = calc_ai_profile(
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self.__mspec, self.__run_parameters.get("sort_type"), ret_df
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)
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@@ -192,7 +245,7 @@ class Roofline:
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console_debug(msg)
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ops_figure = flops_figure = None
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ops_dt_list = flops_dt_list = ""
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ops_dt_list = flops_dt_list = kernel_list = ""
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for dt in self.__run_parameters.get("roofline_data_type", []):
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gpu_arch = getattr(self.__mspec, "gpu_arch", "unknown_arch")
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@@ -245,6 +298,9 @@ class Roofline:
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original_kernel_names = []
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else:
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original_kernel_names = self.__ai_data.get("kernelNames", [])
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if self.__run_parameters.get("kernel_filter", False):
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for name in sorted(self.__args.kernel):
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kernel_list += "_" + name
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num_kernels = len(original_kernel_names)
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@@ -376,18 +432,23 @@ class Roofline:
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if ops_figure:
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ops_figure.write_image(
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self.__run_parameters["workload_dir"]
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+ "/empirRoof_gpu-{}{}.pdf".format(dev_id, ops_dt_list)
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+ "/empirRoof_gpu-{}{}{}.pdf".format(
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dev_id, ops_dt_list, kernel_list
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)
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)
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if flops_figure:
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flops_figure.write_image(
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self.__run_parameters["workload_dir"]
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+ "/empirRoof_gpu-{}{}.pdf".format(dev_id, flops_dt_list)
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+ "/empirRoof_gpu-{}{}{}.pdf".format(
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dev_id, flops_dt_list, kernel_list
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)
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)
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# only save a legend if kernel_names option is toggled
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if self.__run_parameters["include_kernel_names"]:
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self.__figure.write_image(
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self.__run_parameters["workload_dir"] + "/kernelName_legend.pdf"
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self.__run_parameters["workload_dir"]
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+ "/kernelName_legend{}.pdf".format(kernel_list)
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)
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time.sleep(1)
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console_log("roofline", "Empirical Roofline PDFs saved!")
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@@ -697,6 +758,7 @@ class Roofline:
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if profiling_config.get("format_rocprof_output") == "rocpd":
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t_df["pmc_perf"] = rocpd_data.process_rocpd_csv(t_df["pmc_perf"])
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t_df = self.validate_apply_kernel_filter(df=t_df, path=base_path)
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self.__ai_data = calc_ai_profile(
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self.__mspec, self.__run_parameters["sort_type"], t_df
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)
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