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Format source code to PEP8 using Ruff (#36)

Quellcode durchsuchen 
* added ruff docs

* style: Run ruff and black before yapf pass

* yapf -r -i (23 fixes)

* fixed conf.py and ran ruff format .

* fixed conf.py 2

* formatted argparser.py

* formatted src/rocprof_compute_analyze

* formatted src/rocprof_compute_profile

* formatted soc_base.py

* formatted rocprof_compute_tui

* formatted gui_components

* formatted src/utils

* formatted tests/

* format extra files

* cleanup

* fix test_utils.py

* fixed typos

* Update pyproject.toml

* Update README.md

* Update test_utils.py

---------

Signed-off-by: jamessiddeley-amd <James.Siddeley@amd.com>
Co-authored-by: James Siddeley <James.Siddeley@amd.com>
Co-authored-by: systems-assistant[bot] <systems-assistant[bot]@users.noreply.github.com>
Dieser Commit ist enthalten in:
systems-assistant[bot]
2025-08-08 15:32:30 -04:00
committet von GitHub
Ursprung d3f9ab25eb
Commit 58d2a016ce
72 geänderte Dateien mit 3272 neuen und 2395 gelöschten Zeilen
Patch-Datei herunterladen Vergleichs-Datei herunterladen Alle Dateien ausklappen Alle Dateien einklappen
projects/rocprofiler-compute/src/utils/parser.py
+195 -90
Datei anzeigen
@@ -23,7 +23,6 @@
##############################################################################
import ast
import json
import re
@@ -66,10 +65,9 @@ pmc_kernel_top_table_id = 1
# },
# {
# "case": { "$eq": [ $normUnit, "per Sec"]} ,
# "then": {"$divide":[{"$subtract": ["&End_Timestamp", "&Start_Timestamp" ]}, 1000000000]}
# }
# ],
# "default": 1
# "then": {"$divide":[{"$subtract": ["&End_Timestamp",
# "&Start_Timestamp" ]},
# 1000000000]}
# }
# }
supported_denom = {
@@ -84,16 +82,19 @@ build_in_vars = {
"GRBM_GUI_ACTIVE_PER_XCD": "(GRBM_GUI_ACTIVE / $num_xcd)",
"GRBM_COUNT_PER_XCD": "(GRBM_COUNT / $num_xcd)",
"GRBM_SPI_BUSY_PER_XCD": "(GRBM_SPI_BUSY / $num_xcd)",
"numActiveCUs": "TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / $GRBM_GUI_ACTIVE_PER_XCD)), \
0) / $max_waves_per_cu) * 8) + MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) \
/ $GRBM_GUI_ACTIVE_PER_XCD)), 0), $max_waves_per_cu), 8)), $cu_per_gpu))",
"kernelBusyCycles": "ROUND(AVG((((End_Timestamp - Start_Timestamp) / 1000) * $max_sclk)), 0)",
"numActiveCUs": "TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / \
$GRBM_GUI_ACTIVE_PER_XCD)), 0) / $max_waves_per_cu) * 8) + \
MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / \
$GRBM_GUI_ACTIVE_PER_XCD)), 0), $max_waves_per_cu), 8)), $cu_per_gpu))",
"kernelBusyCycles": "ROUND(AVG((((End_Timestamp - Start_Timestamp) / \
1000) * $max_sclk)), 0)",
"hbmBandwidth": "($max_mclk / 1000 * 32 * $num_hbm_channels)",
}
supported_call = {
# If the below has single arg, like(expr), it is a aggr, in which turn to a pd function.
# If it has args like list [], in which turn to a python function.
# If the below has a single arg, like(expr), it is an aggr,
# in which case it turns into a pandas function.
# If it has args like a list [], it turns into a Python function.
"MIN": "to_min",
"MAX": "to_max",
# simple aggr
@@ -243,11 +244,18 @@ class CodeTransformer(ast.NodeTransformer):
def visit_IfExp(self, node):
self.generic_visit(node)
# print("visit_IfExp", type(node.test), type(node.body), type(node.orelse), dir(node))
# print(
# "visit_IfExp",
# type(node.test),
# type(node.body),
# type(node.orelse),
# dir(node),
# )
if isinstance(node.body, ast.Num):
raise Exception(
"Don't support body of IF with number only! Has to be expr with df['column']."
"Don't support body of IF with number only! Has to be expr with "
"df['column']."
)
new_node = ast.Expr(
@@ -289,19 +297,58 @@ class CodeTransformer(ast.NodeTransformer):
def build_eval_string(equation, coll_level, config):
"""
Convert user defined equation string to eval executable string
Convert user defined equation string to eval executable string.
For example,
input: AVG(100 * SQ_ACTIVE_INST_SCA / ( GRBM_GUI_ACTIVE * $numCU ))
output: to_avg(100 * raw_pmc_df["pmc_perf"]["SQ_ACTIVE_INST_SCA"] / \
(raw_pmc_df["pmc_perf"]["GRBM_GUI_ACTIVE"] * numCU))
input: AVG(((TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31) if (TCC_EA_RDREQ_31 != 0) else (0)))
output: to_avg((raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] / raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]).where(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] != 0, 0))
We can not handle the below for now,
input: AVG((0 if (TCC_EA_RDREQ_31 == 0) else (TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31)))
But potential workaound is,
output: to_avg(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"].where(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] == 0, raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] / raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]))
input:
AVG(100 * SQ_ACTIVE_INST_SCA / ( GRBM_GUI_ACTIVE * $numCU ))
output:
to_avg(
100 * raw_pmc_df["pmc_perf"]["SQ_ACTIVE_INST_SCA"] /
(
raw_pmc_df["pmc_perf"]["GRBM_GUI_ACTIVE"] *
numCU
)
)
input:
AVG(
(
TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31
)
if (TCC_EA_RDREQ_31 != 0)
else (0)
)
output:
to_avg(
(
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] /
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]
).where(
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] != 0,
0
)
)
We can not handle the below for now:
input:
AVG(
(
0
if (TCC_EA_RDREQ_31 == 0)
else (
TCC_EA_RDREQ_LEVEL_31 /
TCC_EA_RDREQ_31
)
)
)
But potential workaround is:
output:
to_avg(
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"].where(
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] == 0,
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] /
raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]
)
)
"""
if coll_level is None:
raise Exception("Error: coll_level can not be None.")
@@ -333,7 +380,7 @@ def build_eval_string(equation, coll_level, config):
# apply coll_level
if config.get("format_rocprof_output") == "rocpd":
# Replace SQ_ACCUM_PREV_HIRES with coll_level_ACCUM then ignore coll_level df
s = re.sub(f"SQ_ACCUM_PREV_HIRES", f"{coll_level}_ACCUM", s)
s = re.sub("SQ_ACCUM_PREV_HIRES", f"{coll_level}_ACCUM", s)
s = re.sub(
r"raw_pmc_df", "raw_pmc_df.get('" + schema.pmc_perf_file_prefix + "')", s
)
@@ -420,21 +467,24 @@ def gen_counter_list(formula):
.replace("$denom", "SQ_WAVES")
.replace(
"$numActiveCUs",
"TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / $GRBM_GUI_ACTIVE_PER_XCD})), \
0) / $maxWavesPerCU) * 8) + MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) \
/ $GRBM_GUI_ACTIVE_PER_XCD)), 0), $maxWavesPerCU), 8)), $numCU))",
"TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / "
"$GRBM_GUI_ACTIVE_PER_XCD})), 0) / $maxWavesPerCU) * 8) + "
"MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / "
"$GRBM_GUI_ACTIVE_PER_XCD)), 0), $maxWavesPerCU), 8)), $numCU))",
)
.replace("$", "")
)
for node in ast.walk(tree):
if isinstance(node, ast.Name):
val = str(node.id)[:-4] if str(node.id).endswith("_sum") else str(node.id)
val = (
str(node.id)[:-4] if str(node.id).endswith("_sum") else str(node.id)
)
if val.isupper() and val not in function_filter:
counters.append(val)
visited = True
if val in built_in_counter:
visited = True
except:
except Exception:
pass
return visited, counters
@@ -645,10 +695,7 @@ def build_dfs(archConfigs, filter_metrics, sys_info):
or (data_source_idx == "0") # no filter
or (data_source_idx in filter_metrics)
):
if (
"columnwise" in data_config
and data_config["columnwise"] == True
):
if "columnwise" in data_config and data_config["columnwise"]:
df = pd.DataFrame(
[data_config["source"]], columns=["from_csv_columnwise"]
)
@@ -726,79 +773,93 @@ def eval_metric(dfs, dfs_type, sys_info, raw_pmc_df, debug, config):
ammolite__se_per_gpu = int(sys_info.se_per_gpu)
if np.isnan(ammolite__se_per_gpu) or ammolite__se_per_gpu == 0:
console_warning(
"se_per_gpu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"se_per_gpu is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__pipes_per_gpu = int(sys_info.pipes_per_gpu)
if np.isnan(ammolite__pipes_per_gpu) or ammolite__pipes_per_gpu == 0:
console_warning(
"pipes_per_gpu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"pipes_per_gpu is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__cu_per_gpu = int(sys_info.cu_per_gpu)
if np.isnan(ammolite__cu_per_gpu) or ammolite__cu_per_gpu == 0:
console_warning(
"cu_per_gpu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"cu_per_gpu is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__simd_per_cu = int(sys_info.simd_per_cu) # not used
if np.isnan(ammolite__simd_per_cu) or ammolite__simd_per_cu == 0:
console_warning(
"simd_per_cu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"simd_per_cu is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__sqc_per_gpu = int(sys_info.sqc_per_gpu)
if np.isnan(ammolite__sqc_per_gpu) or ammolite__sqc_per_gpu == 0:
console_warning(
"sqc_per_gpu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"sqc_per_gpu is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__lds_banks_per_cu = int(sys_info.lds_banks_per_cu)
if np.isnan(ammolite__lds_banks_per_cu) or ammolite__lds_banks_per_cu == 0:
console_warning(
"lds_banks_per_cu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"lds_banks_per_cu is not available in sysinfo.csv, please provide the "
"correct value using --specs-correction"
)
ammolite__cur_sclk = float(sys_info.cur_sclk) # not used
if np.isnan(ammolite__cur_sclk) or ammolite__cur_sclk == 0:
console_warning(
"cur_sclk is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"cur_sclk is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__cur_mclk = float(sys_info.cur_mclk) # not used
if np.isnan(ammolite__cur_mclk) or ammolite__cur_mclk == 0:
console_warning(
"cur_mclk is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"cur_mclk is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__max_mclk = float(sys_info.max_mclk)
if np.isnan(ammolite__max_mclk) or ammolite__max_mclk == 0:
console_warning(
"max_mclk is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"max_mclk is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__max_sclk = float(sys_info.max_sclk)
if np.isnan(ammolite__max_sclk) or ammolite__max_sclk == 0:
console_warning(
"max_sclk is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"max_sclk is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__max_waves_per_cu = int(sys_info.max_waves_per_cu)
if np.isnan(ammolite__max_waves_per_cu) or ammolite__max_waves_per_cu == 0:
console_warning(
"max_waver_per_cu is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"max_waver_per_cu is not available in sysinfo.csv, please provide the "
"correct value using --specs-correction"
)
ammolite__num_hbm_channels = float(sys_info.num_hbm_channels)
if np.isnan(ammolite__num_hbm_channels) or ammolite__num_hbm_channels == 0:
console_warning(
"num_hbm_channels is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"num_hbm_channels is not available in sysinfo.csv, please provide the "
"correct value using --specs-correction"
)
ammolite__total_l2_chan = calc_builtin_var("$total_l2_chan", sys_info)
if np.isnan(ammolite__total_l2_chan) or ammolite__total_l2_chan == 0:
console_warning(
"total_l2_chan is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"total_l2_chan is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__num_xcd = int(sys_info.num_xcd)
if np.isnan(ammolite__num_xcd) or ammolite__num_xcd == 0:
console_warning(
"num_xcd is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"num_xcd is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
ammolite__wave_size = int(sys_info.wave_size)
if np.isnan(ammolite__wave_size) or ammolite__wave_size == 0:
console_warning(
"wave_size is not available in sysinfo.csv, please provide the correct value using --specs-correction"
"wave_size is not available in sysinfo.csv, please provide the correct "
"value using --specs-correction"
)
# TODO: fix all $normUnit in Unit column or title
# build and eval all derived build-in global variables
@@ -817,9 +878,9 @@ def eval_metric(dfs, dfs_type, sys_info, raw_pmc_df, debug, config):
except AttributeError as ae:
if ae == "'NoneType' object has no attribute 'get'":
ammolite__build_in[key] = None
ammolite__GRBM_GUI_ACTIVE_PER_XCD = ammolite__build_in["GRBM_GUI_ACTIVE_PER_XCD"]
ammolite__GRBM_COUNT_PER_XCD = ammolite__build_in["GRBM_COUNT_PER_XCD"]
ammolite__GRBM_SPI_BUSY_PER_XCD = ammolite__build_in["GRBM_SPI_BUSY_PER_XCD"]
ammolite__GRBM_GUI_ACTIVE_PER_XCD = ammolite__build_in["GRBM_GUI_ACTIVE_PER_XCD"] # noqa: F841 - Ruff: var utilized during runtime
ammolite__GRBM_COUNT_PER_XCD = ammolite__build_in["GRBM_COUNT_PER_XCD"] # noqa: F841 - Ruff: var utilized during runtime
ammolite__GRBM_SPI_BUSY_PER_XCD = ammolite__build_in["GRBM_SPI_BUSY_PER_XCD"] # noqa: F841 - Ruff: var utilized during runtime
for key, value in build_in_vars.items():
# next pass, we evaluate the builtins the depend on the per-XCD values
@@ -834,12 +895,16 @@ def eval_metric(dfs, dfs_type, sys_info, raw_pmc_df, debug, config):
except AttributeError as ae:
if ae == "'NoneType' object has no attribute 'get'":
ammolite__build_in[key] = None
ammolite__numActiveCUs = ammolite__build_in["numActiveCUs"]
ammolite__kernelBusyCycles = ammolite__build_in["kernelBusyCycles"]
ammolite__hbmBandwidth = ammolite__build_in["hbmBandwidth"]
ammolite__numActiveCUs = ammolite__build_in["numActiveCUs"] # noqa: F841 - Ruff: var utilized during runtime
ammolite__kernelBusyCycles = ammolite__build_in["kernelBusyCycles"] # noqa: F841 - Ruff: var utilized during runtime
ammolite__hbmBandwidth = ammolite__build_in["hbmBandwidth"] # noqa: F841 - Ruff: var utilized during runtime
# Hmmm... apply + lambda should just work
# df['Value'] = df['Value'].apply(lambda s: eval(compile(str(s), '<string>', 'eval')))
# df['Value'] = df['Value'].apply(
# lambda s: eval(
# compile(str(s), '<string>', 'eval')
# )
# )
for id, df in dfs.items():
if dfs_type[id] == "metric_table":
for idx, row in df.iterrows():
@@ -851,7 +916,9 @@ def eval_metric(dfs, dfs_type, sys_info, raw_pmc_df, debug, config):
print("~" * 40 + "\nExpression:")
print(expr, "=", row[expr])
print("Inputs:")
matched_vars = re.findall(r"ammolite__\w+", row[expr])
matched_vars = re.findall(
r"ammolite__\w+", row[expr]
)
if matched_vars:
for v in matched_vars:
print(
@@ -868,7 +935,7 @@ def eval_metric(dfs, dfs_type, sys_info, raw_pmc_df, debug, config):
m = re.match(
r"raw_pmc_df\['(\w+)'\]\['(\w+)'\]", c
)
t = raw_pmc_df[m.group(1)][
t = raw_pmc_df[m.group(1)][ # noqa: F841
m.group(2)
].to_list()
print(c)
@@ -890,19 +957,22 @@ def eval_metric(dfs, dfs_type, sys_info, raw_pmc_df, debug, config):
print("~" * 40)
except TypeError:
console_warning(
"Skipping entry. Encountered a missing counter\n{} has been assigned to None\n{}".format(
expr, np.nan
"Skipping entry. Encountered a missing "
"counter\n{} has been assigned to None\n{}"
.format(
expr,
np.nan,
)
)
except AttributeError as ae:
if (
str(ae)
== "'NoneType' object has no attribute 'get'"
== "'NoneType' object has no attribute "
"'get'"
):
console_warning(
"Skipping entry. Encountered a missing csv\n{}".format(
np.nan
)
"Skipping entry. Encountered a missing "
"csv\n{}".format(np.nan)
)
else:
console_error("analysis", str(ae))
@@ -967,14 +1037,16 @@ def apply_filters(workload, dir, is_gui, debug):
# We pick up kernel names from kerne ids first.
# Then filter valid entries with kernel names.
if workload.filter_kernel_ids:
if all(type(kid) == int for kid in workload.filter_kernel_ids):
if all(isinstance(kid, int) for kid in workload.filter_kernel_ids):
# Verify valid kernel filter
kernels_df = pd.read_csv(str(Path(dir).joinpath("pmc_kernel_top.csv")))
for kernel_id in workload.filter_kernel_ids:
if kernel_id >= len(kernels_df["Kernel_Name"]):
console_error(
"{} is an invalid kernel id. Please enter an id between 0-{}".format(
kernel_id, len(kernels_df["Kernel_Name"]) - 1
"{} is an invalid kernel id. Please enter an id between 0-{}"
.format(
kernel_id,
len(kernels_df["Kernel_Name"]) - 1,
)
)
kernels = []
@@ -992,7 +1064,7 @@ def apply_filters(workload, dir, is_gui, debug):
ret_df = ret_df.loc[
ret_df[schema.pmc_perf_file_prefix]["Kernel_Name"].isin(kernels)
]
elif all(type(kid) == str for kid in workload.filter_kernel_ids):
elif all(isinstance(kid, str) for kid in workload.filter_kernel_ids):
df_cleaned = ret_df[schema.pmc_perf_file_prefix]["Kernel_Name"].apply(
lambda x: x.strip() if isinstance(x, str) else x
)
@@ -1028,7 +1100,8 @@ def apply_filters(workload, dir, is_gui, debug):
def find_key_recursively(data, search_key):
"""
Recursively search for the search_key in the given data (which can be a dict or list).
Recursively search for the search_key in the given data
(which can be a dict or list).
If the key is found, returns the value as a DataFrame.
"""
if isinstance(data, dict):
@@ -1050,7 +1123,6 @@ def find_key_recursively(data, search_key):
def search_key_in_json(file_path, search_key):
# FIXME:
# Load the entire JSON into memory.
# Should not use for large file.
@@ -1081,14 +1153,18 @@ def search_pc_sampling_record(records):
"inst_index": None,
"stall_reason": {
"NONE": 0,
"NO_INSTRUCTION_AVAILABLE": 0, # No instruction available in the instruction cache.
# No instruction available in the instruction cache.
"NO_INSTRUCTION_AVAILABLE": 0,
"ALU_DEPENDENCY": 0, # ALU dependency not resolved.
"WAITCNT": 0,
"INTERNAL_INSTRUCTION": 0, # Wave executes an internal instruction.
"BARRIER_WAIT": 0,
"ARBITER_NOT_WIN": 0, # The instruction did not win the arbiter.
"ARBITER_WIN_EX_STALL": 0, # Arbiter issued an instruction, but the execution pipe pushed it back from execution.
"OTHER_WAIT": 0, # Other types of wait (e.g., wait for XNACK acknowledgment).
"ARBITER_WIN_EX_STALL": 0,
# Arbiter issued an instruction, but the execution pipe
# pushed it back from execution.
"OTHER_WAIT": 0,
# Other types of wait (e.g., wait for XNACK acknowledgment).
"SLEEP_WAIT": 0,
"LAST": 0,
},
@@ -1116,14 +1192,19 @@ def search_pc_sampling_record(records):
and inst_index is not None
):
grouped_data[code_object_id][code_object_offset]["count"] += 1
# NB: the write here could be duplicated. If there is perf issue, We might want to opt it.
# NB: the write here could be duplicated. If there is perf issue,
# We might want to opt it.
grouped_data[code_object_id][code_object_offset]["inst_index"] = inst_index
if len(snapshot):
if issued:
grouped_data[code_object_id][code_object_offset]["count_issued"] += 1
grouped_data[code_object_id][code_object_offset][
"count_issued"
] += 1
else:
grouped_data[code_object_id][code_object_offset]["count_stalled"] += 1
grouped_data[code_object_id][code_object_offset][
"count_stalled"
] += 1
grouped_data[code_object_id][code_object_offset]["stall_reason"][
snapshot.get("stall_reason")[rocp_inst_not_issued_prefix_len:]
] += 1
@@ -1138,7 +1219,8 @@ def search_pc_sampling_record(records):
# print(grouped_data)
# Convert to sorted list of tuples (code_object_id, inst_index, code_object_offset, count)
# Convert to sorted list of tuples:
# (code_object_id, inst_index, code_object_offset, count)
sorted_counts = sorted(
[
(
@@ -1148,7 +1230,8 @@ def search_pc_sampling_record(records):
info["count"],
info["count_issued"],
info["count_stalled"],
# For info["stall_reason"], remove the zero entries, sorting the remaining items by their values in descending order
# For info["stall_reason"], remove the zero entries,
# sorting the remaining items by their values in descending order
sorted(
((k, v) for k, v in info["stall_reason"].items() if v > 0),
key=lambda item: item[1],
@@ -1173,7 +1256,8 @@ def load_pc_sampling_data_per_kernel(
) -> pd.DataFrame:
"""
Load PC sampling raw data from json file with given method and kernel name,
count pc sampling and sort it in the order of compiled asm and associate with kernel source code if available,
count pc sampling and sort it in the order of compiled asm and associate with
kernel source code if available,
then return df.
:param method: "host_trap" or "stochastic".
@@ -1258,11 +1342,25 @@ def load_pc_sampling_data_per_kernel(
(df["code_object_id"] == kernel_info["code_object_id"])
& (df["offset"] > kernel_info["entry_byte_offset"])
& (df["offset"] < kernel_info["potential_end_offset"])
][["inst_index", "offset", "count", "count_issued", "count_stalled", "stall_reason"]]
][
[
"inst_index",
"offset",
"count",
"count_issued",
"count_stalled",
"stall_reason",
]
]
df["offset"] = df["offset"].apply(lambda x: hex(x))
# df["stall_reason"] = df["stall_reason"].apply(lambda x: ', '.join(f"{k}: {v}" for k, v in x))
# df["stall_reason"] = df["stall_reason"].apply(
# lambda x: ', '.join(
# f"{k}: {v}"
# for k, v in x
# )
# )
pc_sample_instructions = search_key_in_json(file_name, "pc_sample_instructions")
# print(pc_sample_instructions)
@@ -1334,7 +1432,9 @@ def load_pc_sampling_data(workload, dir, file_prefix, sorting_type):
# - The default file name is subject to changes from rocprofv3
# - Prioritize stochastic
# - Alternatively, we could check pc_sampling_method in json
csv_file_path = Path.joinpath(Path(dir), file_prefix + "_pc_sampling_stochastic.csv")
csv_file_path = Path.joinpath(
Path(dir), file_prefix + "_pc_sampling_stochastic.csv"
)
if csv_file_path.exists():
pc_sampling_method = "stochastic"
else:
@@ -1352,7 +1452,6 @@ def load_pc_sampling_data(workload, dir, file_prefix, sorting_type):
# No kernel filter, return grouped and sorted csv directly
if not workload.filter_kernel_ids:
df = pd.read_csv(csv_file_path)
# Group by 'Instruction_Comment' and count occurrences
grouped_counts = (
@@ -1379,7 +1478,8 @@ def load_pc_sampling_data(workload, dir, file_prefix, sorting_type):
elif len(workload.filter_kernel_ids) > 1:
console_error(
"PC sampling supports single kernel only! Please specify -k with single kernel."
"PC sampling supports single kernel only! Please specify -k with "
"single kernel."
)
return pd.DataFrame()
@@ -1409,7 +1509,8 @@ def load_pc_sampling_data(workload, dir, file_prefix, sorting_type):
@demarcate
def load_kernel_top(workload, dir, args):
# NB:
# - Do pmc_kernel_top.csv loading before eval_metric because we need the kernel names.
# - Do pmc_kernel_top.csv loading before eval_metric because we need the
# kernel names.
# - There might be a better way/timing to load raw_csv_table.
# FIXME:
@@ -1427,9 +1528,11 @@ def load_kernel_top(workload, dir, args):
tmp[id] = pd.read_csv(file)
else:
console_warning(
f"Couldn't load {file.name}. This may result in missing analysis data."
f"Couldn't load {file.name}. "
"This may result in missing analysis data."
)
# NB: Special case for sysinfo. Probably room for improvement in this whole function design
# NB: Special case for sysinfo. Probably room for improvement in this whole
# function design
elif "from_csv_columnwise" in df.columns and id == 101:
tmp[id] = workload.sys_info.transpose()
# All transposed columns should be marked with a general header
@@ -1447,7 +1550,8 @@ def load_kernel_top(workload, dir, args):
tmp[id].columns = ["Info"]
else:
console_warning(
f"Couldn't load {file.name}. This may result in missing analysis data."
f"Couldn't load {file.name}. "
"This may result in missing analysis data."
)
elif "from_pc_sampling" in df.columns:
tmp[id] = load_pc_sampling_data(
@@ -1513,7 +1617,8 @@ def correct_sys_info(mspec, specs_correction: dict):
if not hasattr(mspec, str(k)):
console_error(
"analyze",
f"Invalid specs correction '{k}'. Please use --specs option to peak valid specs",
f"Invalid specs correction '{k}'. Please use --specs option "
f"to peak valid specs",
)
setattr(mspec, str(k), v)
return mspec.get_class_members()