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Support privateuse1 kineto activities #309

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10 changes: 6 additions & 4 deletions hta/common/trace_stack_filter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,18 +13,20 @@
def get_matching_kernels(
df_ops: pd.DataFrame, df_both: pd.DataFrame, cat_column: str
) -> pd.DataFrame:
"""Get CUDA Kernels launched by operators in df_ops.
"""Get Kernels launched by operators in df_ops.

Args:
df_ops: a DataFrame that contains cpu operators.
df_both: a DataFrame that contains both cpu operators and CUDA kernels.
df_both: a DataFrame that contains both cpu operators and kernels.
cat_column: the column name for cat in string type, which can be either `cat` or `s_cat`
depending on how the DataFrame symbol columns are encoded/decoded.

Returns:
A DataFrame that contains the CUDA kernels launched by ops in df_ops.
A DataFrame that contains the kernels launched by ops in df_ops.
"""
df_runtimes = df_ops.loc[df_ops[cat_column].eq("cuda_runtime")]
df_runtimes = df_ops.loc[
df_ops[cat_column].isin(["cuda_runtime", "privateuse1_runtime"])
]
df_kernels = df_both.loc[df_both["index_correlation"].isin(df_runtimes["index"])]
return df_kernels

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10 changes: 7 additions & 3 deletions hta/common/trace_symbol_table.py
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Expand Up @@ -307,14 +307,18 @@ def add_symbols_to_trace_df(self, trace_df: pd.DataFrame, col: str) -> None:

def get_operator_or_cuda_runtime_mask(self, df: pd.DataFrame) -> pd.Series:
"""Returns a boolean mask you can use with pandas dataframes
to filter events that are CUDA runtime events or operators."""
to filter events that are runtime events or operators."""
cpu_op_id = self.sym_index.get("cpu_op")
cuda_runtime_id = self.sym_index.get("cuda_driver", self.NULL)
cuda_driver_id = self.sym_index.get("cuda_runtime", self.NULL)
cuda_runtime_id = self.sym_index.get("cuda_runtime", self.NULL)
cuda_driver_id = self.sym_index.get("cuda_driver", self.NULL)
privateuse1_runtime_id = self.sym_index.get("privateuse1_runtime", self.NULL)
privateuse1_driver_id = self.sym_index.get("privateuse1_driver", self.NULL)
return (
(df["cat"] == cpu_op_id)
| (df["cat"] == cuda_runtime_id)
| (df["cat"] == cuda_driver_id)
| (df["cat"] == privateuse1_runtime_id)
| (df["cat"] == privateuse1_driver_id)
)

def get_runtime_launch_events_mask(self, df: pd.DataFrame) -> pd.Series:
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4 changes: 3 additions & 1 deletion hta/common/types.py
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Expand Up @@ -147,7 +147,9 @@ def to_grouping_pattern(
)

DEVICE_RUNTIME_CATEGORY_PATTERN = GroupingPattern(
re.compile("(cuda_runtime)|(mtia_runtime)"), False, "Device Runtimes"
re.compile("(cuda_runtime)|(mtia_runtime)|(privateuse1_runtime)"),
False,
"Device Runtimes",
)

MEMORY_CATEGORY_PATTERN = GroupingPattern(
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