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Fix freeze pane, add more metrics #250

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Merged
merged 5 commits into from
Oct 5, 2025
Merged

Fix freeze pane, add more metrics #250

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b93cb03
Fix freeze pane, add more metrics
xadupre Oct 5, 2025
5f23f61
changes
xadupre Oct 5, 2025
3d809b7
spell
xadupre Oct 5, 2025
b1b6f5d
fix
xadupre Oct 5, 2025
2541cd9
add stats
xadupre Oct 5, 2025
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1 change: 1 addition & 0 deletions CHANGELOGS.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ Change Logs
0.7.14
++++++

* :pr:`250`: add variables to track sequence nodes
* :pr:`249`: patches _maybe_broadcast to support a corner case

0.7.13
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2 changes: 1 addition & 1 deletion _unittests/ut_helpers/test_log_helper.py
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Expand Up @@ -268,7 +268,7 @@ def test_cube_logs_performance_cube_time(self):
cube = CubeLogsPerformance(dfs, keep_last_date=True)
cube.load()
ct = cube.clone()
self.assertEqual((52, 111), ct.shape)
self.assertEqual((52, 116), ct.shape)

def test_duplicate(self):
df = pandas.DataFrame(
Expand Down
52 changes: 39 additions & 13 deletions onnx_diagnostic/helpers/log_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1167,7 +1167,7 @@ def to_excel(
df.to_excel(
writer,
sheet_name=name,
freeze_panes=(df.columns.nlevels + df.index.nlevels, df.index.nlevels),
freeze_panes=(df.columns.nlevels + 1, df.index.nlevels),
)
f_highlights[name] = tview.f_highlight
if tview.plots:
Expand Down Expand Up @@ -1222,15 +1222,15 @@ def to_excel(
writer,
sheet_name=name,
freeze_panes=(
sbs_raw.columns.nlevels + sbs_raw.index.nlevels,
sbs_raw.columns.nlevels + 1,
sbs_raw.index.nlevels,
),
)
sbs_agg.to_excel(
writer,
sheet_name=f"{name}-AGG",
freeze_panes=(
sbs_agg.columns.nlevels + sbs_agg.index.nlevels,
sbs_agg.columns.nlevels + 1,
sbs_agg.index.nlevels,
),
)
Expand Down Expand Up @@ -1456,9 +1456,7 @@ def _mkc(m, s):


class CubeLogsPerformance(CubeLogs):
"""
Processes logs coming from experiments.
"""
"""Processes logs coming from experiments."""

def __init__(
self,
Expand Down Expand Up @@ -1511,20 +1509,25 @@ def __init__(
"n_model_faster2x",
"n_model_faster3x",
"n_model_faster4x",
"n_model_faster5x",
"n_node_attention",
"n_node_attention23",
"n_node_rotary_embedding",
"n_node_rotary_embedding23",
"n_node_layer_normalization",
"n_node_layer_normalization23",
"n_node_causal_mask",
"n_node_constant",
"n_node_control_flow",
"n_node_scatter",
"n_node_expand",
"n_node_function",
"n_node_gqa",
"n_node_initializer",
"n_node_initializer_small",
"n_node_constant",
"n_node_layer_normalization",
"n_node_layer_normalization23",
"n_node_reshape",
"n_node_rotary_embedding",
"n_node_rotary_embedding23",
"n_node_scatter",
"n_node_sequence",
"n_node_shape",
"n_node_expand",
"onnx_n_nodes_no_cst",
"peak_gpu_torch",
"peak_gpu_nvidia",
Expand Down Expand Up @@ -1690,6 +1693,11 @@ def first_err(df: pandas.DataFrame) -> pandas.Series:
"time_latency",
gdf(df, "time_latency_eager") > gdf(df, "time_latency", np.inf) * 3.98,
),
n_model_faster5x=lambda df: gpreserve(
df,
"time_latency",
gdf(df, "time_latency_eager") > gdf(df, "time_latency", np.inf) * 4.98,
),
n_node_attention23=lambda df: gpreserve(
df, "time_latency_eager", gdf(df, "op_onnx__Attention")
),
Expand Down Expand Up @@ -1720,6 +1728,11 @@ def first_err(df: pandas.DataFrame) -> pandas.Series:
+ gdf(df, "op_onnx_com.microsoft_DecoderMaskedMultiHeadAttention", 0)
+ gdf(df, "op_onnx_com.microsoft_SparseAttention", 0),
),
n_node_gqa=lambda df: gpreserve(
df,
"time_latency_eager",
gdf(df, "op_onnx_com.microsoft_GroupQueryAttention", 0),
),
n_node_layer_normalization=lambda df: gpreserve(
df,
"time_latency_eager",
Expand Down Expand Up @@ -1764,9 +1777,22 @@ def first_err(df: pandas.DataFrame) -> pandas.Series:
n_node_shape=lambda df: gpreserve(
df, "time_latency_eager", gdf(df, "op_onnx__Shape")
),
n_node_reshape=lambda df: gpreserve(
df, "time_latency_eager", gdf(df, "op_onnx__Reshape")
),
n_node_expand=lambda df: gpreserve(
df, "time_latency_eager", gdf(df, "op_onnx__Expand")
),
n_node_causal_mask=lambda df: gpreserve(
df,
"time_latency_eager",
gdf(df, "op_onnx__CausalMask", 0),
),
n_node_sequence=lambda df: gpreserve(
df,
"time_latency_eager",
gdf(df, "op_onnx__SequenceAt", 0) + gdf(df, "op_onnx__SplitToSequence", 0),
),
)
assert (
formula in lambdas
Expand Down
1 change: 1 addition & 0 deletions onnx_diagnostic/torch_models/validate.py
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Expand Up @@ -1744,6 +1744,7 @@ def _simplify(p):
"constant_folding",
"remove_identity",
"remove_duplicated_initializer",
"remove_duplicated_shape",
"dynamic_dimension_naming",
"inline",
"check",
Expand Down
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