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[Benchmark]: Add model_config sweep mode and model registry #1180

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185f2f7
[Benchmark]: Add model_config sweep mode and expand model registry
noemotiovon Apr 1, 2026
2f68437
make checkstyle
noemotiovon Apr 3, 2026
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121 changes: 84 additions & 37 deletions benchmark/benchmarks_visualizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,50 +319,97 @@ def plot_data(df: pd.DataFrame, config: VisualizationsConfig):

plt.figure(figsize=(10, 6))
sns.set(style="whitegrid")
try:
ax = sns.lineplot(
data=df,
x="x_value",
y="y_value_50",
hue="kernel_provider",
marker="o",
palette="tab10",
errorbar=("ci", None),
)
except Exception:
ax = sns.lineplot(

use_bar_chart = config.sweep_mode == "model_config"

if use_bar_chart:
# Grouped bar chart for model_config sweep
ax = sns.barplot(
data=df,
x="x_value",
y="y_value_50",
hue="kernel_provider",
marker="o",
palette="tab10",
errorbar=None,
edgecolor="black",
linewidth=0.5,
)

# For numeric x axes, show tick labels only at actual data points
if is_numeric_x:
tick_values = sorted(df["x_value"].unique())
ax.set_xticks(tick_values)
ax.set_xticklabels([str(int(v)) if v == int(v) else str(v) for v in tick_values])

# Seaborn can't plot pre-computed error bars, so we need to do it manually
lines = ax.get_lines()
colors = [line.get_color() for line in lines]

for (_, group_data), color in zip(df.groupby("kernel_provider"), colors):
y_error_lower = group_data["y_value_50"] - group_data["y_value_20"]
y_error_upper = group_data["y_value_80"] - group_data["y_value_50"]
y_error = [y_error_lower, y_error_upper]

plt.errorbar(
group_data["x_value"],
group_data["y_value_50"],
yerr=y_error,
fmt="o",
color=color,
capsize=5,
)
# Add error bars on each bar using pre-computed percentiles
providers = df.sort_values("kernel_provider")["kernel_provider"].unique()
x_values = df["x_value"].unique()
n_providers = len(providers)
bar_width = 0.8 / n_providers # seaborn default total width is 0.8

for i, provider in enumerate(providers):
group_data = df[df["kernel_provider"] == provider]
for j, x_val in enumerate(x_values):
row = group_data[group_data["x_value"] == x_val]
if row.empty:
continue
y_val = row["y_value_50"].values[0]
y_err_lower = y_val - row["y_value_20"].values[0]
y_err_upper = row["y_value_80"].values[0] - y_val
bar_x = j + (i - (n_providers - 1) / 2) * bar_width
ax.errorbar(
bar_x,
y_val,
yerr=[[y_err_lower], [y_err_upper]],
fmt="none",
color="black",
capsize=3,
linewidth=1,
)

# Rotate x labels if they are long model config names
if not is_numeric_x:
plt.xticks(rotation=30, ha="right")
else:
# Line chart for token_length sweep
try:
ax = sns.lineplot(
data=df,
x="x_value",
y="y_value_50",
hue="kernel_provider",
marker="o",
palette="tab10",
errorbar=("ci", None),
)
except Exception:
ax = sns.lineplot(
data=df,
x="x_value",
y="y_value_50",
hue="kernel_provider",
marker="o",
palette="tab10",
errorbar=None,
)

# For numeric x axes, show tick labels only at actual data points
if is_numeric_x:
tick_values = sorted(df["x_value"].unique())
ax.set_xticks(tick_values)
ax.set_xticklabels([str(int(v)) if v == int(v) else str(v) for v in tick_values])

# Seaborn can't plot pre-computed error bars, so we need to do it manually
lines = ax.get_lines()
colors = [line.get_color() for line in lines]

for (_, group_data), color in zip(df.groupby("kernel_provider"), colors):
y_error_lower = group_data["y_value_50"] - group_data["y_value_20"]
y_error_upper = group_data["y_value_80"] - group_data["y_value_50"]
y_error = [y_error_lower, y_error_upper]

plt.errorbar(
group_data["x_value"],
group_data["y_value_50"],
yerr=y_error,
fmt="o",
color=color,
capsize=5,
)

plt.legend(title="Kernel Provider")
plt.xlabel(xlabel)
plt.ylabel(ylabel)
Expand Down
203 changes: 149 additions & 54 deletions benchmark/scripts/benchmark_attn_res.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,8 @@

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))

from benchmark_model_configs import MODEL_REGISTRY
from benchmark_model_configs import compute_model_config_sweep_config
from benchmark_model_configs import compute_seq_len_sweep_config
from benchmark_model_configs import estimate_kernel_peak_memory
from benchmark_model_configs import get_benchmark_model_config
Expand Down Expand Up @@ -69,61 +71,154 @@ def bench_memory_attn_res(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunO
return run_memory_benchmark(fn, input.kernel_operation_mode)


if __name__ == "__main__":
args = parse_benchmark_script_args()

model = get_benchmark_model_config(args.model)
probe_seq_len = 1024

def _probe():
probe_input = SingleBenchmarkRunInput(
x=probe_seq_len,
kernel_provider="pytorch",
def _resolve_model_config_attn_res(input: SingleBenchmarkRunInput):
"""Resolve model-config-sweep input into standard setup args."""
cfg = input.extra_benchmark_config
model_info = cfg["model_configs"][input.x]
return _setup_attn_res(
SingleBenchmarkRunInput(
x=cfg["seq_len"],
kernel_provider=input.kernel_provider,
extra_benchmark_config={
"N": 8,
"bsz": 1,
"hidden_size": model.hidden_size,
"dtype": model.dtype,
"eps": 1e-6,
"N": cfg["N"],
"bsz": cfg["bsz"],
"hidden_size": model_info["hidden_size"],
"dtype": model_info["dtype"],
"eps": cfg.get("eps", 1e-6),
},
)
V, fn = _setup_attn_res(probe_input)
return fn()

peak_bytes = estimate_kernel_peak_memory(probe_fn=_probe)
kernel_bpt = peak_bytes // probe_seq_len

config = compute_seq_len_sweep_config(model, kernel_bytes_per_token=kernel_bpt)

common_configs = {
"kernel_name": "attn_res",
"x_name": "T",
"x_label": "sequence length",
"x_values": [2**i for i in range(10, int(math.log2(config.seq_len)) + 1)],
"kernel_providers": ["liger", "pytorch"],
"extra_benchmark_configs": [
{
"N": 8,
"bsz": config.batch_size,
"hidden_size": model.hidden_size,
"dtype": model.dtype,
"eps": 1e-6,
}
],
"overwrite": args.overwrite,
}

run_benchmarks(
bench_test_fn=bench_speed_attn_res,
kernel_operation_modes=["full", "forward", "backward"],
metric_name="speed",
metric_unit="ms",
**common_configs,
)
run_benchmarks(
bench_test_fn=bench_memory_attn_res,
kernel_operation_modes=["full", "forward", "backward"],
metric_name="memory",
metric_unit="MB",
**common_configs,
)


def bench_speed_attn_res_model_config(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput:
V, fn = _resolve_model_config_attn_res(input)
return run_speed_benchmark(fn, input.kernel_operation_mode, [V])


def bench_memory_attn_res_model_config(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput:
V, fn = _resolve_model_config_attn_res(input)
return run_memory_benchmark(fn, input.kernel_operation_mode)


if __name__ == "__main__":
args = parse_benchmark_script_args()

if args.sweep_mode == "model_config":
all_model_configs = list(MODEL_REGISTRY.values())

def _probe_factory(model_cfg, probe_seq_len):
def _probe():
probe_input = SingleBenchmarkRunInput(
x=probe_seq_len,
kernel_provider="pytorch",
extra_benchmark_config={
"N": 8,
"bsz": 1,
"hidden_size": model_cfg.hidden_size,
"dtype": model_cfg.dtype,
"eps": 1e-6,
},
)
V, fn = _setup_attn_res(probe_input)
return fn()

return _probe

sweep = compute_model_config_sweep_config(all_model_configs, probe_fn_factory=_probe_factory, bt=args.bt)

model_configs_info = {
cfg.name: {
"hidden_size": cfg.hidden_size,
"dtype": cfg.dtype,
}
for cfg in sweep.model_configs
}

common_configs = {
"kernel_name": "attn_res",
"x_name": "model_config",
"x_label": "model configuration",
"x_values": [cfg.name for cfg in sweep.model_configs],
"kernel_providers": ["liger", "pytorch"],
"extra_benchmark_configs": [
{
"model_configs": model_configs_info,
"N": 8,
"bsz": sweep.batch_size,
"seq_len": sweep.seq_len,
"eps": 1e-6,
}
],
"overwrite": args.overwrite,
}

run_benchmarks(
bench_test_fn=bench_speed_attn_res_model_config,
kernel_operation_modes=["full", "forward", "backward"],
metric_name="speed",
metric_unit="ms",
**common_configs,
)
run_benchmarks(
bench_test_fn=bench_memory_attn_res_model_config,
kernel_operation_modes=["full", "forward", "backward"],
metric_name="memory",
metric_unit="MB",
**common_configs,
)
else:
model = get_benchmark_model_config(args.model)
probe_seq_len = 1024

def _probe():
probe_input = SingleBenchmarkRunInput(
x=probe_seq_len,
kernel_provider="pytorch",
extra_benchmark_config={
"N": 8,
"bsz": 1,
"hidden_size": model.hidden_size,
"dtype": model.dtype,
"eps": 1e-6,
},
)
V, fn = _setup_attn_res(probe_input)
return fn()

peak_bytes = estimate_kernel_peak_memory(probe_fn=_probe)
kernel_bpt = peak_bytes // probe_seq_len

config = compute_seq_len_sweep_config(model, kernel_bytes_per_token=kernel_bpt)

common_configs = {
"kernel_name": "attn_res",
"x_name": "T",
"x_label": "sequence length",
"x_values": [2**i for i in range(10, int(math.log2(config.seq_len)) + 1)],
"kernel_providers": ["liger", "pytorch"],
"extra_benchmark_configs": [
{
"N": 8,
"bsz": config.batch_size,
"hidden_size": model.hidden_size,
"dtype": model.dtype,
"eps": 1e-6,
}
],
"overwrite": args.overwrite,
}

run_benchmarks(
bench_test_fn=bench_speed_attn_res,
kernel_operation_modes=["full", "forward", "backward"],
metric_name="speed",
metric_unit="ms",
**common_configs,
)
run_benchmarks(
bench_test_fn=bench_memory_attn_res,
kernel_operation_modes=["full", "forward", "backward"],
metric_name="memory",
metric_unit="MB",
**common_configs,
)
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