[misc] split engine_model into json file for nsys profile tool (#23117)

Signed-off-by: Grace Ho <grho@nvidia.com>
Signed-off-by: Grace Ho <146482179+gracehonv@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
Grace Ho
2025-08-19 00:44:53 -07:00
committed by GitHub
parent fda9537c5e
commit 01a08739e0
3 changed files with 135 additions and 186 deletions

View File

@@ -15,132 +15,18 @@ logger = logging.getLogger(__name__)
# helper data class for annotating kernels
class EngineModelData:
# engine + model mappings
engine_model = {
'vllm': {
'llama': {
'layer_anno': {
'Stage': {
'.*': 'layer',
},
'Substage': {
'gemm': 'gemm',
'fused_moe_kernel|GroupProblemShape|group_gemm_starts':
'moe_gemm', #llama4
'moe|sigmoid': 'moe', #llama4
'CatArrayBatched|prepare_inputs': 'prepare_next',
'flash': 'attn',
'ncclDevKernel|cross_device_reduce':
'nccl_and_custom_ar',
'_norm_': 'norm',
'act_and_mul_': 'silu',
'rotary_embedding_kernel': 'rope',
'SoftMax': 'softmax',
'elementwise': 'elementwise',
'fp8_quant': 'quantize',
'reduce_kernel': 'reduce',
'triton': 'triton_kernel',
'CUDA mem': 'non-gpu-H_D_memops',
'.*': 'misc'
}
}
},
'ds': {
'layer_anno': {
'Stage': {
'.*': 'layer',
},
'Substage': {
'block_fp8|gemm_fp8_blockwise':
'block_fp8_gemm',
'fused_moe_kernel|_group_gemm|GroupProblemShape|GemmUniversal':
'moe_gemm',
'gemm|matmul|nvjet':
'gemm',
'moe|sigmoid|expert':
'moe',
'_fwd_|FlashAttn|_mla_|_attn_':
'attn',
'CatArrayBatched':
'prepare_next',
'ncclDevKernel|cross_device_reduce':
'nccl_and_custom_ar',
'Norm|_norm_':
'norm',
'sbtopk':
'topk',
'act_and_mul_':
'activation',
'compute_position_kernel':
'rope',
'elementwise':
'elementwise',
'fp8_quant|quant_fp8|cvt_fp16_to_fp4':
'quantize',
'reduce':
'reduce',
'SoftMax':
'softmax',
'triton':
'triton_kernel',
'CUDA mem':
'non-gpu-H_D_memops',
'.*':
'misc'
}
}
},
'gpt-oss': {
'layer_anno': {
'Stage': {
'.*': 'layer',
},
'Substage': {
'block_fp8|gemm_fp8_blockwise':
'block_fp8_gemm',
'fused_moe_kernel|_group_gemm|GroupProblemShape|GemmUniversal|bmm_'
# this section is triton_moe_gemm
'|matmul_ogs_|_topk_forward|_combined_routing'
'|_sum_bitmatrix_rows|_compute_writeback_idx':
'moe_gemm',
'gemm|matmul|nvjet':
'gemm',
'moe|sigmoid|expert|splitKreduce':
'moe',
'_fwd_|FlashAttn|_mla_|_attn_|_flash_|flash::prepare_varlen|fmha':
'attn',
'CatArrayBatched':
'prepare_next',
'ncclDevKernel|cross_device_reduce':
'nccl_and_custom_ar',
'Norm|_norm_':
'norm',
'sbtopk':
'topk',
'act_and_mul_':
'activation',
'compute_position_kernel':
'rope',
'elementwise':
'elementwise',
'fp8_quant|quant_fp8|cvt_fp16_to_fp4|quantize':
'quantize',
'reduce':
'reduce',
'SoftMax':
'softmax',
'triton':
'triton_kernel',
'CUDA mem':
'non-gpu-H_D_memops',
'.*':
'misc'
}
}
}
},
}
def load_engine_model():
""" returns engine_model built from all json files in the current dir """
import glob
import json
engine_model = {}
json_files = glob.glob(
os.path.join(os.path.dirname(__file__) or ".", "*.json"))
for fname in json_files:
with open(fname, encoding="utf-8") as f:
engine_model.update(json.load(f))
return engine_model
class GPUTrace2Graph:
@@ -148,8 +34,7 @@ class GPUTrace2Graph:
Parses output of nsys report, generates csv and bar chart output
"""
def __init__(self, nsys_cmd):
self.nsys_cmd = nsys_cmd
def __init__(self):
import pandas as pd # avoid importing till needed
self.pd = pd
self.pd.options.mode.copy_on_write = True
@@ -227,7 +112,7 @@ class GPUTrace2Graph:
title = 'Model_Engine'
x = 'Model_Engine'
y = 'Elapsed Time (sec)'
color = 'Substage'
color = 'Category'
""" generate kernel mapping table """
# Sort Model_Engine categories by last field after underscore
df['Model_Engine'] = self.pd.Categorical(
@@ -249,14 +134,13 @@ class GPUTrace2Graph:
Generate data table with columns per Model_Engine into result.html
"""
pivot_df = df.pivot_table(values='Elapsed Time (sec)',
index='Substage',
index='Category',
columns='Model_Engine',
aggfunc='sum',
observed=False).round(2)
# Add sum row at bottom
pivot_df.loc['total_elapsed_sec'] = pivot_df.sum()
pivot_df.fillna('').to_html('temp.html')
print('got')
with (open(f'{output_name}.html', 'a', encoding='utf-8') as
outfile, open('temp.html', encoding='utf-8') as infile):
outfile.write(infile.read())
@@ -264,23 +148,22 @@ class GPUTrace2Graph:
print(f'Finished generating: \n'
f' {output_name}.html for stack bar chart \n'
f' {output_name}.csv for Kernel-Substage mapping')
f' {output_name}.csv for Kernel-Category mapping')
def anno_gpu_kernname(self, df, mapping):
""" add "stage" and "substage" columns """
""" add "Category" column """
def anno_gpu_kernname_helper(name, stage):
for kern_name, val in mapping['layer_anno'][stage].items():
def anno_gpu_kernname_helper(name):
for kern_name, val in mapping.items():
if re.search(kern_name, name):
return val
for stage in ['Stage', 'Substage']:
df[stage] = df['Name'].apply(anno_gpu_kernname_helper, stage=stage)
df['Category'] = df['Name'].apply(anno_gpu_kernname_helper)
def make_nongpu_row(self, df, nongpu_sec):
""" this will append non-gpu time entry at end of df """
nongpu_row = self.pd.DataFrame([df.iloc[-1]])
nongpu_row['Substage'] = nongpu_row['Name'] = 'CPU(non-GPU)'
nongpu_row['Category'] = nongpu_row['Name'] = 'CPU(non-GPU)'
nongpu_row['Instances'] = 1
nongpu_row['Elapsed Time (sec)'] = nongpu_sec
return (nongpu_row)
@@ -302,7 +185,7 @@ class GPUTrace2Graph:
logger.info('generating %s', new_file)
return True
def gen_sum_file(self, file):
def gen_sum_file(self, file, nsys_cmd):
"""
generates sum file from nsys trace with times per kernel and
returns the name of the sum file
@@ -318,17 +201,21 @@ class GPUTrace2Graph:
sum_file = f'{file_dir}/{file_name}_cuda_gpu_kernel_tracesum.csv'
if self.should_gen_file(nsys_stats_file, file):
cmd = [
self.nsys_cmd, 'stats', '-r', 'cuda_gpu_trace', file, '-o',
nsys_cmd, 'stats', '-r', 'cuda_gpu_trace', file, '-o',
f'{file_dir}/{file_name}'
]
cmd_str = ' '.join(cmd)
logger.info('+ %s', cmd_str)
# estimate time based on calibrated 240M/min
file_size_mb = os.path.getsize(file) / 1e6
logger.info(
'nsys stats for %.2f MB file expected to take %.2f min',
file_size_mb, file_size_mb / 240)
try:
subprocess.run(cmd)
subprocess.run(cmd, check=True)
except Exception:
logger.error(
"%s failed, specify --nsys_cmd for correct nsys path",
cmd_str)
logger.error("%s failed; Use --nsys_cmd to specify nsys path",
cmd_str)
exit(1)
logger.info('generating non-overalapped sum %s', sum_file)
self.gen_nonoverlapped_sum_from_gputrace(nsys_stats_file, sum_file)
@@ -336,7 +223,7 @@ class GPUTrace2Graph:
logger.info('Finished generating %s', sum_file)
return sum_file
def gen_graph(self, in_file, out_dir, title):
def gen_graph(self, in_file, out_dir, title, nsys_cmd, engine_model):
""" generates graph and csv file from in_file into out_dir """
# Initialize an empty DataFrame to store combined data
combined_df = self.pd.DataFrame()
@@ -345,17 +232,16 @@ class GPUTrace2Graph:
file_name = os.path.basename(file)
if not file_dir:
file_dir = '.'
sum_file = self.gen_sum_file(file)
sum_file = self.gen_sum_file(file, nsys_cmd)
# read kernel summary file
df = self.pd.read_csv(sum_file)
# annotate kernel to their categories
assert EngineModelData.engine_model.get(engine)
assert EngineModelData.engine_model[engine].get(model)
assert engine_model.get(engine), f'engine {engine} unknown'
assert engine_model[engine].get(model), f'model {model} unknown'
# remove nsys-rep from file_name for shorter x-label
file_name = file_name.replace('.nsys-rep', '')
df['Model_Engine'] = f'{model}_{engine}_{file_name}_{idx}'
self.anno_gpu_kernname(df,
EngineModelData.engine_model[engine][model])
self.anno_gpu_kernname(df, engine_model[engine][model])
# patch in non-gpu time
gpu_sec = round(df['Elapsed Time (sec)'].sum(), 1)
total_sec = round(float(total_sec), 1)
@@ -393,12 +279,12 @@ def main():
"--out_dir results/ --title \"Model=gpt-oss vLLM chart\""),
formatter_class=argparse.RawDescriptionHelpFormatter)
# Build help string showing available engine/model combinations
engine_model_help = []
for engine, models in EngineModelData.engine_model.items():
model_list = list(models.keys())
engine_model_help.append(f"{engine}:[{','.join(model_list)}]")
engine_model_str = ' '.join(engine_model_help)
# load supported engine_model
engine_model_supported = load_engine_model()
# Get a string representation of supported engine/model combinations
engine_model_supported_str = ', '.join(
f"{engine}:[{', '.join(models.keys())}]"
for engine, models in engine_model_supported.items())
parser.add_argument(
'--in_file',
type=parse_tuple,
@@ -408,7 +294,7 @@ def main():
'separated by space. Elapsed_nonprofiled_sec is runtime without '
'profiling used to calculate non-gpu time. Specify 0 to use '
'elapsed time from nsys-rep but that might inflate non-gpu time. '
f'Available engine:[model] are: {engine_model_str} '
f'Available engine:[model] are: {engine_model_supported_str} '
f'Example: --infile d1.nsys-rep,vllm,llama,100 '
'd2.nsys-rep,vllm,gpt-oss,102'),
required=True)
@@ -418,8 +304,9 @@ def main():
help=('nsys cmd, e.g. /usr/bin/nsys, Default: nsys'),
default="nsys")
args = parser.parse_args()
gputrace = GPUTrace2Graph(args.nsys_cmd)
gputrace.gen_graph(args.in_file, args.out_dir, args.title)
gputrace = GPUTrace2Graph()
gputrace.gen_graph(args.in_file, args.out_dir, args.title, args.nsys_cmd,
engine_model_supported)
if __name__ == '__main__':