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https://github.com/NVIDIA/TensorRT-LLM.git
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be9cd719f7
* Update TensorRT-LLM --------- Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com> Co-authored-by: Tayef Shah <tayefshah@gmail.com> Co-authored-by: lfz941 <linfanzai941@gmail.com>
105 lines
2.8 KiB
Python
105 lines
2.8 KiB
Python
import argparse
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template = '''{{
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"num_experts": {num_experts},
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"k": {k},
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"hidden_size": {hidden_size},
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"inter_size": {inter_size},
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"tp_size": {tp_size},
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"ep_size": {ep_size},
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"world_rank": {world_rank},
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"num_tokens": {num_tokens},
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"act_fn": {act_fn},
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"norm_mode": {norm_mode},
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{dtype_string}
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{routing_string}
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{tactic_string}
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"bias": 0
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}}'''
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def make_dtype_string(dtypes=None):
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if dtypes is None:
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return ""
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if not isinstance(dtypes, list):
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dtypes = [dtypes]
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join_term = '","' # Include quotes because they should be strings
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return f'"dtypes": ["{join_term.join(dtypes)}"],'
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def make_routing_string(name=None, values=None, is_distribution=False):
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if values is None and name is None:
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return ""
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values_field = "routing_distribution" if is_distribution else "routing_values"
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if values is None:
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return f'"{values_field}": "{name}",'
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values = f'"{values_field}": [{",".join(map(str, values))}],'
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if name is not None:
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values += f' "routing_values_name": "{name}",'
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return values
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def make_tactic_string(tactic_id=None, tactic_id1=None, tactic_id2=None):
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if tactic_id is not None:
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return f'"tactic_id": {tactic_id},'
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if not tactic_id1 and not tactic_id2:
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return f'"tactic_id": "auto",'
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return f'"tactic_id1": {tactic_id1},\n "tactic_id2": {tactic_id2},'
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def populate_benchmark_config(**kwargs):
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return template.format(**kwargs)
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# Default Mixtral configurations
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num_experts = 8
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k = 2
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hidden_size = 4096
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inter_size = 14336
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tp_size = 4
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ep_size = 1
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world_rank = 0
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act_fn = 3
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norm_mode = 1
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dtype_string = make_dtype_string() # All dtypes
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routing_string = make_routing_string(
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name="uniform",
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is_distribution=True) # Use the default uniform random distribution
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tactic_id1 = '"auto"'
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tactic_id2 = '"auto"'
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configs = []
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for num_tokens in [1, 8, 64, 2048, 65536]:
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configs.append(
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populate_benchmark_config(
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num_experts=num_experts,
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k=k,
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hidden_size=hidden_size,
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inter_size=inter_size,
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tp_size=tp_size,
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ep_size=ep_size,
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world_rank=world_rank,
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num_tokens=num_tokens,
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act_fn=act_fn,
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norm_mode=norm_mode,
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dtype_string=dtype_string,
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routing_string=routing_string,
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tactic_string=make_tactic_string(tactic_id1=tactic_id1,
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tactic_id2=tactic_id2),
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))
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full_string = "[\n" + ",\n".join(configs) + "\n]"
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parser = argparse.ArgumentParser()
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parser.add_argument('filename',
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type=str,
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help='The name of the file to generate',
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nargs='?',
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default="moe-benchmark-file.json")
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args = parser.parse_args()
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with open(args.filename, "w+") as f:
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f.write(full_string)
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