mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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549 lines
19 KiB
Python
549 lines
19 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Literal, Optional
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from pydantic import BaseModel, Extra
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from tensorrt_llm.functional import PositionEmbeddingType
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class BuildConfig(BaseModel, extra=Extra.allow):
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num_layers: int
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num_heads: int
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hidden_size: int
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vocab_size: int
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hidden_act: Optional[str]
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n_positions: int
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max_batch_size: int
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max_input_len: int
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num_kv_heads: Optional[int] = None
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max_output_len: Optional[int] = None
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# TRT builder_optimization_level from 0 to 5
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builder_opt: Optional[int] = None
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inter_size: Optional[int] = None
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rotary_dim: Optional[int] = None
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type_vocab_size: Optional[int] = None
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use_smooth_quant: bool = False
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per_token: bool = False
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per_channel: bool = False
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pre_norm: Optional[bool] = None
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do_layer_norm_before: Optional[bool] = None
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enable_qk_half_accum: bool = False
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enable_context_fmha: bool = True
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# None means using the model family's default value defined in the ctor
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position_embedding_type: Optional[PositionEmbeddingType] = None
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# Only when position embedding is RoPE, this value makes sense, make
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# default value to be None, not 0 or 1 to prevent misuse
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rotary_pct: Optional[float] = None
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bias: bool = True
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class ModelConfig(BaseModel):
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name: str
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family: str
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benchmark_type: Literal["gpt", "bert"]
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build_config: BuildConfig
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_allowed_configs = {
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"gpt_350m":
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ModelConfig(name="gpt_350m",
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family="gpt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=1024,
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vocab_size=51200,
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hidden_act='gelu',
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n_positions=1024,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"gpt_1.5b":
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ModelConfig(name="gpt_1.5b",
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family="gpt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=48,
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num_heads=25,
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hidden_size=1600,
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vocab_size=51200,
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hidden_act='gelu',
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n_positions=1024,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"gpt_175b":
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ModelConfig(name="gpt_175b",
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family="gpt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=96,
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num_heads=96,
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hidden_size=12288,
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vocab_size=51200,
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hidden_act='gelu',
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n_positions=2048,
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max_batch_size=64,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"gpt_350m_sq_per_tensor":
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ModelConfig(name="gpt_350m_sq_per_tensor",
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family="gpt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=1024,
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vocab_size=51200,
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hidden_act='gelu',
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n_positions=1024,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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use_smooth_quant=True,
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)),
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"gpt_350m_sq_per_token_channel":
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ModelConfig(name="gpt_350m_sq_per_token_channel",
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family="gpt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=1024,
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vocab_size=51200,
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hidden_act='gelu',
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n_positions=1024,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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use_smooth_quant=True,
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per_token=True,
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per_channel=True,
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)),
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"gpt-next_2b":
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ModelConfig(name="gpt-next_2b",
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family="gpt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=2048,
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vocab_size=256000,
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hidden_act='swiglu',
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n_positions=1024,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_pct=0.5,
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bias=False,
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)),
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"opt_350m":
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ModelConfig(name="opt_350m",
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family="opt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=1024,
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vocab_size=50272,
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hidden_act='relu',
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n_positions=2048,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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pre_norm=False,
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do_layer_norm_before=False,
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)),
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"opt_2.7b":
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ModelConfig(name="opt_2.7b",
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family="opt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=32,
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num_heads=32,
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hidden_size=2560,
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vocab_size=50272,
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hidden_act='relu',
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n_positions=2048,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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pre_norm=False,
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do_layer_norm_before=True,
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)),
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"opt_6.7b":
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ModelConfig(name="opt_6.7b",
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family="opt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=32,
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num_heads=32,
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hidden_size=4096,
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vocab_size=50272,
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hidden_act='relu',
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n_positions=2048,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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pre_norm=False,
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do_layer_norm_before=True,
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)),
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"opt_66b":
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ModelConfig(name="opt_66b",
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family="opt",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=64,
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num_heads=72,
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hidden_size=9216,
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vocab_size=50272,
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hidden_act='relu',
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n_positions=2048,
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max_batch_size=64,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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pre_norm=True,
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do_layer_norm_before=True,
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)),
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"llama_7b":
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ModelConfig(name="llama_7b",
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family="llama",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=32,
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num_heads=32,
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hidden_size=4096,
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vocab_size=32000,
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hidden_act='silu',
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n_positions=2048,
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inter_size=11008,
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max_batch_size=128,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"llama_13b":
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ModelConfig(name="llama_13b",
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family="llama",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=40,
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num_heads=40,
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hidden_size=5120,
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vocab_size=32000,
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hidden_act='silu',
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n_positions=2048,
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inter_size=13824,
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max_batch_size=128,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"llama_30b":
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ModelConfig(name="llama_30b",
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family="llama",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=60,
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num_heads=52,
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hidden_size=6656,
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vocab_size=32000,
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hidden_act='silu',
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n_positions=2048,
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inter_size=17920,
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max_batch_size=64,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"llama_70b":
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ModelConfig(name="llama_70b",
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family="llama",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=80,
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num_heads=64,
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num_kv_heads=8,
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hidden_size=8192,
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vocab_size=32000,
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hidden_act='silu',
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n_positions=2048,
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inter_size=28672,
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max_batch_size=64,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"llama_70b_sq_per_tensor":
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ModelConfig(name="llama_70b_sq_per_tensor",
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family="llama",
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benchmark_type="gpt",
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build_config=BuildConfig(num_layers=80,
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num_heads=64,
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num_kv_heads=8,
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hidden_size=8192,
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vocab_size=32000,
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hidden_act='silu',
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n_positions=2048,
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inter_size=28672,
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max_batch_size=128,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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use_smooth_quant=True)),
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"gptj_6b":
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ModelConfig(name="gptj_6b",
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family="gptj",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=28,
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num_heads=16,
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hidden_size=4096,
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vocab_size=50401,
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hidden_act='gelu',
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n_positions=1024,
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rotary_dim=64,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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)),
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"gptneox_20b":
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ModelConfig(name="gptneox_20b",
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family="gptneox",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=44,
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num_heads=64,
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hidden_size=6144,
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vocab_size=50432,
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hidden_act='gelu',
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n_positions=2048,
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rotary_dim=24,
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max_batch_size=16,
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max_input_len=512,
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max_output_len=512,
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builder_opt=None,
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)),
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"chatglm_6b":
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ModelConfig(name="chatglm_6b",
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family="chatglm",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=28,
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num_heads=32,
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hidden_size=4096,
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vocab_size=130528,
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hidden_act='gelu',
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n_positions=2048,
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max_batch_size=256,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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remove_input_padding=False,
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)),
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"bloom_560m":
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ModelConfig(name="bloom_560m",
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family="bloom",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=1024,
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vocab_size=250880,
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hidden_act=None,
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n_positions=2048,
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max_batch_size=8,
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max_input_len=1024,
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max_output_len=1024,
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builder_opt=None,
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)),
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"bloom_176b":
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ModelConfig(name="bloom_176b",
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family="bloom",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=70,
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num_heads=112,
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hidden_size=14336,
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vocab_size=250880,
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hidden_act=None,
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n_positions=2048,
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max_batch_size=8,
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max_input_len=1024,
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max_output_len=1024,
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builder_opt=None,
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)),
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"bert_base":
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ModelConfig(name="bert_base",
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family="bert",
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benchmark_type="bert",
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build_config=BuildConfig(
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num_layers=12,
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num_heads=12,
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hidden_size=768,
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vocab_size=30522,
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type_vocab_size=2,
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hidden_act='gelu',
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n_positions=1024,
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max_batch_size=256,
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max_input_len=512,
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builder_opt=None,
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enable_qk_half_accum=False,
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enable_context_fmha=False,
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)),
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"bert_large":
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ModelConfig(name="bert_large",
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family="bert",
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benchmark_type="bert",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=16,
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hidden_size=1024,
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vocab_size=30522,
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type_vocab_size=2,
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hidden_act='gelu',
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n_positions=1024,
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max_batch_size=64,
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max_input_len=512,
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builder_opt=None,
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enable_qk_half_accum=False,
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enable_context_fmha=False,
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)),
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"falcon_rw_1b":
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ModelConfig(name="falcon_rw_1b",
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family="falcon",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=24,
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num_heads=32,
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hidden_size=2048,
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vocab_size=50304,
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hidden_act=None,
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n_positions=2048,
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max_batch_size=256,
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max_input_len=1024,
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max_output_len=1024,
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builder_opt=None,
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bias=True,
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use_alibi=True,
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parallel_attention=False,
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new_decoder_architecture=False,
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)),
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"falcon_7b":
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ModelConfig(name="falcon_7b",
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family="falcon",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=32,
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num_heads=71,
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num_kv_heads=1,
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hidden_size=4544,
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vocab_size=65024,
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hidden_act=None,
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n_positions=2048,
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max_batch_size=128,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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bias=False,
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use_alibi=False,
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parallel_attention=True,
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new_decoder_architecture=False,
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)),
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"falcon_40b":
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ModelConfig(name="falcon_40b",
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family="falcon",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=60,
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num_heads=128,
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num_kv_heads=8,
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hidden_size=8192,
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vocab_size=65024,
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hidden_act=None,
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n_positions=2048,
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max_batch_size=64,
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max_input_len=512,
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max_output_len=200,
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builder_opt=None,
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bias=False,
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use_alibi=False,
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parallel_attention=True,
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new_decoder_architecture=False,
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)),
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"falcon_180b":
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ModelConfig(name="falcon_180b",
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family="falcon",
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benchmark_type="gpt",
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build_config=BuildConfig(
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num_layers=80,
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num_heads=232,
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num_kv_heads=8,
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hidden_size=14848,
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vocab_size=65024,
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hidden_act=None,
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n_positions=2048,
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max_batch_size=8,
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max_input_len=1024,
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max_output_len=1024,
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builder_opt=None,
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bias=False,
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use_alibi=False,
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parallel_attention=True,
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new_decoder_architecture=False,
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)),
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}
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def get_allowed_models(benchmark_type=None):
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if benchmark_type is None:
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return set(_allowed_configs.keys())
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else:
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return set(i.name for i in _allowed_configs.values()
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if i.benchmark_type == benchmark_type)
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|
|
|
def get_build_config(model_name):
|
|
if model_name in _allowed_configs:
|
|
return dict(_allowed_configs[model_name].build_config)
|
|
else:
|
|
raise KeyError(f'Unexpected model: {model_name}. Please add the model '
|
|
'to allowed_configs.py')
|
|
|
|
|
|
def get_model_family(model_name):
|
|
if model_name in _allowed_configs:
|
|
return _allowed_configs[model_name].family
|
|
else:
|
|
raise KeyError(f'Unexpected model: {model_name}. Please add the model '
|
|
'to allowed_configs.py')
|