File size: 3,841 Bytes
69262b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | # Copyright 2026 The TensorMind team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TensorMind model configuration"""
try:
from transformers.configuration_utils import PreTrainedConfig
from transformers.modeling_rope_utils import RopeParameters
except ImportError:
from transformers.configuration_utils import PretrainedConfig as PreTrainedConfig
RopeParameters = None
class TensorMindConfig(PreTrainedConfig):
model_type = "tensormind"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size: int | None = 32768,
hidden_size: int | None = 1024,
intermediate_size: int | None = 4096,
num_hidden_layers: int | None = 32,
num_attention_heads: int | None = 16,
num_key_value_heads: int | None = 8,
hidden_act: str | None = "silu",
max_position_embeddings: int | None = 32768,
initializer_range: float | None = 0.02,
rms_norm_eps: int | None = 1e-6,
use_cache: bool | None = True,
tie_word_embeddings: bool | None = True,
attention_bias: bool | None = False,
attention_dropout: float | None = 0.0,
pad_token_id: int | None = None,
bos_token_id: int | None = None,
eos_token_id: int | None = None,
rope_parameters: RopeParameters | dict[str, RopeParameters] | None = {
"rope_type": "default",
"rope_theta": 10000.0,
# YaRN
# "factor": 4.0,
# "original_max_position_embeddings": 32768,
# "attention_factor": 1.0,
# "beta_fast": 32.0,
# "beta_slow": 1.0,
},
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.rope_parameters = rope_parameters
self.tie_word_embeddings = tie_word_embeddings
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self._attn_implementation = "sdpa"
super().__init__(**kwargs)
__all__ = ["TensorMindConfig"]
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