File size: 3,955 Bytes
9e3709f | 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 98 99 100 101 102 103 104 | from transformers.configuration_utils import PretrainedConfig
class SarvamMoEConfig(PretrainedConfig):
model_type = "sarvam_moe"
def __init__(
self,
vocab_size=262144,
hidden_size=4096,
intermediate_size=8192,
num_hidden_layers=19,
num_attention_heads=16,
num_key_value_heads=4,
hidden_act="silu",
use_qkv_bias=False,
use_bias=False,
rms_norm_eps=1e-06,
tie_word_embeddings=False,
embedding_dropout=0.0,
attention_dropout=0.0,
output_dropout=0.0,
initializer_range=0.006,
max_position_embeddings=4096,
rope_theta=10000.0,
use_cache=True,
max_window_layers=19,
rope_scaling=None,
pad_token_id=0,
eos_token_id=1,
num_experts=128,
num_shared_experts=1,
num_experts_per_tok=6,
n_group=1,
topk_group=1,
moe_intermediate_size=1024,
first_k_dense_replace=1,
head_dim=256,
output_router_logits=False,
use_qk_norm=True,
moe_router_enable_expert_bias=True,
routed_scaling_factor=2.5,
attn_implementation: str = "eager",
**kwargs,
):
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.use_qkv_bias = use_qkv_bias
self.use_bias = use_bias
self.rms_norm_eps = rms_norm_eps
self.embedding_dropout = embedding_dropout
self.attention_dropout = attention_dropout
self.output_dropout = output_dropout
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.use_cache = use_cache
self.max_window_layers = max_window_layers
self.head_dim = head_dim or hidden_size // num_attention_heads
self.rope_scaling = rope_scaling
self.use_qk_norm = use_qk_norm
self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
self.routed_scaling_factor = routed_scaling_factor
self.num_experts = num_experts
self.num_shared_experts = num_shared_experts
self.num_experts_per_tok = num_experts_per_tok
self.n_group = n_group
self.topk_group = topk_group
self.moe_intermediate_size = moe_intermediate_size
self.first_k_dense_replace = first_k_dense_replace
self.output_router_logits = output_router_logits
self.attn_implementation = attn_implementation
self._attn_implementation = attn_implementation
self.base_model_tp_plan = {
"layers.*.attention.query_key_value": "colwise",
"layers.*.attention.dense": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
"layers.*.mlp.experts.*.gate_proj": "colwise",
"layers.*.mlp.experts.*.up_proj": "colwise",
"layers.*.mlp.experts.*.down_proj": "rowwise",
"layers.*.mlp.shared_experts.gate_proj": "colwise",
"layers.*.mlp.shared_experts.up_proj": "colwise",
"layers.*.mlp.shared_experts.down_proj": "rowwise",
}
self.base_model_pp_plan = {
"word_embeddings": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
super().__init__(
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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