init

vllm/transformers_utils/configs/cpm.py

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+# coding=utf-8
+# Copyright 2022 The OpenBMB team.
+#
+# 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.
+
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from transformers.configuration_utils import PretrainedConfig
+from typing_extensions import TypedDict
+
+import math
+
+
+class CPMDragonflyConfig(PretrainedConfig):
+    model_type = "cpm_dragonfly"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "scale_emb": "scale_emb",
+        "scale_depth": "scale_depth",
+        "scale": "scale",
+        "attention_scale": "attention_scale",
+        "qk_norm": "qk_norm",
+        "ffn_gated": "ffn_gated",
+    } # model specific to common
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        dim_head=128,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        dropout_p=0.0,
+        hidden_act="silu",
+        scale=True,
+        scale_emb: float=1.,
+        scale_depth: float=-1,
+        dim_model_base:int=None,
+        rms_norm_eps=1e-5,
+        init_std=0.02,
+        half: bool = True,
+        half_type = 'bf16',
+        mask_modules: Optional[List[Tuple[bool, bool]]] = None,
+        use_flash_attn: bool = True,
+        flash_attn_mask_shape="1d",
+        flash_impl="cuda",
+        base=10000,
+        non_checkpointing_layers_num:int = 0,
+        attention_scale=1,
+        qk_norm=False,
+        ffn_gated=True,
+        tie_lm_head=False,
+        max_position_embeddings=2048,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.dim_head = dim_head
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.max_position_embeddings = max_position_embeddings
+        self.dropout_p = dropout_p
+        self.hidden_act = hidden_act
+        self.scale = scale
+        self.scale_emb = scale_emb
+        self.half = half
+        self.half_type = half_type
+        self.dim_model_base = dim_model_base
+        self.scale_depth = scale_depth
+        self.rms_norm_eps = rms_norm_eps
+        self.init_std = init_std
+        self.flash_impl = flash_impl
+        self.mask_modules = mask_modules
+        self.use_flash_attn = use_flash_attn
+        self.flash_attn_mask_shape = flash_attn_mask_shape
+        self.base = base
+        self.attention_scale=attention_scale
+        self.qk_norm = qk_norm
+        self.ffn_gated = ffn_gated
+        self.non_checkpointing_layers_num = non_checkpointing_layers_num
+        self.tie_lm_head = tie_lm_head
+        self.use_bfloat16 = True if self.half_type == 'bf16' else False
+        super().__init__(architectures=["CPMDragonflyForCausalLM"])
+    
+    @property
+    def scale_width(self,):
+        if self.scale:
+            return self.hidden_size / self.dim_model_base
+        else:
+            return 1.
+    
+    @property
+    def scale_states(self,):
+        if self.scale:
+            return self.scale_depth / math.sqrt(self.num_hidden_layers)
+        else:
+            return 1.