Upload correct optimizer states

configuration_yulanmini.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" YuLanMinimodel configuration"""
+
+import math
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+YULANMINI_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class YuLanMiniConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`YuLanMiniModel`]. It is used to instantiate an YuLanMini
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the YuLanMini-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the YuLanMinimodel. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`YuLanMiniModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. YuLanMini1 supports up to 2048 tokens,
+            YuLanMini2 up to 4096, CodeYuLanMiniup to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalYuLanMini/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import YuLanMiniModel, YuLanMiniConfig
+
+    >>> # Initializing a YuLanMini-7b style configuration
+    >>> configuration = YuLanMiniConfig()
+
+    >>> # Initializing a model from the YuLanMini-7b style configuration
+    >>> model = YuLanMiniModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "yulanmini"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=99000,
+        hidden_size=1920,
+        intermediate_size=4800,
+        num_hidden_layers=56,
+        num_attention_heads=30,
+        num_key_value_heads=6,
+
+        # 不常用变量
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,  # /home/u20140041/pretrain-mini/preprocess/modify_tokenizer/1731
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        use_sliding_window=False,
+        sliding_window=4096,
+        rope_scaling=None,
+        attention_bias=True,  # qwen
+        attention_dropout=0.0,
+        # 放缩embedding grad
+        shrink_alpha=1,
+        shrink_alpha2=1,
+        use_liger=False,
+        # 初始化
+        initializer_range=0.014434,
+        init_scale_o=10.582218,
+        model_reproduce="transformer",
+        # 下面是为了muparam设置的参数，需要保证：默认值是不使用任何muparam的部分
+        hidden_states_shrink=1,
+        dim_model_base=None,
+        dim_ffn_base_init=None,  # 新版muparam没有使用了
+        dim_model_base_init=None,
+        dim_model_base_attn=None,
+        dim_model_base_lmh=None,
+        dim_model_base_logits=None,
+        dim_model_base_lr=None,
+        scale_emb=1,
+        # qk_layernorm
+        qk_layernorm=False,
+        layer_norm_eps=1e-6,
+        embedding_ln=False,
+        embedding_rmsln=False,
+        ln_scale=1.,
+        z_loss=0.0001,
+        # wesar
+        wesar_weights=True,
+        embed_tokens_alpha=1,
+        q_proj_alpha=1,
+        k_proj_alpha=1,
+        v_proj_alpha=1,
+        o_proj_alpha=1,
+        down_proj_alpha=1,
+        gate_up_proj_alpha=1,
+        input_layernorm_alpha=1,
+        post_attention_layernorm_alpha=1,
+        norm_alpha=1,
+        lm_head_alpha=1,
+        use_norm_alpha=True,
+        use_emb_alpha=False,
+        rms_type="llama",
+        num_steps_trained_before_this_epoch=0,
+        num_epochs_trained_before_this_epoch=0,
+        # 加速
+        gradient_checkpointing_step=7,
+        **kwargs,
+    ):
+        # 训练states，每个epoch更新，epoch内部不会变。比如训练到第4轮数据，这两个的值都是第三轮最后一步的值（epochs=3, steps=xxx），只要是在第4轮，无论是多少步，都是第三轮的值，由update_trained_steps_and_epochs控制是否更新
+        self.num_steps_trained_before_this_epoch = num_steps_trained_before_this_epoch
+        self.num_epochs_trained_before_this_epoch = num_epochs_trained_before_this_epoch
+
+        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.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if use_sliding_window else None
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_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.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.shrink_alpha = shrink_alpha
+        self.use_liger = use_liger
+        self.init_scale_o = init_scale_o
+        self.hidden_states_shrink = 1 / math.sqrt(num_hidden_layers) if hidden_states_shrink == "muparam" else hidden_states_shrink
+        self.dim_model_base = dim_model_base if dim_model_base is not None else hidden_size
+        self.dim_model_base_init = dim_model_base_init
+        self.dim_model_base_attn = dim_model_base_attn if dim_model_base_attn is not None else (hidden_size // num_attention_heads)  # 初始化为1则是使用1/H_dim
+        self.dim_model_base_lmh = dim_model_base_lmh if dim_model_base_lmh is not None else 1  # 初始化为1则是不放缩lm_head的init
+        self.scale_emb = scale_emb if scale_emb is not None else 1
+        self.model_reproduce=model_reproduce if model_reproduce is not None else "transformer"
+        self.dim_model_base_logits = dim_model_base_logits if dim_model_base_logits is not None else hidden_size
+        self.dim_model_base_lr = dim_model_base_lr if dim_model_base_lr is not None else hidden_size
+
+        self.qk_layernorm = qk_layernorm
+        self.layer_norm_eps = layer_norm_eps
+        self.embedding_ln = embedding_ln
+        self.embedding_rmsln = embedding_rmsln
+        self.ln_scale = ln_scale
+        self.z_loss = z_loss
+
+        if embedding_ln and embedding_rmsln:
+            raise ValueError("Only one of embedding_ln and embedding_rmsln should be True")
+
+        self.wesar_weights = wesar_weights
+        self.embed_tokens_alpha = embed_tokens_alpha
+        self.q_proj_alpha = q_proj_alpha
+        self.k_proj_alpha = k_proj_alpha
+        self.v_proj_alpha = v_proj_alpha
+        self.o_proj_alpha = o_proj_alpha
+        self.down_proj_alpha = down_proj_alpha
+        self.gate_up_proj_alpha = gate_up_proj_alpha
+        self.input_layernorm_alpha = input_layernorm_alpha
+        self.post_attention_layernorm_alpha = post_attention_layernorm_alpha
+        self.norm_alpha = norm_alpha
+        self.lm_head_alpha = lm_head_alpha
+        self.use_norm_alpha = use_norm_alpha
+        self.use_emb_alpha = use_emb_alpha
+        self.rms_type = rms_type
+
+        self.gradient_checkpointing_step = gradient_checkpointing_step
+
+        if self.dim_model_base != hidden_size or self.dim_model_base_init is not None or self.dim_model_base_attn != (hidden_size // num_attention_heads) or self.dim_model_base_lmh != 1:
+            if init_scale_o != 1:
+                raise ValueError("When using muparam, init_scale_o should be 1")
+
+        # multiplier
+        print("Attention放缩：", math.sqrt(self.dim_model_base_attn) / (hidden_size // num_attention_heads))
+        print("Residual链接处的Hidden States放缩：", hidden_states_shrink)
+        print("Logits放缩：", 1 / (hidden_size / self.dim_model_base))
+
+        # initializer
+        if dim_model_base_init is not None:
+            print("o_proj,down_proj初始化STD：", initializer_range / math.sqrt(2 * (hidden_size / dim_model_base_init) * num_hidden_layers))
+            print("gate_proj,up_proj,q_proj,k_proj,v_proj初始化STD：", initializer_range / math.sqrt(self.hidden_size / self.dim_model_base_init))
+        else:
+            print("o_proj,down_proj初始化STD：", initializer_range / init_scale_o)
+            print("gate_proj,up_proj,q_proj,k_proj,v_proj初始化STD：", initializer_range)
+        print("lm_head初始化STD：", initializer_range / math.sqrt(self.dim_model_base_lmh))
+
+        if not tie_word_embeddings and self.scale_emb != 1:
+            raise ValueError("When using scale_emb, tie_word_embeddings should be False")
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        try:
+            import flash_attn
+            self._attn_implementation = "flash_attention_2"
+        except:
+            pass
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

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