Delete configuration_fast_slm.py

configuration_fast_slm.py

@@ -1,246 +0,0 @@
-# coding=utf-8
-# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. 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.
-""" Jamba model configuration"""
-import math
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class FastSLMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a
-    Jamba 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 jamba-small architecture.
-
-    [ai21labs/jamba-small](https://huggingface.co/ai21labs/Jamba-v0.1)
-
-    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 65536):
-            Vocabulary size of the Jamba model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`JambaModel`]
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
-            model has a output word embedding layer.
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 14336):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 8):
-            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 `8`.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        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`.
-        calc_logits_for_entire_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not to calculate logits for entire prompt during generation. If `False`, only the logits of the
-            last prompt token will be calculated, which are the only logits needed for generation. For long sequences,
-            the logits for the entire sequence may use a lot of memory so setting `calc_logits_for_entire_prompt=False`
-            will reduce memory footprint significantly.
-            Note: some generation features may not be available if this is set to `False`.
-        output_router_logits (`bool`, *optional*, defaults to `False`):
-            Whether or not the router logits should be returned by the model. Enabling this will also
-            allow the model to output the auxiliary loss. See [here]() for more details
-        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
-            The aux loss factor for the total loss.
-        pad_token_id (`int`, *optional*, defaults to 0):
-            The id of the padding token.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            The id of the "end-of-sequence" token.
-        sliding_window (`int`, *optional*):
-            Sliding window attention window size. If not specified, will default to `None`.
-        n_ctx (`int`, *optional*, defaults to 262144):
-            This value doesn't have any real effect. The maximum sequence length that this model is intended to be
-            used with. It can be used with longer sequences, but performance may degrade.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        num_experts_per_tok (`int`, *optional*, defaults to 2):
-            The number of experts to root per-token, can be also interpreted as the `top-p` routing
-            parameter
-        num_experts (`int`, *optional*, defaults to 16):
-            Number of experts per Sparse MLP layer.
-        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
-            Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and
-            `causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if
-            `True` and kernels are not available
-        mamba_d_state (`int`, *optional*, defaults to 16):
-            The dimension the mamba state space latents
-        mamba_d_conv (`int`, *optional*, defaults to 4):
-            The size of the mamba convolution kernel
-        mamba_expand (`int`, *optional*, defaults to 2):
-            Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
-        mamba_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
-            Rank of the the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
-        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
-            Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
-        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
-            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
-        mamba_inner_layernorms (`bool`, *optional*, defaults to `True`):
-            Flag indicating whether or not to apply layernorms to internal mamba activations
-
-    """
-
-    model_type = "jamba"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-            self,
-            vocab_size=65536,
-            tie_word_embeddings=False,
-            hidden_size=4096,
-            intermediate_size=14336,
-            num_hidden_layers=32,
-            num_attention_heads=32,
-            num_key_value_heads=8,
-            hidden_act="silu",
-            initializer_range=0.02,
-            rms_norm_eps=1e-6,
-            use_cache=True,
-            calc_logits_for_entire_prompt=False,
-            output_router_logits=False,
-            router_aux_loss_coef=0.001,
-            pad_token_id=0,
-            bos_token_id=1,
-            eos_token_id=2,
-            sliding_window=None,
-            max_position_embeddings=262144,
-            orig_max_position_embeddings=None,
-            attention_dropout=0.0,
-            num_experts_per_tok=2,
-            num_experts=16,
-            use_mamba_kernels=True,
-            mamba_d_state=16,
-            mamba_d_conv=4,
-            mamba_expand=2,
-            mamba_dt_rank="auto",
-            mamba_conv_bias=True,
-            mamba_proj_bias=False,
-            mamba_inner_layernorms=True,
-
-            hybrid_decoder_layer='mamba',
-
-            global_attn_idx=None,
-
-            attn_implementation_new='flash_attention_2',
-
-            mamba2_headdim=64,
-
-            rope_type=None,
-            
-            layer_types=None,
-            
-            ffn_expand_ratio=None,
-            
-            d_conv=4,
-
-            **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.tie_word_embeddings = tie_word_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.sliding_window = sliding_window
-        self.max_position_embeddings = max_position_embeddings
-        self.orig_max_position_embeddings = orig_max_position_embeddings
-        self.attention_dropout = attention_dropout
-
-        # 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.calc_logits_for_entire_prompt = calc_logits_for_entire_prompt
-        self.output_router_logits = output_router_logits
-        self.router_aux_loss_coef = router_aux_loss_coef
-
-        self.num_experts_per_tok = num_experts_per_tok
-        self.num_experts = num_experts
-
-        self.use_mamba_kernels = use_mamba_kernels
-        self.mamba_d_state = mamba_d_state
-        self.mamba_d_conv = mamba_d_conv
-        self.mamba_expand = mamba_expand
-        self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
-        self.mamba_conv_bias = mamba_conv_bias
-        self.mamba_proj_bias = mamba_proj_bias
-        self.mamba_inner_layernorms = mamba_inner_layernorms
-
-        # added by Xin 
-        self.kq_norm = kwargs.pop("kq_norm", None)
-        self.rope = kwargs.pop("rope", False)
-        self.rope_theta = kwargs.pop("rope_theta", 10000.0)
-        self.num_memory_tokens = kwargs.pop("num_memory_tokens", 0)
-        self.attn_hidden_size = kwargs.pop("attn_hidden_size", -1)
-        self.kq_head_dim = kwargs.pop("kq_head_dim", -1)
-        self.v_head_dim = kwargs.pop("v_head_dim", -1)
-
-        #! adhoc change
-        self.new_seq_length = 2048
-
-        self.hybrid_decoder_layer = hybrid_decoder_layer
-
-        self.global_attn_idx = global_attn_idx
-
-        self.attn_implementation_new = attn_implementation_new
-
-        self.mamba2_headdim = mamba2_headdim
-
-        self.rope_type = rope_type
-        
-        self.layer_types = layer_types
-        
-        self.ffn_expand_ratio = ffn_expand_ratio
-        
-        self.d_conv = d_conv
-
-        self.mlp_hidden_act = kwargs.pop("mlp_hidden_act", "silu")
-        
-        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,
-        )