Upload HunYuanMoEV1ForCausalLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "add_classification_head": false,
+  "anyres_pooling_size": 2,
+  "anyres_vit_max_image_size": null,
+  "anyres_vit_two_views": false,
+  "architectures": [
+    "HunYuanMoEV1ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.1,
+  "attention_head_dim": 128,
+  "auto_map": {
+    "AutoConfig": "configuration_hunyuan.HunYuanConfig",
+    "AutoModel": "hunyuan.HunYuanModel",
+    "AutoModelForCausalLM": "hunyuan.HunYuanMoEV1ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "cla_share_factor": 2,
+  "class_num": 0,
+  "dense_list": [
+    4096,
+    0
+  ],
+  "eod_token_id": 127967,
+  "eos_token_id": 127960,
+  "group_limited_greedy": false,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "im_end_id": 6,
+  "im_newline_id": 12,
+  "im_start_id": 5,
+  "image_token_id": 9,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "kv_lora_rank": null,
+  "mask_init_id": 13,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "hunyuan",
+  "moe_drop_tokens": false,
+  "moe_intermediate_size": [
+    3072,
+    3072,
+    3072,
+    3072,
+    3072,
+    3072,
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+    3072,
+    3072,
+    3072,
+    3072,
+    3072,
+    3072,
+    3072
+  ],
+  "moe_layer_num_skipped": 0,
+  "moe_random_routing_dropped_token": false,
+  "moe_topk": [
+    8,
+    8,
+    8,
+    8,
+    8,
+    8,
+    8,
+    8,
+    8,
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+  ],
+  "n_group": null,
+  "norm_topk_prob": true,
+  "norm_type": "rms",
+  "num_attention_heads": 32,
+  "num_experts": 64,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "num_media_embeds": 257,
+  "num_shared_expert": [
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
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+  ],
+  "org_vocab_size": 128167,
+  "pad_id": 127961,
+  "pad_token_id": 127961,
+  "pool_type": "last",
+  "position_embedding_xdrope": false,
+  "pretraining_tp": 1,
+  "q_lora_rank": null,
+  "qk_nope_head_dim": null,
+  "qk_rope_head_dim": null,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "alpha": 1000.0,
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 1.0,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "type": "dynamic"
+  },
+  "rope_theta": 10000.0,
+  "routed_scaling_factor": 1.0,
+  "sep_token_id": 127962,
+  "skip_cls_token": false,
+  "text_end_id": 8,
+  "text_start_id": 7,
+  "tie_word_embeddings": true,
+  "topk_group": null,
+  "torch_dtype": "float16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_cla": false,
+  "use_mixed_mlp_moe": true,
+  "use_mla": false,
+  "use_qk_norm": true,
+  "use_rotary_pos_emb": true,
+  "v_head_dim": null,
+  "video_end_id": 11,
+  "video_start_id": 10,
+  "vit_add_patchemb_bias": false,
+  "vit_input_resolution": 224,
+  "vit_mapping_type": "resampler",
+  "vit_norm_type": "fused",
+  "vit_patch": 1,
+  "vit_path": null,
+  "vit_remove_prenorm": false,
+  "vit_token": 64,
+  "vit_type": null,
+  "vit_used_rms_norm": false,
+  "vocab_size": 128167,
+  "xdrope_section": null
+}

configuration_hunyuan.py

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+# coding=utf-8
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+""" HunYuan model configuration"""
+from torch import nn
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+from typing import List, Union, Optional
+
+
+logger = logging.get_logger(__name__)
+
+
+class HunYuanConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`HunYuanModel`]. It is used to instantiate an
+    HunYuan 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 HunYuan-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 HunYuan model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`HunYuanModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations or shared MLP representations.
+        moe_intermediate_size (`int` or `List`, *optional*, defaults to 11008):
+            Dimension of the MLP representations in MoE. Use a list if you want a different size per layer.
+        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.
+        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/LocalLLaMA/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.
+        use_qk_norm (`bool`, *optional*, defaults to `False`):
+            Whether query and key in attention use norm
+        use_cla (`bool`, *optional*, defaults to `False`):
+            Whether to use CLA in attention
+        cla_share_factor (`int`, *optional*, defaults to 1):
+            The share factor of CLA
+        num_experts (`int` or `List`, *optional*, defaults to 1):
+            The number of experts for moe. If it is a list, it will be used as the number of experts for each layer.
+        num_shared_expert (`int` or `List`, *optional*, defaults to 1):
+            The number of shared experts for moe. If it is a list, it will be used as the number of shared experts for each layer.
+        moe_topk (`int` or `List`, *optional*, defaults to 1):
+            The topk value for moe. If it is a list, it will be used as the topk value for each layer.
+        capacity_factor (Not used) (`float` or `List`, *optional*, defaults to 1.0):
+            The capacity factor for moe. If it is a list, it will be used as the capacity factor for each layer.
+        moe_layer_num_skipped (`int`, *optional*, defaults to 0):
+            First moe_layer_num_skipped layers do not use MoE.
+    """
+
+    model_type = "hunyuan"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=290943,
+        org_vocab_size=290943,
+        hidden_size=4096,
+        intermediate_size: int=11008,
+        moe_intermediate_size: Union[int, List]=None,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        attention_head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        eod_token_id=3,
+        sep_token_id=4,
+        im_start_id=5,
+        im_end_id=6,
+        text_start_id=7,
+        text_end_id=8,
+        image_token_id=9,
+        video_start_id=10,
+        video_end_id=11,
+        im_newline_id=12,
+        mask_init_id=13,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        mlp_bias=False,
+        attention_dropout=0.0,
+        use_qk_norm=False,
+        use_rotary_pos_emb=True,
+        use_cla=False,
+        cla_share_factor=1,
+        norm_type="hf_rms",
+        num_experts: Union[int, List]=1,
+        use_mixed_mlp_moe=False,
+        num_shared_expert: Union[int, List]=1,
+        moe_topk: Union[int, List]=1,
+        # capacity_factor: Union[int, List]=1.0,
+        moe_drop_tokens=False,
+        moe_random_routing_dropped_token=False,
+        use_mla=False,
+        kv_lora_rank=512,
+        q_lora_rank=1536,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        qk_nope_head_dim=128,
+        moe_layer_num_skipped=0,
+        norm_topk_prob=True,
+        routed_scaling_factor=1.0,
+        group_limited_greedy=False,
+        n_group=None,
+        topk_group=None,
+        vit_path=None,
+        num_media_embeds=257,
+        vit_type="AnyResVit",
+        vit_input_resolution=224,
+        vit_token=64,
+        vit_patch=1,
+        vit_mapping_type="simple_conv_mlp",
+        vit_norm_type="fused",
+        vit_used_rms_norm=True,
+        vit_remove_prenorm=True,
+        vit_add_patchemb_bias=True,
+        anyres_vit_max_image_size=2048,
+        anyres_pooling_size=2,
+        anyres_vit_two_views=False,
+        skip_cls_token=False,
+        position_embedding_xdrope=False,
+        xdrope_section=None,
+        add_classification_head=False,
+        class_num=0,
+        pool_type="last",
+        pad_id=-1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.org_vocab_size = org_vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_experts = num_experts
+        self.use_mixed_mlp_moe = use_mixed_mlp_moe
+        self.num_shared_expert = num_shared_expert
+        self.moe_topk = moe_topk
+        # self.capacity_factor = capacity_factor
+        self.moe_drop_tokens = moe_drop_tokens
+        self.moe_random_routing_dropped_token = moe_random_routing_dropped_token
+
+        if attention_head_dim is not None:
+            self.attention_head_dim = attention_head_dim
+        else:
+            self.attention_head_dim = self.hidden_size // num_attention_heads
+
+        # 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.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        # self._rope_scaling_validation()   # TODO: Need validation?
+        self.attention_bias = attention_bias
+        self.mlp_bias = mlp_bias
+        self.attention_dropout = attention_dropout
+        self.use_qk_norm = use_qk_norm
+        self.use_rotary_pos_emb = use_rotary_pos_emb
+        self.use_cla = use_cla
+        self.cla_share_factor = cla_share_factor
+        self.norm_type = norm_type
+        # MLA args
+        self.use_mla = use_mla
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.v_head_dim = v_head_dim
+
+        # DeepSeek related args
+        self.moe_layer_num_skipped = moe_layer_num_skipped
+        self.norm_topk_prob = norm_topk_prob
+        self.routed_scaling_factor = routed_scaling_factor
+        self.group_limited_greedy = group_limited_greedy
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.add_classification_head = add_classification_head
+        self.class_num = class_num
+        self.pool_type = pool_type
+        self.pad_id = pad_id
+
+        if self.class_num is not None:
+            self.dense_list = [self.hidden_size, self.class_num]
+
+        # Vit args
+        self.vit_path = vit_path
+        self.num_media_embeds = num_media_embeds
+        self.vit_type = vit_type
+        self.vit_input_resolution = vit_input_resolution
+        self.vit_token = vit_token
+        self.vit_patch = vit_patch
+        self.vit_mapping_type = vit_mapping_type
+        self.vit_norm_type = vit_norm_type
+        self.vit_used_rms_norm = vit_used_rms_norm
+        self.vit_remove_prenorm = vit_remove_prenorm
+        self.vit_add_patchemb_bias = vit_add_patchemb_bias
+        self.anyres_vit_max_image_size = anyres_vit_max_image_size
+        self.anyres_pooling_size = anyres_pooling_size
+        self.anyres_vit_two_views = anyres_vit_two_views
+        self.skip_cls_token = skip_cls_token
+        self.position_embedding_xdrope = position_embedding_xdrope
+        self.xdrope_section = xdrope_section
+
+        # token id
+        self.eod_token_id = eod_token_id
+        self.im_start_id = im_start_id
+        self.im_end_id = im_end_id
+        self.text_start_id = text_start_id
+        self.text_end_id = text_end_id
+        self.image_token_id = image_token_id
+        self.video_start_id = video_start_id
+        self.video_end_id = video_end_id
+        self.im_newline_id = im_newline_id
+        self.mask_init_id = mask_init_id
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            sep_token_id=sep_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    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` or `type` and `alpha`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        rope_scaling_alpha = self.rope_scaling.get("alpha", 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 and rope_scaling_alpha is None:
+            raise ValueError("`rope_scaling`'s factor or alpha field must be have one, got both of none")
+        if rope_scaling_factor is not None:
+            if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+                raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
+        if rope_scaling_alpha is not None:
+            if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
+                raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 127960,
+  "pad_token_id": 127961,
+  "transformers_version": "4.52.4"
+}

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