[nnx] deepseek example v2

examples/deepseek_v3/config.py

@@ -0,0 +1,177 @@
+import dataclasses
+from typing import TypedDict
+from transformers.utils import logging
+from collections.abc import Callable
+
+from flax.core.frozen_dict import FrozenDict
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+class RopeScalingConf(TypedDict):
+  type: str
+  factor: int
+  mscale: float
+  beta_fast: int
+  beta_slow: int
+  mscale_all_dim: float
+  original_max_position_embeddings: int
+
+
+@dataclasses.dataclass(unsafe_hash=True, eq=True)
+class Config:
+  r"""
+  This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
+  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 DeepSeek-V3.
+  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 129280):
+          Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+          `inputs_ids` passed when calling [`DeepseekV3Model`]
+      hidden_size (`int`, *optional*, defaults to 4096):
+          Dimension of the hidden representations.
+      intermediate_size (`int`, *optional*, defaults to 11008):
+          Dimension of the MLP representations.
+      moe_intermediate_size (`int`, *optional*, defaults to 1407):
+          Dimension of the MoE representations.
+      num_hidden_layers (`int`, *optional*, defaults to 32):
+          Number of hidden layers in the Transformer decoder.
+      num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+          Number of nextn predict layers in the DeepSeekV3 Model.
+      num_attention_heads (`int`, *optional*, defaults to 32):
+          Number of attention heads for each attention layer in the Transformer decoder.
+      n_shared_experts (`int`, *optional*, defaults to None):
+          Number of shared experts, None means dense model.
+      n_routed_experts (`int`, *optional*, defaults to None):
+          Number of routed experts, None means dense model.
+      routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+          Scaling factor or routed experts.
+      topk_method (`str`, *optional*, defaults to `gready`):
+          Topk method used in routed gate.
+      n_group (`int`, *optional*, defaults to None):
+          Number of groups for routed experts.
+      topk_group (`int`, *optional*, defaults to None):
+          Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+      num_experts_per_tok (`int`, *optional*, defaults to None):
+          Number of selected experts, None means dense model.
+      moe_layer_freq (`int`, *optional*, defaults to 1):
+          The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+      first_k_dense_replace (`int`, *optional*, defaults to 0):
+          Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                          \--k dense layers--/
+      norm_topk_prob (`bool`, *optional*, defaults to False):
+          Whether to normalize the weights of the routed experts.
+      scoring_func (`str`, *optional*, defaults to 'softmax'):
+          Method of computing expert weights.
+      aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+          Auxiliary loss weight coefficient.
+      seq_aux = (`bool`, *optional*, defaults to True):
+          Whether to compute the auxiliary loss for each individual sample.
+      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.
+      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 DeepseekV3Model, DeepseekV3Config
+  >>> # Initializing a Deepseek-V3 style configuration
+  >>> configuration = DeepseekV3Config()
+  >>> # Accessing the model configuration
+  >>> configuration = model.config
+  ```"""
+
+  model_type = 'deepseek_v3'
+  keys_to_ignore_at_inference: tuple[str, ...] = ('past_key_values',)
+  vocab_size: int = 129280
+  hidden_size: int = 7168
+  intermediate_size: int = 18432
+  moe_intermediate_size: int = 2048
+  num_hidden_layers: int = 61
+  num_nextn_predict_layers: int = 1
+  num_attention_heads: int = 128
+  num_key_value_heads: int = 128
+  n_shared_experts: int = 1
+  n_routed_experts: int = 256
+  ep_size: int = 1
+  routed_scaling_factor: float = 2.5
+  kv_lora_rank: int = 512
+  q_lora_rank: int | None = 1536
+  qk_rope_head_dim: int = 64
+  v_head_dim: int = 128
+  qk_nope_head_dim: int = 128
+  topk_method: str = 'noaux_tc'
+  n_group: int = 8
+  topk_group: int = 4
+  num_experts_per_tok: int = 8
+  moe_layer_freq: int = 1
+  first_k_dense_replace: int = 3
+  norm_topk_prob: bool = True
+  scoring_func: str = 'sigmoid'
+  aux_loss_alpha: float = 0.001
+  seq_aux: bool = True
+  hidden_act: str | Callable = 'silu'
+  max_position_embeddings: int = 4096
+  initializer_range: float = 0.02
+  rms_norm_eps: float = 1e-6
+  use_cache: bool = True
+  pad_token_id: int | None = None
+  bos_token_id: int = 0
+  eos_token_id: int = 1
+  pretraining_tp: int = 1
+  tie_word_embeddings: bool = False
+  rope_theta: float = 10000.0
+  rope_scaling: RopeScalingConf | None = None
+  attention_bias: bool = False
+  attention_dropout: float = 0.0
+
+  def __post_init__(self):
+    # for backward compatibility
+    if self.num_key_value_heads is None:
+      self.num_key_value_heads = self.num_attention_heads
+
+    if self.rope_scaling is not None:
+      self.rope_scaling = FrozenDict(self.rope_scaling)
+
+def get_config():
+  return Config()

examples/deepseek_v3/deepseek/configuration_deepseek.py

@@ -0,0 +1,210 @@
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+class DeepseekV3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
+    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 DeepSeek-V3.
+
+    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 129280):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV3Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+            Number of nextn predict layers in the DeepSeekV3 Model.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to None):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to False):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'softmax'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        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.
+        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 DeepseekV3Model, DeepseekV3Config
+
+    >>> # Initializing a Deepseek-V3 style configuration
+    >>> configuration = DeepseekV3Config()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        moe_intermediate_size = 2048,
+        num_hidden_layers=61,
+        num_nextn_predict_layers=1,
+        num_attention_heads=128,
+        num_key_value_heads=128,
+        n_shared_experts = 1,
+        n_routed_experts = 256,
+        ep_size = 1,
+        routed_scaling_factor = 2.5,
+        kv_lora_rank = 512,
+        q_lora_rank = 1536,
+        qk_rope_head_dim = 64,
+        v_head_dim = 128,
+        qk_nope_head_dim = 128,
+        topk_method = 'noaux_tc',
+        n_group = 8,
+        topk_group = 4,
+        num_experts_per_tok = 8,
+        moe_layer_freq = 1,
+        first_k_dense_replace = 3,
+        norm_topk_prob = True,
+        scoring_func = 'sigmoid',
+        aux_loss_alpha = 0.001,
+        seq_aux = True,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = 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_nextn_predict_layers = num_nextn_predict_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        self.aux_loss_alpha = aux_loss_alpha
+        self.seq_aux = seq_aux
+        # 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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        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,
+        )