[nnx] deepseek example

examples/deepseek_v3/config.py

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+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)

examples/deepseek_v3/configs/base.py

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+# Copyright 2024 The Flax Authors.
+#
+# 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.
+# ============================================================================
+"""Base configuration for the model."""
+
+import dataclasses
+from typing import Literal
+
+
+@dataclasses.dataclass
+class ModelArgs:
+  """Data class for defining model arguments and hyperparameters.
+
+  Attributes:
+      max_batch_size (int): Maximum batch size.
+      max_seq_len (int): Maximum sequence length.
+      dtype (Literal["bf16", "fp8"]): Data type for computations.
+      vocab_size (int): Vocabulary size.
+      dim (int): Model dimension.
+      inter_dim (int): Intermediate dimension for MLP layers.
+      moe_inter_dim (int): Intermediate dimension for MoE layers.
+      n_layers (int): Number of transformer layers.
+      n_dense_layers (int): Number of dense layers in the model.
+      n_heads (int): Number of attention heads.
+      n_routed_experts (int): Number of routed experts for MoE layers.
+      n_shared_experts (int): Number of shared experts for MoE layers.
+      n_activated_experts (int): Number of activated experts in MoE layers.
+      n_expert_groups (int): Number of expert groups.
+      n_limited_groups (int): Number of limited groups for MoE routing.
+      score_func (Literal["softmax", "sigmoid"]): Scoring function for MoE
+        routing.
+      route_scale (float): Scaling factor for routing scores.
+      q_lora_rank (int): LoRA rank for query projections.
+      kv_lora_rank (int): LoRA rank for key-value projections.
+      qk_nope_head_dim (int): Dimension for query-key projections without
+        positional embeddings.
+      qk_rope_head_dim (int): Dimension for query-key projections with rotary
+        embeddings.
+      v_head_dim (int): Dimension for value projections.
+      original_seq_len (int): Original sequence length.
+      rope_theta (float): Base for rotary positional encoding.
+      rope_factor (float): Scaling factor for extended sequence lengths.
+      beta_fast (int): Fast beta correction factor.
+      beta_slow (int): Slow beta correction factor.
+      mscale (float): Scaling factor for extended attention.
+      world_size (int): World size.
+      rank (int): Rank.
+      block_size (int): Block size.
+      gemm_impl (Literal["bf16", "fp8"] | None): Implementation for GEMM
+        operations.
+      attn_impl (Literal["naive", "absorb"]): Implementation for attention
+        operations.
+  """
+
+  max_batch_size: int = 8
+  max_seq_len: int = 4096 * 4
+  dtype: Literal["bf16", "fp8"] = "bf16"
+  vocab_size: int = 102400
+  dim: int = 2048
+  inter_dim: int = 10944
+  moe_inter_dim: int = 1408
+  n_layers: int = 27
+  n_dense_layers: int = 1
+  n_heads: int = 16
+  # moe
+  n_routed_experts: int = 64
+  n_shared_experts: int = 2
+  n_activated_experts: int = 6
+  n_expert_groups: int = 1
+  n_limited_groups: int = 1
+  score_func: Literal["softmax", "sigmoid"] = "softmax"
+  route_scale: float = 1.0
+  # mla
+  q_lora_rank: int = 0
+  kv_lora_rank: int = 512
+  qk_nope_head_dim: int = 128
+  qk_rope_head_dim: int = 64
+  v_head_dim: int = 128
+  # yarn
+  original_seq_len: int = 4096
+  rope_theta: float = 10000.0
+  rope_factor: float = 40
+  beta_fast: int = 32
+  beta_slow: int = 1
+  mscale: float = 1.0
+  # misc
+  world_size: int = 1
+  rank: int = 0
+  block_size: int = 128
+  gemm_impl: Literal["bf16", "fp8"] | None = "bf16"
+  attn_impl: Literal["naive", "absorb"] = "absorb"

examples/deepseek_v3/configs/config_16B.py

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+# Copyright 2024 The Flax Authors.
+#
+# 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.
+# ============================================================================
+"""Configuration for the 16B model."""
+
+from configs.base import ModelArgs
+
+
+def get_config():
+  """Returns the configuration for the model."""
+  return ModelArgs(
+      vocab_size=102400,
+      dim=2048,
+      inter_dim=10944,
+      moe_inter_dim=1408,
+      n_layers=27,
+      n_dense_layers=1,
+      n_heads=16,
+      n_routed_experts=64,
+      n_shared_experts=2,
+      n_activated_experts=6,
+      route_scale=1.0,
+      q_lora_rank=0,
+      kv_lora_rank=512,
+      qk_nope_head_dim=128,
+      qk_rope_head_dim=64,
+      v_head_dim=128,
+      mscale=0.707,
+  )

examples/deepseek_v3/configs/config_236B.py

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+# Copyright 2024 The Flax Authors.
+#
+# 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.
+# ============================================================================
+"""Configuration for the 236B model."""
+
+from configs.base import ModelArgs
+
+
+def get_config():
+  """Returns the configuration for the model."""
+  return ModelArgs(
+      vocab_size=102400,
+      dim=5120,
+      inter_dim=12288,
+      moe_inter_dim=1536,
+      n_layers=60,
+      n_dense_layers=1,
+      n_heads=128,
+      n_routed_experts=160,
+      n_shared_experts=2,
+      n_activated_experts=6,
+      n_expert_groups=8,
+      n_limited_groups=3,
+      route_scale=16.0,
+      q_lora_rank=1536,
+      kv_lora_rank=512,
+      qk_nope_head_dim=128,
+      qk_rope_head_dim=64,
+      v_head_dim=128,
+  )