diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000000000000000000000000000000000000..85e6d86c1707eab26462f3979384667b228ed363
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,96 @@
+{%- macro render_content(msg) -%}
+ {%- set c = msg.get('content') -%}
+ {%- if c is string -%}
+ {{ c }}
+ {%- elif c is not none -%}
+ {% for content in c -%}
+ {% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%}
+ <|media_start|>image<|media_content|><|media_pad|><|media_end|>
+ {% else -%}
+ {{ content['text'] }}
+ {%- endif -%}
+ {%- endfor -%}
+ {%- endif -%}
+{%- endmacro -%}
+
+{% macro set_roles(message) -%}
+ {%- set role_name = message.get('name') or message['role'] -%}
+ {%- if message['role'] == 'user' -%}
+ <|im_user|>{{role_name}}<|im_middle|>
+ {%- elif message['role'] == 'assistant' -%}
+ <|im_assistant|>{{role_name}}<|im_middle|>
+ {%- else -%}
+ <|im_system|>{{role_name}}<|im_middle|>
+ {%- endif -%}
+{%- endmacro -%}
+
+
+{%- macro render_toolcalls(message) -%}
+ <|tool_calls_section_begin|>
+ {%- for tool_call in message['tool_calls'] -%}
+ {%- set formatted_id = tool_call['id'] -%}
+ <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|>
+ {%- endfor -%}
+ <|tool_calls_section_end|>
+{%- endmacro -%}
+
+
+{# Find last non-tool-call assisitant message #}
+{%- set ns = namespace(last_non_tool_call_assistant_msg=-1) -%}
+{%- for idx in range(messages|length-1, -1, -1) -%}
+ {%- if messages[idx]['role'] == 'assistant' and not messages[idx].get('tool_calls') -%}
+ {%- set ns.last_non_tool_call_assistant_msg = idx -%}
+ {%- break -%}
+ {%- endif -%}
+{%- endfor -%}
+
+{# split all messages into history & suffix, reasoning_content in suffix should be reserved.#}
+{%- set hist_msgs = messages[:ns.last_non_tool_call_assistant_msg+1] -%}
+{%- set suffix_msgs = messages[ns.last_non_tool_call_assistant_msg+1:] -%}
+
+{%- if tools -%}
+ <|im_system|>tool_declare<|im_middle|>{{ tools | tojson(separators=(',', ':')) }}<|im_end|>
+{%- endif -%}
+
+{%- for message in hist_msgs -%}
+ {%- if loop.first and messages[0]['role'] != 'system' -%}
+ <|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|>
+ {%- endif -%}
+ {{set_roles(message)}}
+ {%- if message['role'] == 'assistant' -%}
+ {{render_content(message)}}
+ {%- if message.get('tool_calls') -%}
+ {{render_toolcalls(message)}}
+ {%- endif -%}
+ {%- elif message['role'] == 'tool' -%}
+ {%- set tool_call_id = message.tool_call_id -%}
+ ## Return of {{ tool_call_id }}
+{{render_content(message)}}
+ {%- elif message['content'] is not none -%}
+ {{render_content(message)}}
+ {%- endif -%}
+ <|im_end|>
+{%- endfor -%}
+
+{%- for message in suffix_msgs -%}
+ {{set_roles(message)}}
+ {%- if message['role'] == 'assistant' -%}
+ {%- set rc = message.get('reasoning_content', '') -%}
+ {{rc}}{{render_content(message)}}
+ {%- if message.get('tool_calls') -%}
+ {{render_toolcalls(message)}}
+ {%- endif -%}
+ {%- elif message['role'] == 'tool' -%}
+ {%- set tool_call_id = message.tool_call_id -%}
+ ## Return of {{ tool_call_id }}
+{{render_content(message)}}
+ {%- elif message['content'] is not none -%}
+ {{render_content(message)}}
+ {%- endif -%}
+ <|im_end|>
+{%- endfor -%}
+
+
+{%- if add_generation_prompt -%}
+ <|im_assistant|>assistant<|im_middle|>
+{%- endif -%}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c210b47cdad1c04abe5954fdef0ceddc1cd475f0
--- /dev/null
+++ b/config.json
@@ -0,0 +1,219 @@
+{
+ "_attn_implementation_autoset": false,
+ "architectures": [
+ "DeepseekV3ForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_deepseek.DeepseekV3Config",
+ "AutoModel": "modeling_deepseek.DeepseekV3Model",
+ "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
+ },
+ "aux_loss_alpha": 0.001,
+ "bos_token_id": 163584,
+ "dtype": "bfloat16",
+ "eos_token_id": 163586,
+ "ep_size": 1,
+ "first_k_dense_replace": 1,
+ "hidden_act": "silu",
+ "hidden_size": 7168,
+ "initializer_range": 0.02,
+ "intermediate_size": 18432,
+ "kv_lora_rank": 512,
+ "max_position_embeddings": 262144,
+ "model_type": "deepseek_v3",
+ "moe_intermediate_size": 2048,
+ "moe_layer_freq": 1,
+ "n_group": 1,
+ "n_routed_experts": 384,
+ "n_shared_experts": 1,
+ "norm_topk_prob": true,
+ "num_attention_heads": 64,
+ "num_experts_per_tok": 8,
+ "num_hidden_layers": 61,
+ "num_key_value_heads": 64,
+ "num_nextn_predict_layers": 0,
+ "pad_token_id": 163839,
+ "pretraining_tp": 1,
+ "q_lora_rank": 1536,
+ "qk_nope_head_dim": 128,
+ "qk_rope_head_dim": 64,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": {
+ "beta_fast": 1.0,
+ "beta_slow": 1.0,
+ "factor": 64.0,
+ "mscale": 1.0,
+ "mscale_all_dim": 1.0,
+ "original_max_position_embeddings": 4096,
+ "type": "yarn"
+ },
+ "rope_theta": 50000.0,
+ "routed_scaling_factor": 2.827,
+ "scoring_func": "sigmoid",
+ "seq_aux": true,
+ "tie_word_embeddings": false,
+ "topk_group": 1,
+ "topk_method": "noaux_tc",
+ "transformers_version": "4.57.1",
+ "use_cache": true,
+ "v_head_dim": 128,
+ "vocab_size": 163840,
+ "quantization_config": {
+ "config_groups": {
+ "group_0": {
+ "input_activations": {
+ "dynamic": false,
+ "num_bits": 4,
+ "type": "float",
+ "group_size": 16
+ },
+ "weights": {
+ "dynamic": false,
+ "num_bits": 4,
+ "type": "float",
+ "group_size": 16
+ },
+ "targets": [
+ "Linear"
+ ]
+ }
+ },
+ "ignore": [
+ "lm_head",
+ "model.layers.0*",
+ "model.layers.1.mlp.shared_experts*",
+ "model.layers.1.self_attn*",
+ "model.layers.10.mlp.shared_experts*",
+ "model.layers.10.self_attn*",
+ "model.layers.11.mlp.shared_experts*",
+ "model.layers.11.self_attn*",
+ "model.layers.12.mlp.shared_experts*",
+ "model.layers.12.self_attn*",
+ "model.layers.13.mlp.shared_experts*",
+ "model.layers.13.self_attn*",
+ "model.layers.14.mlp.shared_experts*",
+ "model.layers.14.self_attn*",
+ "model.layers.15.mlp.shared_experts*",
+ "model.layers.15.self_attn*",
+ "model.layers.16.mlp.shared_experts*",
+ "model.layers.16.self_attn*",
+ "model.layers.17.mlp.shared_experts*",
+ "model.layers.17.self_attn*",
+ "model.layers.18.mlp.shared_experts*",
+ "model.layers.18.self_attn*",
+ "model.layers.19.mlp.shared_experts*",
+ "model.layers.19.self_attn*",
+ "model.layers.2.mlp.shared_experts*",
+ "model.layers.2.self_attn*",
+ "model.layers.20.mlp.shared_experts*",
+ "model.layers.20.self_attn*",
+ "model.layers.21.mlp.shared_experts*",
+ "model.layers.21.self_attn*",
+ "model.layers.22.mlp.shared_experts*",
+ "model.layers.22.self_attn*",
+ "model.layers.23.mlp.shared_experts*",
+ "model.layers.23.self_attn*",
+ "model.layers.24.mlp.shared_experts*",
+ "model.layers.24.self_attn*",
+ "model.layers.25.mlp.shared_experts*",
+ "model.layers.25.self_attn*",
+ "model.layers.26.mlp.shared_experts*",
+ "model.layers.26.self_attn*",
+ "model.layers.27.mlp.shared_experts*",
+ "model.layers.27.self_attn*",
+ "model.layers.28.mlp.shared_experts*",
+ "model.layers.28.self_attn*",
+ "model.layers.29.mlp.shared_experts*",
+ "model.layers.29.self_attn*",
+ "model.layers.3.mlp.shared_experts*",
+ "model.layers.3.self_attn*",
+ "model.layers.30.mlp.shared_experts*",
+ "model.layers.30.self_attn*",
+ "model.layers.31.mlp.shared_experts*",
+ "model.layers.31.self_attn*",
+ "model.layers.32.mlp.shared_experts*",
+ "model.layers.32.self_attn*",
+ "model.layers.33.mlp.shared_experts*",
+ "model.layers.33.self_attn*",
+ "model.layers.34.mlp.shared_experts*",
+ "model.layers.34.self_attn*",
+ "model.layers.35.mlp.shared_experts*",
+ "model.layers.35.self_attn*",
+ "model.layers.36.mlp.shared_experts*",
+ "model.layers.36.self_attn*",
+ "model.layers.37.mlp.shared_experts*",
+ "model.layers.37.self_attn*",
+ "model.layers.38.mlp.shared_experts*",
+ "model.layers.38.self_attn*",
+ "model.layers.39.mlp.shared_experts*",
+ "model.layers.39.self_attn*",
+ "model.layers.4.mlp.shared_experts*",
+ "model.layers.4.self_attn*",
+ "model.layers.40.mlp.shared_experts*",
+ "model.layers.40.self_attn*",
+ "model.layers.41.mlp.shared_experts*",
+ "model.layers.41.self_attn*",
+ "model.layers.42.mlp.shared_experts*",
+ "model.layers.42.self_attn*",
+ "model.layers.43.mlp.shared_experts*",
+ "model.layers.43.self_attn*",
+ "model.layers.44.mlp.shared_experts*",
+ "model.layers.44.self_attn*",
+ "model.layers.45.mlp.shared_experts*",
+ "model.layers.45.self_attn*",
+ "model.layers.46.mlp.shared_experts*",
+ "model.layers.46.self_attn*",
+ "model.layers.47.mlp.shared_experts*",
+ "model.layers.47.self_attn*",
+ "model.layers.48.mlp.shared_experts*",
+ "model.layers.48.self_attn*",
+ "model.layers.49.mlp.shared_experts*",
+ "model.layers.49.self_attn*",
+ "model.layers.5.mlp.shared_experts*",
+ "model.layers.5.self_attn*",
+ "model.layers.50.mlp.shared_experts*",
+ "model.layers.50.self_attn*",
+ "model.layers.51.mlp.shared_experts*",
+ "model.layers.51.self_attn*",
+ "model.layers.52.mlp.shared_experts*",
+ "model.layers.52.self_attn*",
+ "model.layers.53.mlp.shared_experts*",
+ "model.layers.53.self_attn*",
+ "model.layers.54.mlp.shared_experts*",
+ "model.layers.54.self_attn*",
+ "model.layers.55.mlp.shared_experts*",
+ "model.layers.55.self_attn*",
+ "model.layers.56.mlp.shared_experts*",
+ "model.layers.56.self_attn*",
+ "model.layers.57.mlp.shared_experts*",
+ "model.layers.57.self_attn*",
+ "model.layers.58.mlp.shared_experts*",
+ "model.layers.58.self_attn*",
+ "model.layers.59.mlp.shared_experts*",
+ "model.layers.59.self_attn*",
+ "model.layers.6.mlp.shared_experts*",
+ "model.layers.6.self_attn*",
+ "model.layers.60.mlp.shared_experts*",
+ "model.layers.60.self_attn*",
+ "model.layers.7.mlp.shared_experts*",
+ "model.layers.7.self_attn*",
+ "model.layers.8.mlp.shared_experts*",
+ "model.layers.8.self_attn*",
+ "model.layers.9.mlp.shared_experts*",
+ "model.layers.9.self_attn*"
+ ],
+ "quant_algo": "NVFP4",
+ "kv_cache_scheme": {
+ "dynamic": false,
+ "num_bits": 8,
+ "type": "float"
+ },
+ "producer": {
+ "name": "modelopt",
+ "version": "0.40.0.dev66+gbe64f6b1d.d20251119"
+ },
+ "quant_method": "modelopt"
+ }
+}
\ No newline at end of file
diff --git a/configuration_deepseek.py b/configuration_deepseek.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b86a084abd1013a46244394f876aa0375b88ca
--- /dev/null
+++ b/configuration_deepseek.py
@@ -0,0 +1,212 @@
+# Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py
+
+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,
+ )
\ No newline at end of file
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..80efe619b184f822f14c45337feb7761b519419a
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,5 @@
+{
+ "eos_token_id": 163586,
+ "max_length": 262144,
+ "transformers_version": "4.57.1"
+}
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fed26353c7b87bf76aa941503e534617609f1fe55fce36812e37900e1d9c3a21
+size 26530147
diff --git a/modeling_deepseek.py b/modeling_deepseek.py
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index 0000000000000000000000000000000000000000..59d235414dab96ac1672cb0010d2c89c2a760d39
--- /dev/null
+++ b/modeling_deepseek.py
@@ -0,0 +1,1871 @@
+# coding=utf-8
+# Copyright 2023 DeepSeek-AI 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.
+""" PyTorch DeepSeek model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_causal_attention_mask,
+)
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import (
+ ALL_LAYERNORM_LAYERS,
+ is_torch_greater_or_equal_than_1_13,
+)
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_2_available,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_deepseek import DeepseekV3Config
+import torch.distributed as dist
+import numpy as np
+
+if is_flash_attn_2_available():
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+ if not is_torch_greater_or_equal_than_1_13:
+ import torch.fx
+
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeepseekV3Config"
+
+def _get_usable_past_kv_length(cache: Cache, new_seq_length: int, layer_idx: int = 0) -> int:
+ """Compute the usable past length for the given cache and upcoming new sequence length.
+
+ This mirrors the previous `get_usable_length(new_seq_length, layer_idx)` behavior that existed in
+ Transformers < 4.45, while being compatible with the new Cache API.
+ """
+ try:
+ previous_length = cache.get_seq_length(layer_idx)
+ # Dynamic layers return -1, static layers return an int
+ max_length = cache.get_max_cache_shape(layer_idx)
+ if max_length is not None and max_length != -1 and previous_length + new_seq_length > max_length:
+ return max_length - new_seq_length
+ return previous_length
+ except Exception:
+ # Best-effort fallback
+ return cache.get_seq_length(layer_idx) if hasattr(cache, "get_seq_length") else 0
+
+
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(
+ torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
+ )
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+class DeepseekV3RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ DeepseekV3RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(DeepseekV3RMSNorm)
+
+
+class DeepseekV3RotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (
+ self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+ )
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ # Build here to make `torch.jit.trace` work.
+ self._set_cos_sin_cache(
+ seq_len=max_position_embeddings,
+ device=self.inv_freq.device,
+ dtype=torch.get_default_dtype(),
+ )
+ self.max_seq_len_cached = None
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+ )
+
+ freqs = torch.outer(t, self.inv_freq.to(t.device))
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
+ )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV3
+class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+ """DeepseekV3RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(
+ self,
+ dim,
+ max_position_embeddings=2048,
+ base=10000,
+ device=None,
+ scaling_factor=1.0,
+ ):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+ )
+ t = t / self.scaling_factor
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV3
+class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+ """DeepseekV3RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+ def __init__(
+ self,
+ dim,
+ max_position_embeddings=2048,
+ base=10000,
+ device=None,
+ scaling_factor=1.0,
+ ):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings)
+ - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (
+ base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+ )
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+ )
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Inverse dim formula to find dim based on number of rotations
+def yarn_find_correction_dim(
+ num_rotations, dim, base=10000, max_position_embeddings=2048
+):
+ return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+ 2 * math.log(base)
+ )
+
+
+# Find dim range bounds based on rotations
+def yarn_find_correction_range(
+ low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
+):
+ low = math.floor(
+ yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
+ )
+ high = math.ceil(
+ yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
+ )
+ return max(low, 0), min(high, dim - 1) # Clamp values just in case
+
+
+def yarn_get_mscale(scale=1, mscale=1):
+ if scale <= 1:
+ return 1.0
+ return 0.1 * mscale * math.log(scale) + 1.0
+
+
+def yarn_linear_ramp_mask(min, max, dim):
+ if min == max:
+ max += 0.001 # Prevent singularity
+
+ linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+ ramp_func = torch.clamp(linear_func, 0, 1)
+ return ramp_func
+
+
+class DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding):
+
+ def __init__(
+ self,
+ dim,
+ max_position_embeddings=2048,
+ base=10000,
+ device=None,
+ scaling_factor=1.0,
+ original_max_position_embeddings=4096,
+ beta_fast=32,
+ beta_slow=1,
+ mscale=1,
+ mscale_all_dim=0,
+ ):
+ self.scaling_factor = scaling_factor
+ self.original_max_position_embeddings = original_max_position_embeddings
+ self.beta_fast = beta_fast
+ self.beta_slow = beta_slow
+ self.mscale = mscale
+ self.mscale_all_dim = mscale_all_dim
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ dim = self.dim
+
+ freq_extra = 1.0 / (
+ self.base
+ ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+ )
+ freq_inter = 1.0 / (
+ self.scaling_factor
+ * self.base
+ ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+ )
+
+ low, high = yarn_find_correction_range(
+ self.beta_fast,
+ self.beta_slow,
+ dim,
+ self.base,
+ self.original_max_position_embeddings,
+ )
+ inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
+ device=device, dtype=torch.float32
+ )
+ inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(seq_len, device=device, dtype=torch.float32)
+
+ freqs = torch.outer(t, inv_freq)
+
+ _mscale = float(
+ yarn_get_mscale(self.scaling_factor, self.mscale)
+ / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
+ )
+
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer(
+ "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
+ )
+ self.register_buffer(
+ "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
+ )
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+
+ b, h, s, d = q.shape
+ q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+ b, h, s, d = k.shape
+ k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class DeepseekV3MLP(nn.Module):
+ def __init__(self, config, hidden_size=None, intermediate_size=None):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+ self.intermediate_size = (
+ config.intermediate_size if intermediate_size is None else intermediate_size
+ )
+
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+class MoEGate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.top_k = config.num_experts_per_tok
+ self.n_routed_experts = config.n_routed_experts
+ self.routed_scaling_factor = config.routed_scaling_factor
+ self.scoring_func = config.scoring_func
+ self.seq_aux = config.seq_aux
+ self.topk_method = config.topk_method
+ self.n_group = config.n_group
+ self.topk_group = config.topk_group
+
+ # topk selection algorithm
+ self.norm_topk_prob = config.norm_topk_prob
+ self.gating_dim = config.hidden_size
+ self.weight = nn.Parameter(
+ torch.empty((self.n_routed_experts, self.gating_dim))
+ )
+ if self.topk_method == "noaux_tc":
+ self.e_score_correction_bias = nn.Parameter(
+ torch.empty((self.n_routed_experts))
+ )
+ self.reset_parameters()
+
+ def reset_parameters(self) -> None:
+ import torch.nn.init as init
+
+ init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+ def forward(self, hidden_states):
+ bsz, seq_len, h = hidden_states.shape
+ ### compute gating score
+ hidden_states = hidden_states.view(-1, h)
+ logits = F.linear(
+ hidden_states.type(torch.float32), self.weight.type(torch.float32), None
+ )
+ if self.scoring_func == "sigmoid":
+ scores = logits.sigmoid()
+ else:
+ raise NotImplementedError(
+ f"insupportable scoring function for MoE gating: {self.scoring_func}"
+ )
+
+ ### select top-k experts
+ if self.topk_method == "noaux_tc":
+ assert not self.training
+ scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
+ group_scores = (
+ scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1)
+ ) # [n, n_group]
+ group_idx = torch.topk(
+ group_scores, k=self.topk_group, dim=-1, sorted=False
+ )[
+ 1
+ ] # [n, top_k_group]
+ group_mask = torch.zeros_like(group_scores) # [n, n_group]
+ group_mask.scatter_(1, group_idx, 1) # [n, n_group]
+ score_mask = (
+ group_mask.unsqueeze(-1)
+ .expand(
+ bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
+ )
+ .reshape(bsz * seq_len, -1)
+ ) # [n, e]
+ tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) # [n, e]
+ _, topk_idx = torch.topk(
+ tmp_scores, k=self.top_k, dim=-1, sorted=False
+ )
+ topk_weight = scores.gather(1, topk_idx)
+ else:
+ raise NotImplementedError(
+ f"insupportable TopK function for MoE gating: {self.topk_method}"
+ )
+
+ ### norm gate to sum 1
+ if self.top_k > 1 and self.norm_topk_prob:
+ denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+ topk_weight = topk_weight / denominator
+ topk_weight = topk_weight * self.routed_scaling_factor # must multiply the scaling factor
+
+ return topk_idx, topk_weight
+
+class DeepseekV3MoE(nn.Module):
+ """
+ A mixed expert module containing shared experts.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.num_experts_per_tok = config.num_experts_per_tok
+
+ if hasattr(config, "ep_size") and config.ep_size > 1:
+ assert config.ep_size == dist.get_world_size()
+ self.ep_size = config.ep_size
+ self.experts_per_rank = config.n_routed_experts // config.ep_size
+ self.ep_rank = dist.get_rank()
+ self.experts = nn.ModuleList(
+ [
+ (
+ DeepseekV3MLP(
+ config, intermediate_size=config.moe_intermediate_size
+ )
+ if i >= self.ep_rank * self.experts_per_rank
+ and i < (self.ep_rank + 1) * self.experts_per_rank
+ else None
+ )
+ for i in range(config.n_routed_experts)
+ ]
+ )
+ else:
+ self.ep_size = 1
+ self.experts_per_rank = config.n_routed_experts
+ self.ep_rank = 0
+ self.experts = nn.ModuleList(
+ [
+ DeepseekV3MLP(
+ config, intermediate_size=config.moe_intermediate_size
+ )
+ for i in range(config.n_routed_experts)
+ ]
+ )
+ self.gate = MoEGate(config)
+ if config.n_shared_experts is not None:
+ intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+ self.shared_experts = DeepseekV3MLP(
+ config=config, intermediate_size=intermediate_size
+ )
+
+ def forward(self, hidden_states):
+ identity = hidden_states
+ orig_shape = hidden_states.shape
+ topk_idx, topk_weight = self.gate(hidden_states)
+ hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+ flat_topk_idx = topk_idx.view(-1)
+ if not self.training:
+ y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
+ if self.config.n_shared_experts is not None:
+ y = y + self.shared_experts(identity)
+ return y
+
+ @torch.no_grad()
+ def moe_infer(self, x, topk_ids, topk_weight):
+ cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+ cnts.scatter_(1, topk_ids, 1)
+ tokens_per_expert = cnts.sum(dim=0)
+ idxs = topk_ids.view(-1).argsort()
+ sorted_tokens = x[idxs // topk_ids.shape[1]]
+ sorted_tokens_shape = sorted_tokens.shape
+ if self.ep_size > 1:
+ tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
+ tokens_per_expert_group = tokens_per_expert.new_empty(
+ tokens_per_expert.shape[0]
+ )
+ dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
+ output_splits = (
+ tokens_per_expert_group.view(self.ep_size, -1)
+ .sum(1)
+ .cpu()
+ .numpy()
+ .tolist()
+ )
+ gathered_tokens = sorted_tokens.new_empty(
+ tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
+ )
+ input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
+ dist.all_to_all(
+ list(gathered_tokens.split(output_splits)),
+ list(sorted_tokens.split(input_split_sizes)),
+ )
+ tokens_per_expert_post_gather = tokens_per_expert_group.view(
+ self.ep_size, self.experts_per_rank
+ ).sum(dim=0)
+ gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
+ s = 0
+ for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
+ gatherd_idxs[s : s + k] = i % self.experts_per_rank
+ s += k
+ gatherd_idxs = gatherd_idxs.argsort()
+ sorted_tokens = gathered_tokens[gatherd_idxs]
+ tokens_per_expert = tokens_per_expert_post_gather
+ tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+ outputs = []
+ start_idx = 0
+ for i, num_tokens in enumerate(tokens_per_expert):
+ end_idx = start_idx + num_tokens
+ if num_tokens == 0:
+ continue
+ expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+ tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+ expert_out = expert(tokens_for_this_expert)
+ outputs.append(expert_out)
+ start_idx = end_idx
+
+ outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+ if self.ep_size > 1:
+ new_x = torch.empty_like(outs)
+ new_x[gatherd_idxs] = outs
+ gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
+ dist.all_to_all(
+ list(gathered_tokens.split(input_split_sizes)),
+ list(new_x.split(output_splits)),
+ )
+ outs = gathered_tokens
+
+ new_x = torch.empty_like(outs)
+ new_x[idxs] = outs
+ final_out = (
+ new_x.view(*topk_ids.shape, -1)
+ .type(topk_weight.dtype)
+ .mul_(topk_weight.unsqueeze(dim=-1))
+ .sum(dim=1)
+ .type(new_x.dtype)
+ )
+ return final_out
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(
+ batch, num_key_value_heads, n_rep, slen, head_dim
+ )
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV3
+class DeepseekV3Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.q_lora_rank = config.q_lora_rank
+ self.qk_rope_head_dim = config.qk_rope_head_dim
+ self.kv_lora_rank = config.kv_lora_rank
+ self.v_head_dim = config.v_head_dim
+ self.qk_nope_head_dim = config.qk_nope_head_dim
+ self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
+
+ self.is_causal = True
+
+ if self.q_lora_rank is None:
+ self.q_proj = nn.Linear(
+ self.hidden_size, self.num_heads * self.q_head_dim, bias=False
+ )
+ else:
+ self.q_a_proj = nn.Linear(
+ self.hidden_size, config.q_lora_rank, bias=config.attention_bias
+ )
+ self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank)
+ self.q_b_proj = nn.Linear(
+ config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
+ )
+
+ self.kv_a_proj_with_mqa = nn.Linear(
+ self.hidden_size,
+ config.kv_lora_rank + config.qk_rope_head_dim,
+ bias=config.attention_bias,
+ )
+ self.kv_a_layernorm = DeepseekV3RMSNorm(config.kv_lora_rank)
+ self.kv_b_proj = nn.Linear(
+ config.kv_lora_rank,
+ self.num_heads
+ * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
+ bias=False,
+ )
+
+ self.o_proj = nn.Linear(
+ self.num_heads * self.v_head_dim,
+ self.hidden_size,
+ bias=config.attention_bias,
+ )
+ self._init_rope()
+
+ self.softmax_scale = self.q_head_dim ** (-0.5)
+ if self.config.rope_scaling is not None:
+ mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
+ scaling_factor = self.config.rope_scaling["factor"]
+ if mscale_all_dim:
+ mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+ self.softmax_scale = self.softmax_scale * mscale * mscale
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = DeepseekV3RotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ if scaling_type == "linear":
+ self.rotary_emb = DeepseekV3LinearScalingRotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ self.rotary_emb = DeepseekV3DynamicNTKScalingRotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "yarn":
+ kwargs = {
+ key: self.config.rope_scaling[key]
+ for key in [
+ "original_max_position_embeddings",
+ "beta_fast",
+ "beta_slow",
+ "mscale",
+ "mscale_all_dim",
+ ]
+ if key in self.config.rope_scaling
+ }
+ self.rotary_emb = DeepseekV3YarnRotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ **kwargs,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return (
+ tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim)
+ .transpose(1, 2)
+ .contiguous()
+ )
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ bsz, q_len, _ = hidden_states.size()
+
+ if self.q_lora_rank is None:
+ q = self.q_proj(hidden_states)
+ else:
+ q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+ q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+ q_nope, q_pe = torch.split(
+ q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+ )
+
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+ compressed_kv, k_pe = torch.split(
+ compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+ )
+ k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+ kv = (
+ self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+ .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+ .transpose(1, 2)
+ )
+
+ k_nope, value_states = torch.split(
+ kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
+ )
+ kv_seq_len = value_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += _get_usable_past_kv_length(past_key_value, kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+ q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
+
+ query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+ query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+ query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+ key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+ key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+ key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(
+ key_states, value_states, self.layer_idx, cache_kwargs
+ )
+
+ attn_weights = (
+ torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
+ )
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+ assert attention_mask is not None
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(
+ attn_weights, dim=-1, dtype=torch.float32
+ ).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(
+ attn_weights, p=self.attention_dropout, training=self.training
+ )
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV3
+class DeepseekV3FlashAttention2(DeepseekV3Attention):
+ """
+ DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ # DeepseekV3FlashAttention2 attention does not support output_attentions
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ if self.q_lora_rank is None:
+ q = self.q_proj(hidden_states)
+ else:
+ q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+ q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+ q_nope, q_pe = torch.split(
+ q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+ )
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+ compressed_kv, k_pe = torch.split(
+ compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+ )
+ k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+ kv = (
+ self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+ .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+ .transpose(1, 2)
+ )
+
+ k_nope, value_states = torch.split(
+ kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
+ )
+ kv_seq_len = value_states.shape[-2]
+
+ kv_seq_len = value_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += _get_usable_past_kv_length(past_key_value, kv_seq_len, self.layer_idx)
+
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
+
+ query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+ query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+ query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+ key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+ key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+ key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+
+ if self.q_head_dim != self.v_head_dim:
+ value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(
+ key_states, value_states, self.layer_idx, cache_kwargs
+ )
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (DeepseekV3RMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ if input_dtype == torch.float32:
+ # Handle the case where the model is quantized
+ if hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ elif torch.is_autocast_enabled():
+ target_dtype = torch.get_autocast_gpu_dtype()
+ else:
+ target_dtype = (
+ self.q_proj.weight.dtype
+ if self.q_lora_rank is None
+ else self.q_a_proj.weight.dtype
+ )
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = self._flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ dropout=dropout_rate,
+ softmax_scale=self.softmax_scale,
+ )
+ if self.q_head_dim != self.v_head_dim:
+ attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+ attn_output = attn_output.reshape(
+ bsz, q_len, self.num_heads * self.v_head_dim
+ ).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+ def _flash_attention_forward(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ query_length,
+ dropout=0.0,
+ softmax_scale=None,
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`int`, *optional*):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV3FlashAttention2 __init__.
+ causal = self.is_causal and query_length != 1
+
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ (
+ query_states,
+ key_states,
+ value_states,
+ indices_q,
+ cu_seq_lens,
+ max_seq_lens,
+ ) = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+
+ attn_output = pad_input(
+ attn_output_unpad, indices_q, batch_size, query_length
+ )
+ else:
+ attn_output = flash_attn_func(
+ query_states,
+ key_states,
+ value_states,
+ dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+
+ return attn_output
+
+ def _upad_input(
+ self, query_layer, key_layer, value_layer, attention_mask, query_length
+ ):
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+ key_layer = index_first_axis(
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+ indices_k,
+ )
+ value_layer = index_first_axis(
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+ indices_k,
+ )
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
+ indices_k,
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
+ query_layer, attention_mask
+ )
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+ATTENTION_CLASSES = {
+ "eager": DeepseekV3Attention,
+ "flash_attention_2": DeepseekV3FlashAttention2,
+}
+
+
+class DeepseekV3DecoderLayer(nn.Module):
+ def __init__(self, config: DeepseekV3Config, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
+ config=config, layer_idx=layer_idx
+ )
+
+ self.mlp = (
+ DeepseekV3MoE(config)
+ if (
+ config.n_routed_experts is not None
+ and layer_idx >= config.first_k_dense_replace
+ and layer_idx % config.moe_layer_freq == 0
+ )
+ else DeepseekV3MLP(config)
+ )
+ self.input_layernorm = DeepseekV3RMSNorm(
+ config.hidden_size, eps=config.rms_norm_eps
+ )
+ self.post_attention_layernorm = DeepseekV3RMSNorm(
+ config.hidden_size, eps=config.rms_norm_eps
+ )
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ **kwargs,
+ ) -> Tuple[
+ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+ ]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+
+DeepseekV3_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`DeepseekV3Config`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+ DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3PreTrainedModel(PreTrainedModel):
+ config_class = DeepseekV3Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["DeepseekV3DecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+DeepseekV3_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+ DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3Model(DeepseekV3PreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`]
+
+ Args:
+ config: DeepseekV3Config
+ """
+
+ def __init__(self, config: DeepseekV3Config):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(
+ config.vocab_size, config.hidden_size, self.padding_idx
+ )
+ self.layers = nn.ModuleList(
+ [
+ DeepseekV3DecoderLayer(config, layer_idx)
+ for layer_idx in range(config.num_hidden_layers)
+ ]
+ )
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+ self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = (
+ output_attentions
+ if output_attentions is not None
+ else self.config.output_attentions
+ )
+ output_hidden_states = (
+ output_hidden_states
+ if output_hidden_states is not None
+ else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError(
+ "You cannot specify both input_ids and inputs_embeds at the same time"
+ )
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape[:2]
+ elif inputs_embeds is not None:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ past_key_values_length = 0
+ if use_cache:
+ use_legacy_cache = not isinstance(past_key_values, Cache)
+ if use_legacy_cache:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = _get_usable_past_kv_length(past_key_values, seq_length, 0) if past_key_values is not None else 0
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length,
+ seq_length + past_key_values_length,
+ dtype=torch.long,
+ device=device,
+ )
+ position_ids = position_ids.unsqueeze(0)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if self._use_flash_attention_2:
+ # 2d mask is passed through the layers
+ attention_mask = (
+ attention_mask
+ if (attention_mask is not None and 0 in attention_mask)
+ else None
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = (
+ next_decoder_cache.to_legacy_cache()
+ if use_legacy_cache
+ else next_decoder_cache
+ )
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+ if v is not None
+ )
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = DeepseekV3Model(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(
+ output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+ )
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM
+
+ >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = (
+ output_attentions
+ if output_attentions is not None
+ else self.config.output_attentions
+ )
+ output_hidden_states = (
+ output_hidden_states
+ if output_hidden_states is not None
+ else self.config.output_hidden_states
+ )
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ **kwargs,
+ ):
+ if past_key_values is not None:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length(0)
+ past_length = cache_length
+ try:
+ max_cache_length = past_key_values.get_max_cache_shape(0)
+ if max_cache_length == -1:
+ max_cache_length = None
+ except Exception:
+ max_cache_length = None
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if (
+ attention_mask is not None
+ and attention_mask.shape[1] > input_ids.shape[1]
+ ):
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1] :]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(
+ past_state.index_select(0, beam_idx.to(past_state.device))
+ for past_state in layer_past
+ ),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The DeepseekV3 Model transformer with a sequence classification head on top (linear layer).
+
+ [`DeepseekV3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = DeepseekV3Model(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError(
+ "Cannot handle batch sizes > 1 if no padding token is defined."
+ )
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ sequence_lengths = (
+ torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+ ).to(logits.device)
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[
+ torch.arange(batch_size, device=logits.device), sequence_lengths
+ ]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (
+ labels.dtype == torch.long or labels.dtype == torch.int
+ ):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(
+ pooled_logits.view(-1, self.num_labels), labels.view(-1)
+ )
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..a5f1fc6cf7baf89dbf68fd49ecfe9f7bc626de3f
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,34 @@
+{
+ "additional_special_tokens": [
+ "<|im_end|>",
+ "<|im_user|>",
+ "<|im_assistant|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "[EOT]",
+ "<|im_system|>",
+ "<|im_middle|>"
+ ],
+ "bos_token": {
+ "content": "[BOS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "[EOS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": "[EOS]",
+ "unk_token": {
+ "content": "[UNK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/tiktoken.model b/tiktoken.model
new file mode 100644
index 0000000000000000000000000000000000000000..b4149a6e17a01b6442187f39890f89bc2fe8d309
--- /dev/null
+++ b/tiktoken.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6c497a7469b33ced9c38afb1ad6e47f03f5e5dc05f15930799210ec050c5103
+size 2795286
diff --git a/tokenization_kimi.py b/tokenization_kimi.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c9c7eb2d481a59b2d115ec5cceb1458b20347f0
--- /dev/null
+++ b/tokenization_kimi.py
@@ -0,0 +1,349 @@
+import os
+import tiktoken
+
+from logging import getLogger
+from pathlib import Path
+from typing import (
+ cast,
+ Tuple,
+ Dict,
+ Iterator,
+ List,
+ Union,
+ Optional,
+)
+from shutil import copyfile
+from tiktoken.load import load_tiktoken_bpe
+from tokenizers import AddedToken, pre_tokenizers, Regex
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
+from typing import Any
+
+
+logger = getLogger(__name__)
+VOCAB_FILES_NAMES = {"vocab_file": "tiktoken.model"}
+
+
+class TikTokenTokenizer(PreTrainedTokenizer):
+ """
+ Tokenizing and encoding/decoding text using the Tiktoken tokenizer. See megatron/tokenizer/tiktoken_tokenizer.py.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ The path to the Tiktoken model file.
+ bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|begin_of_text|>",`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+ eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|end_of_text|>"`):
+ The end of sequence token.
+ unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_249|>"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead. The second to last item in special_tokens.
+ pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_250|>"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ additional_special_tokens (list of `str`, *optional*):
+ A tuple or a list of additional tokens, which will be marked as `special`, meaning that they will be
+ skipped when decoding if `skip_special_tokens` is set to `True`.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ model_input_names = ["input_ids", "attention_mask"]
+
+ special_tokens: Dict[str, int]
+
+ num_reserved_special_tokens = 256
+
+ pat_str = "|".join(
+ [
+ r"""[\p{Han}]+""",
+ r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
+ r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
+ r"""\p{N}{1,3}""",
+ r""" ?[^\s\p{L}\p{N}]+[\r\n]*""",
+ r"""\s*[\r\n]+""",
+ r"""\s+(?!\S)""",
+ r"""\s+""",
+ ]
+ )
+
+ def __init__(
+ self,
+ vocab_file,
+ bos_token: Union[str, AddedToken]="[BOS]",
+ eos_token: Union[str, AddedToken]="[EOS]",
+ unk_token: Union[str, AddedToken, None]=None,
+ pad_token: Union[str, AddedToken, None]=None,
+ additional_special_tokens: List[str]=None,
+ added_tokens_decoder: Optional[dict] = None,
+ **kwargs,
+ ):
+ assert os.path.isfile(vocab_file), vocab_file
+
+ if additional_special_tokens is None:
+ additional_special_tokens = [
+ "<|im_end|>",
+ "<|im_user|>",
+ "<|im_assistant|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "[EOT]",
+ "<|im_system|>",
+ "<|im_middle|>",
+ ]
+
+ special_tokens_mapping = {
+ i: added_tokens_decoder[i].content for i in added_tokens_decoder
+ }
+
+ self.vocab_file = vocab_file
+ mergeable_ranks = load_tiktoken_bpe(vocab_file)
+ num_base_tokens = len(mergeable_ranks)
+ self.special_tokens = {
+ special_tokens_mapping.get(i, f"<|reserved_token_{i}|>"): i
+ for i in range(
+ num_base_tokens, num_base_tokens + self.num_reserved_special_tokens + 2
+ )
+ }
+
+
+
+ self.model = tiktoken.Encoding(
+ name=Path(vocab_file).name,
+ pat_str=self.pat_str,
+ mergeable_ranks=mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ logger.info(f"Reloaded tiktoken model from {vocab_file}")
+
+ self.n_words: int = self.model.n_vocab
+ # BOS / EOS token IDs
+ self.bos_id: int = self.special_tokens[str(bos_token)]
+ self.eos_id: int = self.special_tokens[str(eos_token)]
+ logger.info(
+ f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
+ )
+
+ self.pad_id: int = self.special_tokens[str(pad_token)]
+ self.unk_id: int = self.special_tokens[str(unk_token)]
+
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+
+ self.decoder = {}
+ for i in range(self.n_words):
+ # Taken from https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
+ decoding = ''.join([
+ self.byte_encoder[ord(char)] for char in
+ self.model.decode_single_token_bytes(i).decode('latin-1')
+ ])
+ self.decoder[i] = decoding
+
+ self.encoder = {}
+ for i in range(self.n_words):
+ if i in self.decoder:
+ self.encoder[self.decoder[i]] = i
+
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ additional_special_tokens=additional_special_tokens,
+ added_tokens_decoder=added_tokens_decoder,
+ **kwargs,
+ )
+ self.all_special_ids_set = set(self.all_special_ids)
+
+ def encode(
+ self,
+ text: str,
+ allow_special_tokens: bool = True,
+ **kwargs
+ ) -> List[int]:
+ """
+ Encodes a string into a list of token IDs.
+
+ Args:
+ text (str): The input string to be encoded.
+
+ Returns:
+ list[int]: A list of token IDs.
+ """
+ # If there are other args, we should call super().encode because there are a lot of code
+ # to handle those args. supper().encode finally will call _tokenize and _convert_token_to_id.
+ # NOTE: our encode method is not compatible with the super().encode method,
+ # e.g. split_special_tokens' default is True in our encode method.
+ if len(kwargs) > 0:
+ logger.warning( f"Calling super().encode with {kwargs}" )
+ return super().encode(text, **kwargs)
+
+ assert type(text) is str
+
+ # The tiktoken tokenizer can handle <=400k chars without
+ # pyo3_runtime.PanicException.
+ TIKTOKEN_MAX_ENCODE_CHARS = 400_000
+
+ # https://github.com/openai/tiktoken/issues/195
+ # Here we iterate over subsequences and split if we exceed the limit
+ # of max consecutive non-whitespace or whitespace characters.
+ MAX_NO_WHITESPACES_CHARS = 25_000
+
+ texts = self.pre_tokenizer_process(text)
+
+ all_substrs = []
+ for text in texts:
+ substrs = (
+ substr
+ for i in range(0, len(text), TIKTOKEN_MAX_ENCODE_CHARS)
+ for substr in self._split_whitespaces_or_nonwhitespaces(
+ text[i: i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS
+ )
+ )
+ all_substrs.extend(substrs)
+
+ t: List[int] = []
+ for substr in all_substrs:
+ if allow_special_tokens:
+ t.extend(
+ # we should consider special token as a common token
+ self.model.encode(
+ substr,
+ allowed_special="all",
+ )
+ )
+ else:
+ t.extend(
+ # we should consider special token as a common token
+ self.model.encode(
+ substr,
+ disallowed_special=(),
+ )
+ )
+
+ return t
+
+ def decode(
+ self,
+ token_ids: Union[int, List[int]],
+ **kwargs
+ ) -> str:
+ """
+ Decodes a list of token IDs into a string.
+
+ Args:
+ token_ids (List[int]): The list of token IDs to be decoded.
+
+ Returns:
+ str: The decoded string.
+ """
+ # If there are other args, we should call super().decode because there are a lot of code
+ # to handle those args. supper().encode finally will call convert_tokens_to_string and _convert_id_to_token.
+ if len(kwargs) > 0:
+ return super().decode(token_ids, **kwargs)
+
+ if type(token_ids) is int:
+ token_ids = [token_ids]
+
+ return self.model.decode(cast(List[int], token_ids))
+
+ @staticmethod
+ def _split_whitespaces_or_nonwhitespaces(
+ s: str, max_consecutive_slice_len: int
+ ) -> Iterator[str]:
+ """
+ Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len`
+ consecutive whitespaces or consecutive non-whitespaces.
+ """
+ current_slice_len = 0
+ current_slice_is_space = s[0].isspace() if len(s) > 0 else False
+ slice_start = 0
+
+ for i in range(len(s)):
+ is_now_space = s[i].isspace()
+
+ if current_slice_is_space ^ is_now_space:
+ current_slice_len = 1
+ current_slice_is_space = is_now_space
+ else:
+ current_slice_len += 1
+ if current_slice_len > max_consecutive_slice_len:
+ yield s[slice_start:i]
+ slice_start = i
+ current_slice_len = 1
+ yield s[slice_start:]
+
+ def pre_tokenizer_process(self, text: str) -> List[str]:
+ """
+ pre-tokenizes the input text into a list of tokens.
+ This method is used to split the input text into smaller chunks for internal processing.
+ """
+ return [text]
+
+
+ """ ----- Below are the abstract methods required by PreTrainedTokenizer ----- """
+ @property
+ def vocab_size(self) -> int:
+ return self.n_words
+
+ def get_vocab(self) -> Dict[str, int]:
+ return self.encoder
+
+ def _tokenize(self, text: str, **kwargs) -> List[str]:
+ return [
+ self.decoder[t]
+ for t in self.encode(text)
+ ]
+
+ def _convert_token_to_id(self, token: str) -> int:
+ return self.encoder.get(token, self.unk_id)
+
+ def _convert_id_to_token(self, index: int) -> str:
+ return self.decoder.get(index)
+
+ @staticmethod
+ def clean_up_tokenization(out_string: str) -> str:
+ return out_string
+
+ def convert_tokens_to_string(self, tokens: List[str]) -> str:
+ text = ''.join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', 'replace')
+ return text
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+ if not os.path.isdir(save_directory):
+ raise ValueError(f"vocabulary path ({save_directory}) should be a directory")
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+
+ return (out_vocab_file,)
+
+
+
+ def apply_chat_template(
+ self, conversation, tools: Optional[list[dict]] = None,
+ tokenize: bool = False,
+ add_generation_prompt: bool = True,
+ **kwargs
+ ):
+ tools = deep_sort_dict(tools)
+ return super().apply_chat_template(conversation,
+ tools=tools,
+ tokenize=tokenize,
+ add_generation_prompt=add_generation_prompt,
+ **kwargs)
+
+
+def deep_sort_dict(obj: Any) -> Any:
+ if isinstance(obj, dict):
+ return {k: deep_sort_dict(v) for k, v in sorted(obj.items())}
+ if isinstance(obj, list):
+ return [deep_sort_dict(item) for item in obj]
+ return obj
+
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b98343420352549a9fda49355e7b28e35852dced
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,180 @@
+{
+ "added_tokens_decoder": {
+ "163584": {
+ "content": "[BOS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163585": {
+ "content": "[EOS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163586": {
+ "content": "<|im_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163587": {
+ "content": "<|im_user|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163588": {
+ "content": "<|im_assistant|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163590": {
+ "content": "<|start_header_id|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163591": {
+ "content": "<|end_header_id|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163593": {
+ "content": "[EOT]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163594": {
+ "content": "<|im_system|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163595": {
+ "content": "<|tool_calls_section_begin|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163596": {
+ "content": "<|tool_calls_section_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163597": {
+ "content": "<|tool_call_begin|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163598": {
+ "content": "<|tool_call_argument_begin|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163599": {
+ "content": "<|tool_call_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163601": {
+ "content": "<|im_middle|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163606": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163607": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "163838": {
+ "content": "[UNK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "163839": {
+ "content": "[PAD]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "additional_special_tokens": [
+ "<|im_end|>",
+ "<|im_user|>",
+ "<|im_assistant|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "[EOT]",
+ "<|im_system|>",
+ "<|im_middle|>"
+ ],
+ "auto_map": {
+ "AutoTokenizer": [
+ "tokenization_kimi.TikTokenTokenizer",
+ null
+ ]
+ },
+ "bos_token": "[BOS]",
+ "clean_up_tokenization_spaces": false,
+ "eos_token": "[EOS]",
+ "extra_special_tokens": {},
+ "model_max_length": 1000000000000000019884624838656,
+ "pad_token": "[EOS]",
+ "tokenizer_class": "TikTokenTokenizer",
+ "unk_token": "[UNK]"
+}