# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from collections.abc import Callable import torch import torch.nn as nn from ... import initialization as init from ...cache_utils import Cache, DynamicCache from ...masking_utils import create_causal_mask from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring from ..clip.modeling_clip import CLIPMLP from ..gemma2.modeling_gemma2 import Gemma2ForCausalLM from ..llama.modeling_llama import ( LlamaDecoderLayer, LlamaModel, LlamaPreTrainedModel, LlamaRotaryEmbedding, apply_rotary_pos_emb, eager_attention_forward, ) from ..llama4.modeling_llama4 import Llama4TextL2Norm from ..qwen3.modeling_qwen3 import Qwen3Attention from .configuration_nanochat import NanoChatConfig class NanoChatRMSNorm(Llama4TextL2Norm): pass class NanoChatRotaryEmbedding(LlamaRotaryEmbedding): pass def rotate_half(x): """Rotates half the hidden dims of the input with flipped signs for NanoChat.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((x2, -x1), dim=-1) class NanoChatAttention(Qwen3Attention): def __init__(self, config: NanoChatConfig, layer_idx: int): super().__init__(config, layer_idx) del self.sliding_window del self.layer_type self.q_norm = NanoChatRMSNorm(eps=config.rms_norm_eps) self.k_norm = NanoChatRMSNorm(eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # RoPE -> Norm (instead of usual Norm -> RoPE) query_states = self.q_norm(query_states) key_states = self.k_norm(key_states) if past_key_values is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class NanoChatMLP(CLIPMLP): def __init__(self, config): super().__init__(config) self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) class NanoChatDecoderLayer(LlamaDecoderLayer): def __init__(self, config: NanoChatConfig, layer_idx: int): super().__init__() self.input_layernorm = NanoChatRMSNorm(eps=config.rms_norm_eps) self.post_attention_layernorm = NanoChatRMSNorm(eps=config.rms_norm_eps) @auto_docstring class NanoChatPreTrainedModel(LlamaPreTrainedModel): def _init_weights(self, module: nn.Module) -> None: PreTrainedModel._init_weights(self, module) if isinstance(module, NanoChatAttention): init.normal_( module.o_proj.weight, mean=0.0, std=self.config.initializer_range / math.sqrt(2 * self.config.num_hidden_layers), ) @auto_docstring class NanoChatModel(LlamaModel): def __init__(self, config: NanoChatConfig): super().__init__(config) self.norm = NanoChatRMSNorm(eps=config.rms_norm_eps) def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, cache_position: torch.LongTensor | None = None, use_cache: bool | None = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position: torch.Tensor = ( torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = create_causal_mask( config=self.config, input_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) hidden_states = self.norm(hidden_states) # Additional norm before the layers for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, attention_mask=causal_mask, position_embeddings=position_embeddings, position_ids=position_ids, past_key_values=past_key_values, cache_position=cache_position, **kwargs, ) hidden_states = self.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) @auto_docstring class NanoChatForCausalLM(Gemma2ForCausalLM): _tp_plan = {"lm_head": "colwise_gather_output"} def forward(self, **super_kwargs) -> CausalLMOutputWithPast: r""" Example: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM >>> model = AutoModelForCausalLM.from_pretrained("karpathy/nanochat-d32") >>> tokenizer = AutoTokenizer.from_pretrained("karpathy/nanochat-d32") >>> conversation = [ {"role": "user", "content": "What is the capital of France?"}, ] >>> inputs = tokenizer.apply_chat_template( conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(device) >>> with torch.no_grad(): >>> outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False) >>> generated_tokens = outputs[0, inputs["input_ids"].shape[1] :] >>> output = tokenizer.decode(generated_tokens, skip_special_tokens=True) ```""" super().forward(**super_kwargs) __all__ = [ "NanoChatPreTrainedModel", "NanoChatModel", "NanoChatForCausalLM", ]