"""HuggingFace custom modeling for Nemotron-Dense checkpoints. Loaded via `AutoModelForCausalLM.from_pretrained(..., trust_remote_code=True)` using the `auto_map` field in `config.json`. RMSNorm + squared_relu MLP + GQA. Uses transformers >=4.38 DynamicCache.update() API. NemotronDenseConfig is a standalone PretrainedConfig (not a NemotronConfig subclass) for version stability; recent transformers versions migrated `rope_theta` into a `rope_parameters` dict on NemotronConfig, which breaks direct attribute access. We follow the modern convention and use `rope_parameters` exclusively; both the HF modeling code below and the vLLM plugin read RoPE settings from this dict. """ import torch import torch.nn as nn import torch.nn.functional as F from transformers import PretrainedConfig, PreTrainedModel from transformers.activations import ACT2FN from transformers.cache_utils import DynamicCache from transformers.generation import GenerationMixin from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast class NemotronDenseConfig(PretrainedConfig): model_type = "nemotron_dense" def __init__( self, vocab_size=131072, hidden_size=2048, intermediate_size=9216, num_hidden_layers=28, num_attention_heads=16, head_dim=128, num_key_value_heads=8, hidden_act="relu2", max_position_embeddings=131072, norm_eps=1e-5, rope_parameters=None, tie_word_embeddings=False, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.head_dim = head_dim self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.norm_eps = norm_eps self.rope_parameters = rope_parameters or { "rope_theta": 100000000.0, "partial_rotary_factor": 1.0, } super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: 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) class NemotronDenseRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-5): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): return F.rms_norm(hidden_states, self.weight.shape, self.weight, self.variance_epsilon) class NemotronDenseRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=131072, base=100000000.0, 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, dtype=torch.int64).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) emb = emb.unsqueeze(1) cos = emb.cos() sin = emb.sin() return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) def rotate_half(x): x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None): q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class NemotronDenseMLP(nn.Module): def __init__(self, config): super().__init__() self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): return self.down_proj(self.act_fn(self.up_proj(x))) class NemotronDenseAttention(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = getattr(config, "head_dim", None) or self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) self.rotary_emb = NemotronDenseRotaryEmbedding( self.head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_parameters["rope_theta"], ) def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_values=None, use_cache=False, **kwargs): bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) past_len = past_key_values.get_seq_length(self.layer_idx) if past_key_values is not None else 0 if position_ids is None: position_ids = torch.arange(past_len, past_len + q_len, dtype=torch.long, device=hidden_states.device).unsqueeze(0) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_values is not None: key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) kv_seq_len = key_states.shape[-2] key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) if attention_mask is not None: if attention_mask.dim() == 2: attention_mask = attention_mask[:, None, None, :kv_seq_len].to(torch.bool) if q_len > 1: causal = torch.tril( torch.ones(q_len, kv_seq_len, dtype=torch.bool, device=hidden_states.device), diagonal=kv_seq_len - q_len, ) attention_mask = attention_mask & causal[None, None, :, :] else: attention_mask = attention_mask[:, :, :, :kv_seq_len] is_causal = attention_mask is None and q_len > 1 attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=0.0, is_causal=is_causal, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) return attn_output, None class NemotronDenseDecoderLayer(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() self.self_attn = NemotronDenseAttention(config, layer_idx=layer_idx) self.mlp = NemotronDenseMLP(config) self.input_layernorm = NemotronDenseRMSNorm(config.hidden_size, eps=config.norm_eps) self.post_attention_layernorm = NemotronDenseRMSNorm(config.hidden_size, eps=config.norm_eps) def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_values=None, use_cache=False, **kwargs): residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class NemotronDenseModel(PreTrainedModel): config_class = NemotronDenseConfig base_model_prefix = "model" def __init__(self, config: NemotronDenseConfig): super().__init__(config) self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList( [NemotronDenseDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)] ) self.norm = NemotronDenseRMSNorm(config.hidden_size, eps=config.norm_eps) self.post_init() def _init_weights(self, module): if isinstance(module, NemotronDenseRotaryEmbedding): inv_freq = 1.0 / (module.base ** (torch.arange(0, module.dim, 2, dtype=torch.int64).float() / module.dim)) try: import transformers.initialization as init init.copy_(module.inv_freq, inv_freq) except (ImportError, AttributeError): module.inv_freq.copy_(inv_freq) def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, new_embeddings): self.embeddings = new_embeddings def forward(self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, use_cache=None, return_dict=None, **kwargs): 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 if use_cache and past_key_values is None: past_key_values = DynamicCache() hidden_states = self.embeddings(input_ids) for decoder_layer in self.layers: hidden_states = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, ) hidden_states = self.norm(hidden_states) if not return_dict: return tuple(v for v in [hidden_states, past_key_values] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) class NemotronDenseForCausalLM(PreTrainedModel, GenerationMixin): config_class = NemotronDenseConfig base_model_prefix = "model" def __init__(self, config: NemotronDenseConfig): super().__init__(config) self.model = NemotronDenseModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.post_init() def forward(self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, labels=None, use_cache=None, return_dict=None, **kwargs): return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, return_dict=return_dict, **kwargs, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() loss_fct = nn.CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) 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, ) def get_input_embeddings(self): return self.model.get_input_embeddings() def set_input_embeddings(self, new_embeddings): return self.model.set_input_embeddings(new_embeddings) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_decoder(self): return self.model def set_decoder(self, decoder): self.model = decoder # `prepare_inputs_for_generation` intentionally not overridden: # transformers.GenerationMixin's default already handles next_sequence_length, # inputs_embeds first-iteration injection, left-padded position_ids, compilable # caches, and past_key_values forwarding. Inheriting it gets us all of those # correctly without us having to keep our override in sync with HF.