# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from modular_openpangu_dense.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_openpangu_dense.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2022 EleutherAI 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. from typing import Callable, Optional, Union import torch import torch.nn.functional as F import torch_npu from torch_npu.contrib import transfer_to_npu if "910" in torch.npu.get_device_name(): NPU_ATTN_INFR = True print("[INFO] torch_npu detected. Using NPU fused infer attention.") else: NPU_ATTN_INFR = False from einops import rearrange from torch import nn from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.integrations import use_kernel_forward_from_hub from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import GradientCheckpointingLayer from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_rope_utils import dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging from .configuration_openpangu_dense import PanguEmbeddedConfig logger = logging.get_logger(__name__) def aggregate_hidden_through_time( input_hidden, merge_conv, sliding_window=2, decay_coeff=0.5, restore_sliding_window=False, history_cache=None ): """ input_hidden.shape = (B, S, H) return.shape = (B, S, H) """ B, S, H = input_hidden.shape # concat zeors to the lefe of the first token if history_cache is None: history_cache = torch.zeros((B, H, sliding_window - 1), device=input_hidden.device, dtype=input_hidden.dtype) else: history_cache = history_cache.permute(0, 2, 1) conv_input = torch.cat( [history_cache, input_hidden.permute(0, 2, 1)], # input_hidden (B, S, H) -> (B, H, S) dim=-1, ) conv_output = merge_conv(conv_input) # (B, H, S) -> (B, S, H) return conv_output.permute(0, 2, 1) class WindowBuffer: def __init__(self, win_size, decay_coeff, use_cache, aggregate_fn): self.win_size = win_size self.decay_coeff = decay_coeff self.use_cache = use_cache self.aggregate_fn = aggregate_fn self.buffer = None def get_aggregated_hidden(self, hidden_states): if not self.use_cache: self.buffer = None return aggregate_hidden_through_time(hidden_states, self.aggregate_fn, sliding_window=self.win_size) B, S, H = hidden_states.shape if S > 1: # prefill, generate first token win_input = aggregate_hidden_through_time(hidden_states, self.aggregate_fn, sliding_window=self.win_size) self.buffer = hidden_states[:, -(self.win_size - 1) :] else: # decode stage win_input = aggregate_hidden_through_time( hidden_states, self.aggregate_fn, sliding_window=self.win_size, history_cache=self.buffer ) if self.win_size > 2: self.buffer = torch.cat([self.buffer[:, -(self.win_size - 2) :], hidden_states], dim=1) else: self.buffer = hidden_states return win_input @use_kernel_forward_from_hub("RMSNorm") class PanguEmbeddedRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ PanguEmbeddedRMSNorm 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) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class PanguEmbeddedRotaryEmbedding(nn.Module): def __init__(self, config: PanguEmbeddedConfig, device=None): super().__init__() base_dim = config.head_dim rotary_percent = config.rotary_percent dim = base_dim if rotary_percent < 1.0: dim = int(dim * rotary_percent) if dim % 2 != 0: dim += 1 rotary_base = config.rope_theta inv_freq = 1.0 / (rotary_base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.attention_scaling = 1.0 if device is not None: inv_freq = inv_freq.to(device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq self.dim = dim @torch.no_grad() @dynamic_rope_update def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) class PanguEmbeddedMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.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 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) 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) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights def apply_rotary_pos_emb( q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, unsqueeze_dim: int = 1 ): """ Applies Rotary Position Embedding to the query and key tensors, handling cases where rotary_percent < 1.0 by only rotating a subset of the dimensions. ATTENTION: This version assumes cos/sin tensors are already the full rotation dimension (D_rot), consistent with some Megatron/Fusion implementations, rather than the standard HF (D_rot/2) format. Args: q (`torch.Tensor`): The query tensor [Batch, Heads, Seq, Head_Dim]. k (`torch.Tensor`): The key tensor [Batch, Heads, Seq, Head_Dim]. cos (`torch.Tensor`): The cosine part of the rotary embedding [Batch, Seq, Head_Dim_Rotary]. <--- FULL D_ROT sin (`torch.Tensor`): The sine part of the rotary embedding [Batch, Seq, Head_Dim_Rotary]. <--- FULL D_ROT unsqueeze_dim (`int`, *optional*, defaults to 1): The dimension to unsqueeze cos/sin for broadcasting (usually the Heads dimension). Returns: `tuple(torch.Tensor)` comprising of the rotated query and key tensors. """ rot_dim = cos.shape[-1] q_rope, q_pass = q[..., :rot_dim], q[..., rot_dim:] k_rope, k_pass = k[..., :rot_dim], k[..., rot_dim:] cos_broad = cos.unsqueeze(unsqueeze_dim) # [B, 1, S, Dim] sin_broad = sin.unsqueeze(unsqueeze_dim) # [B, 1, S, Dim] q_embed_rope = (q_rope * cos_broad) + (rotate_half(q_rope) * sin_broad) k_embed_rope = (k_rope * cos_broad) + (rotate_half(k_rope) * sin_broad) q_embed = torch.cat((q_embed_rope, q_pass), dim=-1) k_embed = torch.cat((k_embed_rope, k_pass), dim=-1) return q_embed, k_embed class PanguEmbeddedAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: PanguEmbeddedConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = config.head_dim self.num_key_value_groups = config.num_key_value_groups self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.bias) self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias) self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias) self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias) 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.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.qk_nope_dim = config.qk_nope_dim self.qk_rope_dim = config.qk_rope_dim self.v_channels = config.v_channels self.num_key_value_heads = config.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.attn_groupnorm = config.attn_groupnorm self.attn_elementwise_gate = config.attn_elementwise_gate self.param_sink_number = config.param_sink_number self.param_sink_with_value = config.param_sink_with_value self.num_attention_heads = config.num_attention_heads self.rotary_emb = PanguEmbeddedRotaryEmbedding(config=config) if self.param_sink_number > 0: self.param_sink_query = torch.zeros( (self.param_sink_number, self.num_heads, self.head_dim), dtype=config.torch_dtype ) self.param_sink_num_heads_per_partition = self.num_key_value_heads self.param_sink_key = torch.nn.Parameter( torch.empty( (self.param_sink_number, self.param_sink_num_heads_per_partition, self.head_dim), dtype=config.torch_dtype, ) ) if self.param_sink_with_value: self.param_sink_value = torch.nn.Parameter( torch.empty( (self.param_sink_number, self.param_sink_num_heads_per_partition, self.v_channels), dtype=config.torch_dtype, ) ) else: self.param_sink_value = torch.zeros( (self.param_sink_number, self.param_sink_num_heads_per_partition, self.v_channels), dtype=config.torch_dtype, ) if self.attn_groupnorm: self.groupnorm = PanguEmbeddedRMSNorm(hidden_size=self.head_dim, eps=config.rms_norm_eps) if self.attn_elementwise_gate: self.attention_gate = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, 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, cache_position: Optional[torch.LongTensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) bsz, q_len, _ = hidden_states.size() 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) if self.attn_elementwise_gate: gate_score = self.attention_gate(hidden_states) else: gate_score = None kv_seq_len = q_len is_prefill = past_key_value.get_usable_length(kv_seq_len, self.layer_idx) == 0 if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) 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 key_states/v caching, please make sure to initialize the attention class " "with a layer index." ) cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) kv_seq_len = key_states.shape[-2] if self.param_sink_number > 0: batch_size = query_states.shape[0] if is_prefill: param_sink_query = ( self.param_sink_query.permute(1, 0, 2) .unsqueeze(0) .expand(batch_size, -1, -1, -1) .to(query_states.device) ) query_states = torch.cat([param_sink_query, query_states], dim=2) q_len += self.param_sink_number param_sink_key = ( self.param_sink_key.permute(1, 0, 2).unsqueeze(0).expand(batch_size, -1, -1, -1).to(key_states.device) ) param_sink_value = ( self.param_sink_value.permute(1, 0, 2) .unsqueeze(0) .expand(batch_size, -1, -1, -1) .to(value_states.device) ) key_states = torch.cat([param_sink_key, key_states], dim=2) value_states = torch.cat([param_sink_value, value_states], dim=2) kv_seq_len += self.param_sink_number if not self.training and NPU_ATTN_INFR: q_len_current = query_states.shape[2] kv_len_current = key_states.shape[2] param_sink_number = self.config.param_sink_number # Causal Mask if is_prefill: causal_mask_npu = ( torch.triu(torch.ones([q_len_current, kv_len_current]), diagonal=1) .bool() .unsqueeze(0) .unsqueeze(0) .to(query_states.device) ) original_mask = ~attention_mask.bool() expanded_mask = F.pad( original_mask.float(), (param_sink_number, 0, param_sink_number, 0), mode="constant", value=1.0 ).bool() attention_mask_npu = (expanded_mask) & (~causal_mask_npu) else: original_mask = ~attention_mask.bool() attention_mask_npu = F.pad( original_mask.float(), (param_sink_number, 0, 0, 0), mode="constant", value=1.0 ).bool() attention_mask_npu = ~attention_mask_npu.bool() attn_output, _ = torch_npu.npu_fused_infer_attention_score( query_states, key_states, value_states, num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads, input_layout="BNSD", atten_mask=attention_mask_npu, scale=self.scaling, ) attn_output = attn_output.transpose(1, 2) # (bsz, q_len, num_heads * head_dim) attn_weights = None else: 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, sliding_window=self.sliding_window, position_ids=position_ids, ) if self.param_sink_number > 0 and is_prefill: # (bsz, q_len_original, hidden_dim) attn_output = attn_output[:, self.param_sink_number :, :] if self.attn_groupnorm: attn_output = self.groupnorm(attn_output) if self.attn_elementwise_gate: core_attn_out_reshaped = rearrange(attn_output, "s b h d -> s b (h d)", h=self.num_attention_heads) core_attn_out_reshaped = core_attn_out_reshaped * F.sigmoid(gate_score) attn_output = rearrange(core_attn_out_reshaped, "s b (h d) -> s b h d", h=self.num_attention_heads) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class PanguEmbeddedDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: PanguEmbeddedConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = PanguEmbeddedAttention(config=config, layer_idx=layer_idx) self.mlp = PanguEmbeddedMLP(config) self.input_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.attention_type = config.layer_types[layer_idx] if layer_idx == 0 or layer_idx == config.num_hidden_layers - 1: self.start_end = True else: self.start_end = False if self.start_end: self.router_sliding_window = config.router_sliding_window self.router_win_decay = config.router_win_decay self.merge_conv = torch.nn.Conv1d( config.hidden_size, config.hidden_size, self.router_sliding_window, groups=config.hidden_size, bias=False, ) self.window_buffer = WindowBuffer( self.router_sliding_window, self.router_win_decay, True, self.merge_conv.forward ) 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: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, _ = 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, cache_position=cache_position, **kwargs, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states if self.start_end and self.router_sliding_window: win_input = self.window_buffer.get_aggregated_hidden(hidden_states) else: win_input = hidden_states hidden_states = self.post_attention_layernorm(win_input) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs @auto_docstring class PanguEmbeddedPreTrainedModel(PreTrainedModel): config_class = PanguEmbeddedConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PanguEmbeddedDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn_3 = True _supports_flash_attn_2 = True _supports_sdpa = True _supports_flex_attn = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True _supports_attention_backend = True _keys_to_ignore_on_load_unexpected = [r"model\.layers\.27.*"] 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_() elif isinstance(module, PanguEmbeddedRMSNorm): module.weight.data.fill_(1.0) @auto_docstring class PanguEmbeddedModel(PanguEmbeddedPreTrainedModel): def __init__(self, config: PanguEmbeddedConfig): 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( [PanguEmbeddedDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.rotary_emb = PanguEmbeddedRotaryEmbedding(config=config) self.gradient_checkpointing = False self.norms = nn.ModuleList( [ PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps), PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps), ] ) # 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 @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> 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 if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if not isinstance(past_key_values, (type(None), Cache)): raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache() 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.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) if not isinstance(causal_mask_mapping := attention_mask, dict): mask_kwargs = { "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, } causal_mask_mapping = { "full_attention": create_causal_mask(**mask_kwargs), "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs), } hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask_mapping[decoder_layer.attention_type], position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norms[0](hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... @auto_docstring class PanguEmbeddedForCausalLM(PanguEmbeddedPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = PanguEmbeddedModel(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 @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[KwargsForCausalLM], ) -> CausalLMOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., 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, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, PanguEmbeddedForCausalLM >>> model = PanguEmbeddedForCausalLM.from_pretrained("meta-PanguEmbedded/PanguEmbedded-2-7b-hf") >>> tokenizer = AutoTokenizer.from_pretrained("meta-PanguEmbedded/PanguEmbedded-2-7b-hf") >>> 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 ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs: BaseModelOutputWithPast = 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, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = ["PanguEmbeddedForCausalLM", "PanguEmbeddedModel", "PanguEmbeddedPreTrainedModel"]