# coding=utf-8 # Copyright 2023 the Falcon authors and 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. """PyTorch Falcon model.""" import math from typing import TYPE_CHECKING, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss from torch.nn import functional as F from ...activations import get_activation from ...cache_utils import Cache, DynamicCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import ( AttentionMaskConverter, ) from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from ...utils.deprecation import deprecate_kwarg from .configuration_falcon import FalconConfig if TYPE_CHECKING: from ...configuration_utils import PretrainedConfig if is_flash_attn_available(): from ...modeling_flash_attention_utils import _flash_attention_forward logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b" _CONFIG_FOR_DOC = "FalconConfig" # NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations. # In order not to degrade the quality of our HF-port, we keep these characteristics in the final model. class FalconLinear(nn.Linear): def forward(self, input: torch.Tensor) -> torch.Tensor: hidden_states = input @ self.weight.T if self.bias is None: return hidden_states return hidden_states + self.bias # 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=None, 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`, *optional*): Deprecated and unused. 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.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Falcon class FalconRotaryEmbedding(nn.Module): def __init__(self, config: FalconConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" 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.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) 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) def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: batch_size, seq_length = attention_mask.shape closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) base = torch.tensor( 2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 ) powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) slopes = torch.pow(base, powers) if closest_power_of_2 != num_heads: extra_base = torch.tensor( 2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 ) num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32) slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) # Note: alibi will added to the attention bias that will be applied to the query, key product of attention # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) # => the query_length dimension will then be broadcasted correctly # This is more or less identical to T5's relative position bias: # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] alibi = slopes[..., None].bfloat16() * arange_tensor return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) # Copied from transformers.models.bloom.modeling_bloom.dropout_add def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor: """ Dropout add function Args: x (`torch.tensor`): input tensor residual (`torch.tensor`): residual tensor prob (`float`): dropout probability training (`bool`): training mode """ out = F.dropout(x, p=prob, training=training) out = residual + out return out class FalconAttention(nn.Module): def __init__(self, config: FalconConfig, layer_idx=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.split_size = self.hidden_size self.hidden_dropout = config.hidden_dropout self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.is_causal = True self._use_sdpa = config._attn_implementation == "sdpa" self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) if self.head_dim * self.num_heads != self.hidden_size: raise ValueError( f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:" f" {self.num_heads})." ) # Layer-wise attention scaling self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim) self.beta = self.inv_norm_factor if config.new_decoder_architecture: qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim elif config.multi_query: qkv_out_dim = self.hidden_size + 2 * self.head_dim else: qkv_out_dim = 3 * self.hidden_size self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias) self.new_decoder_architecture = config.new_decoder_architecture self.multi_query = config.multi_query self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias) self.attention_dropout = nn.Dropout(config.attention_dropout) self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1 # TODO (raushan): remove in v4.46 (RoPE is computed in the model, not in the decoder layers) if config.rotary: self.rotary_emb = FalconRotaryEmbedding(config=self.config) def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv` Args: fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim] Returns: query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim] value: [batch_size, seq_length, num_heads, head_dim] """ if self.new_decoder_architecture: batch, seq_len, _ = fused_qkv.shape qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim) query = qkv[:, :, :, :-2] key = qkv[:, :, :, [-2]] value = qkv[:, :, :, [-1]] key = torch.broadcast_to(key, query.shape) value = torch.broadcast_to(value, query.shape) query, key, value = [x.flatten(2, 3) for x in (query, key, value)] return query, key, value elif not self.multi_query: batch_size, seq_length, three_times_hidden_size = fused_qkv.shape fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim) return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :] else: batch_size, seq_length, three_times_hidden_size = fused_qkv.shape fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim) return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :] # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads def _merge_heads(self, x: torch.Tensor) -> torch.Tensor: """ Merge heads together over the last dimension Args: x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim] Returns: torch.tensor: [batch_size, seq_length, num_heads * head_dim] """ # What we want to achieve is: # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim batch_size_and_num_heads, seq_length, _ = x.shape batch_size = batch_size_and_num_heads // self.num_heads # First view to decompose the batch size # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim x = x.view(batch_size, self.num_heads, seq_length, self.head_dim) # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim x = x.permute(0, 2, 1, 3) # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim) def forward( self, hidden_states: torch.Tensor, alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, layer_past: Optional[Cache] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ): fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads # 3 x [batch_size, seq_length, num_heads, head_dim] (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) batch_size, query_length, _, _ = query_layer.shape query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim) key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) if alibi is None: cos, sin = position_embeddings query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin) if layer_past is not None: cache_kwargs = {"cache_position": cache_position} if alibi is None: cache_kwargs.update({"sin": sin, "cos": cos}) key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs) kv_length = key_layer.shape[-2] if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None: # For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. query_layer = query_layer.contiguous() key_layer = key_layer.contiguous() value_layer = value_layer.contiguous() if attention_mask is not None: attention_mask = attention_mask[:, :, :, : key_layer.shape[-2]] if alibi is None: if self._use_sdpa and not output_attentions: # We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an # inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True` # The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not # create a causal mask in case query_length == 1. is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_layer, key_layer, value_layer, attn_mask=attention_mask, dropout_p=0.0, is_causal=is_causal, ) attention_scores = None else: attention_scores = query_layer @ key_layer.transpose(-1, -2) attention_scores /= math.sqrt(self.head_dim) attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype) # It is unclear why neither dropout nor head_mask is applied here (while it is with alibi). attn_output = attention_scores @ value_layer attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim) attn_output = attn_output.permute(0, 2, 1, 3) attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) attn_output = self.dense(attn_output) if output_attentions: return attn_output, layer_past, attention_scores else: return attn_output, layer_past else: if self._use_sdpa and not output_attentions and head_mask is None: # We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an # inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True` is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_layer, key_layer, value_layer, attn_mask=attention_mask, dropout_p=self.attention_dropout.p if self.training else 0.0, is_causal=is_causal, ) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) attn_output = self.dense(attn_output) else: matmul_result = query_layer @ key_layer.transpose(-1, -2) # change view to [batch_size, num_heads, q_length, kv_length] attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length) # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length] input_dtype = attention_scores.dtype # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38` if input_dtype == torch.float16 or input_dtype == torch.bfloat16: attention_scores = attention_scores.to(torch.float32) attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1) attention_logits *= self.inv_norm_factor attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype) # [batch_size, num_heads, q_length, kv_length] attention_probs = self.attention_dropout(attention_probs) if head_mask is not None: attention_probs = attention_probs * head_mask # change view [batch_size, num_heads, q_length, kv_length] attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length) # matmul: [batch_size * num_heads, q_length, head_dim] attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1) # change view [batch_size, q_length, num_heads * head_dim] attn_output = self._merge_heads(attn_output) attn_output = self.dense(attn_output) if output_attentions: return attn_output, layer_past, attention_probs else: return attn_output, layer_past class FalconFlashAttention2(FalconAttention): """ Falcon flash attention module. This module inherits from `FalconAttention` 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 alignment, 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 = flash_attn_supports_top_left_mask() def forward( self, hidden_states: torch.Tensor, alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, layer_past: Optional[Cache] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC ): fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads # 3 x [batch_size, seq_length, num_heads, head_dim] (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) batch_size, query_length, _, _ = query_layer.shape query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim) key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim) if alibi is None: cos, sin = position_embeddings query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin) if layer_past is not None: cache_kwargs = {"cache_position": cache_position} if alibi is None: cache_kwargs.update({"sin": sin, "cos": cos}) key_layer, value_layer = layer_past.update(key_layer, value_layer, 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_layer = query_layer.transpose(1, 2) key_layer = key_layer.transpose(1, 2) value_layer = value_layer.transpose(1, 2) if alibi is not None: raise ValueError("`alibi` is not supported when `use_flash_attn` is True") attn_dropout = self.config.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 float16 just to be sure everything works as expected. input_dtype = query_layer.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.query_key_value.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_layer = query_layer.to(target_dtype) key_layer = key_layer.to(target_dtype) value_layer = value_layer.to(target_dtype) attn_output = _flash_attention_forward( query_layer, key_layer, value_layer, attention_mask, query_length, position_ids=position_ids, dropout=attn_dropout, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) attn_output = self.dense(attn_weights) if not output_attentions: attn_weights = None return attn_output, layer_past, attn_weights class FalconMLP(nn.Module): def __init__(self, config: FalconConfig): super().__init__() hidden_size = config.hidden_size self.dense_h_to_4h = FalconLinear(hidden_size, config.ffn_hidden_size, bias=config.bias) self.act = get_activation(config.activation) self.dense_4h_to_h = FalconLinear(config.ffn_hidden_size, hidden_size, bias=config.bias) self.hidden_dropout = config.hidden_dropout def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.act(self.dense_h_to_4h(x)) x = self.dense_4h_to_h(x) return x FALCON_ATTENTION_CLASSES = { "eager": FalconAttention, "sdpa": FalconAttention, # FalconAttention originally implemented both a forward with & without SDPA "flash_attention_2": FalconFlashAttention2, } class FalconDecoderLayer(nn.Module): def __init__(self, config: FalconConfig, layer_idx=None): super().__init__() hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = FalconMLP(config) self.hidden_dropout = config.hidden_dropout self.config = config if config.num_ln_in_parallel_attn is None and config.new_decoder_architecture: config.num_ln_in_parallel_attn = 2 if not config.parallel_attn: self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) else: if config.num_ln_in_parallel_attn == 2: # The layer norm before self-attention self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) # The layer norm before the MLP self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) else: self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) def forward( self, hidden_states: torch.Tensor, alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, layer_past: Optional[Union[Cache, Tuple[torch.Tensor, torch.Tensor]]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC **kwargs, ): residual = hidden_states if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2: attention_layernorm_out = self.ln_attn(hidden_states) mlp_layernorm_out = self.ln_mlp(hidden_states) else: attention_layernorm_out = self.input_layernorm(hidden_states) # Self attention. attn_outputs = self.self_attention( attention_layernorm_out, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, alibi=alibi, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, position_embeddings=position_embeddings, ) attention_output = attn_outputs[0] if not self.config.new_decoder_architecture: if self.config.parallel_attn: mlp_layernorm_out = attention_layernorm_out else: residual = dropout_add( attention_output, residual, self.config.attention_dropout, training=self.training ) mlp_layernorm_out = self.post_attention_layernorm(residual) if ( self.config.new_decoder_architecture and self.config.parallel_attn and self.config.num_ln_in_parallel_attn == 1 ): mlp_layernorm_out = attention_layernorm_out outputs = attn_outputs[1:] # MLP. mlp_output = self.mlp(mlp_layernorm_out) if self.config.new_decoder_architecture or self.config.parallel_attn: mlp_output += attention_output output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training) if use_cache: outputs = (output,) + outputs else: outputs = (output,) + outputs[1:] return outputs # hidden_states, past_kv, attentions FALCON_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 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 ([`FalconConfig`]): 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. """ FALCON_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-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, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - 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)`. attention_mask (`torch.FloatTensor` 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) 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) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. 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. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). 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 [`~file_utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ class FalconPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FalconConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["FalconDecoderLayer"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module: nn.Module): """Initialize the weights.""" if isinstance(module, nn.Linear) or isinstance(module, FalconLinear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) # Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa @classmethod def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig": _is_bettertransformer = getattr(cls, "use_bettertransformer", False) if _is_bettertransformer: return config if not hard_check_only: config._attn_implementation = "sdpa" return config @add_start_docstrings( "The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.", FALCON_START_DOCSTRING, ) class FalconModel(FalconPreTrainedModel): def __init__(self, config: FalconConfig): super().__init__(config) self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.use_alibi = config.alibi # Embedding + LN Embedding self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim) # Transformer blocks self.h = nn.ModuleList([FalconDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self._use_sdpa = config._attn_implementation == "sdpa" # Final Layer Norm self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.rotary_emb = FalconRotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.word_embeddings def set_input_embeddings(self, new_embeddings: torch.Tensor): self.word_embeddings = new_embeddings @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: 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, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: 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 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: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) # Compute alibi tensor: check build_alibi_tensor documentation alibi = None past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 batch_size, seq_length, _ = inputs_embeds.shape if self.use_alibi: mask = ( torch.ones( (batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long ) if attention_mask is None else attention_mask ) alibi = build_alibi_tensor(mask, self.num_heads, dtype=inputs_embeds.dtype) if cache_position is None: cache_position = torch.arange( past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions, head_mask, alibi ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape batch_size x num_heads x N x N # head_mask has shape n_layer x batch x num_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) next_decoder_cache = None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, alibi, causal_mask, position_ids, head_mask[i], past_key_values, use_cache, output_attentions, cache_position, position_embeddings, ) else: outputs = block( hidden_states, layer_past=past_key_values, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = outputs[0] if use_cache is True: next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) # Add last hidden state hidden_states = self.ln_f(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, head_mask: torch.Tensor, alibi: torch.Tensor, ): # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if ( self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions and head_mask is None and alibi is None ): if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min batch_size, sequence_length, _ = input_tensor.shape if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) # We take care to integrate alibi bias in the causal_mask here if head_mask is None and alibi is not None: alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:]) causal_mask = torch.masked_fill( alibi / math.sqrt(self.config.hidden_size // self.num_heads), causal_mask < -1, min_dtype, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @add_start_docstrings( "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).", FALCON_START_DOCSTRING, ) class FalconForCausalLM(FalconPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: FalconConfig): super().__init__(config) self.transformer = FalconModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings: torch.Tensor): self.lm_head = new_embeddings @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) hidden_states = transformer_outputs[0] slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep lm_logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function( lm_logits, labels, vocab_size=self.config.vocab_size, **kwargs, ) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def _reorder_cache( self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. Output shares the same memory storage as `past`. """ # Get a copy of `beam_idx` on all the devices where we need those indices. device_to_beam_idx = { past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past } reordered_past = tuple( ( layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]), layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]), ) for layer_past in past ) return reordered_past @add_start_docstrings( """ The Falcon Model transformer with a sequence classification head on top (linear layer). [`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) 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). """, FALCON_START_DOCSTRING, ) class FalconForSequenceClassification(FalconPreTrainedModel): def __init__(self, config: FalconConfig): super().__init__(config) self.num_labels = config.num_labels self.transformer = FalconModel(config) self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., 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.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, 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: last_non_pad_token = -1 elif input_ids is not None: # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32) last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) else: last_non_pad_token = -1 logger.warning_once( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token] loss = None if labels is not None: 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, labels) 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, ) @add_start_docstrings( """ Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, FALCON_START_DOCSTRING, ) class FalconForTokenClassification(FalconPreTrainedModel): def __init__(self, config: FalconConfig): super().__init__(config) self.num_labels = config.num_labels self.transformer = FalconModel(config) if getattr(config, "classifier_dropout", None) is not None: classifier_dropout = config.classifier_dropout elif getattr(config, "hidden_dropout", None) is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., 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.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, 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] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: batch_size, seq_length = labels.shape loss_fct = CrossEntropyLoss() loss = loss_fct( logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) ) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, FALCON_START_DOCSTRING, ) class FalconForQuestionAnswering(FalconPreTrainedModel): def __init__(self, config): super().__init__(config) self.transformer = FalconModel(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "FalconForCausalLM", "FalconModel", "FalconPreTrainedModel", "FalconForSequenceClassification", "FalconForTokenClassification", "FalconForQuestionAnswering", ]