# 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 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 ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_available, logging, ) from .configuration_falcon import FalconConfig if is_flash_attn_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa logger = logging.get_logger(__name__) FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [ "tiiuae/falcon-40b", "tiiuae/falcon-40b-instruct", "tiiuae/falcon-7b", "tiiuae/falcon-7b-instruct", "tiiuae/falcon-rw-7b", "tiiuae/falcon-rw-1b", ] _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 # rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...) def rotate_half(x): x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(padding_mask): seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) # TODO (joao): Is this the same implementation as in Llama? If so, let's make them the same and add the copy facilities class FalconRotaryEmbedding(nn.Module): """Implementation of RotaryEmbedding from GPT-NeoX. This implementation is designed to operate on queries and keys that are compatible with `[batch_size, n_heads_per_partition, seq_len, head_dim]` (e.g. MinGPTAttention format). """ def __init__(self, head_dim: int, base=10000, max_position_embeddings=2048): super().__init__() self.base = base self.max_position_embeddings = max_position_embeddings inv_freq = 1.0 / (self.base ** (torch.arange(0, head_dim, 2).float() / head_dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self.head_dim = head_dim self.seq_len_cached = -1 self.cos_cached: torch.Tensor | None = None self.sin_cached: torch.Tensor | None = None def _set_cos_sin_cache(self, seq_len, device, dtype): self.seq_len_cached = seq_len t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(device) if dtype in [torch.float16, torch.bfloat16]: emb = emb.float() self.cos_cached = emb.cos()[None, :, :] self.sin_cached = emb.sin()[None, :, :] self.cos_cached = self.cos_cached.type(dtype) self.sin_cached = self.sin_cached.type(dtype) def cos_sin( self, seq_len: int, past_key_values_length: int, position_ids: torch.Tensor, device="cpu", dtype=torch.bfloat16 ) -> torch.Tensor: total_length = seq_len + past_key_values_length if total_length > self.seq_len_cached: self._set_cos_sin_cache(total_length, device, dtype) # the cached tensors need to update their devices (for example, after we change the model's device) self.cos_cached = self.cos_cached.to(device) self.sin_cached = self.sin_cached.to(device) # Gather cos, sin at the designated position ids cos = self.cos_cached.squeeze(0)[position_ids] # [bs, seq_len, dim] sin = self.sin_cached.squeeze(0)[position_ids] # [bs, seq_len, dim] return cos, sin def forward(self, query, key, past_key_values_length, position_ids): _, seq_len, _ = query.shape cos, sin = self.cos_sin(seq_len, past_key_values_length, position_ids, query.device, query.dtype) # Query and key's shapes are [bs * num_heads, seq_len, dim], might need manual expansion. Ifs and elses used to # avoid unnecessary repeat_interleave operations. query_expansion_factor = int(query.shape[0] / cos.shape[0]) if query_expansion_factor > 1: query_cos = torch.repeat_interleave(cos, query_expansion_factor, dim=0) query_sin = torch.repeat_interleave(sin, query_expansion_factor, dim=0) else: query_cos, query_sin = cos, sin key_expansion_factor = int(key.shape[0] / cos.shape[0]) if key_expansion_factor > 1: if key_expansion_factor != query_expansion_factor: key_cos = torch.repeat_interleave(cos, key_expansion_factor, dim=0) key_sin = torch.repeat_interleave(sin, key_expansion_factor, dim=0) else: key_cos, key_sin = query_cos, query_sin else: key_cos, key_sin = cos, sin return (query * query_cos) + (rotate_half(query) * query_sin), (key * key_cos) + (rotate_half(key) * key_sin) class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding): """FalconRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, head_dim: int, base=10000, max_position_embeddings=2048, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(head_dim, base, max_position_embeddings) def _set_cos_sin_cache(self, seq_len, device, dtype): self.seq_len_cached = seq_len t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) # This line is the only difference from FalconRotaryEmbedding._set_cos_sin_cache t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(device) if dtype in [torch.float16, torch.bfloat16]: emb = emb.float() self.cos_cached = emb.cos()[None, :, :] self.sin_cached = emb.sin()[None, :, :] self.cos_cached = self.cos_cached.type(dtype) self.sin_cached = self.sin_cached.type(dtype) class FalconDynamicNTKScalingRotaryEmbedding(FalconRotaryEmbedding): """ FalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla """ def __init__(self, head_dim: int, base=10000, max_position_embeddings=2048, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(head_dim, base, max_position_embeddings) def _set_cos_sin_cache(self, seq_len, device, dtype): self.seq_len_cached = seq_len # This if block is the only difference from FalconRotaryEmbedding._set_cos_sin_cache if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.head_dim / (self.head_dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.head_dim, 2).float().to(device) / self.head_dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(device) if dtype in [torch.float16, torch.bfloat16]: emb = emb.float() self.cos_cached = emb.cos()[None, :, :] self.sin_cached = emb.sin()[None, :, :] self.cos_cached = self.cos_cached.type(dtype) self.sin_cached = self.sin_cached.type(dtype) def _make_causal_mask( input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int ) -> torch.BoolTensor: """ Make causal mask used for self-attention. This mask does not take the existing attention mask into account - it just blocks tokens from attending forwards in the sequence. The output shape will be `[batch_size, 1, target_length, target_length+past_key_values_length]`. """ batch_size, target_length = input_ids_shape mask = torch.triu(torch.ones((target_length, target_length), dtype=torch.bool, device=device), diagonal=1) # If past_key_values_length is 0 this is an empty tensor and the concatenation is a no-op. # This code style is an unfortunate consequence of getting your TF engineer to port models; doing it this # way avoids a data-dependent conditional, which will help me when I have to port this to XLA later. past_mask = torch.zeros((target_length, past_key_values_length), dtype=torch.bool, device=device) mask = torch.cat([past_mask, mask], dim=-1) expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length) return expanded_mask def _expand_mask(mask: torch.Tensor, past_key_values_length: int) -> torch.BoolTensor: """ Expands attention_mask from `[batch_size, seq_length]` to `[batch_size, 1, seq_length, seq_length + past_length]`. """ batch_size, total_length = mask.shape seq_length = total_length - past_key_values_length if past_key_values_length is not None else total_length expanded_mask = ~(mask[:, None, None, :].to(torch.bool)) return expanded_mask.expand(batch_size, 1, seq_length, total_length) 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`, *required*): input tensor residual (`torch.tensor`, *required*): residual tensor prob (`float`, *required*): dropout probability training (`bool`, *required*): training mode """ out = F.dropout(x, p=prob, training=training) out = residual + out return out class FalconAttention(nn.Module): def __init__(self, config: FalconConfig): 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 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})." ) self.maybe_rotary = self._init_rope() if config.rotary else lambda q, k, t, p: (q, k) # 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 def _init_rope(self): if self.config.rope_scaling is None: rotary_emb = FalconRotaryEmbedding( self.head_dim, base=self.config.rope_theta, max_position_embeddings=self.config.max_position_embeddings, ) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": rotary_emb = FalconLinearScalingRotaryEmbedding( self.head_dim, base=self.config.rope_theta, max_position_embeddings=self.config.max_position_embeddings, scaling_factor=scaling_factor, ) elif scaling_type == "dynamic": rotary_emb = FalconDynamicNTKScalingRotaryEmbedding( self.head_dim, base=self.config.rope_theta, max_position_embeddings=self.config.max_position_embeddings, scaling_factor=scaling_factor, ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") return rotary_emb 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`, *required*): [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`, *required*): [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[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, padding_mask: Optional[torch.LongTensor] = None, ): 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) past_kv_length = 0 if layer_past is None else layer_past[0].shape[1] query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length, position_ids) if layer_past is not None: past_key, past_value = layer_past # concatenate along seq_length dimension: # - key: [batch_size * self.num_heads, kv_length, head_dim] # - value: [batch_size * self.num_heads, kv_length, head_dim] key_layer = torch.cat((past_key, key_layer), dim=1) value_layer = torch.cat((past_value, value_layer), dim=1) _, kv_length, _ = key_layer.shape if use_cache: present = (key_layer, value_layer) else: present = None float_min = torch.finfo(query_layer.dtype).min attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float_min).to(query_layer.dtype) query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim) key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim) value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim) if alibi is None: if hasattr(F, "scaled_dot_product_attention") and not output_attentions: # TODO: deprecate this once we add FA2 support in Falcon logger.warning_once( "The current implementation of Falcon calls `torch.scaled_dot_product_attention` directly, this will be deprecated in the" " future in favor of the `BetterTransformer` API. Please install the latest optimum library with `pip install -U optimum` and call " "`model.to_bettertransformer()` to benefit from `torch.scaled_dot_product_attention` and future performance optimizations." ) attn_output = F.scaled_dot_product_attention( query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False ) 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_float, dim=-1, dtype=hidden_states.dtype ) 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) output_tensor = self.dense(attn_output) if output_attentions: return output_tensor, present, attention_scores else: return output_tensor, present 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) # Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by # adding (alibi * self.inv_norm_factor) to attention_mask_float. I think this would be mathematically # equivalent and more performant, but there might be a numerical difference. If you're reading this # and you'd like to experiment and maybe file a PR, feel free! 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_float, 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] context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1) # change view [batch_size, q_length, num_heads * head_dim] context_layer = self._merge_heads(context_layer) output_tensor = self.dense(context_layer) if output_attentions: return output_tensor, present, attention_probs else: return output_tensor, present 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 forward( self, hidden_states: torch.Tensor, alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, padding_mask: Optional[torch.LongTensor] = None, ): 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) past_kv_length = 0 if layer_past is None else layer_past[0].shape[1] query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length, position_ids) if layer_past is not None and use_cache: past_key, past_value = layer_past # concatenate along seq_length dimension: # - key: [batch_size * self.num_heads, kv_length, head_dim] # - value: [batch_size * self.num_heads, kv_length, head_dim] key_layer = torch.cat((past_key, key_layer), dim=1) value_layer = torch.cat((past_value, value_layer), dim=1) _, kv_seq_length, _ = key_layer.shape torch_dtype = query_layer.dtype past_key_value = (key_layer, value_layer) if use_cache else None query_layer = ( query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim).transpose(1, 2).to(torch_dtype) ) key_layer = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim).transpose(1, 2).to(torch_dtype) value_layer = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim).transpose(1, 2).to(torch_dtype) if alibi is not None: raise ValueError("`alibi` is not supported when `use_flash_attn` is True") attn_dropout = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. input_dtype = query_layer.dtype if input_dtype == torch.float32: logger.warning_once( "The input hidden states seems to be silently casted in float32, this might be related to" " the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" " float16." ) query_layer = query_layer.to(torch.float16) key_layer = key_layer.to(torch.float16) value_layer = value_layer.to(torch.float16) attn_output = self._flash_attention_forward( query_layer, key_layer, value_layer, padding_mask, query_length, dropout=attn_dropout ) 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, past_key_value, attn_weights # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward def _flash_attention_forward( self, query_states, key_states, value_states, padding_mask, query_length, dropout=0.0, softmax_scale=None ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API padding_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) """ # Contains at least one padding token in the sequence if padding_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, padding_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=True, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=True ) return attn_output # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. padding_mask = padding_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) class FalconMLP(nn.Module): def __init__(self, config: FalconConfig): super().__init__() hidden_size = config.hidden_size self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias) self.act = nn.GELU() self.dense_4h_to_h = FalconLinear(4 * 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 class FalconDecoderLayer(nn.Module): def __init__(self, config: FalconConfig): super().__init__() hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.self_attention = ( FalconAttention(config) if not getattr(config, "_flash_attn_2_enabled", False) else FalconFlashAttention2(config) ) self.mlp = FalconMLP(config) self.hidden_dropout = config.hidden_dropout self.config = config if config.new_decoder_architecture: # 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) if not config.parallel_attn: self.post_attention_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[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, padding_mask: Optional[torch.LongTensor] = None, ): residual = hidden_states if self.config.new_decoder_architecture: 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, padding_mask=padding_mask, ) 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) 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, present, 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 (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`): Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. Each element of `past_key_values` is a tuple (past_key, past_value): - past_key: [batch_size * num_heads, head_dim, kv_length] - past_value: [batch_size * num_heads, kv_length, head_dim] 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. """ 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 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) # Copied from transformers.models.bloom.modeling_bloom.BloomPreTrainedModel._set_gradient_checkpointing with BloomModel->FalconModel def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False): if isinstance(module, FalconModel): module.gradient_checkpointing = value @staticmethod def _convert_cache_to_standard_format( past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: """ Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size, num_heads, ...])) """ batch_size_times_num_heads, kv_length, head_dim = past_key_value[0][0].shape # [batch_size * self.num_heads, kv_length, head_dim] -> [batch_size, num_heads, kv_length, head_dim] # Note that don't want to use self.num_attention_heads because the number of heads may vary depending # on whether we use multi_query attention. num_heads = batch_size_times_num_heads // batch_size return tuple( ( layer_past[0].view(batch_size, num_heads, kv_length, head_dim), layer_past[1].view(batch_size, num_heads, kv_length, head_dim), ) for layer_past in past_key_value ) @staticmethod def _convert_to_rw_cache( past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]] ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape batch_size_times_num_heads = batch_size * num_heads # [batch_size, num_heads, kv_length, head_dim] -> [batch_size * num_heads, kv_length, head_dim] return tuple( ( layer_past[0].view(batch_size_times_num_heads, kv_length, head_dim), layer_past[1].view(batch_size_times_num_heads, kv_length, head_dim), ) for layer_past in past_key_value ) @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) for _ in range(config.num_hidden_layers)]) # Final Layer Norm self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.word_embeddings @staticmethod def _prepare_attn_mask( attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int ) -> torch.BoolTensor: # Create a causal mask # The attention mask we receive as input should cover the whole extended sequence, including any past # cache, so its shape should be [batch_size, seq_length + past_key_values_length] # The output shape will be [batch_size, 1, seq_length, seq_length + past_key_values_length] if input_shape[1] + past_key_values_length != attention_mask.shape[1]: raise ValueError( "Attention mask shape should be (batch_size, seq_length + past_key_values_length)" f" but is {attention_mask.shape} with input_ids shape {input_shape} and past length" f" {past_key_values_length}." ) combined_attention_mask = None device = attention_mask.device _, seq_length = input_shape if seq_length > 1: combined_attention_mask = _make_causal_mask( input_shape, device=device, past_key_values_length=past_key_values_length ) # [batch_size, seq_length + past_key_values_length] -> [batch_size, 1, seq_length, seq_length + past_key_values_length] expanded_attn_mask = _expand_mask(attention_mask, past_key_values_length=past_key_values_length) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask ) return combined_attention_mask 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[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, ) -> 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 not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either input_ids or inputs_embeds") if past_key_values is None: past_key_values = tuple([None] * len(self.h)) else: past_key_values = self._convert_to_rw_cache(past_key_values) # 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) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) hidden_states = inputs_embeds presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None # Compute alibi tensor: check build_alibi_tensor documentation past_key_values_length = 0 if past_key_values[0] is not None: past_key_values_length = past_key_values[0][0].shape[1] # 1 because RW-cache, not standard format if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=hidden_states.device) padding_mask = None else: attention_mask = attention_mask.to(hidden_states.device) if 0 in attention_mask: padding_mask = attention_mask else: padding_mask = None if self.use_alibi: alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) else: alibi = None if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0).view(-1, seq_length) else: position_ids = position_ids.view(-1, seq_length).long() causal_mask = self._prepare_attn_mask( attention_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length, ) for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) return custom_forward outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, alibi, causal_mask, position_ids, head_mask[i], padding_mask, ) else: outputs = block( hidden_states, layer_past=layer_past, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi, padding_mask=padding_mask, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (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,) if presents is not None: presents = self._convert_cache_to_standard_format(presents, batch_size) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @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): _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 def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, **kwargs, ) -> dict: if past_key_values is not None: input_ids = input_ids[:, -1:] # Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE. if not self.transformer.use_alibi and attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) return { "input_ids": input_ids, "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } @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[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, ) -> 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]` """ 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, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() batch_size, seq_length, vocab_size = shift_logits.shape # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct( shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length) ) 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: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1).to(logits.device) else: sequence_lengths = -1 logger.warning( 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), sequence_lengths] 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, )