# coding=utf-8 # Copyright 2022 HuggingFace Inc. team and BigScience workshop. # # 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 BLOOM model.""" import os import os.path as osp import math import warnings 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 transformers.file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging try: from flash_attn import flash_attn_func except ImportError: flash_attn_func = None from transformers.models.bloom.configuration_bloom import BloomConfig as CCBloomConfig # from transformers.models.llama.modeling_llama import LlamaDynamicNTKScalingRotaryEmbedding, LlamaLinearScalingRotaryEmbedding, LlamaRotaryEmbedding logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "bigscience/bloom-560m" _CONFIG_FOR_DOC = "CCBloomConfig" BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST = [ "bigscience/bigscience-small-testing", "bigscience/bloom-560m", "bigscience/bloom-1b1", "bigscience/bloom-1b7", "bigscience/bloom-3b", "bigscience/bloom-7b1", "bigscience/bloom", ] 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. """ batch_size, target_length = input_ids_shape mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device) # ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround seq_ids = torch.arange(target_length, device=device) mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :] if past_key_values_length > 0: mask[:, :past_key_values_length] = False 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, tgt_length: int) -> torch.BoolTensor: """ Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`. """ batch_size, src_length = mask.shape tgt_length = tgt_length if tgt_length is not None else src_length expanded_mask = ~(mask[:, None, None, :].to(torch.bool)) return expanded_mask.expand(batch_size, 1, tgt_length, src_length) def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: """ Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value `softmax(l+a) = softmax(l)`. Based on https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742 TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly. Args: Returns tensor shaped (batch_size * num_heads, 1, max_seq_len) attention_mask (`torch.Tensor`): Token-wise attention mask, this should be of shape (batch_size, max_seq_len). num_heads (`int`, *required*): number of heads dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`): dtype of the output 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] * arange_tensor return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) 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*): esidual tensor prob (`float`, *required*): dropout probability training (`bool`, *required*): training mode """ out = F.dropout(x, p=prob, training=training) out = residual + out return out def bloom_gelu_forward(x: torch.Tensor) -> torch.Tensor: """ Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to make the model jitable. Args: x (`torch.tensor`, *required*): input hidden states """ return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))) def bloom_gelu_back(g: torch.Tensor, x: torch.Tensor) -> torch.Tensor: """ gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) Args: g (`torch.tensor`, *required*): gradient output tensor x (`torch.tensor`, *required*): input tensor """ x = x[0] # x is a tuple of 1 element, needs to unpack it first tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)) # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out) return ff * g class GeLUFunction(torch.autograd.Function): @staticmethod def forward(ctx, input: torch.Tensor) -> torch.Tensor: ctx.save_for_backward(input) return bloom_gelu_forward(input) @staticmethod def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: input = ctx.saved_tensors tmp = bloom_gelu_back(grad_output, input) return tmp class BloomGelu(nn.Module): """ BloomBiasGelu wrapper function that make use of the simple function on inference mode to make the model torchscriptable and use the autograd function in training mode to get the accurate results of the gradients Partly copied from Megatron-DeepSpeed code and adapted for our needs See here why autograd functions are not torchscriptable: https://github.com/pytorch/pytorch/issues/22329 """ def __init__(self): super().__init__() def forward(self, x: torch.Tensor) -> torch.Tensor: if self.training: return GeLUFunction.apply(x) else: return bloom_gelu_forward(x) class LlamaRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. # self._set_cos_sin_cache( # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 # ) self.max_seq_len_cached = max_position_embeddings self.cos_cached = None self.sin_cached = None def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.cos_cached = emb.cos()[None, None, :, :].to(dtype) self.sin_cached = emb.sin()[None, None, :, :].to(dtype) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if self.cos_cached is None: self._set_cos_sin_cache(seq_len=self.max_seq_len_cached, device=x.device, dtype=torch.float32) if seq_len > self.max_seq_len_cached: print('reset cos/sin cache') self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32) self.cos_cached = self.cos_cached.to(x.device) self.sin_cached = self.sin_cached.to(x.device) return ( self.cos_cached[:, :, :seq_len, ...], self.sin_cached[:, :, :seq_len, ...], ) class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.cos_cached = emb.cos()[None, None, :, :].to(dtype) self.sin_cached = emb.sin()[None, None, :, :].to(dtype) class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.cos_cached = emb.cos()[None, None, :, :].to(dtype) self.sin_cached = emb.sin()[None, None, :, :].to(dtype) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): ''' q: [bs, seq_len, num_heads, head_dim] k: [bs, seq_len, num_heads, head_dim] cos: [1, 1, seq_len, dim] sin: [1, 1, seq_len, dim] position_ids: [bs, seq_len] ''' # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] cos = cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] sin = sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] # q_embed and k_embed: [bs, seq_len, num_heads, dim] q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin) k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin) return q_embed.to(q.dtype), k_embed.to(k.dtype) class BloomAttention(nn.Module): def __init__(self, config: CCBloomConfig): super().__init__() self.config = config self.pretraining_tp = config.pretraining_tp self.slow_but_exact = config.slow_but_exact self.hidden_size = config.hidden_size self.num_heads = config.n_head self.head_dim = self.hidden_size // self.num_heads self.split_size = self.hidden_size self.hidden_dropout = config.hidden_dropout self.p_attn_dropout = config.attention_dropout self.max_position_embeddings = config.seq_length 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 = 1.0 self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=True) self.dense = nn.Linear(self.hidden_size, self.hidden_size) self.attention_dropout = nn.Dropout(config.attention_dropout) self._init_rope() def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = LlamaLinearScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor ) elif scaling_type == "dynamic": self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Split the last dimension into (num_heads, head_dim) without making any copies, 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] """ 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, :] def forward( self, hidden_states: torch.Tensor, residual: torch.Tensor, # alibi: torch.Tensor, attention_mask: torch.Tensor, causal_mask: torch.Tensor, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, ): fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] # 3 x [batch_size, seq_length, num_heads, head_dim] (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) batch_size, q_length, num_heads, head_dim = query_layer.shape kv_length = key_layer.shape[1] position_ids = attention_mask.cumsum(dim=-1) - 1 if layer_past is not None: kv_length += layer_past[0].shape[1] position_ids = position_ids[:, -1:] ## add rope cos, sin = self.rotary_emb(value_layer, seq_len=kv_length) query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids) # still [bs, seq_len, num_heads, head_dim] if layer_past is not None: past_key, past_value = layer_past # concatenate along seq_length dimension: # - key: [batch_size, kv_length, num_heads, head_dim] # - value: [batch_size, kv_length, num_heads, head_dim] key_layer = torch.cat((past_key, key_layer), dim=1) value_layer = torch.cat((past_value, value_layer), dim=1) if use_cache is True: present = (key_layer, value_layer) else: present = None context_layer = self.compute_qkv_attn(query_layer, key_layer, value_layer, causal_mask, head_mask=head_mask, causal=layer_past is None) # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232 if self.pretraining_tp > 1 and self.slow_but_exact: slices = self.hidden_size / self.pretraining_tp output_tensor = torch.zeros_like(context_layer) for i in range(self.pretraining_tp): output_tensor = output_tensor + F.linear( context_layer[:, :, int(i * slices) : int((i + 1) * slices)], self.dense.weight[:, int(i * slices) : int((i + 1) * slices)], ) else: output_tensor = self.dense(context_layer) output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training) outputs = (output_tensor, present) if output_attentions: outputs += (attention_probs,) return outputs def compute_qkv_attn(self, query_layer, key_layer, value_layer, causal_mask, head_mask=None, causal=False): batch_size, q_length, num_heads, head_dim = query_layer.shape kv_length = key_layer.size(1) query_layer = query_layer.transpose(1, 2) key_layer = key_layer.permute(0, 2, 3, 1) attn_weights = torch.matmul(query_layer, key_layer).mul(self.inv_norm_factor) # attn_weights is [batch_size, num_heads, q_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] # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38` input_dtype = attn_weights.dtype if input_dtype == torch.float16: attn_weights = attn_weights.to(torch.float) attn_weights = torch.masked_fill(attn_weights, causal_mask, torch.finfo(attn_weights.dtype).min) attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_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 v view [bs, num_heads, kv_length, head_dim] value_layer = value_layer.transpose(1, 2) # matmul: [batch_size, num_heads, q_length, head_dim] num = batch_size * num_heads attention_probs = attention_probs.reshape(num, q_length, kv_length) value_layer = value_layer.reshape(num, kv_length, head_dim) context_layer = torch.bmm(attention_probs, value_layer) context_layer = context_layer.reshape(batch_size, num_heads, q_length, head_dim) context_layer = torch.einsum('bhld->blhd', context_layer) context_layer = context_layer.flatten(2) return context_layer class BloomAttentionFlashAttn(BloomAttention): def __init__(self, config: CCBloomConfig): super().__init__(config) def compute_qkv_attn(self, query_layer, key_layer, value_layer, causal_mask, head_mask=None, causal=False): batch_size, q_length, num_heads, head_dim = query_layer.shape # flash attention requires qkv to be [bs, seq_len, num_heads, head_dim] context_layer = flash_attn_func(query_layer, key_layer, value_layer, dropout_p=self.p_attn_dropout, softmax_scale=self.inv_norm_factor, causal=causal) # output is: [batch_size, q_length, num_heads, head_dim] context_layer = context_layer.flatten(2) return context_layer class BloomAttentionTorchFast(BloomAttention): def __init__(self, config: CCBloomConfig): super().__init__(config) def compute_qkv_attn(self, query_layer, key_layer, value_layer, causal_mask, head_mask=None, causal=False): batch_size, q_length, num_heads, head_dim = query_layer.shape kv_length = key_layer.size(1) ## requires qkv to have shape: [batch_size, num_heads, length, head_dim] # query_layer = torch.einsum('blhd->bhld', query_layer) # key_layer = torch.einsum('blhd->bhld', key_layer) # value_layer = torch.einsum('blhd->bhld', value_layer) query_layer = query_layer.transpose(1, 2) key_layer = key_layer.transpose(1, 2) value_layer = value_layer.transpose(1, 2) attn_mask = torch.zeros(batch_size, 1, q_length, kv_length, device=query_layer.device, dtype=query_layer.dtype) attn_mask.masked_fill_(causal_mask, torch.finfo(attn_mask.dtype).min) with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True): context_layer = F.scaled_dot_product_attention(query_layer, key_layer, value_layer, dropout_p=self.p_attn_dropout, attn_mask=attn_mask, scale=self.inv_norm_factor) context_layer = context_layer.transpose(1, 2) # context_layer = torch.einsum('blhd->bhld', context_layer) context_layer = context_layer.flatten(2) return context_layer class BloomMLP(nn.Module): def __init__(self, config: CCBloomConfig): super().__init__() hidden_size = config.hidden_size self.pretraining_tp = config.pretraining_tp self.slow_but_exact = config.slow_but_exact self.dense_h_to_4h = nn.Linear(hidden_size, 4 * hidden_size) self.gelu_impl = BloomGelu() self.dense_4h_to_h = nn.Linear(4 * hidden_size, hidden_size) self.hidden_dropout = config.hidden_dropout def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor: hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states)) if self.pretraining_tp > 1 and self.slow_but_exact: intermediate_output = torch.zeros_like(residual) slices = self.dense_4h_to_h.weight.shape[-1] / self.pretraining_tp for i in range(self.pretraining_tp): intermediate_output = intermediate_output + F.linear( hidden_states[:, :, int(i * slices) : int((i + 1) * slices)], self.dense_4h_to_h.weight[:, int(i * slices) : int((i + 1) * slices)], ) else: intermediate_output = self.dense_4h_to_h(hidden_states) output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training) return output class BloomBlock(nn.Module): def __init__(self, config: CCBloomConfig): super().__init__() self.config = config hidden_size = config.hidden_size self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.num_heads = config.n_head if config.use_flash_attn: self.self_attention = BloomAttentionFlashAttn(config) else: self.self_attention = BloomAttention(config) # self.self_attention = BloomAttentionTorchFast(config) self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = BloomMLP(config) self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm self.hidden_dropout = config.hidden_dropout # @torch.compile def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_mask: torch.Tensor, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, ): # hidden_states: [batch_size, seq_length, hidden_size] # Layer norm at the beginning of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Layer norm post the self attention. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states # Self attention. attn_outputs = self.self_attention( layernorm_output, residual, layer_past=layer_past, attention_mask=attention_mask, causal_mask=causal_mask, # alibi=alibi, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attention_output = attn_outputs[0] outputs = attn_outputs[1:] layernorm_output = self.post_attention_layernorm(attention_output) # Get residual if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = attention_output # MLP. output = self.mlp(layernorm_output, residual) if use_cache: outputs = (output,) + outputs else: outputs = (output,) + outputs[1:] return outputs # hidden_states, present, attentions class NormHead(nn.Module): def __init__(self, hidden_size, vocab_size, bias=False): super().__init__() self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size))) nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) self.first_flag = True def forward(self, hidden_states): if self.training: norm_weight = nn.functional.normalize(self.weight) elif self.first_flag: self.first_flag = False self.weight = nn.Parameter(nn.functional.normalize(self.weight)) norm_weight = self.weight else: norm_weight = self.weight return F.linear(hidden_states, norm_weight) class SmoothEmbedding(nn.Embedding): def __init__(self, vocab_size, embed_dim): super().__init__(vocab_size, embed_dim) def forward(self, input_ids): word_emb = super().forward(input_ids) mean_emb = self.weight.mean(dim=0, keepdim=True) emb = word_emb * 0.9 + mean_emb * 0.1 return emb class BloomPreTrainedModel(PreTrainedModel): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CCBloomConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["BloomBlock"] _skip_keys_device_placement = "past_key_values" def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module: nn.Module): """Initialize the weights.""" if isinstance(module, nn.Linear): # 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) def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False): if isinstance(module, BloomModel): module.gradient_checkpointing = value @staticmethod def _convert_to_standard_cache( 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, ...])) """ # seq_length, num_heads, head_dim = past_key_value[0][0].shape # batch_size_times_num_heads, seq_length, head_dim = past_key_value[0][0].shape # num_heads = batch_size_times_num_heads // batch_size # key: [batch_size, seq_length, num_heads, head_dim] -> [batch_size, num_heads, head_dim, seq_length] # value: [batch_size, seq_length, num_heads, head_dim] -> [batch_size, num_heads, seq_length, head_dim] return tuple( ( layer_past[0].permute(0, 2, 3, 1), layer_past[1].permute(0, 2, 1, 3), ) for layer_past in past_key_value ) @staticmethod def _convert_to_bloom_cache( past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]] ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: """ Converts the cache to the format expected by Bloom, i.e. to tuple(tuple([batch_size * num_heads, ...])) """ # key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size, seq_length, num_heads, head_dim] # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size, seq_length, num_heads, head_dim] return tuple( ( torch.einsum('bhdl->blhd', layer_past[0]), torch.einsum('bhld->blhd', layer_past[1]) ) for layer_past in past_key_value ) BLOOM_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 ([`CCBloomConfig`]): 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. """ BLOOM_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.n_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) 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. """ @add_start_docstrings( "The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.", BLOOM_START_DOCSTRING, ) class BloomModel(BloomPreTrainedModel): _auto_class = 'ccbloom' def __init__(self, config: CCBloomConfig): super().__init__(config) self.embed_dim = config.hidden_size self.num_heads = config.n_head # Embedding + LN Embedding self.word_embeddings = SmoothEmbedding(config.vocab_size, self.embed_dim) # self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim) self.word_embeddings_layernorm = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Transformer blocks self.h = nn.ModuleList([BloomBlock(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 build_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: return build_alibi_tensor(attention_mask, num_heads, dtype) def get_input_embeddings(self): return self.word_embeddings def _prepare_attn_mask( self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int ) -> torch.BoolTensor: # create causal mask # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length] combined_attention_mask = None device = attention_mask.device _, src_length = input_shape if src_length > 1: combined_attention_mask = _make_causal_mask( input_shape, device=device, past_key_values_length=past_key_values_length ) # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length] expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_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(BLOOM_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, 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, **deprecated_arguments, ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: if deprecated_arguments.pop("position_ids", False) is not False: # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` warnings.warn( "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" " passing `position_ids`.", FutureWarning, ) if len(deprecated_arguments) > 0: raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") 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)) # 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.n_layer) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) hidden_states = self.word_embeddings_layernorm(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 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 # Compute alibi tensor: check build_alibi_tensor documentation seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values[0] is not None: past_key_values_length = past_key_values[0][0].shape[2] seq_length_with_past = seq_length_with_past + past_key_values_length if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) else: attention_mask = attention_mask.to(hidden_states.device) ## lower triangular mask causal_mask = None if self.config.use_flash_attn == False: # if True: 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: 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, attention_mask, causal_mask, layer_past, head_mask[i], ) else: outputs = block( hidden_states, layer_past=layer_past, attention_mask=attention_mask, causal_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, ) 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 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 Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, BLOOM_START_DOCSTRING, ) class CCBloomForCausalLM(BloomPreTrainedModel): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] def __init__(self, config: CCBloomConfig): super().__init__(config) self.transformer = BloomModel(config) # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.lm_head = NormHead(config.hidden_size, config.vocab_size) # 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, inputs_embeds: Optional[torch.Tensor] = None, **kwargs, ) -> dict: # only last token for input_ids if past is not None if past_key_values: input_ids = input_ids[:, -1].unsqueeze(-1) # the cache may be in the stardard format (e.g. in contrastive search), convert to bloom's format if needed # if past_key_values[0][0].shape[0] == input_ids.shape[0]: # past_key_values = self._convert_to_bloom_cache(past_key_values) # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs @add_start_docstrings_to_model_forward(BLOOM_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, 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, **deprecated_arguments, ) -> 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]` """ if deprecated_arguments.pop("position_ids", False) is not False: # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` warnings.warn( "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" " passing `position_ids`.", FutureWarning, ) if len(deprecated_arguments) > 0: raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") 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] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # 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`. """ standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx)) # 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 standardized_past ) return self._convert_to_bloom_cache(reordered_past) @add_start_docstrings( """ The Bloom Model transformer with a sequence classification head on top (linear layer). [`BloomForSequenceClassification`] 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). """, BLOOM_START_DOCSTRING, ) class BloomForSequenceClassification(BloomPreTrainedModel): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] def __init__(self, config: CCBloomConfig): super().__init__(config) self.num_labels = config.num_labels self.transformer = BloomModel(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(BLOOM_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, **deprecated_arguments, ) -> 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). """ if deprecated_arguments.pop("position_ids", False) is not False: # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` warnings.warn( "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" " passing `position_ids`.", FutureWarning, ) if len(deprecated_arguments) > 0: raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") 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(-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( """ Bloom 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. """, BLOOM_START_DOCSTRING, ) class BloomForTokenClassification(BloomPreTrainedModel): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] def __init__(self, config: CCBloomConfig): super().__init__(config) self.num_labels = config.num_labels self.transformer = BloomModel(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout elif hasattr(config, "hidden_dropout") and config.hidden_dropout 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(BLOOM_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, **deprecated_arguments, ) -> 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). """ if deprecated_arguments.pop("position_ids", False) is not False: # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` warnings.warn( "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" " passing `position_ids`.", FutureWarning, ) if len(deprecated_arguments) > 0: raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") 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: # move labels to correct device to enable model parallelism labels = labels.to(logits.device) 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 BLOOM 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`). """, BLOOM_START_DOCSTRING, ) class BloomForQuestionAnswering(BloomPreTrainedModel): _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = BloomModel(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(BLOOM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = 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, position_ids=position_ids, 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, )