# 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 math import warnings from typing import Optional, 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 ...cache_utils import Cache, DynamicCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( auto_docstring, is_torch_flex_attn_available, logging, ) from .configuration_bloom import BloomConfig if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from ...integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: """ Link to paper: https://huggingface.co/papers/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`): 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`): input tensor residual (`torch.tensor`): residual tensor prob (`float`): dropout probability training (`bool`): training mode """ out = F.dropout(x, p=prob, training=training) out = residual + out return out 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`): 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`): gradient output tensor x (`torch.tensor`): 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 BloomAttention(nn.Module): def __init__(self, config: BloomConfig, layer_idx: Optional[int] = None): super().__init__() 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 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.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) 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) def _reshape(self, fused_qkv: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Split the last dimension into (num_heads, head_dim) and reshapes to (bs, heads, len, dim) shape without making any copies, results share same memory storage as `fused_qkv` Args: fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim] Returns: query: [batch_size, num_heads, seq_length, head_dim] key: [batch_size, num_heads, seq_length, head_dim] value: [batch_size, num_heads, seq_length, 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) query_layer = fused_qkv[..., 0, :].transpose(1, 2) key_layer = fused_qkv[..., 1, :].transpose(1, 2) value_layer = fused_qkv[..., 2, :].transpose(1, 2) return query_layer, key_layer, value_layer def _merge_heads(self, x: torch.Tensor) -> torch.Tensor: """ Merge heads together over the last dimension Args: x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim] Returns: torch.tensor: [batch_size, seq_length, num_heads * head_dim] """ # What we want to achieve is: # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim batch_size_and_num_heads, seq_length, _ = x.shape batch_size = batch_size_and_num_heads // self.num_heads # First view to decompose the batch size # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim x = x.view(batch_size, self.num_heads, seq_length, self.head_dim) # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim x = x.permute(0, 2, 1, 3) # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim) def forward( self, hidden_states: torch.Tensor, residual: torch.Tensor, alibi: torch.Tensor, attention_mask: torch.Tensor, layer_past: Optional[Cache] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, ): batch_size, q_length, _ = hidden_states.shape fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] # 3 x [batch_size, num_heads, seq_length, head_dim] query_layer, key_layer, value_layer = self._reshape(fused_qkv) if layer_past is not None: cache_kwargs = {"cache_position": cache_position} key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs) # reshape qkv for further computations query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim) key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2) value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim) # [batch_size * num_heads, q_length, kv_length] attention_scores = alibi.baddbmm( batch1=query_layer, batch2=key_layer, beta=self.beta, alpha=self.inv_norm_factor, ) # change view to [batch_size, num_heads, q_length, kv_length] attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]] attn_weights = attn_weights + causal_mask # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.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 x num_heads, q_length, kv_length] attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1) # matmul: [batch_size * num_heads, q_length, head_dim] context_layer = torch.bmm(attention_probs_reshaped, value_layer) # change view [batch_size, q_length, num_heads * head_dim] context_layer = self._merge_heads(context_layer) # 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) return output_tensor, attention_probs class BloomMLP(nn.Module): def __init__(self, config: BloomConfig): 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(GradientCheckpointingLayer): def __init__(self, config: BloomConfig, layer_idx: Optional[int] = None): super().__init__() hidden_size = config.hidden_size self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.num_heads = config.n_head self.self_attention = BloomAttention(config, layer_idx) 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 def forward( self, hidden_states: torch.Tensor, alibi: torch.Tensor, attention_mask: torch.Tensor, layer_past: Optional[Cache] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, cache_position: Optional[torch.LongTensor] = None, ): # 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. attention_output, attn_weights = self.self_attention( layernorm_output, residual, layer_past=layer_past, attention_mask=attention_mask, alibi=alibi, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) 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) return output, attn_weights # hidden_states, attentions @auto_docstring class BloomPreTrainedModel(PreTrainedModel): config: BloomConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["BloomBlock"] _skip_keys_device_placement = "past_key_values" _can_compile_fullgraph = 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): # 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) @auto_docstring class BloomModel(BloomPreTrainedModel): def __init__(self, config: BloomConfig): super().__init__(config) self.embed_dim = config.hidden_size self.num_heads = config.n_head # Embedding + LN Embedding 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, layer_idx=i) for i 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 set_input_embeddings(self, new_embeddings: torch.Tensor): self.word_embeddings = new_embeddings @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **deprecated_arguments, ) -> Union[tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: r""" 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.get_seq_length()` (`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) """ 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 None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache if not isinstance(past_key_values, (type(None), Cache)): raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.") if use_cache and past_key_values is None: past_key_values = DynamicCache() batch_size, seq_length, _ = inputs_embeds.shape past_length = past_key_values.get_seq_length() if past_key_values is not None else 0 seq_length_with_past = seq_length + past_length if cache_position is None: cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device) # 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) hidden_states = self.word_embeddings_layernorm(inputs_embeds) 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 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) alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = block( hidden_states, layer_past=past_key_values, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi, cache_position=cache_position, ) hidden_states = outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (outputs[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, past_key_values, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype = input_tensor.dtype sequence_length = input_tensor.shape[1] if using_compilable_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @auto_docstring( custom_intro=""" The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """ ) class BloomForCausalLM(BloomPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: BloomConfig): super().__init__(config) self.transformer = BloomModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def set_output_embeddings(self, new_embeddings: torch.Tensor): self.lm_head = new_embeddings def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, use_cache=True, **kwargs, ): # Overwritten because of the fixed-shape attention mask creation # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case. # (we can't check exception 3 while compiling) # Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and # generate the first token for each sequence. Later use the generated Input ids for continuation. if past_key_values is not None: if inputs_embeds is not None and input_ids.shape[1] == 0: # Exception 4 inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :] elif ( inputs_embeds is not None # Exception 1 or cache_position[-1] >= input_ids.shape[1] # Exception 3 ): input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]: model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None} else: # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the # input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in # the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture. model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None} # This part differs from other models because BLOOM needs a 2D mask to construct alibi tensor # The only difference is the usage of 2D instead of 4D mask, but the shape will be static if isinstance(past_key_values, StaticCache) and attention_mask is not None: target_length = past_key_values.get_max_cache_shape() batch_size, seq_length = attention_mask.shape diff = target_length - seq_length new_attn_mask = torch.zeros(batch_size, diff, device=attention_mask.device, dtype=attention_mask.dtype) attention_mask = torch.cat( [attention_mask, new_attn_mask], dim=-1, ) model_inputs.update( { "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, } ) return model_inputs @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **deprecated_arguments, ) -> Union[tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" 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.get_seq_length()` (`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) 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]` """ # Bloom has deprecated kwargs, so we need to pop num_items_in_batch explicitly num_items_in_batch = deprecated_arguments.pop("num_items_in_batch", None) 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, cache_position=cache_position, ) 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) # Flatten the tokens loss = self.loss_function( lm_logits, labels, vocab_size=self.config.vocab_size, num_items_in_batch=num_items_in_batch, ) 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, ) @auto_docstring( custom_intro=""" 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). """ ) class BloomForSequenceClassification(BloomPreTrainedModel): def __init__(self, config: BloomConfig): 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() @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, 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""" 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.get_seq_length()` (`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) 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: last_non_pad_token = -1 elif input_ids is not None: # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32) last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) else: last_non_pad_token = -1 logger.warning_once( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @auto_docstring class BloomForTokenClassification(BloomPreTrainedModel): def __init__(self, config: BloomConfig): 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() @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, 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""" 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.get_seq_length()` (`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) 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, ) @auto_docstring class BloomForQuestionAnswering(BloomPreTrainedModel): 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() @auto_docstring 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""" 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.get_seq_length()` (`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) """ 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, ) __all__ = [ "BloomForCausalLM", "BloomModel", "BloomPreTrainedModel", "BloomForSequenceClassification", "BloomForTokenClassification", "BloomForQuestionAnswering", ]