diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py index 1ddc3516ba2..bb9c4565d11 100644 --- a/src/transformers/cache_utils.py +++ b/src/transformers/cache_utils.py @@ -1016,7 +1016,9 @@ class StaticCache(Cache): self.dtype = dtype if dtype is not None else torch.float32 self.num_key_value_heads = ( - config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads + config.num_attention_heads + if getattr(config, "num_key_value_heads", None) is None + else config.num_key_value_heads ) self.key_cache: List[torch.Tensor] = [] diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py index 3e3c7803a61..998288bd38d 100644 --- a/src/transformers/generation/utils.py +++ b/src/transformers/generation/utils.py @@ -1473,7 +1473,7 @@ class GenerationMixin: # NOTE: self.dtype is not compatible with torch.compile, as it calls `self.parameters()`. # Workaround: trust the lm_head, whose attribute name is somewhat consistent across generative # models. May cause trobles with non-text modalities. - cache_dtype = self.lm_head.weight.dtype + cache_dtype = self.get_output_embeddings().weight.dtype cache_kwargs = { "config": self.config, diff --git a/src/transformers/models/codegen/modeling_codegen.py b/src/transformers/models/codegen/modeling_codegen.py index a8df9ed7f3f..6452c2afa0b 100644 --- a/src/transformers/models/codegen/modeling_codegen.py +++ b/src/transformers/models/codegen/modeling_codegen.py @@ -22,6 +22,8 @@ from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, StaticCache +from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_utils import PreTrainedModel from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging @@ -34,6 +36,60 @@ _CHECKPOINT_FOR_DOC = "Salesforce/codegen-2B-mono" _CONFIG_FOR_DOC = "CodeGenConfig" +# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + 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: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + # Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor: inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim)) @@ -57,20 +113,19 @@ def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Ten class CodeGenAttention(nn.Module): - def __init__(self, config): + def __init__(self, config, layer_idx=None): super().__init__() max_positions = config.max_position_embeddings - self.register_buffer( - "causal_mask", - torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( - 1, 1, max_positions, max_positions - ), - persistent=False, - ) - self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) + 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.embed_dim = config.hidden_size self.num_attention_heads = config.num_attention_heads @@ -114,27 +169,17 @@ class CodeGenAttention(nn.Module): attention_mask=None, head_mask=None, ): - # compute causal mask from causal mask buffer - query_length, key_length = query.size(-2), key.size(-2) - causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length] - # Keep the attention weights computation in fp32 to avoid overflow issues query = query.to(torch.float32) key = key.to(torch.float32) attn_weights = torch.matmul(query, key.transpose(-1, -2)) - attn_weights = attn_weights / self.scale_attn - mask_value = torch.finfo(attn_weights.dtype).min - # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. - # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` - mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) - attn_weights = torch.where(causal_mask, attn_weights, mask_value) - if attention_mask is not None: - # Apply the attention mask - attn_weights = attn_weights + attention_mask + causal_mask = attention_mask[:, :, :, : key.shape[-2]] + attn_weights += causal_mask + attn_weights = attn_weights / self.scale_attn attn_weights = nn.Softmax(dim=-1)(attn_weights) attn_weights = attn_weights.to(value.dtype) attn_weights = self.attn_dropout(attn_weights) @@ -150,12 +195,13 @@ class CodeGenAttention(nn.Module): def forward( self, hidden_states: Optional[torch.FloatTensor], - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[ Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]], @@ -200,18 +246,16 @@ class CodeGenAttention(nn.Module): key = key.permute(0, 2, 1, 3) query = query.permute(0, 2, 1, 3) + # Note that this cast is quite ugly, but is not implemented before ROPE as k_rot in the original codebase is always in fp32. + # Reference: https://github.com/salesforce/CodeGen/blob/f210c3bb1216c975ad858cd4132c0fdeabf4bfc2/codegen1/jaxformer/hf/codegen/modeling_codegen.py#L38 if layer_past is not None: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - - if use_cache is True: - # Note that this cast is quite ugly, but is not implemented before ROPE as k_rot in the original codebase is always in fp32. - # Reference: https://github.com/salesforce/CodeGen/blob/f210c3bb1216c975ad858cd4132c0fdeabf4bfc2/codegen1/jaxformer/hf/codegen/modeling_codegen.py#L38 - present = (key.to(hidden_states.dtype), value) - else: - present = None + cache_kwargs = { + "sin": sin, + "cos": cos, + "partial_rotation_size": self.rotary_dim, + "cache_position": cache_position, + } + key, value = layer_past.update(key.to(hidden_states.dtype), value, self.layer_idx, cache_kwargs) # compute self-attention: V x Softmax(QK^T) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) @@ -220,7 +264,7 @@ class CodeGenAttention(nn.Module): attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) - outputs = (attn_output, present) + outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) @@ -250,22 +294,23 @@ class CodeGenMLP(nn.Module): # Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->CodeGen class CodeGenBlock(nn.Module): # Ignore copy - def __init__(self, config): + def __init__(self, config, layer_idx=None): super().__init__() inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) - self.attn = CodeGenAttention(config) + self.attn = CodeGenAttention(config, layer_idx) self.mlp = CodeGenMLP(inner_dim, config) def forward( self, hidden_states: Optional[torch.FloatTensor], - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) @@ -277,6 +322,7 @@ class CodeGenBlock(nn.Module): head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] @@ -303,6 +349,9 @@ class CodeGenPreTrainedModel(PreTrainedModel): supports_gradient_checkpointing = True _no_split_modules = ["CodeGenBlock"] _skip_keys_device_placement = "past_key_values" + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) @@ -374,6 +423,23 @@ CODEGEN_INPUTS_DOCSTRING = r""" 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. + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. @@ -382,6 +448,10 @@ CODEGEN_INPUTS_DOCSTRING = r""" more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. """ @@ -397,7 +467,7 @@ class CodeGenModel(CodeGenPreTrainedModel): self.vocab_size = config.vocab_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.drop = nn.Dropout(config.embd_pdrop) - self.h = nn.ModuleList([CodeGenBlock(config) for _ in range(config.n_layer)]) + self.h = nn.ModuleList([CodeGenBlock(config, layer_idx=i) for i in range(config.n_layer)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads) @@ -421,7 +491,7 @@ class CodeGenModel(CodeGenPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, @@ -431,6 +501,7 @@ class CodeGenModel(CodeGenPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( @@ -439,85 +510,62 @@ class CodeGenModel(CodeGenPreTrainedModel): 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: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - batch_size = input_ids.shape[0] - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size = inputs_embeds.shape[0] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) - device = input_ids.device if input_ids is not None else inputs_embeds.device + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False - if token_type_ids is not None: - token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) - if past_key_values is None: - past_length = 0 - past_key_values = tuple([None] * len(self.h)) - else: - past_length = past_key_values[0][0].size(-2) + use_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + use_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + if not self.training: + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + + seq_length = inputs_embeds.shape[1] + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device) if position_ids is None: - position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) - position_ids = position_ids.unsqueeze(0) + position_ids = cache_position.unsqueeze(0) - # Attention mask. - if attention_mask is not None: - if batch_size <= 0: - raise ValueError("batch_size has to be defined and > 0") - attention_mask = attention_mask.view(batch_size, -1) - # We create a 3D attention mask from a 2D tensor mask. - # Sizes are [batch_size, 1, 1, to_seq_length] - # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] - # this attention mask is more simple than the triangular masking of causal attention - # used in OpenAI GPT, we just need to prepare the broadcast dimension here. - attention_mask = attention_mask[:, None, None, :] - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and the dtype's smallest value for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility - attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x num_attention_heads x N x N # head_mask has shape n_layer x batch x num_attention_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.wte(input_ids) - hidden_states = inputs_embeds if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, seq_length) token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) + output_shape = (-1, seq_length, hidden_states.size(-1)) - output_shape = input_shape + (hidden_states.size(-1),) - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " - "`use_cache=False`..." - ) - use_cache = False - - presents = () if use_cache else None + next_decoder_cache = None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None - for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) @@ -526,26 +574,28 @@ class CodeGenModel(CodeGenPreTrainedModel): block.__call__, hidden_states, None, - attention_mask, + causal_mask, position_ids, head_mask[i], use_cache, output_attentions, + cache_position, ) else: outputs = block( hidden_states=hidden_states, - layer_past=layer_past, - attention_mask=attention_mask, + layer_past=past_key_values, + attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) hidden_states = outputs[0] if use_cache is True: - presents = presents + (outputs[1],) + next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) @@ -557,16 +607,94 @@ class CodeGenModel(CodeGenPreTrainedModel): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + 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 tuple( + v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None + ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=presents, + past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + 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 = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_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 == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + @add_start_docstrings( """ @@ -591,26 +719,31 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel): def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings - def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, past_key_values=None, **kwargs): - token_type_ids = kwargs.get("token_type_ids", None) - # Omit tokens covered by past_key_values - if past_key_values: - past_length = past_key_values[0][0].shape[2] + # Copied from transformers.models.gpt_neo.modeling_gpt_neo.GPTNeoForCausalLM.prepare_inputs_for_generation + def prepare_inputs_for_generation( + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + past_key_values=None, + inputs_embeds=None, + cache_position=None, + use_cache=True, + **kwargs, + ): + # 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 + if past_key_values is not None: + if inputs_embeds is not None: # Exception 1 + 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] - # Some generation methods already pass only the last input ID - if input_ids.shape[1] > past_length: - remove_prefix_length = past_length - else: - # Default to old behavior: keep only final ID - remove_prefix_length = input_ids.shape[1] - 1 - - input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] - attention_mask = kwargs.get("attention_mask", None) - position_ids = kwargs.get("position_ids", None) - if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 @@ -618,19 +751,45 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel): if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] + # 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. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + # 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: + if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: - model_inputs = {"input_ids": input_ids.contiguous()} + model_inputs = {"input_ids": input_ids} + + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if inputs_embeds is not None: + batch_size, sequence_length = inputs_embeds.shape + device = inputs_embeds.device + else: + batch_size, sequence_length = input_ids.shape + device = input_ids.device + + dtype = self.lm_head.weight.dtype + min_dtype = torch.finfo(dtype).min + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) model_inputs.update( { - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, - "attention_mask": attention_mask, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, "token_type_ids": token_type_ids, + "attention_mask": attention_mask, } ) return model_inputs @@ -644,7 +803,7 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, @@ -655,6 +814,7 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): @@ -676,6 +836,7 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel): output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + cache_position=cache_position, ) hidden_states = transformer_outputs[0] diff --git a/src/transformers/models/falcon/modeling_falcon.py b/src/transformers/models/falcon/modeling_falcon.py index 37385dd9fd6..5ddc1fba9ef 100644 --- a/src/transformers/models/falcon/modeling_falcon.py +++ b/src/transformers/models/falcon/modeling_falcon.py @@ -24,10 +24,9 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss from torch.nn import functional as F from ...activations import get_activation +from ...cache_utils import Cache, DynamicCache, StaticCache from ...modeling_attn_mask_utils import ( AttentionMaskConverter, - _prepare_4d_causal_attention_mask, - _prepare_4d_causal_attention_mask_for_sdpa, ) from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, @@ -62,6 +61,60 @@ _CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b" _CONFIG_FOR_DOC = "FalconConfig" +# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + 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: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + # NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations. # In order not to degrade the quality of our HF-port, we keep these characteristics in the final model. class FalconLinear(nn.Linear): @@ -244,7 +297,7 @@ def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: class FalconAttention(nn.Module): - def __init__(self, config: FalconConfig): + def __init__(self, config: FalconConfig, layer_idx=None): super().__init__() self.config = config @@ -257,6 +310,13 @@ class FalconAttention(nn.Module): self.rope_theta = config.rope_theta self.is_causal = True self._use_sdpa = config._attn_implementation == "sdpa" + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) if self.head_dim * self.num_heads != self.hidden_size: raise ValueError( @@ -373,10 +433,11 @@ class FalconAttention(nn.Module): alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, - layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + layer_past: Optional[Cache] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, + cache_position: Optional[torch.LongTensor] = None, ): fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads @@ -391,25 +452,24 @@ class FalconAttention(nn.Module): kv_seq_len = key_layer.shape[-2] if layer_past is not None: - kv_seq_len += layer_past[0].shape[-2] + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += layer_past.get_seq_length(self.layer_idx) if alibi is None: cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len) query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids) if layer_past is not None: - past_key, past_value = layer_past - # concatenate along seq_length dimension: - # - key: [batch_size, self.num_heads, kv_length, head_dim] - # - value: [batch_size, self.num_heads, kv_length, head_dim] - key_layer = torch.cat((past_key, key_layer), dim=-2) - value_layer = torch.cat((past_value, value_layer), dim=-2) + cache_kwargs = {"cache_position": cache_position} + if alibi is None: + cache_kwargs.update({"sin": sin, "cos": cos}) + key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs) kv_length = key_layer.shape[-2] - if use_cache: - present = (key_layer, value_layer) - else: - present = None - if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None: # For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. @@ -417,6 +477,9 @@ class FalconAttention(nn.Module): key_layer = key_layer.contiguous() value_layer = value_layer.contiguous() + if attention_mask is not None: + attention_mask = attention_mask[:, :, :, : key_layer.shape[-2]] + if alibi is None: if self._use_sdpa and not output_attentions: # We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an @@ -448,9 +511,9 @@ class FalconAttention(nn.Module): attn_output = self.dense(attn_output) if output_attentions: - return attn_output, present, attention_scores + return attn_output, layer_past, attention_scores else: - return attn_output, present + return attn_output, layer_past else: if self._use_sdpa and not output_attentions and head_mask is None: @@ -502,9 +565,9 @@ class FalconAttention(nn.Module): attn_output = self.dense(attn_output) if output_attentions: - return attn_output, present, attention_probs + return attn_output, layer_past, attention_probs else: - return attn_output, present + return attn_output, layer_past class FalconFlashAttention2(FalconAttention): @@ -529,10 +592,11 @@ class FalconFlashAttention2(FalconAttention): alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, - layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + layer_past: Optional[Cache] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, + cache_position: Optional[torch.LongTensor] = None, ): fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads @@ -547,20 +611,22 @@ class FalconFlashAttention2(FalconAttention): kv_seq_len = key_layer.shape[-2] if layer_past is not None: - kv_seq_len += layer_past[0].shape[-2] + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += layer_past.get_seq_length(self.layer_idx) if alibi is None: cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len) query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids) - if layer_past is not None and use_cache: - past_key, past_value = layer_past - # concatenate along seq_length dimension: - # - key: [batch_size, self.num_heads, kv_length, head_dim] - # - value: [batch_size, self.num_heads, kv_length, head_dim] - key_layer = torch.cat((past_key, key_layer), dim=-2) - value_layer = torch.cat((past_value, value_layer), dim=-2) - - past_key_value = (key_layer, value_layer) if use_cache else None + if layer_past is not None: + cache_kwargs = {"cache_position": cache_position} + if alibi is None: + cache_kwargs.update({"sin": sin, "cos": cos}) + key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. @@ -614,7 +680,7 @@ class FalconFlashAttention2(FalconAttention): if not output_attentions: attn_weights = None - return attn_output, past_key_value, attn_weights + return attn_output, layer_past, attn_weights class FalconMLP(nn.Module): @@ -641,12 +707,12 @@ FALCON_ATTENTION_CLASSES = { class FalconDecoderLayer(nn.Module): - def __init__(self, config: FalconConfig): + def __init__(self, config: FalconConfig, layer_idx=None): super().__init__() hidden_size = config.hidden_size self.num_heads = config.num_attention_heads - self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config) + self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = FalconMLP(config) self.hidden_dropout = config.hidden_dropout self.config = config @@ -672,10 +738,11 @@ class FalconDecoderLayer(nn.Module): alibi: Optional[torch.Tensor], attention_mask: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, - layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + layer_past: Optional[Union[Cache, Tuple[torch.Tensor, torch.Tensor]]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False, + cache_position: Optional[torch.LongTensor] = None, **kwargs, ): residual = hidden_states @@ -696,6 +763,7 @@ class FalconDecoderLayer(nn.Module): head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) attention_output = attn_outputs[0] @@ -731,7 +799,7 @@ class FalconDecoderLayer(nn.Module): else: outputs = (output,) + outputs[1:] - return outputs # hidden_states, present, attentions + return outputs # hidden_states, past_kv, attentions FALCON_START_DOCSTRING = r""" @@ -762,14 +830,23 @@ FALCON_INPUTS_DOCSTRING = r""" [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) - past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`): - Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see - `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have - their past given to this model should not be passed as `input_ids` as they have already been computed. + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. - 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] + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: @@ -806,6 +883,10 @@ FALCON_INPUTS_DOCSTRING = r""" more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. """ @@ -821,6 +902,9 @@ class FalconPreTrainedModel(PreTrainedModel): _no_split_modules = ["FalconDecoderLayer"] _supports_flash_attn_2 = True _supports_sdpa = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) @@ -877,7 +961,7 @@ class FalconModel(FalconPreTrainedModel): self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim) # Transformer blocks - self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.h = nn.ModuleList([FalconDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self._use_sdpa = config._attn_implementation == "sdpa" @@ -904,7 +988,7 @@ class FalconModel(FalconPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, @@ -913,6 +997,7 @@ class FalconModel(FalconPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( @@ -921,38 +1006,35 @@ class FalconModel(FalconPreTrainedModel): 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 (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) - if past_key_values is None: - past_key_values = tuple([None] * len(self.h)) + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) - hidden_states = inputs_embeds - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - presents = () if use_cache else None - all_self_attentions = () if output_attentions else None - all_hidden_states = () if output_hidden_states else None - # Compute alibi tensor: check build_alibi_tensor documentation - past_key_values_length = 0 - if past_key_values[0] is not None: - past_key_values_length = past_key_values[0][0].shape[-2] + use_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + use_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + if not self.training: + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + alibi = None + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + batch_size, seq_length, _ = inputs_embeds.shape if self.use_alibi: mask = ( torch.ones( @@ -961,67 +1043,32 @@ class FalconModel(FalconPreTrainedModel): if attention_mask is None else attention_mask ) - alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype) - else: - alibi = None - if position_ids is None: - device = input_ids.device if input_ids is not None else inputs_embeds.device - position_ids = torch.arange( - past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device - ) - position_ids = position_ids.unsqueeze(0) + alibi = build_alibi_tensor(mask, self.num_heads, dtype=inputs_embeds.dtype) - if self._use_flash_attention_2: - # 2d mask is passed through the layers - attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None - elif self._use_sdpa and not output_attentions: - # output_attentions=True can not be supported when using SDPA, and we fall back on - # the manual implementation that requires a 4D causal mask in all cases. - if alibi is None: - attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - ) - elif head_mask is None: - alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:]) - - # We don't call _prepare_4d_causal_attention_mask_for_sdpa as we need to mask alibi using the 4D attention_mask untouched. - attention_mask = _prepare_4d_causal_attention_mask( - attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length - ) - - # We take care to integrate alibi bias in the attention_mask here. - min_dtype = torch.finfo(alibi.dtype).min - attention_mask = torch.masked_fill( - alibi / math.sqrt(self.config.hidden_size // self.num_heads), - attention_mask < -1, - min_dtype, - ) - - # From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend - # produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213 - if seq_length > 1 and attention_mask.device.type == "cuda": - attention_mask = AttentionMaskConverter._unmask_unattended(attention_mask, min_dtype=min_dtype) - else: - # PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case. - attention_mask = _prepare_4d_causal_attention_mask( - attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length - ) - else: - # 4d mask is passed through the layers - attention_mask = _prepare_4d_causal_attention_mask( - attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + if cache_position is None: + cache_position = torch.arange( + past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions, head_mask, alibi + ) + # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape batch_size x num_heads x N x N # head_mask has shape n_layer x batch x num_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + hidden_states = inputs_embeds - for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + next_decoder_cache = None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) @@ -1030,28 +1077,30 @@ class FalconModel(FalconPreTrainedModel): block.__call__, hidden_states, alibi, - attention_mask, + causal_mask, position_ids, head_mask[i], - layer_past, + past_key_values, use_cache, output_attentions, + cache_position, ) else: outputs = block( hidden_states, - layer_past=layer_past, - attention_mask=attention_mask, + layer_past=past_key_values, + attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi, + cache_position=cache_position, ) hidden_states = outputs[0] if use_cache is True: - presents = presents + (outputs[1],) + next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) @@ -1062,16 +1111,110 @@ class FalconModel(FalconPreTrainedModel): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + 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 tuple( + v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None + ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, - past_key_values=presents, + past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + head_mask: torch.Tensor, + alibi: torch.Tensor, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if ( + self.config._attn_implementation == "sdpa" + and not using_static_cache + and not output_attentions + and head_mask is None + and alibi is None + ): + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + batch_size, sequence_length, _ = input_tensor.shape + if using_static_cache: + target_length = past_key_values.get_max_length() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + ) + + # We take care to integrate alibi bias in the causal_mask here + if head_mask is None and alibi is not None: + alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:]) + causal_mask = torch.masked_fill( + alibi / math.sqrt(self.config.hidden_size // self.num_heads), + causal_mask < -1, + min_dtype, + ) + + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + @add_start_docstrings( "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).", @@ -1097,23 +1240,22 @@ class FalconForCausalLM(FalconPreTrainedModel): def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, - past_key_values: Optional[torch.Tensor] = None, + past_key_values: Optional[Union[Cache, torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, + cache_position: Optional[torch.LongTensor] = None, + use_cache: bool = True, **kwargs, ) -> dict: + # 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 if past_key_values is not None: - past_length = past_key_values[0][0].shape[2] - - # Some generation methods already pass only the last input ID - if input_ids.shape[1] > past_length: - remove_prefix_length = past_length - else: - # Default to old behavior: keep only final ID - remove_prefix_length = input_ids.shape[1] - 1 - - input_ids = input_ids[:, remove_prefix_length:] + if inputs_embeds is not None: # Exception 1 + 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] # Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE. if not self.transformer.use_alibi and attention_mask is not None and position_ids is None: @@ -1123,16 +1265,43 @@ class FalconForCausalLM(FalconPreTrainedModel): if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] - if inputs_embeds is not None and past_key_values is None: + # 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. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if inputs_embeds is not None: + batch_size, sequence_length = inputs_embeds.shape + device = inputs_embeds.device + else: + batch_size, sequence_length = input_ids.shape + device = input_ids.device + + dtype = self.lm_head.weight.dtype + min_dtype = torch.finfo(dtype).min + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) + model_inputs.update( { "position_ids": position_ids, + "cache_position": cache_position, "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), + "use_cache": use_cache, "attention_mask": attention_mask, } ) @@ -1147,7 +1316,7 @@ class FalconForCausalLM(FalconPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, @@ -1157,6 +1326,7 @@ class FalconForCausalLM(FalconPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): @@ -1178,6 +1348,7 @@ class FalconForCausalLM(FalconPreTrainedModel): output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + cache_position=cache_position, ) hidden_states = transformer_outputs[0] diff --git a/src/transformers/models/git/modeling_git.py b/src/transformers/models/git/modeling_git.py index 27de8c688be..581f2b3947b 100644 --- a/src/transformers/models/git/modeling_git.py +++ b/src/transformers/models/git/modeling_git.py @@ -25,6 +25,7 @@ from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache from ...file_utils import ModelOutput from ...modeling_attn_mask_utils import _prepare_4d_attention_mask from ...modeling_outputs import ( @@ -124,13 +125,20 @@ class GitEmbeddings(nn.Module): class GitSelfAttention(nn.Module): - def __init__(self, config, position_embedding_type=None): + def __init__(self, config, position_embedding_type=None, layer_idx=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) + 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.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) @@ -161,46 +169,31 @@ class GitSelfAttention(nn.Module): hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, - past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, pixel_values_present: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) cutoff = self.image_patch_tokens if pixel_values_present else 0 + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) if past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([key_layer[:, :, :cutoff, :], past_key_value[0], key_layer[:, :, -1:, :]], dim=2) - value_layer = torch.cat( - [value_layer[:, :, :cutoff, :], past_key_value[1], value_layer[:, :, -1:, :]], dim=2 + # NOTE: like in other caches, we store the text component. In GIT it means we discard the image component. + key_layer_past, value_layer_past = past_key_value.update( + key_layer[:, :, cutoff:, :], value_layer[:, :, cutoff:, :], self.layer_idx ) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([key_layer[:, :, :cutoff, :], key_layer_past], dim=2) + value_layer = torch.cat([value_layer[:, :, :cutoff, :], value_layer_past], dim=2) query_layer = self.transpose_for_scores(mixed_query_layer) - use_cache = past_key_value is not None - # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. - # Further calls to cross_attention layer can then reuse all cross-attention - # key/value_states (first "if" case) - # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of - # all previous decoder key/value_states. Further calls to uni-directional self-attention - # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) - # if encoder bi-directional self-attention `past_key_value` is always `None` - # NOTE: like in other caches, we store the text component. In GIT it means we discard the image component. - past_key_value = ( - key_layer[:, :, cutoff:, :], - value_layer[:, :, cutoff:, :], - ) - # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] - if use_cache: + if past_key_value is not None: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) @@ -269,11 +262,10 @@ GIT_SELF_ATTENTION_CLASSES = { class GitAttention(nn.Module): - # Copied from transformers.models.bert.modeling_bert.BertAttention.__init__ with Bert->Git,BERT->GIT - def __init__(self, config, position_embedding_type=None): + def __init__(self, config, position_embedding_type=None, layer_idx=None): super().__init__() self.self = GIT_SELF_ATTENTION_CLASSES[config._attn_implementation]( - config, position_embedding_type=position_embedding_type + config, position_embedding_type=position_embedding_type, layer_idx=layer_idx ) self.output = GitSelfOutput(config) self.pruned_heads = set() @@ -302,7 +294,7 @@ class GitAttention(nn.Module): hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, - past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, pixel_values_present: Optional[bool] = False, ) -> Tuple[torch.Tensor]: @@ -351,11 +343,11 @@ class GitOutput(nn.Module): class GitLayer(nn.Module): - def __init__(self, config): + def __init__(self, config, layer_idx=None): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 - self.attention = GitAttention(config) + self.attention = GitAttention(config, layer_idx=layer_idx) self.intermediate = GitIntermediate(config) self.output = GitOutput(config) @@ -364,18 +356,17 @@ class GitLayer(nn.Module): hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, - past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, pixel_values_present: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, + past_key_value=past_key_value, pixel_values_present=pixel_values_present, ) attention_output = self_attention_outputs[0] @@ -401,11 +392,10 @@ class GitLayer(nn.Module): class GitEncoder(nn.Module): - # Copied from transformers.models.bert.modeling_bert.BertEncoder.__init__ with Bert->Git def __init__(self, config): super().__init__() self.config = config - self.layer = nn.ModuleList([GitLayer(config) for _ in range(config.num_hidden_layers)]) + self.layer = nn.ModuleList([GitLayer(config, i) for i in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( @@ -413,7 +403,7 @@ class GitEncoder(nn.Module): hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, @@ -427,16 +417,23 @@ class GitEncoder(nn.Module): ) use_cache = False + use_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache) and not self.training: + use_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None - - next_decoder_cache = () if use_cache else None + next_decoder_cache = None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( @@ -444,7 +441,7 @@ class GitEncoder(nn.Module): hidden_states, attention_mask, layer_head_mask, - past_key_value, + past_key_values, output_attentions, ) else: @@ -452,26 +449,30 @@ class GitEncoder(nn.Module): hidden_states, attention_mask, layer_head_mask, - past_key_value, + past_key_values, output_attentions, pixel_values_present, ) hidden_states = layer_outputs[0] if use_cache: - next_decoder_cache += (layer_outputs[-1],) + next_decoder_cache = layer_outputs[-1] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + if not return_dict: return tuple( v for v in [ hidden_states, - next_decoder_cache, + next_cache, all_hidden_states, all_self_attentions, ] @@ -479,7 +480,7 @@ class GitEncoder(nn.Module): ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, + past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @@ -494,6 +495,8 @@ class GitPreTrainedModel(PreTrainedModel): config_class = GitConfig base_model_prefix = "git" supports_gradient_checkpointing = True + _supports_cache_class = True + _supports_quantized_cache = True def _init_weights(self, module): """Initialize the weights""" @@ -569,6 +572,23 @@ GIT_INPUTS_DOCSTRING = r""" 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. + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. @@ -1136,19 +1156,13 @@ class GitModel(GitPreTrainedModel): pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]: r""" - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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`). @@ -1195,7 +1209,13 @@ class GitModel(GitPreTrainedModel): seq_length = input_shape[1] # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + past_key_values_length = 0 + if past_key_values is not None: + past_key_values_length = ( + past_key_values[0][0].shape[2] + if not isinstance(past_key_values, Cache) + else past_key_values.get_seq_length() + ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head @@ -1327,7 +1347,7 @@ class GitForCausalLM(GitPreTrainedModel): head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.Tensor]] = None, + past_key_values: Optional[Union[Cache, List[torch.Tensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, @@ -1338,12 +1358,6 @@ class GitForCausalLM(GitPreTrainedModel): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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`). @@ -1522,7 +1536,16 @@ class GitForCausalLM(GitPreTrainedModel): ): # cut decoder_input_ids if past_key_values is used if past_key_values is not None: - input_ids = input_ids[:, -1:] + past_length = past_key_values.get_seq_length() + + # Some generation methods already pass only the last input ID + if input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = input_ids.shape[1] - 1 + + input_ids = input_ids[:, remove_prefix_length:] # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly input_shape = input_ids.shape diff --git a/src/transformers/models/gpt_neo/modeling_gpt_neo.py b/src/transformers/models/gpt_neo/modeling_gpt_neo.py index f5f8ebb2a9d..8335268e84a 100755 --- a/src/transformers/models/gpt_neo/modeling_gpt_neo.py +++ b/src/transformers/models/gpt_neo/modeling_gpt_neo.py @@ -23,7 +23,8 @@ from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN -from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask +from ...cache_utils import Cache, DynamicCache, StaticCache +from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask from ...modeling_outputs import ( BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, @@ -68,6 +69,60 @@ _CONFIG_FOR_DOC = "GPTNeoConfig" _CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neo-1.3B" +# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + 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: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + def load_tf_weights_in_gpt_neo(model, config, gpt_neo_checkpoint_path): """Load tf checkpoints in a pytorch model""" try: @@ -149,7 +204,7 @@ def load_tf_weights_in_gpt_neo(model, config, gpt_neo_checkpoint_path): class GPTNeoSelfAttention(nn.Module): - def __init__(self, config, attention_type): + def __init__(self, config, attention_type, layer_id=None): super().__init__() self.config = config @@ -170,6 +225,7 @@ class GPTNeoSelfAttention(nn.Module): self.attn_dropout = nn.Dropout(float(config.attention_dropout)) self.resid_dropout = nn.Dropout(float(config.resid_dropout)) self.is_causal = True + self.layer_id = layer_id self.embed_dim = config.hidden_size self.num_heads = config.num_heads @@ -208,6 +264,7 @@ class GPTNeoSelfAttention(nn.Module): attn_weights = torch.matmul(query, key.transpose(-1, -2)) + # Apply sliding window masking for local attention layers query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min @@ -216,9 +273,9 @@ class GPTNeoSelfAttention(nn.Module): mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights, mask_value) - if attention_mask is not None: - # Apply the attention mask - attn_weights = attn_weights + attention_mask + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key.shape[-2]] + attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.to(value.dtype) @@ -240,6 +297,7 @@ class GPTNeoSelfAttention(nn.Module): head_mask=None, use_cache=False, output_attentions=False, + cache_position=None, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) @@ -250,15 +308,8 @@ class GPTNeoSelfAttention(nn.Module): value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - - if use_cache is True: - present = (key, value) - else: - present = None + cache_kwargs = {"cache_position": cache_position} + key, value = layer_past.update(key, value, self.layer_id, cache_kwargs) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) @@ -266,11 +317,11 @@ class GPTNeoSelfAttention(nn.Module): attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) - outputs = (attn_output, present) + outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) - return outputs # a, present, (attentions) + return outputs # a, past_kv, (attentions) class GPTNeoFlashAttention2(GPTNeoSelfAttention): @@ -297,6 +348,7 @@ class GPTNeoFlashAttention2(GPTNeoSelfAttention): head_mask=None, use_cache=False, output_attentions=False, + cache_position=None, ): bsz, _, _ = hidden_states.size() @@ -309,15 +361,8 @@ class GPTNeoFlashAttention2(GPTNeoSelfAttention): value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - - if use_cache is True: - present = (key, value) - else: - present = None + cache_kwargs = {"cache_position": cache_position} + key, value = layer_past.update(key, value, self.layer_id, cache_kwargs) query_length = query.shape[2] tgt_len = key.shape[2] @@ -330,6 +375,9 @@ class GPTNeoFlashAttention2(GPTNeoSelfAttention): attn_dropout = self.config.attention_dropout if self.training else 0.0 + if attention_mask is not None: # no matter the length, we just slice it + attention_mask = attention_mask[:, :, :, : key.shape[-2]] + # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. @@ -371,7 +419,7 @@ class GPTNeoFlashAttention2(GPTNeoSelfAttention): attn_output = self.out_proj(attn_weights_reshaped) attn_output = self.resid_dropout(attn_output) - outputs = (attn_output, present) + outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights_reshaped,) @@ -392,7 +440,9 @@ class GPTNeoAttention(nn.Module): self.attention_type = self.attention_layers[layer_id] if self.attention_type in ["global", "local"]: - self.attention = GPT_NEO_ATTENTION_CLASSES[config._attn_implementation](config, self.attention_type) + self.attention = GPT_NEO_ATTENTION_CLASSES[config._attn_implementation]( + config, self.attention_type, layer_id + ) else: raise NotImplementedError( "Only attn layer types 'global' and 'local' exist, but got `config.attention_layers`: " @@ -407,6 +457,7 @@ class GPTNeoAttention(nn.Module): head_mask=None, use_cache=False, output_attentions=False, + cache_position=None, ): return self.attention( hidden_states, @@ -415,6 +466,7 @@ class GPTNeoAttention(nn.Module): head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) @@ -436,7 +488,7 @@ class GPTNeoMLP(nn.Module): class GPTNeoBlock(nn.Module): - def __init__(self, config, layer_id): + def __init__(self, config, layer_id=None): super().__init__() hidden_size = config.hidden_size inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size @@ -453,6 +505,7 @@ class GPTNeoBlock(nn.Module): head_mask=None, use_cache=False, output_attentions=False, + cache_position=None, ): residual = hidden_states hidden_states = self.ln_1(hidden_states) @@ -463,6 +516,7 @@ class GPTNeoBlock(nn.Module): head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] @@ -480,7 +534,7 @@ class GPTNeoBlock(nn.Module): else: outputs = (hidden_states,) + outputs[1:] - return outputs # hidden_states, present, (attentions, cross_attentions) + return outputs # hidden_states, past_kv, attentions class GPTNeoPreTrainedModel(PreTrainedModel): @@ -496,6 +550,9 @@ class GPTNeoPreTrainedModel(PreTrainedModel): _no_split_modules = ["GPTNeoBlock"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = False # TODO: needs a HybridCache def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) @@ -547,10 +604,23 @@ GPT_NEO_INPUTS_DOCSTRING = r""" [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) - past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_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. + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: @@ -595,6 +665,10 @@ GPT_NEO_INPUTS_DOCSTRING = r""" more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. """ @@ -611,7 +685,6 @@ class GPTNeoModel(GPTNeoPreTrainedModel): self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) self.drop = nn.Dropout(float(config.embed_dropout)) self.h = nn.ModuleList([GPTNeoBlock(config, layer_id=i) for i in range(config.num_layers)]) - self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.gradient_checkpointing = False @@ -633,7 +706,7 @@ class GPTNeoModel(GPTNeoPreTrainedModel): def forward( self, input_ids: Optional[torch.Tensor] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + past_key_values: Optional[Union[Cache, Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, @@ -643,6 +716,7 @@ class GPTNeoModel(GPTNeoPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( @@ -651,58 +725,10 @@ class GPTNeoModel(GPTNeoPreTrainedModel): 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: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - device = input_ids.device if input_ids is not None else inputs_embeds.device - - if token_type_ids is not None: - token_type_ids = token_type_ids.view(-1, input_shape[-1]) - - if past_key_values is None: - past_length = 0 - past_key_values = tuple([None] * len(self.h)) - else: - past_length = past_key_values[0][0].size(-2) - - if position_ids is None: - position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) - position_ids = position_ids.unsqueeze(0) - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x num_heads x N x N - # head_mask has shape n_layer x batch x num_heads x N x N - head_mask = self.get_head_mask(head_mask, self.config.num_layers) - - if inputs_embeds is None: - inputs_embeds = self.wte(input_ids) - position_embeds = self.wpe(position_ids) - hidden_states = inputs_embeds + position_embeds - - # Attention mask. - if self._use_flash_attention_2: - # 2d mask is passed through the layers - attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None - else: - # 4d mask is passed through the layers - attention_mask = _prepare_4d_causal_attention_mask(attention_mask, input_shape, inputs_embeds, past_length) - - if token_type_ids is not None: - token_type_embeds = self.wte(token_type_ids) - hidden_states = hidden_states + token_type_embeds - - hidden_states = self.drop(hidden_states) - - output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) if self.gradient_checkpointing and self.training: if use_cache: @@ -711,10 +737,51 @@ class GPTNeoModel(GPTNeoPreTrainedModel): ) use_cache = False - presents = () if use_cache else None + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + + use_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache) and not self.training: + use_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + if not self.training: + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + + seq_length = inputs_embeds.shape[1] + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device) + + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x num_heads x N x N + # head_mask has shape n_layer x batch x num_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.num_layers) + position_embeds = self.wpe(position_ids) + hidden_states = inputs_embeds + position_embeds + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, seq_length) + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + + hidden_states = self.drop(hidden_states) + output_shape = (-1, seq_length, hidden_states.size(-1)) + + next_decoder_cache = None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None - for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) @@ -723,24 +790,26 @@ class GPTNeoModel(GPTNeoPreTrainedModel): block.__call__, hidden_states, None, - attention_mask, + causal_mask, head_mask[i], use_cache, output_attentions, + cache_position, ) else: outputs = block( hidden_states, - layer_past=layer_past, - attention_mask=attention_mask, + layer_past=past_key_values, + attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) hidden_states = outputs[0] - if use_cache is True: - presents = presents + (outputs[1],) + if use_cache: + next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) @@ -752,16 +821,94 @@ class GPTNeoModel(GPTNeoPreTrainedModel): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + 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 tuple( + v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None + ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=presents, + past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + 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 = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_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 == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + @add_start_docstrings( """ @@ -787,26 +934,30 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel): def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings - def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): - token_type_ids = kwargs.get("token_type_ids", None) - # Omit tokens covered by past_key_values - if past_key_values: - past_length = past_key_values[0][0].shape[2] + def prepare_inputs_for_generation( + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + past_key_values=None, + inputs_embeds=None, + cache_position=None, + use_cache=True, + **kwargs, + ): + # 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 + if past_key_values is not None: + if inputs_embeds is not None: # Exception 1 + 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] - # Some generation methods already pass only the last input ID - if input_ids.shape[1] > past_length: - remove_prefix_length = past_length - else: - # Default to old behavior: keep only final ID - remove_prefix_length = input_ids.shape[1] - 1 - - input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] - attention_mask = kwargs.get("attention_mask", None) - position_ids = kwargs.get("position_ids", None) - if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 @@ -814,22 +965,47 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel): if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] + # 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. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + # 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: + if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if inputs_embeds is not None: + batch_size, sequence_length = inputs_embeds.shape + device = inputs_embeds.device + else: + batch_size, sequence_length = input_ids.shape + device = input_ids.device + + dtype = self.lm_head.weight.dtype + min_dtype = torch.finfo(dtype).min + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) + model_inputs.update( { - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, - "attention_mask": attention_mask, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, "token_type_ids": token_type_ids, + "attention_mask": attention_mask, } ) - return model_inputs @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING) @@ -841,7 +1017,7 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel): def forward( self, input_ids: Optional[torch.Tensor] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + past_key_values: Optional[Union[Cache, Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, @@ -852,6 +1028,7 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): @@ -873,6 +1050,7 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel): output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + cache_position=cache_position, ) hidden_states = transformer_outputs[0] @@ -957,7 +1135,7 @@ class GPTNeoForSequenceClassification(GPTNeoPreTrainedModel): def forward( self, input_ids: Optional[torch.Tensor] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + past_key_values: Optional[Union[Cache, Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, @@ -1081,7 +1259,7 @@ class GPTNeoForTokenClassification(GPTNeoPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, diff --git a/src/transformers/models/gpt_neox/modeling_gpt_neox.py b/src/transformers/models/gpt_neox/modeling_gpt_neox.py index 32988e88df3..3e72eec0728 100755 --- a/src/transformers/models/gpt_neox/modeling_gpt_neox.py +++ b/src/transformers/models/gpt_neox/modeling_gpt_neox.py @@ -23,13 +23,14 @@ from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, StaticCache from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) -from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa +from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, @@ -52,6 +53,60 @@ _REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neox-20b" _CONFIG_FOR_DOC = "GPTNeoXConfig" +# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + 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: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + class GPTNeoXPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained @@ -64,6 +119,9 @@ class GPTNeoXPreTrainedModel(PreTrainedModel): _no_split_modules = ["GPTNeoXLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True _supports_sdpa = True def _init_weights(self, module): @@ -82,7 +140,7 @@ class GPTNeoXPreTrainedModel(PreTrainedModel): class GPTNeoXAttention(nn.Module): - def __init__(self, config): + def __init__(self, config, layer_idx=None): super().__init__() self.config = config self.num_attention_heads = config.num_attention_heads @@ -98,11 +156,18 @@ class GPTNeoXAttention(nn.Module): self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) self._init_rope() + 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.norm_factor = self.head_size**-0.5 self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.attention_bias) self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias) self.attention_dropout = nn.Dropout(config.attention_dropout) self.is_causal = True + self.layer_idx = layer_idx def _init_bias(self, max_positions, device=None): self.register_buffer( @@ -146,9 +211,11 @@ class GPTNeoXAttention(nn.Module): attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None, - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + padding_mask: Optional[torch.Tensor] = None, + cache_position: Optional[torch.LongTensor] = None, ): # Apply attention-specific projections and rope query, key, value, present = self._attn_projections_and_rope( @@ -199,9 +266,8 @@ class GPTNeoXAttention(nn.Module): position_ids: torch.LongTensor, layer_past: Optional[Tuple[torch.Tensor]] = None, use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ): - has_layer_past = layer_past is not None - # Compute QKV # Attention heads [batch, seq_len, hidden_size] # --> [batch, seq_len, (np * 3 * head_size)] @@ -225,22 +291,31 @@ class GPTNeoXAttention(nn.Module): # Compute token offset for rotary embeddings (when decoding) seq_len = key.shape[-2] - if has_layer_past: - seq_len += layer_past[0].shape[-2] + if layer_past is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + seq_len += layer_past.get_seq_length(self.layer_idx) + cos, sin = self.rotary_emb(value, seq_len=seq_len) query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) query = torch.cat((query, query_pass), dim=-1) key = torch.cat((key, key_pass), dim=-1) # Cache QKV values - if has_layer_past: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - present = (key, value) if use_cache else None + if layer_past is not None: + cache_kwargs = { + "sin": sin, + "cos": cos, + "partial_rotation_size": self.rotary_emb.dim, + "cache_position": cache_position, + } + key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) - return query, key, value, present + return query, key, value, layer_past def _attn(self, query, key, value, attention_mask=None, head_mask=None): # q, k, v: [bs, num_attention_heads, seq_len, attn_head_size] @@ -277,9 +352,9 @@ class GPTNeoXAttention(nn.Module): mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device) attn_scores = torch.where(causal_mask, attn_scores, mask_value) - if attention_mask is not None: - # Apply the attention mask - attn_scores = attn_scores + attention_mask + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key.shape[-2]] + attn_scores = attn_scores + causal_mask attn_weights = nn.functional.softmax(attn_scores, dim=-1) attn_weights = attn_weights.to(value.dtype) @@ -316,13 +391,18 @@ class GPTNeoXFlashAttention2(GPTNeoXAttention): attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None, - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ): # Apply attention-specific projections and rope query, key, value, present = self._attn_projections_and_rope( - hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache + hidden_states=hidden_states, + position_ids=position_ids, + layer_past=layer_past, + use_cache=use_cache, + cache_position=cache_position, ) query_length = query.shape[-2] @@ -384,7 +464,7 @@ class GPTNeoXFlashAttention2(GPTNeoXAttention): ) attn_output = self.dense(attn_output) - outputs = (attn_output, present) + outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) @@ -398,8 +478,8 @@ class GPTNeoXSdpaAttention(GPTNeoXAttention): to adapt to the SDPA API. """ - def __init__(self, config): - super().__init__(config) + def __init__(self, config, layer_idx=None): + super().__init__(config, layer_idx=layer_idx) # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom # attn_mask, so we need to call `.contiguous()`. This was fixed in torch==2.2.0. @@ -415,6 +495,7 @@ class GPTNeoXSdpaAttention(GPTNeoXAttention): layer_past: Optional[Tuple[torch.Tensor]] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ): if output_attentions or head_mask is not None: logger.warning_once( @@ -431,15 +512,24 @@ class GPTNeoXSdpaAttention(GPTNeoXAttention): layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) bsz, q_len, _ = hidden_states.size() # Apply attention-specific projections and rope query, key, value, present = self._attn_projections_and_rope( - hidden_states=hidden_states, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache + hidden_states=hidden_states, + position_ids=position_ids, + layer_past=layer_past, + use_cache=use_cache, + cache_position=cache_position, ) + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key.shape[-2]] + # GPT-neo-X casts query and key in fp32 to apply rotary embedding in full precision target_dtype = value.dtype if query.dtype != target_dtype: @@ -455,13 +545,13 @@ class GPTNeoXSdpaAttention(GPTNeoXAttention): # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. - is_causal = True if attention_mask is None and q_len > 1 else False + is_causal = True if causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query=query, key=key, value=value, - attn_mask=attention_mask, + attn_mask=causal_mask, dropout_p=self.attention_dropout.p if self.training else 0.0, is_causal=is_causal, ) @@ -624,14 +714,14 @@ GPT_NEOX_ATTENTION_CLASSES = { class GPTNeoXLayer(nn.Module): - def __init__(self, config): + def __init__(self, config, layer_idx): super().__init__() self.use_parallel_residual = config.use_parallel_residual self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_attention_dropout = nn.Dropout(config.hidden_dropout) self.post_mlp_dropout = nn.Dropout(config.hidden_dropout) - self.attention = GPT_NEOX_ATTENTION_CLASSES[config._attn_implementation](config) + self.attention = GPT_NEOX_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = GPTNeoXMLP(config) def forward( @@ -641,8 +731,9 @@ class GPTNeoXLayer(nn.Module): position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ): attention_layer_outputs = self.attention( self.input_layernorm(hidden_states), @@ -652,6 +743,7 @@ class GPTNeoXLayer(nn.Module): head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights) attn_output = self.post_attention_dropout(attn_output) @@ -722,6 +814,23 @@ GPT_NEOX_INPUTS_DOCSTRING = r""" 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. + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. @@ -730,6 +839,10 @@ GPT_NEOX_INPUTS_DOCSTRING = r""" more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. """ @@ -744,7 +857,7 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel): self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) self.emb_dropout = nn.Dropout(config.hidden_dropout) - self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)]) + self.layers = nn.ModuleList([GPTNeoXLayer(config, i) for i in range(config.num_hidden_layers)]) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self._attn_implementation = config._attn_implementation @@ -774,18 +887,14 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel): position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: r""" - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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`). @@ -797,50 +906,42 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel): return_dict = return_dict if return_dict is not None else self.config.use_return_dict use_cache = use_cache if use_cache is not None else self.config.use_cache - 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: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) - batch_size, seq_length = input_shape - - if past_key_values is None: - past_length = 0 - past_key_values = tuple([None] * self.config.num_hidden_layers) - else: - past_length = past_key_values[0][0].size(-2) - - if position_ids is None: - device = input_ids.device if input_ids is not None else inputs_embeds.device - position_ids = torch.arange(past_length, seq_length + past_length, dtype=torch.long, device=device) - position_ids = position_ids.unsqueeze(0) + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids) - # Attention mask. - attention_mask = attention_mask.view(batch_size, -1) if attention_mask is not None else None - if self._attn_implementation == "flash_attention_2": - attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None - elif self._attn_implementation == "sdpa" and not output_attentions and head_mask is None: - attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( - attention_mask=attention_mask, - input_shape=(batch_size, seq_length), - inputs_embeds=inputs_embeds, - past_key_values_length=past_length, - ) - else: - attention_mask = _prepare_4d_causal_attention_mask( - attention_mask=attention_mask, - input_shape=(batch_size, seq_length), - inputs_embeds=inputs_embeds, - past_key_values_length=past_length, - ) + use_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + use_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + if not self.training: + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + + seq_length = inputs_embeds.shape[1] + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device) + + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head @@ -848,20 +949,14 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel): # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - hidden_states = self.emb_dropout(inputs_embeds) - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - presents = () if use_cache else None + next_decoder_cache = None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None - for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)): + for i, layer in enumerate( + self.layers, + ): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) @@ -869,26 +964,28 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel): outputs = self._gradient_checkpointing_func( layer.__call__, hidden_states, - attention_mask, + causal_mask, position_ids, head_mask[i], use_cache, None, output_attentions, + cache_position, ) else: outputs = layer( hidden_states, - attention_mask=attention_mask, + attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], - layer_past=layer_past, + layer_past=past_key_values, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) hidden_states = outputs[0] if use_cache is True: - presents = presents + (outputs[1],) + next_decoder_cache = outputs[1] if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],) @@ -897,16 +994,92 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + if not return_dict: - return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=presents, + past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_attentions, ) + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + 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 = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_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 == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + @add_start_docstrings( """GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_NEOX_START_DOCSTRING @@ -938,26 +1111,15 @@ class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel): position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape - `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape - `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are - only required when the model is used as a decoder in a Sequence to Sequence model. - - Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see - `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are @@ -997,6 +1159,7 @@ class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel): output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + cache_position=cache_position, ) hidden_states = outputs[0] @@ -1024,24 +1187,27 @@ class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel): attentions=outputs.attentions, ) + # can't be copied from llama, gpt-neox has emebd_out and not lm_head def prepare_inputs_for_generation( - self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + cache_position=None, + position_ids=None, + use_cache=True, + **kwargs, ): - input_shape = input_ids.shape - # cut decoder_input_ids if past is used + # 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 if past_key_values is not None: - past_length = past_key_values[0][0].shape[2] + if inputs_embeds is not None: # Exception 1 + 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] - # Some generation methods already pass only the last input ID - if input_ids.shape[1] > past_length: - remove_prefix_length = past_length - else: - # Default to old behavior: keep only final ID - remove_prefix_length = input_ids.shape[1] - 1 - - input_ids = input_ids[:, remove_prefix_length:] - - position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 @@ -1049,24 +1215,46 @@ class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel): if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_shape) + # 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. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) # 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: + if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} + + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if inputs_embeds is not None: + batch_size, sequence_length = inputs_embeds.shape + device = inputs_embeds.device + else: + batch_size, sequence_length = input_ids.shape + device = input_ids.device + + dtype = self.embed_out.weight.dtype + min_dtype = torch.finfo(dtype).min + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) + model_inputs.update( { - "attention_mask": attention_mask, - "past_key_values": past_key_values, "position_ids": position_ids, - "use_cache": kwargs.get("use_cache"), + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, + "attention_mask": attention_mask, } ) - return model_inputs def _reorder_cache(self, past_key_values, beam_idx): @@ -1117,7 +1305,7 @@ class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel): position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, @@ -1229,7 +1417,7 @@ class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, diff --git a/src/transformers/models/gptj/modeling_gptj.py b/src/transformers/models/gptj/modeling_gptj.py index fa658d9e057..39b0f1fc268 100644 --- a/src/transformers/models/gptj/modeling_gptj.py +++ b/src/transformers/models/gptj/modeling_gptj.py @@ -24,6 +24,8 @@ from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, StaticCache +from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, @@ -55,6 +57,60 @@ _REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B" _CONFIG_FOR_DOC = "GPTJConfig" +# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + 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: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor: inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim)) sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float() @@ -80,23 +136,22 @@ def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Ten class GPTJAttention(nn.Module): - def __init__(self, config): + def __init__(self, config, layer_idx=None): super().__init__() self.config = config max_positions = config.max_position_embeddings - self.register_buffer( - "bias", - torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( - 1, 1, max_positions, max_positions - ), - persistent=False, - ) - self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) self.is_causal = True + 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.embed_dim = config.hidden_size self.num_attention_heads = config.num_attention_heads @@ -152,27 +207,16 @@ class GPTJAttention(nn.Module): attention_mask=None, head_mask=None, ): - # compute causal mask from causal mask buffer - query_length, key_length = query.size(-2), key.size(-2) - causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] - # Keep the attention weights computation in fp32 to avoid overflow issues query = query.to(torch.float32) key = key.to(torch.float32) attn_weights = torch.matmul(query, key.transpose(-1, -2)) - - mask_value = torch.finfo(attn_weights.dtype).min - # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. - # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` - mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) - attn_weights = torch.where(causal_mask, attn_weights, mask_value) - attn_weights = attn_weights / self.scale_attn - if attention_mask is not None: - # Apply the attention mask - attn_weights = attn_weights + attention_mask + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key.shape[-2]] + attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.to(value.dtype) @@ -196,12 +240,13 @@ class GPTJAttention(nn.Module): def forward( self, hidden_states: torch.FloatTensor, - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[ Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]], @@ -245,17 +290,13 @@ class GPTJAttention(nn.Module): query = query.permute(0, 2, 1, 3) if layer_past is not None: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - - if use_cache is True: - # Note that this cast is quite ugly, but is not implemented before ROPE as the original codebase keeps the key in float32 all along the computation. - # Reference: https://github.com/kingoflolz/mesh-transformer-jax/blob/f8315e3003033b23f21d78361b288953064e0e76/mesh_transformer/layers.py#L128 - present = (key.to(hidden_states.dtype), value) - else: - present = None + cache_kwargs = { + "sin": sin, + "cos": cos, + "partial_rotation_size": self.rotary_dim, + "cache_position": cache_position, + } + key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) # compute self-attention: V x Softmax(QK^T) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) @@ -264,7 +305,7 @@ class GPTJAttention(nn.Module): attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) - outputs = (attn_output, present) + outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) @@ -290,12 +331,13 @@ class GPTJFlashAttention2(GPTJAttention): def forward( self, hidden_states: torch.FloatTensor, - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[ Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]], @@ -343,17 +385,13 @@ class GPTJFlashAttention2(GPTJAttention): # value: batch_size x num_attention_heads x seq_length x head_dim if layer_past is not None: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - - if use_cache is True: - # Note that this cast is quite ugly, but is not implemented before ROPE as the original codebase keeps the key in float32 all along the computation. - # Reference: https://github.com/kingoflolz/mesh-transformer-jax/blob/f8315e3003033b23f21d78361b288953064e0e76/mesh_transformer/layers.py#L128 - present = (key.to(hidden_states.dtype), value) - else: - present = None + cache_kwargs = { + "sin": sin, + "cos": cos, + "partial_rotation_size": self.rotary_dim, + "cache_position": cache_position, + } + key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) # The Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim @@ -412,7 +450,7 @@ class GPTJFlashAttention2(GPTJAttention): attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) - outputs = (attn_output, present) + outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) @@ -445,22 +483,23 @@ class GPTJMLP(nn.Module): class GPTJBlock(nn.Module): - def __init__(self, config): + def __init__(self, config, layer_idx=None): super().__init__() inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) - self.attn = GPTJ_ATTENTION_CLASSES[config._attn_implementation](config) + self.attn = GPTJ_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = GPTJMLP(inner_dim, config) def forward( self, hidden_states: Optional[torch.FloatTensor], - layer_past: Optional[Tuple[torch.Tensor]] = None, + layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) @@ -472,6 +511,7 @@ class GPTJBlock(nn.Module): head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] @@ -500,6 +540,9 @@ class GPTJPreTrainedModel(PreTrainedModel): _no_split_modules = ["GPTJBlock"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True _supports_param_buffer_assignment = False def __init__(self, *inputs, **kwargs): @@ -572,6 +615,23 @@ GPTJ_INPUTS_DOCSTRING = r""" 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. + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. @@ -580,6 +640,10 @@ GPTJ_INPUTS_DOCSTRING = r""" more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. """ PARALLELIZE_DOCSTRING = r""" @@ -643,7 +707,7 @@ class GPTJModel(GPTJPreTrainedModel): self.vocab_size = config.vocab_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.drop = nn.Dropout(config.embd_pdrop) - self.h = nn.ModuleList([GPTJBlock(config) for _ in range(config.n_layer)]) + self.h = nn.ModuleList([GPTJBlock(config, layer_idx=i) for i in range(config.n_layer)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Model parallel @@ -714,7 +778,7 @@ class GPTJModel(GPTJPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, @@ -724,6 +788,7 @@ class GPTJModel(GPTJPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( @@ -732,73 +797,10 @@ class GPTJModel(GPTJPreTrainedModel): 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: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - batch_size = input_ids.shape[0] - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size = inputs_embeds.shape[0] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - device = input_ids.device if input_ids is not None else inputs_embeds.device - - if token_type_ids is not None: - token_type_ids = token_type_ids.view(-1, input_shape[-1]) - - if past_key_values is None: - past_length = 0 - past_key_values = tuple([None] * len(self.h)) - else: - past_length = past_key_values[0][0].size(-2) - - if position_ids is None: - position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) - position_ids = position_ids.unsqueeze(0) - - if not self._use_flash_attention_2: - # Attention mask. - if attention_mask is not None: - if batch_size <= 0: - raise ValueError("batch_size has to be defined and > 0") - attention_mask = attention_mask.view(batch_size, -1) - # We create a 3D attention mask from a 2D tensor mask. - # Sizes are [batch_size, 1, 1, to_seq_length] - # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] - # this attention mask is more simple than the triangular masking of causal attention - # used in OpenAI GPT, we just need to prepare the broadcast dimension here. - attention_mask = attention_mask[:, None, None, :] - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and the dtype's smallest value for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility - attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x num_attention_heads x N x N - # head_mask has shape n_layer x batch x num_attention_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.wte(input_ids) - - hidden_states = inputs_embeds - - if token_type_ids is not None: - token_type_embeds = self.wte(token_type_ids) - hidden_states = hidden_states + token_type_embeds - - hidden_states = self.drop(hidden_states) - - output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) if self.gradient_checkpointing and self.training: if use_cache: @@ -807,19 +809,64 @@ class GPTJModel(GPTJPreTrainedModel): ) use_cache = False - presents = () if use_cache else None + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + + use_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + use_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + if not self.training: + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + + seq_length = inputs_embeds.shape[1] + if cache_position is None: + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device + ) + + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x num_attention_heads x N x N + # head_mask has shape n_layer x batch x num_attention_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + hidden_states = inputs_embeds + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, seq_length) + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + + hidden_states = self.drop(hidden_states) + output_shape = (-1, seq_length, hidden_states.size(-1)) + + next_decoder_cache = None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None - for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + for i, block in enumerate(self.h): # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) + # Ensure layer_past is on same device as hidden_states (might not be correct) - if layer_past is not None: - layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) + if past_key_values is not None: + past_key_values.key_cache = past_key_values.key_cache.to(hidden_states.device) + past_key_values.value_cache = past_key_values.value_cache.to(hidden_states.device) + # Ensure that attention_mask is always on the same device as hidden_states - if attention_mask is not None: - attention_mask = attention_mask.to(hidden_states.device) + if causal_mask is not None: + causal_mask = causal_mask.to(hidden_states.device) if isinstance(head_mask, torch.Tensor): head_mask = head_mask.to(hidden_states.device) if output_hidden_states: @@ -830,26 +877,28 @@ class GPTJModel(GPTJPreTrainedModel): block.__call__, hidden_states, None, - attention_mask, + causal_mask, position_ids, head_mask[i], use_cache, output_attentions, + cache_position, ) else: outputs = block( hidden_states=hidden_states, - layer_past=layer_past, - attention_mask=attention_mask, + layer_past=past_key_values, + attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, + cache_position=cache_position, ) hidden_states = outputs[0] if use_cache is True: - presents = presents + (outputs[1],) + next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) @@ -867,16 +916,94 @@ class GPTJModel(GPTJPreTrainedModel): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + 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 tuple( + v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None + ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=presents, + past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + 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 = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_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 == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + @add_start_docstrings( """ @@ -936,26 +1063,31 @@ class GPTJForCausalLM(GPTJPreTrainedModel): def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings - def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): - token_type_ids = kwargs.get("token_type_ids", None) - # Omit tokens covered by past_key_values - if past_key_values: - past_length = past_key_values[0][0].shape[2] + # Copied from transformers.models.gpt_neo.modeling_gpt_neo.GPTNeoForCausalLM.prepare_inputs_for_generation + def prepare_inputs_for_generation( + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + past_key_values=None, + inputs_embeds=None, + cache_position=None, + use_cache=True, + **kwargs, + ): + # 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 + if past_key_values is not None: + if inputs_embeds is not None: # Exception 1 + 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] - # Some generation methods already pass only the last input ID - if input_ids.shape[1] > past_length: - remove_prefix_length = past_length - else: - # Default to old behavior: keep only final ID - remove_prefix_length = input_ids.shape[1] - 1 - - input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] - attention_mask = kwargs.get("attention_mask", None) - position_ids = kwargs.get("position_ids", None) - if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 @@ -963,22 +1095,47 @@ class GPTJForCausalLM(GPTJPreTrainedModel): if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] + # 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. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + # 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: + if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if inputs_embeds is not None: + batch_size, sequence_length = inputs_embeds.shape + device = inputs_embeds.device + else: + batch_size, sequence_length = input_ids.shape + device = input_ids.device + + dtype = self.lm_head.weight.dtype + min_dtype = torch.finfo(dtype).min + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) + model_inputs.update( { - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, - "attention_mask": attention_mask, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, "token_type_ids": token_type_ids, + "attention_mask": attention_mask, } ) - return model_inputs @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @@ -991,7 +1148,7 @@ class GPTJForCausalLM(GPTJPreTrainedModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, @@ -1002,6 +1159,7 @@ class GPTJForCausalLM(GPTJPreTrainedModel): output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): @@ -1023,6 +1181,7 @@ class GPTJForCausalLM(GPTJPreTrainedModel): output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + cache_position=cache_position, ) hidden_states = transformer_outputs[0] diff --git a/src/transformers/models/idefics/modeling_idefics.py b/src/transformers/models/idefics/modeling_idefics.py index 63eac6cf528..3532219f3d6 100644 --- a/src/transformers/models/idefics/modeling_idefics.py +++ b/src/transformers/models/idefics/modeling_idefics.py @@ -30,7 +30,8 @@ from torch.nn import CrossEntropyLoss from ... import PreTrainedModel from ...activations import ACT2FN -from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask_for_sdpa +from ...cache_utils import Cache, DynamicCache, StaticCache +from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import ModelOutput from ...modeling_utils import PretrainedConfig from ...pytorch_utils import ALL_LAYERNORM_LAYERS @@ -50,6 +51,60 @@ logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "IdeficsConfig" +# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + 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: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + @dataclass class IdeficsBaseModelOutputWithPast(ModelOutput): """ @@ -184,11 +239,13 @@ def expand_inputs_for_generation( def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) - # only last token for inputs_ids if past is defined in kwargs - if past_key_values: - input_ids = input_ids[:, -1].unsqueeze(-1) + cache_position = kwargs.get("cache_position", None) + # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens + if past_key_values is not None: + if input_ids.shape[1] != cache_position.shape[0]: + input_ids = input_ids[:, cache_position] if token_type_ids is not None: - token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + token_type_ids = token_type_ids[:, -input_ids.shape[1] :] attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) @@ -200,6 +257,9 @@ def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs): if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) + # 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. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + pixel_values = kwargs.get("pixel_values", None) image_encoder_embeddings = kwargs.get("image_encoder_embeddings", None) perceiver_embeddings = kwargs.get("perceiver_embeddings", None) @@ -210,6 +270,7 @@ def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs): "input_ids": input_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), + "cache_position": cache_position, "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, @@ -541,6 +602,7 @@ class IdeficsAttention(nn.Module): is_cross_attention: bool = False, config: PretrainedConfig = None, qk_layer_norms: bool = False, + layer_idx: int = None, ): super().__init__() self.hidden_size = hidden_size @@ -549,6 +611,14 @@ class IdeficsAttention(nn.Module): self.dropout = dropout self.is_causal = True + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + if (self.head_dim * num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" @@ -615,6 +685,7 @@ class IdeficsAttention(nn.Module): past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # if key_value_states are provided this layer is used as a cross-attention layer is_cross_attention = self.is_cross_attention or key_value_states is not None @@ -634,18 +705,17 @@ class IdeficsAttention(nn.Module): kv_seq_len = key_states.shape[-2] if past_key_value is not None: - kv_seq_len += past_key_value[0].shape[-2] + kv_seq_len += cache_position[0] + if not is_cross_attention: cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len)) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) # [bsz, nh, t, hd] if past_key_value is not None: - # reuse k, v, self_attention - key_states = torch.cat([past_key_value[0], key_states], dim=2) - value_states = torch.cat([past_key_value[1], value_states], dim=2) - - past_key_value = (key_states, value_states) if use_cache else None + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) if self.qk_layer_norms: query_states = self.q_layer_norm(query_states) @@ -700,7 +770,7 @@ class IdeficsAttention(nn.Module): # this was adapted from LlamaDecoderLayer class IdeficsDecoderLayer(nn.Module): - def __init__(self, config: IdeficsConfig): + def __init__(self, config: IdeficsConfig, layer_idx: int = None): super().__init__() self.hidden_size = config.hidden_size self.self_attn = IdeficsAttention( @@ -708,6 +778,7 @@ class IdeficsDecoderLayer(nn.Module): num_heads=config.num_attention_heads, dropout=config.dropout, config=config, + layer_idx=layer_idx, ) self.mlp = IdeficsMLP( hidden_size=self.hidden_size, @@ -726,6 +797,7 @@ class IdeficsDecoderLayer(nn.Module): past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: @@ -753,6 +825,7 @@ class IdeficsDecoderLayer(nn.Module): past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, + cache_position=cache_position, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states @@ -944,6 +1017,7 @@ class IdeficsPreTrainedModel(PreTrainedModel): supports_gradient_checkpointing = True _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"] _supports_sdpa = True + _supports_cache_class = True def _init_weights(self, module): # important: this ported version of Idefics isn't meant for training from scratch - only @@ -1031,6 +1105,10 @@ LLAMA_INPUTS_DOCSTRING = r""" more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. """ @@ -1076,7 +1154,9 @@ class IdeficsModel(IdeficsPreTrainedModel): perceiver_config.resampler_n_latents, ) - self.layers = nn.ModuleList([IdeficsDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.layers = nn.ModuleList( + [IdeficsDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)] + ) self.cross_layer_interval = config.cross_layer_interval num_cross_layers = config.num_hidden_layers // self.cross_layer_interval @@ -1132,6 +1212,7 @@ class IdeficsModel(IdeficsPreTrainedModel): output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, IdeficsBaseModelOutputWithPast]: device = input_ids.device if input_ids is not None else inputs_embeds.device @@ -1143,22 +1224,38 @@ class IdeficsModel(IdeficsPreTrainedModel): return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # retrieve input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both decoder_input_ids and decoder_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 decoder_input_ids or decoder_inputs_embeds") + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) - seq_length_with_past = seq_length - past_key_values_length = 0 + 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 past_key_values 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 inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + if not self.training: + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.45. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + return_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + + batch_size, seq_length, _ = inputs_embeds.shape + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + seq_length_with_past = seq_length + past_key_values_length + + if cache_position is None: + cache_position = torch.arange( + past_key_values_length, past_key_values_length + inputs_embeds.shape[1], device=inputs_embeds.device + ) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation @@ -1229,37 +1326,27 @@ class IdeficsModel(IdeficsPreTrainedModel): device ) - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) # embed positions if attention_mask is None: attention_mask = torch.ones( (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device ) - attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( - attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + + attention_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None - next_decoder_cache = () if use_cache else None + next_decoder_cache = None for idx, decoder_layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) - past_key_value = past_key_values[idx] if past_key_values is not None else None - def vblock( main_block, hidden_states, @@ -1274,6 +1361,7 @@ class IdeficsModel(IdeficsPreTrainedModel): layer_idx, cross_layer_interval, gated_cross_attn_layers, + cache_position, ): # TODO(ls): Add cross attention values to respective lists if layer_idx % cross_layer_interval == 0: @@ -1297,12 +1385,13 @@ class IdeficsModel(IdeficsPreTrainedModel): past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, + cache_position=cache_position, ) return layer_outputs if self.gradient_checkpointing and self.training: - past_key_value = None + past_key_values = None if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." @@ -1315,7 +1404,7 @@ class IdeficsModel(IdeficsPreTrainedModel): hidden_states, attention_mask, position_ids, - past_key_value, + past_key_values, image_hidden_states, image_attention_mask, cross_attention_gate, @@ -1324,6 +1413,7 @@ class IdeficsModel(IdeficsPreTrainedModel): idx, self.cross_layer_interval, self.gated_cross_attn_layers, + cache_position, ) else: layer_outputs = vblock( @@ -1331,7 +1421,7 @@ class IdeficsModel(IdeficsPreTrainedModel): hidden_states, attention_mask=attention_mask, position_ids=position_ids, - past_key_value=past_key_value, + past_key_value=past_key_values, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, cross_attention_gate=cross_attention_gate, @@ -1340,12 +1430,13 @@ class IdeficsModel(IdeficsPreTrainedModel): layer_idx=idx, cross_layer_interval=self.cross_layer_interval, gated_cross_attn_layers=self.gated_cross_attn_layers, + cache_position=cache_position, ) hidden_states = layer_outputs[0] if use_cache: - next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) @@ -1357,6 +1448,8 @@ class IdeficsModel(IdeficsPreTrainedModel): all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None + if return_legacy_cache: + next_cache = next_cache.to_legacy_cache() image_hidden_states = image_hidden_states.view(batch_size, num_images, image_seq_len, image_hidden_size) if not return_dict: return tuple( @@ -1372,6 +1465,78 @@ class IdeficsModel(IdeficsPreTrainedModel): image_hidden_states=image_hidden_states, ) + # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + 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 = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_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 == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + class IdeficsForVisionText2Text(IdeficsPreTrainedModel): _keys_to_ignore_on_load_missing = [r"lm_head.weight"] @@ -1450,6 +1615,7 @@ class IdeficsForVisionText2Text(IdeficsPreTrainedModel): output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False, return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, IdeficsCausalLMOutputWithPast]: r""" Args: @@ -1508,6 +1674,7 @@ class IdeficsForVisionText2Text(IdeficsPreTrainedModel): output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, + cache_position=cache_position, ) hidden_states = outputs[0] @@ -1567,13 +1734,13 @@ class IdeficsForVisionText2Text(IdeficsPreTrainedModel): outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, - standardize_cache_format: bool = False, + **kwargs, ) -> Dict[str, Any]: model_kwargs = super()._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder, - standardize_cache_format, + **kwargs, ) if "image_attention_mask" in model_kwargs: diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py index 65fc384b632..17f788b26e2 100644 --- a/tests/generation/test_utils.py +++ b/tests/generation/test_utils.py @@ -59,7 +59,7 @@ if is_torch_available(): ImageGPTForCausalImageModeling, SpeechEncoderDecoderModel, ) - from transformers.cache_utils import DynamicCache, EncoderDecoderCache, QuantoQuantizedCache + from transformers.cache_utils import DynamicCache, EncoderDecoderCache, QuantoQuantizedCache, StaticCache from transformers.generation import ( BeamSampleDecoderOnlyOutput, BeamSampleEncoderDecoderOutput, @@ -1769,6 +1769,53 @@ class GenerationTesterMixin: ) ) + def test_generate_with_static_cache(self): + """ + Tests if StaticCache works if we set attn_implementation=static when generation. + This doesn't test if generation quality is good, but tests that models with + self._supports_static_cache don't throw an error when generating and return + a StaticCache object at the end. + """ + for model_class in self.all_generative_model_classes: + if not model_class._supports_static_cache: + self.skipTest(reason="This model does not support the static cache format") + + config, input_ids, attention_mask = self._get_input_ids_and_config() + if config.is_encoder_decoder: + self.skipTest(reason="This model is encoder-decoder and has Encoder-Decoder Cache") + + config.use_cache = True + config.is_decoder = True + batch_size, seq_length = input_ids.shape + max_new_tokens = 20 + + model = model_class(config).to(torch_device).eval() + generation_kwargs = { + "max_length": None, + "max_new_tokens": max_new_tokens, + "cache_implementation": "static", + "return_dict_in_generate": True, # Required to return `past_key_values` + } + + max_cache_len = seq_length + max_new_tokens + head_dim = ( + model.config.head_dim + if hasattr(model.config, "head_dim") + else model.config.hidden_size // model.config.num_attention_heads + ) + num_key_value_heads = ( + model.config.num_attention_heads + if getattr(config, "num_key_value_heads", None) is None + else model.config.num_key_value_heads + ) + num_hidden_layers = config.num_hidden_layers + results = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs) + + cache_shape = (batch_size, num_key_value_heads, max_cache_len, head_dim) + self.assertTrue(isinstance(results.past_key_values, StaticCache)) + self.assertTrue(len(results.past_key_values.key_cache) == num_hidden_layers) + self.assertTrue(results.past_key_values.key_cache[0].shape == cache_shape) + @require_quanto def test_generate_with_quant_cache(self): for model_class in self.all_generative_model_classes: diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 148f33e048d..4a29942641e 100755 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -4587,6 +4587,44 @@ class ModelTesterMixin: normalized_1 = F.softmax(out_shared_prefix_last_tokens) torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4) + def test_static_cache_matches_dynamic(self): + """ + Tests that generating with static cache give almost same results as with dynamic cache. + This test does not compile the model and check only logits similarity for numerical precision + errors. + """ + if len(self.all_generative_model_classes) == 0: + self.skipTest( + reason="Model architecture has no generative classes, and thus not necessarily supporting 4D masks" + ) + + for model_class in self.all_generative_model_classes: + if not model_class._supports_static_cache: + self.skipTest(f"{model_class.__name__} does not support static cache") + + if not model_class._supports_cache_class: + self.skipTest(f"{model_class.__name__} does not support cache class") + + config, inputs = self.model_tester.prepare_config_and_inputs_for_common() + if getattr(config, "sliding_window", 0) > 0: + self.skipTest(f"{model_class.__name__} with sliding window attention is not supported by this test") + + model = model_class(config).to(device=torch_device, dtype=torch.float32) + model.eval() + + dynamic_out = model.generate( + **inputs, do_sample=False, max_new_tokens=10, output_logits=True, return_dict_in_generate=True + ) + static_out = model.generate( + **inputs, + do_sample=False, + max_new_tokens=10, + cache_implementation="static", + output_logits=True, + return_dict_in_generate=True, + ) + self.assertTrue(torch.allclose(dynamic_out.logits[0], static_out.logits[0], rtol=1e-3, atol=1e-4)) + # For now, Let's focus only on GPU for `torch.compile` @slow @require_torch_gpu