diff --git a/iotdb-core/ainode/iotdb/ainode/core/model/sundial/modeling_sundial.py b/iotdb-core/ainode/iotdb/ainode/core/model/sundial/modeling_sundial.py index b6e627500ea0..544193e4d9c6 100644 --- a/iotdb-core/ainode/iotdb/ainode/core/model/sundial/modeling_sundial.py +++ b/iotdb-core/ainode/iotdb/ainode/core/model/sundial/modeling_sundial.py @@ -156,7 +156,7 @@ def forward( past_key_value: Optional[Cache] = None, output_attentions: bool = False, **kwargs, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) @@ -175,7 +175,7 @@ def forward( kv_seq_len = key_states.shape[-2] if past_key_value is not None: - kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + kv_seq_len += past_key_value.get_seq_length(self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin, position_ids @@ -238,15 +238,13 @@ def forward( hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, + past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, - use_cache: Optional[bool] = False, **kwargs, ) -> Tuple[ torch.FloatTensor, - torch.FloatTensor, - Optional[torch.FloatTensor], - Optional[torch.FloatTensor], + Optional[torch.Tensor], + Optional[Cache], ]: residual = hidden_states @@ -259,7 +257,6 @@ def forward( position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, - use_cache=use_cache, ) hidden_states = residual + hidden_states @@ -272,8 +269,6 @@ def forward( if not output_attentions: self_attn_weights = None - if not use_cache: - present_key_value = None return hidden_states, self_attn_weights, present_key_value @@ -317,9 +312,10 @@ def forward( input_ids: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[ + Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor]]] + ] = None, inputs_embeds: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, @@ -335,7 +331,6 @@ def forward( if output_hidden_states is not None else self.config.output_hidden_states ) - use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict @@ -359,17 +354,25 @@ def forward( inputs_embeds = self.embed_layer(input_ids) seq_length = inputs_embeds.shape[1] - if self.gradient_checkpointing and self.training: - if use_cache: - use_cache = False - past_key_values_length = 0 + use_legacy_cache = False - if use_cache: + if past_key_values is not None: use_legacy_cache = not isinstance(past_key_values, Cache) + # Converts the legacy cache which is tuple into an equivalent Cache. Used for backward compatibility. if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) - past_key_values_length = past_key_values.get_usable_length(seq_length) + # Suppose the sequence length of each layer is the same + past_key_values_length = past_key_values.get_seq_length() + + # When training + checkpoints, caching is usually disabled (just do not transfer) + if ( + self.gradient_checkpointing + and self.training + and isinstance(past_key_values, Cache) + ): + past_key_values = None + past_key_values_length = 0 if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device @@ -412,7 +415,6 @@ def forward( position_ids, past_key_values, output_attentions, - use_cache, ) else: layer_outputs = decoder_layer( @@ -421,7 +423,6 @@ def forward( position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, - use_cache=use_cache, ) hidden_states = layer_outputs[0] @@ -429,7 +430,7 @@ def forward( if output_attentions: all_self_attns += (layer_outputs[1],) - if use_cache: + if isinstance(past_key_values, Cache): next_decoder_cache = layer_outputs[2] hidden_states = self.norm(hidden_states) @@ -438,7 +439,7 @@ def forward( all_hidden_states += (hidden_states,) next_cache = None - if use_cache: + if isinstance(past_key_values, Cache): next_cache = ( next_decoder_cache.to_legacy_cache() if use_legacy_cache @@ -484,12 +485,13 @@ def forward( input_ids: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[ + Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor]]] + ] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.FloatTensor] = None, loss_masks: Optional[torch.FloatTensor] = None, mask_y: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, @@ -525,7 +527,6 @@ def forward( position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, - use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, @@ -604,16 +605,9 @@ def prepare_inputs_for_generation( # Omit tokens covered by past_key_values if past_key_values is not None: if isinstance(past_key_values, Cache): - cache_length = past_key_values.get_seq_length() - if isinstance(past_key_values, DynamicCache): - past_length = past_key_values.seen_tokens - else: - past_length = cache_length - - max_cache_length = past_key_values.get_max_length() + past_length = past_key_values.get_seq_length() else: - cache_length = past_length = past_key_values[0][0].shape[2] - max_cache_length = None + past_length = past_key_values[0][0].shape[2] # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where @@ -622,26 +616,13 @@ def prepare_inputs_for_generation( if attention_mask is not None and attention_mask.shape[1] > ( input_ids.shape[1] // self.config.input_token_len ): - input_ids = input_ids[ - :, - -(attention_mask.shape[1] - past_length) - * self.config.input_token_len :, - ] + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < (input_ids.shape[1] // self.config.input_token_len): input_ids = input_ids[:, past_length * self.config.input_token_len :] # 3 - Otherwise (past_length >= (input_ids.shape[1] // self.config.input_token_len)), let's assume input_ids only has unprocessed tokens. - # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. - if ( - max_cache_length is not None - and attention_mask is not None - and cache_length + (input_ids.shape[1] // self.config.input_token_len) - > max_cache_length - ): - attention_mask = attention_mask[:, -max_cache_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 @@ -662,7 +643,6 @@ def prepare_inputs_for_generation( { "position_ids": position_ids, "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "revin": revin, "num_samples": num_samples, diff --git a/iotdb-core/ainode/iotdb/ainode/core/model/sundial/ts_generation_mixin.py b/iotdb-core/ainode/iotdb/ainode/core/model/sundial/ts_generation_mixin.py index 045711616607..f09621f2cb0a 100644 --- a/iotdb-core/ainode/iotdb/ainode/core/model/sundial/ts_generation_mixin.py +++ b/iotdb-core/ainode/iotdb/ainode/core/model/sundial/ts_generation_mixin.py @@ -83,7 +83,7 @@ def generate( outputs = (outputs * stdev) + means return outputs - def _greedy_search( + def _sample( self, input_ids: torch.Tensor, logits_processor: Optional[LogitsProcessorList] = None, @@ -269,7 +269,7 @@ def _greedy_search( horizon_length = next_tokens.shape[-1] // self.config.input_token_len past_key_values = model_kwargs.get("past_key_values") - if past_key_values is None: + if past_key_values is None or generate_results is None: generate_results = next_tokens else: generate_results = torch.cat([generate_results, next_tokens], dim=-1) @@ -328,9 +328,13 @@ def _update_model_kwargs_for_generation( standardize_cache_format: bool = False, ) -> Dict[str, Any]: # update past_key_values - model_kwargs["past_key_values"] = self._extract_past_from_model_output( - outputs, standardize_cache_format=standardize_cache_format - ) + if "past_key_values" in outputs: + model_kwargs["past_key_values"] = outputs.past_key_values + elif "mems" in outputs: + model_kwargs["past_key_values"] = outputs.mems + elif "past_buckets_states" in outputs: + model_kwargs["past_key_values"] = outputs.past_buckets_states + if getattr(outputs, "state", None) is not None: model_kwargs["state"] = outputs.state diff --git a/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/modeling_timer.py b/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/modeling_timer.py index 9eb3caebf984..37bf56dfc59a 100644 --- a/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/modeling_timer.py +++ b/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/modeling_timer.py @@ -148,7 +148,7 @@ def forward( past_key_value: Optional[Cache] = None, output_attentions: bool = False, **kwargs, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) @@ -167,7 +167,7 @@ def forward( kv_seq_len = key_states.shape[-2] if past_key_value is not None: - kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + kv_seq_len += past_key_value.get_seq_length(self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin, position_ids @@ -230,15 +230,13 @@ def forward( hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, + past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, - use_cache: Optional[bool] = False, **kwargs, ) -> Tuple[ torch.FloatTensor, - torch.FloatTensor, - Optional[torch.FloatTensor], - Optional[torch.FloatTensor], + Optional[torch.Tensor], + Optional[Cache], ]: residual = hidden_states @@ -249,7 +247,6 @@ def forward( position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, - use_cache=use_cache, ) hidden_states = residual + hidden_states hidden_states = self.norm1(hidden_states) @@ -263,8 +260,6 @@ def forward( if not output_attentions: self_attn_weights = None - if not use_cache: - present_key_value = None return hidden_states, self_attn_weights, present_key_value @@ -272,7 +267,7 @@ class TimerPreTrainedModel(PreTrainedModel): config_class = TimerConfig base_model_prefix = "model" supports_gradient_checkpointing = True - _no_split_modules = ["TimeMoeDecoderLayer"] + _no_split_modules = ["TimeDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = False @@ -308,9 +303,10 @@ def forward( input_ids: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[ + Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor]]] + ] = None, inputs_embeds: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, @@ -326,7 +322,6 @@ def forward( if output_hidden_states is not None else self.config.output_hidden_states ) - use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict @@ -350,17 +345,24 @@ def forward( inputs_embeds = self.embed_layer(input_ids) seq_length = inputs_embeds.shape[1] - if self.gradient_checkpointing and self.training: - if use_cache: - use_cache = False - past_key_values_length = 0 + use_legacy_cache = False - if use_cache: + if past_key_values is not None: use_legacy_cache = not isinstance(past_key_values, Cache) + # Converts the legacy cache which is tuple into an equivalent Cache. Used for backward compatibility. if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) - past_key_values_length = past_key_values.get_usable_length(seq_length) + past_key_values_length = past_key_values.get_seq_length() + + # When training + checkpoints, caching is usually disabled (just do not transfer) + if ( + self.gradient_checkpointing + and self.training + and isinstance(past_key_values, Cache) + ): + past_key_values = None + past_key_values_length = 0 if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device @@ -403,7 +405,6 @@ def forward( position_ids, past_key_values, output_attentions, - use_cache, ) else: layer_outputs = decoder_layer( @@ -412,7 +413,6 @@ def forward( position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, - use_cache=use_cache, ) hidden_states = layer_outputs[0] @@ -420,7 +420,7 @@ def forward( if output_attentions: all_self_attns += (layer_outputs[1],) - if use_cache: + if isinstance(past_key_values, Cache): next_decoder_cache = layer_outputs[2] hidden_states = self.norm(hidden_states) @@ -429,7 +429,7 @@ def forward( all_hidden_states += (hidden_states,) next_cache = None - if use_cache: + if isinstance(past_key_values, Cache): next_cache = ( next_decoder_cache.to_legacy_cache() if use_legacy_cache @@ -477,11 +477,12 @@ def forward( input_ids: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, + past_key_values: Optional[ + Union[Cache, tuple[tuple[torch.Tensor, torch.Tensor]]] + ] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.FloatTensor] = None, loss_masks: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, @@ -514,7 +515,6 @@ def forward( position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, - use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, @@ -556,7 +556,7 @@ def forward( predictions = predictions * std + mean if not return_dict: output = (predictions,) + outputs[1:] - return (loss) + output if loss is not None else output + return (loss,) + output if loss is not None else output return MoeCausalLMOutputWithPast( loss=loss, @@ -595,16 +595,9 @@ def prepare_inputs_for_generation( # Omit tokens covered by past_key_values if past_key_values is not None: if isinstance(past_key_values, Cache): - cache_length = past_key_values.get_seq_length() - if isinstance(past_key_values, DynamicCache): - past_length = past_key_values.seen_tokens - else: - past_length = cache_length - - max_cache_length = past_key_values.get_max_length() + past_length = past_key_values.get_seq_length() else: - cache_length = past_length = past_key_values[0][0].shape[2] - max_cache_length = None + past_length = past_key_values[0][0].shape[2] # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where @@ -620,15 +613,6 @@ def prepare_inputs_for_generation( input_ids = input_ids[:, past_length * self.config.input_token_len :] # 3 - Otherwise (past_length >= (input_ids.shape[1] // self.config.input_token_len)), let's assume input_ids only has unprocessed tokens. - # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. - if ( - max_cache_length is not None - and attention_mask is not None - and cache_length + (input_ids.shape[1] // self.config.input_token_len) - > max_cache_length - ): - attention_mask = attention_mask[:, -max_cache_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 @@ -649,7 +633,6 @@ def prepare_inputs_for_generation( { "position_ids": position_ids, "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "revin": revin, } diff --git a/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/ts_generation_mixin.py b/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/ts_generation_mixin.py index 165d3c55e448..18f711b8e1a1 100644 --- a/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/ts_generation_mixin.py +++ b/iotdb-core/ainode/iotdb/ainode/core/model/timerxl/ts_generation_mixin.py @@ -78,7 +78,7 @@ def generate( **kwargs, ) - def _greedy_search( + def _sample( self, input_ids: torch.Tensor, logits_processor: Optional[LogitsProcessorList] = None, @@ -315,9 +315,13 @@ def _update_model_kwargs_for_generation( standardize_cache_format: bool = False, ) -> Dict[str, Any]: # update past_key_values - model_kwargs["past_key_values"] = self._extract_past_from_model_output( - outputs, standardize_cache_format=standardize_cache_format - ) + if "past_key_values" in outputs: + model_kwargs["past_key_values"] = outputs.past_key_values + elif "mems" in outputs: + model_kwargs["past_key_values"] = outputs.mems + elif "past_buckets_states" in outputs: + model_kwargs["past_key_values"] = outputs.past_buckets_states + if getattr(outputs, "state", None) is not None: model_kwargs["state"] = outputs.state diff --git a/iotdb-core/ainode/poetry.lock b/iotdb-core/ainode/poetry.lock index 1cae5ebb4691..7a22506d06fe 100644 --- a/iotdb-core/ainode/poetry.lock +++ b/iotdb-core/ainode/poetry.lock @@ -1,24 +1,24 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. 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---- torch = ">=2.7.0" torchmetrics = "^1.8.0" -transformers = "==4.40.1" -tokenizers = ">=0.19,<0.20" +transformers = "==4.56.2" +tokenizers = ">=0.22.0,<=0.23.0" huggingface_hub = "^0.34.4" safetensors = "^0.6.2" einops = "^0.8.1" @@ -118,6 +118,9 @@ hf_xet = ">=1.1.9" # ---- Tooling ---- black = "25.1.0" isort = "6.0.1" +setuptools = ">=75.3.0" +joblib = ">=1.4.2" +urllib3 = ">=2.2.3" [tool.poetry.scripts] ainode = "iotdb.ainode.core.script:main"