diff --git a/python/llm/src/ipex_llm/transformers/models/qwen2_moe.py b/python/llm/src/ipex_llm/transformers/models/qwen2_moe.py index fe1245a4..466f2706 100644 --- a/python/llm/src/ipex_llm/transformers/models/qwen2_moe.py +++ b/python/llm/src/ipex_llm/transformers/models/qwen2_moe.py @@ -61,6 +61,20 @@ import os KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256)) +from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask_for_sdpa +from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask +from transformers.modeling_outputs import MoeModelOutputWithPast + +try: + from transformers.cache_utils import Cache, DynamicCache +except ImportError: + Cache = Tuple[torch.Tensor] +import logging +from transformers import logging + + +logger = logging.get_logger(__name__) + def qwen2moe_model_forward( self, @@ -79,7 +93,7 @@ def qwen2moe_model_forward( if use_cache and use_quantize_kv_cache(self.layers[0].mlp.shared_expert.up_proj, input_ids): if not isinstance(past_key_values, DynamicFp8Cache): past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values) - return Qwen2MoeModel.forward( + return qwen2_moe_model_forward_internal( self=self, input_ids=input_ids, attention_mask=attention_mask, @@ -94,6 +108,188 @@ def qwen2moe_model_forward( ) +def qwen2_moe_model_forward_internal( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, +) -> Union[Tuple, MoeModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None \ + else self.config.output_attentions + output_router_logits = ( + output_router_logits if output_router_logits is not None else + self.config.output_router_logits + ) + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else + self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + invalidInputError(False, "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: + invalidInputError(False, + "You have to specify decoder_input_ids or decoder_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 + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + 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) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, + dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" \ + and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + invalidInputError( + False, + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of" + " Qwen2MoE. Make sure to call `tokenizer.padding_side='left'`" + " before tokenizing the input." + ) + + if self._attn_implementation == "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._attn_implementation == "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. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + 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, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_router_logits = () if output_router_logits else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + output_router_logits, + use_cache, + ) + else: + # ipex-llm changes + curr_device = decoder_layer.input_layernorm.weight.device + if attention_mask is not None: + attention_mask = attention_mask.to(curr_device) + if position_ids is not None: + position_ids = position_ids.to(curr_device) + # ipex-llm changes end + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + output_router_logits=output_router_logits, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if output_router_logits and layer_outputs[-1] is not None: + all_router_logits += (layer_outputs[-1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_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_cache, all_hidden_states, all_self_attns, + all_router_logits] if v is not None + ) + return MoeModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + router_logits=all_router_logits, + ) + + def qwen2moe_attention_forward( self, hidden_states: torch.Tensor,