Add basic optimization for Qwen2.5 omni (#13022)
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Yishuo Wang
GitHub
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61c2e9c271
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300eb01d98
2 changed files with 320 additions and 0 deletions
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@ -1074,6 +1074,9 @@ def _optimize_pre(model, qtype=None):
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elif model.config.model_type == "deepseek_v3" and model.config.hidden_size == 2048:
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from ipex_llm.transformers.models.deepseek import padding_mla_v_hd
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model.apply(padding_mla_v_hd)
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elif model.config.model_type == "qwen2_5_omni":
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from ipex_llm.transformers.models.qwen2_5_omni import merge_qkv
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model.apply(merge_qkv)
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return model
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@ -2043,7 +2046,38 @@ def _optimize_post(model):
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convert_forward(model, module.DeepseekV3Model, deepseek_model_forward)
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convert_forward(model, module.DeepseekV3Attention, deepseek_attention_forward)
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convert_forward(model, module.DeepseekV3MoE, deepseek_moe_forward)
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elif model.config.model_type == "qwen2_5_omni":
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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# llm opt
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from ipex_llm.transformers.models.qwen2_5_omni import qwen2_5_omni_attention_forward
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from ipex_llm.transformers.models.qwen2_5_omni import qwen2_5_omni_thinker_model_forward
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from ipex_llm.transformers.models.qwen2 import qwen2_mlp_forward
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from ipex_llm.transformers.models.common import rms_norm_forward
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convert_forward(model.thinker.model, module.Qwen2_5OmniAttention,
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qwen2_5_omni_attention_forward)
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convert_forward(model.thinker.model, module.Qwen2_5OmniSdpaAttention,
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qwen2_5_omni_attention_forward)
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convert_forward(model.thinker.model, module.Qwen2_5OmniThinkerModel,
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qwen2_5_omni_thinker_model_forward)
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convert_forward(model.thinker.model, module.Qwen2MLP, qwen2_mlp_forward)
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convert_forward(model, module.Qwen2RMSNorm, rms_norm_forward)
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# vision opt
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from ipex_llm.transformers.models.qwen2_vl import qwen2_vision_get_dtype
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from ipex_llm.transformers.models.qwen2_5_omni import qwen2_5_omni_vision_attention_forward
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convert_forward(model.thinker.visual, module.Qwen2_5OmniVisionAttention,
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qwen2_5_omni_vision_attention_forward)
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convert_forward(model.thinker.visual, module.Qwen2_5OmniVisionSdpaAttention,
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qwen2_5_omni_vision_attention_forward)
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# tts opt
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if hasattr(model, "talker"):
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convert_forward(model.talker, module.Qwen2_5OmniAttention,
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qwen2_5_omni_attention_forward)
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convert_forward(model.talker, module.Qwen2_5OmniThinkerModel,
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qwen2_5_omni_thinker_model_forward)
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return model
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286
python/llm/src/ipex_llm/transformers/models/qwen2_5_omni.py
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286
python/llm/src/ipex_llm/transformers/models/qwen2_5_omni.py
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@ -0,0 +1,286 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/3a1ead0aabed473eafe527915eea8c197d424356/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
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# which is licensed under Apache License 2.0
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import math
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import torch
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from typing import Optional, Tuple, List, Union
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from transformers.cache_utils import Cache
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import Qwen2_5OmniAttention
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from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import apply_rotary_pos_emb_vision
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from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import apply_multimodal_rotary_pos_emb
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.kv import DynamicNormalCache
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from ipex_llm.transformers.models.common import merge_qkv_base
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from ipex_llm.transformers.models.common import attention_softmax
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from ipex_llm.transformers.models.utils import use_sdp_non_causal
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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, Qwen2_5OmniAttention)
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def qwen2_5_omni_attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor]=None,
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position_embeddings: Tuple[torch.Tensor, torch.Tensor]=None,
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):
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bsz, q_len, _ = hidden_states.size()
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qkv = self.qkv_proj(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
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qkv = qkv.transpose(1, 2)
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query_states, key_states, value_states = qkv.split([self.num_heads,
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self.num_key_value_heads,
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self.num_key_value_heads], dim=1)
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cos, sin = position_embeddings
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if query_states.device.type == "xpu":
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import xe_addons
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xe_addons.rotary_half_with_cache_inplaced(query_states, key_states, cos, sin)
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else:
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query_states, key_states = apply_multimodal_rotary_pos_emb(
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query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
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)
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, None)
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attn_weights = None
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attn_output = scaled_dot_product_attention(
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query_states, key_states, value_states,
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attention_mask, q_len == key_states.size(2)
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, -1)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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def qwen2_5_omni_thinker_model_forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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invalidInputError((input_ids is None) ^ (inputs_embeds is None),
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"You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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# ipex-llm changes start: kv cache
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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use_cache = True if inputs_embeds.device.type == "xpu" else use_cache
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# torch.jit.trace() doesn't support cache objects in the output
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if use_cache and not torch.jit.is_tracing():
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if not isinstance(past_key_values, DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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# ipex-llm changes end
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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# the hard coded `3` is for temporal, height and width.
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if position_ids is None:
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position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1)
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elif position_ids.dim() == 2:
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position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
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causal_mask = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
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)
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hidden_states = inputs_embeds
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# create position embeddings to be shared across the decoder layers
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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# ipex-llm changes start: rotary embedding
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if inputs_embeds.device.type == "xpu":
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cos, sin = position_embeddings
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mrope_section = self.config.rope_scaling["mrope_section"] * 2
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cos = torch.cat([m[i % 3] for i, m in
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enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(1)
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sin = torch.cat([m[i % 3] for i, m in
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enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(1)
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position_embeddings = cos.contiguous(), sin.contiguous()
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# ipex-llm changes end
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
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if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def qwen2_5_omni_vision_attention_forward(
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self,
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hidden_states: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor = None
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) -> torch.Tensor:
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seq_length = hidden_states.shape[0]
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q = self.q(hidden_states).reshape(seq_length, self.num_heads, -1)
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k = self.k(hidden_states).reshape(seq_length, self.num_heads, -1)
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v = self.v(hidden_states).reshape(seq_length, self.num_heads, -1)
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q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
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k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
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# q, k, v: [seq_length, num_heads, head_dim]
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seq_lens = cu_seqlens.tolist()
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invalidInputError(seq_lens[0] == 0 and seq_lens[-1] == seq_length,
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"unexpected input")
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head_dim = q.size(-1)
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if use_sdp_non_causal(head_dim, q.device, q.dtype):
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image_num = len(seq_lens) - 1
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image_size = seq_lens[1] - seq_lens[0]
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guessed_seq_lens = torch.arange(0, (image_num + 1) * image_size, image_size,
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dtype=cu_seqlens.dtype, device=cu_seqlens.device)
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if (guessed_seq_lens == cu_seqlens).all():
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q = q.view(image_num, image_size, self.num_heads, head_dim).permute(0, 2, 1, 3)
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k = k.view(image_num, image_size, self.num_heads, head_dim).permute(0, 2, 1, 3)
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v = v.view(image_num, image_size, self.num_heads, head_dim).permute(0, 2, 1, 3)
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# q, k, v: [image_num, num_heads, image_size, head_dim]
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attn_output = scaled_dot_product_attention(
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q, k.contiguous(), v.contiguous(),
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None, False
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)
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attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
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attn_output = attn_output.view(seq_length, self.num_heads, head_dim)
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# attn_output: [seq_length, num_heads, head_dim]
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else:
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q = q.transpose(0, 1).unsqueeze(0)
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k = k.transpose(0, 1).unsqueeze(0).contiguous()
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v = v.transpose(0, 1).unsqueeze(0).contiguous()
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# q, k, v: [1, num_heads, seq_length, head_dim]
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attn_outputs = []
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for i in range(image_num):
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start_idx = seq_lens[i]
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end_idx = seq_lens[i + 1]
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tmp_q = q[:, :, start_idx:end_idx, :]
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tmp_k = k[:, :, start_idx:end_idx, :]
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tmp_v = v[:, :, start_idx:end_idx, :]
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attn_output = scaled_dot_product_attention(
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tmp_q, tmp_k, tmp_v,
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None, False
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)
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attn_output = attn_output.permute(0, 2, 1, 3)
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# attn_output: [1, seq_length, num_heads, head_dim]
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attn_outputs.append(attn_output)
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attn_output = torch.cat(attn_outputs, dim=1).squeeze(0)
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# attn_output: [seq_length, num_heads, head_dim]
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else:
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attention_mask = torch.full(
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[1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype
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)
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for i in range(1, len(seq_lens)):
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attention_mask[..., seq_lens[i - 1]:seq_lens[i], seq_lens[i - 1]:seq_lens[i]] = 0
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q = q.transpose(0, 1)
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k = k.transpose(0, 1)
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v = v.transpose(0, 1)
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# q, k, v: [num_heads, seq_length, head_dim]
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attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(head_dim)
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attn_weights = attn_weights + attention_mask
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attn_weights = attention_softmax(attn_weights)
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attn_output = torch.matmul(attn_weights, v)
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attn_output = attn_output.transpose(0, 1)
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# attn_output: [seq_length, num_heads, head_dim]
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attn_output = attn_output.reshape(seq_length, -1)
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attn_output = self.proj(attn_output)
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return attn_output
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