Refactor qwen2 moe (#11244)
This commit is contained in:
Yishuo Wang
GitHub
parent
7b753dc8ca
commit
ef8e9b2ecd
2 changed files with 40 additions and 475 deletions
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@ -713,6 +713,9 @@ def _optimize_pre(model):
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if model.config.model_type == "qwen2":
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if model.config.model_type == "qwen2":
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from ipex_llm.transformers.models.qwen2 import merge_qkv
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from ipex_llm.transformers.models.qwen2 import merge_qkv
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model.apply(merge_qkv)
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model.apply(merge_qkv)
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if model.config.model_type == "qwen2_moe":
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from ipex_llm.transformers.models.qwen2_moe import merge_qkv
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model.apply(merge_qkv)
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if model.config.model_type == "stablelm":
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if model.config.model_type == "stablelm":
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# For stablelm-zephyr-3b and stablelm-2-zephyr-1_6b
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# For stablelm-zephyr-3b and stablelm-2-zephyr-1_6b
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from ipex_llm.transformers.models.stablelm import merge_qkv
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from ipex_llm.transformers.models.stablelm import merge_qkv
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@ -1305,8 +1308,8 @@ def _optimize_post(model, lightweight_bmm=False):
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modeling_module_name = model.__class__.__module__
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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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module = importlib.import_module(modeling_module_name)
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from ipex_llm.transformers.models.qwen2_moe import qwen2moe_moeblock_forward
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from ipex_llm.transformers.models.qwen2_moe import qwen2moe_moeblock_forward
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from ipex_llm.transformers.models.qwen2_moe import qwen2moe_attention_forward
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from ipex_llm.transformers.models.qwen2_moe import qwen2moe_model_forward
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from ipex_llm.transformers.models.qwen2_moe import qwen2moe_model_forward
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from ipex_llm.transformers.models.qwen2 import qwen2_attention_forward
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convert_forward(model,
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convert_forward(model,
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module.Qwen2MoeModel,
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module.Qwen2MoeModel,
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qwen2moe_model_forward)
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qwen2moe_model_forward)
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@ -1321,7 +1324,10 @@ def _optimize_post(model, lightweight_bmm=False):
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llama_mlp_forward)
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llama_mlp_forward)
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convert_forward(model,
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convert_forward(model,
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module.Qwen2MoeAttention,
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module.Qwen2MoeAttention,
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qwen2moe_attention_forward)
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qwen2_attention_forward)
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convert_forward(model,
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module.Qwen2MoeSdpaAttention,
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qwen2_attention_forward)
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elif model.config.model_type == "cohere":
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elif model.config.model_type == "cohere":
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# for CohereForAI/c4ai-command-r-v01
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# for CohereForAI/c4ai-command-r-v01
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modeling_module_name = model.__class__.__module__
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modeling_module_name = model.__class__.__module__
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@ -37,39 +37,20 @@
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# limitations under the License.
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# limitations under the License.
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""" PyTorch Qwen2MoE model."""
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""" PyTorch Qwen2MoE model."""
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import math
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import torch
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import torch
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import torch.nn.functional as F
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import torch.nn.functional as F
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import torch.nn as nn
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import torch.utils.checkpoint
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import torch.utils.checkpoint
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import warnings
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from typing import Optional, Tuple, Union, List
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from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
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from ipex_llm.transformers.models.llama import repeat_kv
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
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from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import decoding_fast_path_qtype_check
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import use_flash_attention
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from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
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from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeModel, apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.kv import DynamicFp8Cache
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import os
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from transformers.models.qwen2_moe.modeling_qwen2_moe import (
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_prepare_4d_causal_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask,
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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Qwen2MoeAttention,
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)
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask_for_sdpa
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask
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from transformers.modeling_outputs import MoeModelOutputWithPast
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from transformers.modeling_outputs import MoeModelOutputWithPast
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from transformers.cache_utils import Cache, DynamicCache
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try:
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from transformers.cache_utils import Cache, DynamicCache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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import logging
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from transformers import logging
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from transformers import logging
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@ -90,9 +71,12 @@ def qwen2moe_model_forward(
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return_dict: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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):
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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 = use_cache if use_cache is not None else self.config.use_cache
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if use_cache and use_quantize_kv_cache(self.layers[0].mlp.shared_expert.up_proj, input_ids):
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use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.shared_expert.up_proj, input_ids)
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if not isinstance(past_key_values, DynamicFp8Cache):
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if use_cache:
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if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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return qwen2_moe_model_forward_internal(
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return qwen2_moe_model_forward_internal(
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self=self,
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self=self,
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input_ids=input_ids,
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input_ids=input_ids,
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@ -290,452 +274,27 @@ def qwen2_moe_model_forward_internal(
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)
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)
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def qwen2moe_attention_forward(
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def merge_qkv(module: torch.nn.Module):
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self,
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if isinstance(module, Qwen2MoeAttention):
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hidden_states: torch.Tensor,
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new_weight = torch.cat([
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attention_mask: Optional[torch.Tensor] = None,
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module.q_proj.weight.data,
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position_ids: Optional[torch.LongTensor] = None,
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module.k_proj.weight.data,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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module.v_proj.weight.data,
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output_attentions: bool = False,
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], dim=0)
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use_cache: bool = False,
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new_bias = torch.cat([
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**kwargs,
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module.q_proj.bias.data,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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module.k_proj.bias.data,
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if use_quantize_kv_cache(self.q_proj, hidden_states):
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module.v_proj.bias.data,
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forward_function = qwen2moe_attention_forward_quantized
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], dim=-1)
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elif hidden_states.device.type == "cpu":
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forward_function = qwen2moe_attention_forward_sdpa
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else:
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forward_function = qwen2moe_attention_forward_origin
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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**kwargs,
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)
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qkv_proj = torch.nn.Linear(0, 0, bias=True)
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qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
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qkv_proj.bias = torch.nn.Parameter(new_bias, requires_grad=False)
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qkv_proj.in_features = new_weight.size(1)
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qkv_proj.out_features = new_weight.size(0)
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module.qkv_proj = qkv_proj
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def qwen2moe_attention_forward_quantized(
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del module.q_proj, module.k_proj, module.v_proj
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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[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37."
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"Please make sure use `attention_mask` instead.`"
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)
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use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len,
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self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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invalidInputError(self.layer_idx is not None,
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"The cache structure has changed since version v4.36. "
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f"If you are using {self.__class__.__name__} "
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"for auto-regressive decoding with k/v caching, "
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"please make sure to initialize the attention class "
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"with a layer index.")
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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if use_fuse_rope:
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query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
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sin, cos, "qwen2_moe",
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position_ids)
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else:
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, cache_kwargs)
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if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
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and not hidden_states.requires_grad:
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import xe_addons
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attn_weights = xe_addons.query_key_fp8_matmul(query_states, key_states)
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else:
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key_states, value_states = restore_fp8_kv_cache(key_states,
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value_states, query_states.dtype)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
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attn_weights = attn_weights / math.sqrt(self.head_dim)
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invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
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("Attention weights should be of size "
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f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
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"but is {attn_weights.size()}"))
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if attention_mask is not None:
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invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
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(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
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f" but is {attention_mask.size()}"))
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights,
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p=self.attention_dropout, training=self.training)
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if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
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and not hidden_states.requires_grad:
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import xe_addons
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attn_output = xe_addons.attn_value_fp8_matmul(attn_weights, value_states)
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else:
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attn_output = torch.matmul(attn_weights, value_states)
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invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
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"`attn_output` should be of size "
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f"{(bsz, self.num_heads, q_len, self.head_dim)},"
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f" but is {attn_output.size()}")
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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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 qwen2moe_attention_forward_origin(
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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[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37. "
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"Please make sure use `attention_mask` instead.`"
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)
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
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qtype_check = decoding_fast_path_qtype_check(self.q_proj)
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decoding_fast_path = (qtype_check and use_fuse_rope
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and enough_kv_room and bsz * q_len == 1)
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decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
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if decoding_fast_path:
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hidden_states = hidden_states.view(1, -1)
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cache_k = past_key_value.key_cache[self.layer_idx]
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cache_v = past_key_value.value_cache[self.layer_idx]
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kv_seq_len = cache_k.shape[-2]
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import xe_linear
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args = [hidden_states, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight,
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self.q_proj.bias, self.k_proj.bias, self.v_proj.bias, position_ids, cache_k,
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cache_v, self.q_proj.weight.qtype, self.v_proj.weight.qtype, kv_seq_len,
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self.head_dim, self.rotary_emb.base]
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query_states, key_states, value_states = xe_linear.forward_qkv_bias(*args)
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kv_seq_len += 1
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if self.layer_idx == 0:
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past_key_value._seen_tokens = kv_seq_len
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past_key_value.key_cache[self.layer_idx] = key_states
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past_key_value.value_cache[self.layer_idx] = value_states
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else:
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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|
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query_states = query_states.view(bsz, q_len,
|
|
||||||
self.num_heads, self.head_dim).transpose(1, 2)
|
|
||||||
key_states = key_states.view(bsz, q_len,
|
|
||||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
||||||
value_states = value_states.view(bsz, q_len,
|
|
||||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
||||||
|
|
||||||
kv_seq_len = key_states.shape[-2]
|
|
||||||
if past_key_value is not None:
|
|
||||||
if self.layer_idx is None:
|
|
||||||
invalidInputError(
|
|
||||||
False,
|
|
||||||
"The cache structure has changed since version v4.36. "
|
|
||||||
f"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 += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
||||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
||||||
if use_fuse_rope:
|
|
||||||
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
|
|
||||||
sin, cos, "qwen2_moe",
|
|
||||||
position_ids)
|
|
||||||
else:
|
|
||||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
|
||||||
cos, sin, position_ids)
|
|
||||||
if past_key_value is not None:
|
|
||||||
if self.layer_idx == 0:
|
|
||||||
past_key_value._seen_tokens += key_states.shape[-2]
|
|
||||||
|
|
||||||
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
||||||
past_key_value.key_cache.append(key_states)
|
|
||||||
past_key_value.value_cache.append(value_states)
|
|
||||||
else:
|
|
||||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
||||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
||||||
|
|
||||||
if not enough_kv_room:
|
|
||||||
# allocate new
|
|
||||||
new_c_k, new_c_v = extend_kv_cache(bsz,
|
|
||||||
self.num_key_value_heads, # Support GQA
|
|
||||||
self.head_dim,
|
|
||||||
cache_k.size(2),
|
|
||||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
||||||
dtype=cache_k.dtype,
|
|
||||||
device=device)
|
|
||||||
|
|
||||||
new_c_k[:] = cache_k
|
|
||||||
new_c_v[:] = cache_v
|
|
||||||
cache_k = new_c_k
|
|
||||||
cache_v = new_c_v
|
|
||||||
|
|
||||||
key_states, value_states = append_kv_cache(cache_k,
|
|
||||||
cache_v,
|
|
||||||
key_states,
|
|
||||||
value_states)
|
|
||||||
|
|
||||||
# update past_key_value
|
|
||||||
past_key_value.key_cache[self.layer_idx] = key_states
|
|
||||||
past_key_value.value_cache[self.layer_idx] = value_states
|
|
||||||
# repeat k/v heads if n_kv_heads < n_heads
|
|
||||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
||||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
||||||
|
|
||||||
if not self.training and not hidden_states.requires_grad and \
|
|
||||||
use_flash_attention(query_states, key_states, attention_mask):
|
|
||||||
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
|
||||||
key_states.to(device, dtype=torch.float16),
|
|
||||||
value_states.to(device, dtype=torch.float16),
|
|
||||||
is_causal=True)
|
|
||||||
attn_weights = None
|
|
||||||
else:
|
|
||||||
attn_weights = torch.matmul(query_states,
|
|
||||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
||||||
|
|
||||||
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
|
|
||||||
("Attention weights should be of size "
|
|
||||||
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
|
|
||||||
"but is {attn_weights.size()}"))
|
|
||||||
|
|
||||||
if attention_mask is not None:
|
|
||||||
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
|
|
||||||
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
|
|
||||||
f" but is {attention_mask.size()}"))
|
|
||||||
|
|
||||||
attn_weights = attn_weights + attention_mask
|
|
||||||
|
|
||||||
# upcast attention to fp32
|
|
||||||
attn_weights = nn.functional.softmax(attn_weights,
|
|
||||||
dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
||||||
attn_weights = nn.functional.dropout(attn_weights,
|
|
||||||
p=self.attention_dropout, training=self.training)
|
|
||||||
attn_output = torch.matmul(attn_weights, value_states)
|
|
||||||
|
|
||||||
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
|
||||||
"`attn_output` should be of size "
|
|
||||||
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
|
||||||
f" but is {attn_output.size()}")
|
|
||||||
|
|
||||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
||||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
||||||
|
|
||||||
attn_output = self.o_proj(attn_output)
|
|
||||||
|
|
||||||
if not output_attentions:
|
|
||||||
attn_weights = None
|
|
||||||
|
|
||||||
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|
|
||||||
|
|
||||||
|
|
||||||
def qwen2moe_attention_forward_sdpa(
|
|
||||||
self,
|
|
||||||
hidden_states: torch.Tensor,
|
|
||||||
attention_mask: Optional[torch.Tensor] = None,
|
|
||||||
position_ids: Optional[torch.LongTensor] = None,
|
|
||||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
||||||
output_attentions: bool = False,
|
|
||||||
use_cache: bool = False,
|
|
||||||
**kwargs,
|
|
||||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
||||||
use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
|
|
||||||
|
|
||||||
if "padding_mask" in kwargs:
|
|
||||||
warnings.warn(
|
|
||||||
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
|
|
||||||
"Please make sure use `attention_mask` instead.`"
|
|
||||||
)
|
|
||||||
bsz, q_len, _ = hidden_states.size()
|
|
||||||
device = hidden_states.device
|
|
||||||
|
|
||||||
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
|
|
||||||
|
|
||||||
qtype_check = decoding_fast_path_qtype_check(self.q_proj)
|
|
||||||
decoding_fast_path = (qtype_check and use_fuse_rope
|
|
||||||
and enough_kv_room and bsz * q_len == 1)
|
|
||||||
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
|
|
||||||
if decoding_fast_path:
|
|
||||||
hidden_states = hidden_states.view(1, -1)
|
|
||||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
||||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
||||||
kv_seq_len = cache_k.shape[-2]
|
|
||||||
import xe_linear
|
|
||||||
args = [hidden_states, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight,
|
|
||||||
self.q_proj.bias, self.k_proj.bias, self.v_proj.bias, position_ids, cache_k,
|
|
||||||
cache_v, self.q_proj.weight.qtype, self.v_proj.weight.qtype, kv_seq_len,
|
|
||||||
self.head_dim, self.rotary_emb.base]
|
|
||||||
query_states, key_states, value_states = xe_linear.forward_qkv_bias(*args)
|
|
||||||
kv_seq_len += 1
|
|
||||||
if self.layer_idx == 0:
|
|
||||||
past_key_value._seen_tokens = kv_seq_len
|
|
||||||
past_key_value.key_cache[self.layer_idx] = key_states
|
|
||||||
past_key_value.value_cache[self.layer_idx] = value_states
|
|
||||||
else:
|
|
||||||
query_states = self.q_proj(hidden_states)
|
|
||||||
key_states = self.k_proj(hidden_states)
|
|
||||||
value_states = self.v_proj(hidden_states)
|
|
||||||
|
|
||||||
query_states = query_states.view(bsz, q_len,
|
|
||||||
self.num_heads, self.head_dim).transpose(1, 2)
|
|
||||||
key_states = key_states.view(bsz, q_len,
|
|
||||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
||||||
value_states = value_states.view(bsz, q_len,
|
|
||||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
||||||
|
|
||||||
kv_seq_len = key_states.shape[-2]
|
|
||||||
if past_key_value is not None:
|
|
||||||
if self.layer_idx is None:
|
|
||||||
invalidInputError(
|
|
||||||
False,
|
|
||||||
"The cache structure has changed since version v4.36. "
|
|
||||||
f"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 += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
||||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
||||||
if use_fuse_rope:
|
|
||||||
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
|
|
||||||
sin, cos, "qwen2_moe",
|
|
||||||
position_ids)
|
|
||||||
else:
|
|
||||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
|
||||||
cos, sin, position_ids)
|
|
||||||
if past_key_value is not None:
|
|
||||||
if self.layer_idx == 0:
|
|
||||||
past_key_value._seen_tokens += key_states.shape[-2]
|
|
||||||
|
|
||||||
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
||||||
past_key_value.key_cache.append(key_states)
|
|
||||||
past_key_value.value_cache.append(value_states)
|
|
||||||
else:
|
|
||||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
||||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
||||||
|
|
||||||
if not enough_kv_room:
|
|
||||||
# allocate new
|
|
||||||
new_c_k, new_c_v = extend_kv_cache(bsz,
|
|
||||||
self.num_key_value_heads, # Support GQA
|
|
||||||
self.head_dim,
|
|
||||||
cache_k.size(2),
|
|
||||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
||||||
dtype=cache_k.dtype,
|
|
||||||
device=device)
|
|
||||||
|
|
||||||
new_c_k[:] = cache_k
|
|
||||||
new_c_v[:] = cache_v
|
|
||||||
cache_k = new_c_k
|
|
||||||
cache_v = new_c_v
|
|
||||||
|
|
||||||
key_states, value_states = append_kv_cache(cache_k,
|
|
||||||
cache_v,
|
|
||||||
key_states,
|
|
||||||
value_states)
|
|
||||||
|
|
||||||
# update past_key_value
|
|
||||||
past_key_value.key_cache[self.layer_idx] = key_states
|
|
||||||
past_key_value.value_cache[self.layer_idx] = value_states
|
|
||||||
# repeat k/v heads if n_kv_heads < n_heads
|
|
||||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
||||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
||||||
|
|
||||||
if output_attentions:
|
|
||||||
attn_weights = torch.matmul(query_states,
|
|
||||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
||||||
|
|
||||||
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
|
|
||||||
("Attention weights should be of size "
|
|
||||||
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
|
|
||||||
"but is {attn_weights.size()}"))
|
|
||||||
|
|
||||||
if attention_mask is not None:
|
|
||||||
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
|
|
||||||
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
|
|
||||||
f" but is {attention_mask.size()}"))
|
|
||||||
|
|
||||||
attn_weights = attn_weights + attention_mask
|
|
||||||
|
|
||||||
# upcast attention to fp32
|
|
||||||
attn_weights = nn.functional.softmax(attn_weights,
|
|
||||||
dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
||||||
attn_weights = nn.functional.dropout(attn_weights,
|
|
||||||
p=self.attention_dropout, training=self.training)
|
|
||||||
else:
|
|
||||||
attn_weights = None
|
|
||||||
|
|
||||||
from torch.nn.functional import scaled_dot_product_attention as sdpa
|
|
||||||
attn_output = sdpa(query_states,
|
|
||||||
key_states,
|
|
||||||
value_states,
|
|
||||||
attn_mask=attention_mask,
|
|
||||||
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
||||||
is_causal=self.is_causal and attention_mask is None and q_len > 1)
|
|
||||||
|
|
||||||
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
|
||||||
"`attn_output` should be of size "
|
|
||||||
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
|
||||||
f" but is {attn_output.size()}")
|
|
||||||
|
|
||||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
||||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
||||||
|
|
||||||
attn_output = self.o_proj(attn_output)
|
|
||||||
|
|
||||||
return attn_output, attn_weights, past_key_value
|
|
||||||
|
|
||||||
|
|
||||||
def qwen2moe_moeblock_forward(self, hidden_states: torch.Tensor):
|
def qwen2moe_moeblock_forward(self, hidden_states: torch.Tensor):
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue