refactor stablelm (#11195)
This commit is contained in:
Yishuo Wang
GitHub
parent
a644e9409b
commit
d90cd977d0
1 changed files with 61 additions and 307 deletions
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@ -38,31 +38,19 @@
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#
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import math
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from typing import Optional, Tuple, List, Union
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from typing import Optional, Tuple, List
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import torch
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from torch import nn
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import torch.nn.functional as F
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from transformers.cache_utils import Cache
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from transformers.models.stablelm.modeling_stablelm import repeat_kv
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from transformers.models.stablelm.modeling_stablelm import StableLmAttention, StableLmModel
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from ipex_llm.utils.common import invalidInputError
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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, \
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apply_rotary_pos_emb_cache_freq_xpu
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from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
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restore_fp8_kv_cache, use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
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from ipex_llm.transformers.models.mistral import should_use_fuse_rope, repeat_kv
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try:
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from transformers.cache_utils import Cache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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import os
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
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from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
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def merge_qkv(module: torch.nn.Module):
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@ -92,24 +80,26 @@ def merge_qkv(module: torch.nn.Module):
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def stablelm_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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) -> Union[Tuple, BaseModelOutputWithPast]:
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from ipex_llm.transformers.kv import DynamicFp8Cache
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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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):
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# IPEX-LLM OPT: kv cache and quantize 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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if use_cache and use_quantize_kv_cache_stablelm(self.layers[0].self_attn.head_dim,
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self.layers[0].mlp.up_proj,
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input_ids):
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if not isinstance(past_key_values, DynamicFp8Cache):
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use_quantize_kv = (self.layers[0].self_attn.head_dim in [64, 96, 128]
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and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids))
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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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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 StableLmModel.forward(
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self=self,
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input_ids=input_ids,
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@ -124,10 +114,6 @@ def stablelm_model_forward(
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)
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def use_quantize_kv_cache_stablelm(head_dim: int, linear: torch.nn.Module, x: torch.Tensor) -> bool:
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return (head_dim == 64 or head_dim == 128) and use_quantize_kv_cache(linear, x)
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def stablelm_attention_forward(
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self,
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hidden_states: torch.Tensor,
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@ -137,55 +123,17 @@ def stablelm_attention_forward(
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if use_quantize_kv_cache_stablelm(self.head_dim, self.o_proj, hidden_states):
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forward_function = stablelm_attention_forward_quantized
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else:
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forward_function = stablelm_attention_forward_original
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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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)
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def stablelm_attention_forward_original(
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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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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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# for flash attention
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original_dtype = hidden_states.dtype
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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
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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_heads,
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self.num_heads], dim=1)
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self.num_key_value_heads,
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self.num_key_value_heads], dim=1)
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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__} for "
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"auto-regressive decodingwith k/v caching, please make sure "
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"to initialize the attention class 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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# Partial rotary embedding
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@ -198,8 +146,8 @@ def stablelm_attention_forward_original(
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key_states[..., self.rotary_emb.dim:],
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)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
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if use_fuse_rope:
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# [batch_size, num_heads, seq_length, head_dim // config.partial_rotary_factor]
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if should_use_fuse_rope(hidden_states, position_ids, self.training):
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query_rot, key_rot = apply_rotary_pos_emb_cache_freq_xpu(query_rot,
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key_rot,
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sin,
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@ -214,94 +162,52 @@ def stablelm_attention_forward_original(
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position_ids,
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"stablelm")
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# [batch_size, seq_length, num_heads, head_dim]
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# [batch_size, num_heads, seq_length, head_dim]
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query_states = torch.cat((query_rot, query_pass), dim=-1)
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key_states = torch.cat((key_rot, key_pass), dim=-1)
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if past_key_value is not None:
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# update the number of seen tokens
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if self.layer_idx == 0:
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past_key_value.seen_tokens += key_states.shape[-2]
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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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# reuse k, v, self_attention
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# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
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if len(past_key_value.key_cache) <= self.layer_idx:
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past_key_value.key_cache.append(key_states)
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past_key_value.value_cache.append(value_states)
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else:
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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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if not enough_kv_room:
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# allocate new
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new_c_k, new_c_v = extend_kv_cache(bsz,
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self.num_key_value_heads, # Support GQA
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self.head_dim,
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cache_k.size(2),
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kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device)
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new_c_k[:] = cache_k
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new_c_v[:] = cache_v
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cache_k = new_c_k
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cache_v = new_c_v
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key_states, value_states = append_kv_cache(cache_k, cache_v,
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key_states, value_states)
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# update past_key_value
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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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# 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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if not self.training and not hidden_states.requires_grad and \
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use_flash_attention(query_states, key_states, attention_mask):
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attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
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key_states.to(device, dtype=torch.float16),
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value_states.to(device, dtype=torch.float16),
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is_causal=True)
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attn_weights = None
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elif not self.training and not hidden_states.requires_grad and \
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use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
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# IPEX-LLM OPT: sdp
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attn_weights = None
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if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
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import xe_addons
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attn_output = xe_addons.sdp(query_states, key_states, value_states,
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attention_mask)
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attn_output = attn_output.view(query_states.shape)
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attn_weights = None
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if isinstance(past_key_value, DynamicFp8Cache):
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attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
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attention_mask)
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else:
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attn_output = xe_addons.sdp(query_states, key_states, value_states,
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attention_mask)
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elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
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import xe_addons
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if isinstance(past_key_value, DynamicFp8Cache):
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attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
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value_states, attention_mask)
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else:
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attn_output = xe_addons.sdp_causal(query_states, key_states,
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value_states, attention_mask)
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else:
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attn_weights = torch.matmul(
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query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if isinstance(past_key_value, DynamicFp8Cache):
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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invalidInputError(
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attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
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f" but is {attn_weights.size()}")
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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,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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invalidInputError(
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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 = \
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nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query_states.dtype)
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(value_states.dtype)
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attn_weights = self.attention_dropout(attn_weights)
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attn_output = torch.matmul(attn_weights, value_states)
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invalidInputError(
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attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
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f"`attn_output` should be of size {(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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@ -309,156 +215,4 @@ def stablelm_attention_forward_original(
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if not output_attentions:
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attn_weights = None
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return attn_output.to(original_dtype), attn_weights, past_key_value
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def stablelm_attention_forward_quantized(
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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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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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bsz, q_len, hidden_size = hidden_states.size()
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device = hidden_states.device
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# for flash attention
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original_dtype = hidden_states.dtype
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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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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_heads,
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self.num_heads], dim=1)
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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(
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self.layer_idx is not None,
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f"The cache structure has changed since version v4.36. "
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"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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)
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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# Partial rotary embedding
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query_rot, query_pass = (
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query_states[..., : self.rotary_emb.dim],
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query_states[..., self.rotary_emb.dim:],
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)
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key_rot, key_pass = (
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key_states[..., : self.rotary_emb.dim],
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key_states[..., self.rotary_emb.dim:],
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)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
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if use_fuse_rope:
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query_rot, key_rot = apply_rotary_pos_emb_cache_freq_xpu(query_rot,
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key_rot,
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sin,
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cos,
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"stablelm",
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position_ids)
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else:
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query_rot, key_rot = apply_rotary_pos_emb(query_rot,
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key_rot,
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cos,
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sin,
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position_ids,
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"stablelm")
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# [batch_size, seq_length, num_heads, head_dim]
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query_states = torch.cat((query_rot, query_pass), dim=-1)
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key_states = torch.cat((key_rot, key_pass), dim=-1)
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kv_seq_len = key_states.shape[-2]
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if len(past_key_value.key_cache) <= self.layer_idx:
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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(
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attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}"
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f", but is {attn_weights.size()}")
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if attention_mask is not None:
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invalidInputError(
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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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# at inference time, for memory considerations, may not need to upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1).to(query_states.dtype)
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attn_weights = self.attention_dropout(attn_weights)
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attn_output = torch.matmul(attn_weights, value_states)
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invalidInputError(
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attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}"
|
||||
f", but is {attn_output.size()}")
|
||||
if use_cache:
|
||||
cache_kwargs = None
|
||||
key_states, value_states = past_key_value.update(key_states, value_states,
|
||||
self.layer_idx, cache_kwargs)
|
||||
else:
|
||||
cache_kwargs = None # Specific to RoPE models
|
||||
key_states, value_states = past_key_value.update(key_states, value_states,
|
||||
self.layer_idx, cache_kwargs)
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
||||
query_states.dtype)
|
||||
# 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)
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
||||
else:
|
||||
import xe_addons
|
||||
attn_weights = xe_addons.query_key_fp8_matmul(query_states, key_states)
|
||||
|
||||
attn_weights = attn_weights / math.sqrt(self.head_dim)
|
||||
|
||||
invalidInputError(
|
||||
attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
|
||||
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}"
|
||||
f", 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
|
||||
|
||||
# at inference time, for memory considerations, may not need to upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
||||
attn_weights = self.attention_dropout(attn_weights)
|
||||
|
||||
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
else:
|
||||
import xe_addons
|
||||
attn_output = xe_addons.attn_value_fp8_matmul(attn_weights, value_states)
|
||||
|
||||
attn_output_size = (bsz, self.num_heads, q_len, self.head_dim)
|
||||
invalidInputError(attn_output.size() == attn_output_size,
|
||||
f"`attn_output` should be of size {attn_output_size},"
|
||||
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(original_dtype), attn_weights, past_key_value
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
|
|
|||
Loading…
Reference in a new issue