Guoqiong Song
GitHub
parent
84d5f40936
commit
63681af97e
5 changed files with 262 additions and 9 deletions
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@ -19,7 +19,12 @@ pip install einops # additional package required for falcon-7b-instruct and falc
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### 2. (Optional) Download Model and Replace File
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If you select the Falcon models ([tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) or [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct)), please note that their code (`modelling_RW.py`) does not support KV cache at the moment. To address issue, we have provided two updated files ([falcon-7b-instruct/modelling_RW.py](./falcon-7b-instruct/modelling_RW.py) and [falcon-40b-instruct/modelling_RW.py](./falcon-40b-instruct/modelling_RW.py)), which can be used to achieve the best performance using BigDL-LLM INT4 optimizations with KV cache support.
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After transformers 4.36, only transformer models are supported since remote code diverges from transformer model code, make sure set `trust_remote_code=False`.
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```python
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=False)
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```
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#### 2.1 Download Model
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You could use the following code to download [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) or [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) with a specific snapshot id. Please note that the `modelling_RW.py` files that we provide are based on these specific commits.
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@ -43,11 +43,11 @@ if __name__ == '__main__':
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# which convert the relevant layers in the model into INT4 format
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True)
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trust_remote_code=False)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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trust_remote_code=False)
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# Generate predicted tokens
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with torch.inference_mode():
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@ -30,7 +30,12 @@ pip install einops # additional package required for falcon-7b-instruct to condu
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### 2. (Optional) Download Model and Replace File
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If you select the Falcon model ([tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)), please note that their code (`modelling_RW.py`) does not support KV cache at the moment. To address issue, we have provided updated file ([falcon-7b-instruct/modelling_RW.py](./falcon-7b-instruct/modelling_RW.py)), which can be used to achieve the best performance using BigDL-LLM INT4 optimizations with KV cache support.
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After transformers 4.36, only transformer models are supported since remote code diverges from transformer model code, make sure set `trust_remote_code=False`.
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```python
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=False)
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```
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#### 2.1 Download Model
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You could use the following code to download [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) with a specific snapshot id. Please note that the `modelling_RW.py` files that we provide are based on these specific commits.
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@ -818,11 +818,20 @@ def _optimize_post(model, lightweight_bmm=False):
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elif "FalconForCausalLM" in model.config.architectures:
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if model.config.hidden_size != 4544:
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# falcon-180b and new falcon-40b
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from bigdl.llm.transformers.models.falcon import falcon_attention_forward
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convert_forward(model,
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module.FalconAttention,
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falcon_attention_forward
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)
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if version.parse(trans_version) >= version.parse("4.36.0"):
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# transformers version >= 4.36.0
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from bigdl.llm.transformers.models.falcon import falcon_attention_forward_4_36
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convert_forward(model,
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module.FalconAttention,
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falcon_attention_forward_4_36
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)
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else:
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from bigdl.llm.transformers.models.falcon import falcon_attention_forward
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convert_forward(model,
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module.FalconAttention,
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falcon_attention_forward
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)
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elif model.config.model_type == "baichuan" and model.config.vocab_size == 125696:
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# baichuan2
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if model.config.hidden_size == 4096:
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@ -39,11 +39,50 @@ import torch
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from torch.nn import functional as F
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from bigdl.llm.utils.common import invalidInputError
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from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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import warnings
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For
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example, this can be used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze
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cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the
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dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids]
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have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape
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[batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k.
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Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim],
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then set unsqueeze_dim=2.
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Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary
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Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def rw_attention_forward_7b(
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self,
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hidden_states: torch.Tensor,
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@ -592,3 +631,198 @@ def falcon_attention_forward(
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return output_tensor, present, attention_probs
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else:
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return output_tensor, present
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def falcon_attention_forward_4_36(
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self,
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hidden_states: torch.Tensor,
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alibi: Optional[torch.Tensor],
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attention_mask: torch.Tensor,
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position_ids: Optional[torch.LongTensor]=None,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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head_mask: Optional[torch.Tensor]=None,
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use_cache: bool=False,
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output_attentions: bool=False,
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**kwargs,
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):
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""" based on transformers==4.36.0
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https://github.com/huggingface/transformers/blob/v4.36.0/src/transformers/models/falcon/modeling_falcon.py
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"""
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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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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, query_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(
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batch_size, self.num_heads, query_length, self.head_dim)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size, num_kv_heads, query_length, self.head_dim)
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value_layer = value_layer.transpose(1, 2).reshape(
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batch_size, num_kv_heads, query_length, self.head_dim)
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kv_seq_len = key_layer.shape[-2]
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device = hidden_states.device
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if layer_past is not None:
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kv_seq_len += layer_past[0].shape[-2]
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if alibi is None:
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cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
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query_layer, key_layer = apply_rotary_pos_emb(
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query_layer, key_layer, cos, sin, position_ids)
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if layer_past is not None:
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# reuse k, v, self_attention
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cache_k = layer_past[0].view(batch_size, self.num_heads, -1, self.head_dim)
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cache_v = layer_past[1].view(batch_size, self.num_heads, -1, self.head_dim)
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if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(
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batch_size,
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self.num_heads,
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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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)
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new_cache_k[:] = cache_k
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new_cache_v[:] = cache_v
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cache_k = new_cache_k
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cache_v = new_cache_v
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key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(
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batch_size,
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self.num_heads,
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self.head_dim,
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kv_seq_len,
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max_cache_length,
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dtype=key_layer.dtype,
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device=device
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)
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new_key_states[:] = key_layer
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new_value_states[:] = value_layer
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key_layer = new_key_states
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value_layer = new_value_states
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query_layer = query_layer.view(batch_size, self.num_heads, -1, self.head_dim)
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key_layer = key_layer.view(batch_size, self.num_heads, -1, self.head_dim)
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value_layer = value_layer.view(batch_size, self.num_heads, -1, self.head_dim)
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kv_length = key_layer.shape[-2]
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if use_cache:
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present = (key_layer, value_layer)
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else:
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present = None
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# SDPA with memory-efficient backend is currently (torch==2.1.2)
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# bugged with non-contiguous inputs with custom attn_mask,
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# Reference: https://github.com/pytorch/pytorch/issues/112577.
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if query_layer.device.type == "cuda" and attention_mask is not None:
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query_layer = query_layer.contiguous()
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key_layer = key_layer.contiguous()
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value_layer = value_layer.contiguous()
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if alibi is None:
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if self._use_sdpa and not output_attentions:
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attn_output = F.scaled_dot_product_attention(
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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0.0,
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# The query_length > 1 is necessary to match with
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# AttentionMaskConverter.to_causal_4d that does not create a causal mask in case
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# query_length == 1.
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is_causal=self.is_causal and attention_mask is None and query_length > 1,
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)
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attention_scores = None
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else:
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attention_scores = query_layer @ key_layer.transpose(-1, -2)
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attention_scores /= math.sqrt(self.head_dim)
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attention_scores = F.softmax(
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attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
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# It is unclear why neither dropout nor head_mask is applied here
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# (while it is with alibi).
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attn_output = attention_scores @ value_layer
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attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
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attn_output = attn_output.permute(0, 2, 1, 3)
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attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
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attn_output = self.dense(attn_output)
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if output_attentions:
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return attn_output, present, attention_scores
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else:
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return attn_output, present
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else:
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if self._use_sdpa and not output_attentions and head_mask is None:
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attn_output = F.scaled_dot_product_attention(
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query_layer,
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key_layer,
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value_layer,
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attn_mask=attention_mask,
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dropout_p=self.attention_dropout.p if self.training else 0.0,
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is_causal=self.is_causal and attention_mask is None and query_length > 1,
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)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(
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batch_size, query_length, self.num_heads * self.head_dim)
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attn_output = self.dense(attn_output)
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else:
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matmul_result = query_layer @ key_layer.transpose(-1, -2)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attention_scores = matmul_result.view(
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batch_size, self.num_heads, query_length, kv_length)
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# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype -
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# [batch_size, num_heads, q_length, kv_length]
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input_dtype = attention_scores.dtype
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# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a
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# minimum value of `-3.4e+38`
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if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
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attention_scores = attention_scores.to(torch.float32)
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attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
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attention_logits *= self.inv_norm_factor
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attention_probs = F.softmax(
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attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
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# [batch_size, num_heads, q_length, kv_length]
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attention_probs = self.attention_dropout(attention_probs)
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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# change view [batch_size, num_heads, q_length, kv_length]
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attention_probs_reshaped = attention_probs.view(
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batch_size, self.num_heads, query_length, kv_length)
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# matmul: [batch_size * num_heads, q_length, head_dim]
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attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
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# change view [batch_size, q_length, num_heads * head_dim]
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attn_output = self._merge_heads(attn_output)
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attn_output = self.dense(attn_output)
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if output_attentions:
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return attn_output, present, attention_probs
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else:
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return attn_output, present
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Reference in a new issue