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Remove --sdp-attention, --xformers flags (#5126)
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@ -231,8 +231,6 @@ List of command-line flags
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| `--load-in-8bit` | Load the model with 8-bit precision (using bitsandbytes). |
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| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
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| `--no-cache` | Set `use_cache` to `False` while generating text. This reduces VRAM usage slightly, but it comes at a performance cost. |
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| `--xformers` | Use xformer's memory efficient attention. This is really old and probably doesn't do anything. |
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| `--sdp-attention` | Use PyTorch 2.0's SDP attention. Same as above. |
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| `--trust-remote-code` | Set `trust_remote_code=True` while loading the model. Necessary for some models. |
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| `--no_use_fast` | Set use_fast=False while loading the tokenizer (it's True by default). Use this if you have any problems related to use_fast. |
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| `--use_flash_attention_2` | Set use_flash_attention_2=True while loading the model. |
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@ -1,171 +0,0 @@
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import math
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import sys
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import modules.shared as shared
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from modules.logging_colors import logger
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if shared.args.xformers:
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try:
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import xformers.ops
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except Exception:
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logger.error("xformers not found! Please install it before trying to use it.", file=sys.stderr)
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def hijack_llama_attention():
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import transformers.models.llama.modeling_llama
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if shared.args.xformers:
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transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
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logger.info("Replaced attention with xformers_attention")
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elif shared.args.sdp_attention:
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transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward
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logger.info("Replaced attention with sdp_attention")
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def xformers_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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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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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_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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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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# [bsz, nh, t, hd]
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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# We only apply xformers optimizations if we don't need to output the whole attention matrix
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if not output_attentions:
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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# This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
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# We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
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if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
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# input and output should be of form (bsz, q_len, num_heads, head_dim)
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attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None)
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else:
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# input and output should be of form (bsz, q_len, num_heads, head_dim)
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attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask())
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attn_weights = None
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else:
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2)
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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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return attn_output, attn_weights, past_key_value
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def sdp_attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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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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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_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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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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# [bsz, nh, t, hd]
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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# We only apply sdp attention if we don't need to output the whole attention matrix
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if not output_attentions:
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attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False)
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attn_weights = None
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else:
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2)
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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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return attn_output, attn_weights, past_key_value
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@ -21,7 +21,7 @@ from transformers import (
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)
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import modules.shared as shared
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from modules import RoPE, llama_attn_hijack, sampler_hijack
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from modules import RoPE, sampler_hijack
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from modules.logging_colors import logger
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from modules.models_settings import get_model_metadata
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from modules.relative_imports import RelativeImport
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@ -97,10 +97,6 @@ def load_model(model_name, loader=None):
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else:
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tokenizer = load_tokenizer(model_name, model)
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# Hijack attention with xformers
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if any((shared.args.xformers, shared.args.sdp_attention)):
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llama_attn_hijack.hijack_llama_attention()
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shared.settings.update({k: v for k, v in metadata.items() if k in shared.settings})
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if loader.lower().startswith('exllama'):
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shared.settings['truncation_length'] = shared.args.max_seq_len
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@ -98,8 +98,6 @@ group.add_argument('--disk-cache-dir', type=str, default='cache', help='Director
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group.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision (using bitsandbytes).')
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group.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
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group.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces VRAM usage slightly, but it comes at a performance cost.')
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group.add_argument('--xformers', action='store_true', help='Use xformer\'s memory efficient attention. This is really old and probably doesn\'t do anything.')
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group.add_argument('--sdp-attention', action='store_true', help='Use PyTorch 2.0\'s SDP attention. Same as above.')
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group.add_argument('--trust-remote-code', action='store_true', help='Set trust_remote_code=True while loading the model. Necessary for some models.')
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group.add_argument('--force-safetensors', action='store_true', help='Set use_safetensors=True while loading the model. This prevents arbitrary code execution.')
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group.add_argument('--no_use_fast', action='store_true', help='Set use_fast=False while loading the tokenizer (it\'s True by default). Use this if you have any problems related to use_fast.')
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