feat✨: 使得MOELORA支持task_id以及其他参数的传递
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@ -1,29 +1,346 @@
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# from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention,
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from transformers.models.qwen2_vl.modeling_qwen2_vl import *
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from transformers.cache_utils import DynamicCache
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from transformers.modeling_outputs import BaseModelOutputWithPast
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import torch
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from typing import Optional, List, Union, Tuple
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from torch.nn import CrossEntropyLoss
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from torch import nn
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from torch.nn import functional as F
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from torch import Tensor
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class LinearLayer(nn.Linear):
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def forward(self, input: Tensor, **kwargs) -> Tensor:
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return F.linear(input, self.weight, self.bias)
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class Qwen2VLAttention_modified(Qwen2VLAttention):
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def __init__(self, config: Qwen2VLConfig, layer_idx: Optional[int] = None):
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super().__init__(config, layer_idx)
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self.q_proj = LinearLayer(
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self.hidden_size, self.num_heads * self.head_dim, bias=True
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)
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self.k_proj = LinearLayer(
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self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
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)
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self.v_proj = LinearLayer(
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self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
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)
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self.o_proj = LinearLayer(
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self.num_heads * self.head_dim, self.hidden_size, bias=False
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[
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Tuple[torch.Tensor, torch.Tensor]
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] = None, # will become mandatory in v4.46
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**kwargs,
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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)
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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(
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bsz, q_len, self.num_heads, self.head_dim
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).transpose(1, 2)
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key_states = key_states.view(
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bsz, q_len, self.num_key_value_heads, self.head_dim
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).transpose(1, 2)
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value_states = value_states.view(
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bsz, q_len, self.num_key_value_heads, self.head_dim
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).transpose(1, 2)
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if position_embeddings is None:
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logger.warning_once(
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"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
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"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
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"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
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"removed and `position_embeddings` will be mandatory."
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)
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_multimodal_rotary_pos_emb(
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query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
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)
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if past_key_value is not None:
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"cache_position": cache_position,
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} # Specific to RoPE models
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, cache_kwargs
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)
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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(
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query_states, key_states.transpose(2, 3)
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) / math.sqrt(self.head_dim)
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# Fix precision issues in Qwen2-VL float16 inference
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# Replace inf values with zeros in attention weights to prevent NaN propagation
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if query_states.dtype == torch.float16:
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attn_weights = torch.where(
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torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights
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)
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(
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attn_weights, dim=-1, dtype=torch.float32
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).to(query_states.dtype)
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attn_weights = nn.functional.dropout(
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attn_weights, p=self.attention_dropout, training=self.training
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)
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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).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, -1)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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class Qwen2VLSdpaAttention_modified(Qwen2VLAttention_modified):
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"""
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Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
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`Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
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SDPA API.
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"""
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# Adapted from Qwen2Attention.forward
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[
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Tuple[torch.Tensor, torch.Tensor]
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] = None, # will become mandatory in v4.46
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if output_attentions:
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# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
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logger.warning_once(
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"Qwen2VLModel is using Qwen2VLSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
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'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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return super().forward(
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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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cache_position=cache_position,
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**kwargs,
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)
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states, **kwargs)
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key_states = self.k_proj(hidden_states, **kwargs)
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value_states = self.v_proj(hidden_states, **kwargs)
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query_states = query_states.view(
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bsz, q_len, self.num_heads, self.head_dim
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).transpose(1, 2)
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key_states = key_states.view(
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bsz, q_len, self.num_key_value_heads, self.head_dim
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).transpose(1, 2)
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value_states = value_states.view(
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bsz, q_len, self.num_key_value_heads, self.head_dim
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).transpose(1, 2)
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if position_embeddings is None:
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logger.warning_once(
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"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
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"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
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"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
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"removed and `position_embeddings` will be mandatory."
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)
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_multimodal_rotary_pos_emb(
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query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
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)
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if past_key_value is not None:
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"cache_position": cache_position,
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} # Specific to RoPE models
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, cache_kwargs
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)
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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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causal_mask = attention_mask
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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# SDPA with memory-efficient backend is currently (torch==2.1.2) 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_states.device.type == "cuda" and attention_mask is not None:
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query_states = query_states.contiguous()
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key_states = key_states.contiguous()
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value_states = value_states.contiguous()
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# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
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# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
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# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
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is_causal = True if causal_mask is None and q_len > 1 else False
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_states,
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key_states,
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value_states,
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attn_mask=causal_mask,
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dropout_p=self.attention_dropout if self.training else 0.0,
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is_causal=is_causal,
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.view(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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return attn_output, None, past_key_value
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QWEN2_VL_ATTENTION_CLASSES = {
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"eager": Qwen2VLAttention,
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"flash_attention_2": Qwen2VLFlashAttention2,
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"sdpa": Qwen2VLSdpaAttention,
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"sdpa": Qwen2VLSdpaAttention_modified,
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}
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class Qwen2VLDecoderLayer_modified(Qwen2VLDecoderLayer):
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def __init__(self, config: Qwen2VLConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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self.self_attn = QWEN2_VL_ATTENTION_CLASSES[config._attn_implementation](
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config, layer_idx
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)
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def 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: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[
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Tuple[torch.Tensor, torch.Tensor]
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] = None, # will become mandatory in v4.46
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**kwargs,
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) -> Tuple[
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torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
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]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
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`(batch, sequence_length)` where padding elements are indicated by 0.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
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(see `past_key_values`).
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
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Indices depicting the position of the input sequence tokens in the sequence.
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position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
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Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
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with `head_dim` being the embedding dimension of each attention head.
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kwargs (`dict`, *optional*):
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Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
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into the model
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"""
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights, present_key_value = self.self_attn(
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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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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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if use_cache:
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outputs += (present_key_value,)
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return outputs
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class Qwen2VLModel_modified(Qwen2VLModel):
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def __init__(self, config):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[Qwen2VLDecoderLayer_modified(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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[
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Qwen2VLDecoderLayer_modified(config, layer_idx)
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for layer_idx in range(config.num_hidden_layers)
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]
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)
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def forward(
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@ -174,7 +491,16 @@ class Qwen2VLModel_modified(Qwen2VLModel):
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class Qwen2VLForConditionalGeneration_modified(Qwen2VLForConditionalGeneration):
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def __init__(self, config):
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super().__init__(config)
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self.visual = Qwen2VisionTransformerPretrainedModel._from_config(
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config.vision_config
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)
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self.model = Qwen2VLModel_modified(config)
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self.vocab_size = config.vocab_size
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.rope_deltas = None # cache rope_deltas here
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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@ -151,9 +151,10 @@ class MMOELoraLinear(nn.Module, MMOELoraLayer):
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def forward(self, x: torch.Tensor, *args, **kwargs):
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self._check_forward_args(x, *args, **kwargs)
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adapter_names = kwargs.pop("adapter_names", None)
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task_id = kwargs.pop(
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"task_id", torch.tensor([0] * len(x), dtype=torch.long).to(x.device)
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)
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# task_id = kwargs.pop(
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# "task_id", torch.tensor([0] * len(x), dtype=torch.long).to(x.device)
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# )
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task_id = kwargs.pop("task_id", torch.tensor([0] * len(x), dtype=torch.long))
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previous_dtype = x.dtype
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if self.disable_adapters: # No adapter
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