840 lines
35 KiB
Python
840 lines
35 KiB
Python
# 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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if is_flash_attn_2_available():
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from flash_attn import flash_attn_varlen_func
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from transformers.modeling_flash_attention_utils import _flash_attention_forward
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else:
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flash_attn_varlen_func = None
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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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class Qwen2VLFlashAttention2_modified(Qwen2VLAttention_modified):
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"""
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Qwen2VL flash attention module, following Qwen2VL attention module. This module inherits from `Qwen2VLAttention`
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as the weights of the module stays untouched. The only required change would be on the forward pass
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where it needs to correctly call the public API of flash attention and deal with padding tokens
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in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
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config.max_window_layers layers.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
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# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
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self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
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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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):
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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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# Because the input can be padded, the absolute sequence length depends on the max position id.
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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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dropout_rate = 0.0 if not self.training else self.attention_dropout
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in float16 just to be sure everything works as expected.
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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# Reashape to the expected shape for Flash Attention
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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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if (
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self.config.use_sliding_window
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and getattr(self.config, "sliding_window", None) is not None
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and self.layer_idx >= self.config.max_window_layers
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):
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sliding_window = self.config.sliding_window
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else:
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sliding_window = None
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attn_output = _flash_attention_forward(
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query_states,
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key_states,
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value_states,
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attention_mask,
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q_len,
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dropout=dropout_rate,
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sliding_window=sliding_window,
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is_causal=self.is_causal,
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use_top_left_mask=self._flash_attn_uses_top_left_mask,
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)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
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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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QWEN2_VL_ATTENTION_CLASSES = {
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"eager": Qwen2VLAttention,
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"flash_attention_2": Qwen2VLFlashAttention2_modified,
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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*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
|
with `head_dim` being the embedding dimension of each attention head.
|
|
kwargs (`dict`, *optional*):
|
|
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
|
into the model
|
|
"""
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
class Qwen2VLModel_modified(Qwen2VLModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
Qwen2VLDecoderLayer_modified(config, layer_idx)
|
|
for layer_idx in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
output_attentions = (
|
|
output_attentions
|
|
if output_attentions is not None
|
|
else self.config.output_attentions
|
|
)
|
|
output_hidden_states = (
|
|
output_hidden_states
|
|
if output_hidden_states is not None
|
|
else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = (
|
|
return_dict if return_dict is not None else self.config.use_return_dict
|
|
)
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError(
|
|
"You must specify exactly one of input_ids or inputs_embeds"
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
# torch.jit.trace() doesn't support cache objects in the output
|
|
if use_cache and past_key_values is None and not torch.jit.is_tracing():
|
|
past_key_values = DynamicCache()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = (
|
|
past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
)
|
|
cache_position = torch.arange(
|
|
past_seen_tokens,
|
|
past_seen_tokens + inputs_embeds.shape[1],
|
|
device=inputs_embeds.device,
|
|
)
|
|
|
|
# the hard coded `3` is for temporal, height and width.
|
|
if position_ids is None:
|
|
position_ids = cache_position.view(1, 1, -1).expand(
|
|
3, inputs_embeds.shape[0], -1
|
|
)
|
|
elif position_ids.dim() == 2:
|
|
position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask,
|
|
inputs_embeds,
|
|
cache_position,
|
|
past_key_values,
|
|
output_attentions,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = None
|
|
|
|
for decoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
hidden_states,
|
|
causal_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
cache_position,
|
|
position_embeddings,
|
|
**kwargs,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
class Qwen2VLForConditionalGeneration_modified(Qwen2VLForConditionalGeneration):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.visual = Qwen2VisionTransformerPretrainedModel._from_config(
|
|
config.vision_config
|
|
)
|
|
self.model = Qwen2VLModel_modified(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.rope_deltas = None # cache rope_deltas here
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
pixel_values_videos: Optional[torch.FloatTensor] = None,
|
|
image_grid_thw: Optional[torch.LongTensor] = None,
|
|
video_grid_thw: Optional[torch.LongTensor] = None,
|
|
rope_deltas: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Union[Tuple, Qwen2VLCausalLMOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
|
|
|
|
>>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
|
|
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
|
|
|
|
>>> messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "image"},
|
|
{"type": "text", "text": "What is shown in this image?"},
|
|
],
|
|
},
|
|
]
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
|
>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
|
|
```"""
|
|
|
|
output_attentions = (
|
|
output_attentions
|
|
if output_attentions is not None
|
|
else self.config.output_attentions
|
|
)
|
|
output_hidden_states = (
|
|
output_hidden_states
|
|
if output_hidden_states is not None
|
|
else self.config.output_hidden_states
|
|
)
|
|
return_dict = (
|
|
return_dict if return_dict is not None else self.config.use_return_dict
|
|
)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.model.embed_tokens(input_ids)
|
|
if pixel_values is not None:
|
|
pixel_values = pixel_values.type(self.visual.get_dtype())
|
|
image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
|
|
n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
|
|
n_image_features = image_embeds.shape[0]
|
|
if n_image_tokens != n_image_features:
|
|
raise ValueError(
|
|
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
|
|
)
|
|
image_mask = (
|
|
(input_ids == self.config.image_token_id)
|
|
.unsqueeze(-1)
|
|
.expand_as(inputs_embeds)
|
|
.to(inputs_embeds.device)
|
|
)
|
|
image_embeds = image_embeds.to(
|
|
inputs_embeds.device, inputs_embeds.dtype
|
|
)
|
|
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
|
|
|
|
if pixel_values_videos is not None:
|
|
pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
|
|
video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
|
|
n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
|
|
n_video_features = video_embeds.shape[0]
|
|
if n_video_tokens != n_video_features:
|
|
raise ValueError(
|
|
f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
|
|
)
|
|
video_mask = (
|
|
(input_ids == self.config.video_token_id)
|
|
.unsqueeze(-1)
|
|
.expand_as(inputs_embeds)
|
|
.to(inputs_embeds.device)
|
|
)
|
|
video_embeds = video_embeds.to(
|
|
inputs_embeds.device, inputs_embeds.dtype
|
|
)
|
|
inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.to(inputs_embeds.device)
|
|
|
|
# if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
|
|
if (
|
|
position_ids is None
|
|
and input_ids is not None
|
|
and (attention_mask is None or attention_mask.ndim == 2)
|
|
):
|
|
# calculate RoPE index once per generation in the pre-fill stage only
|
|
if (
|
|
cache_position is not None and cache_position[0] == 0
|
|
) or self.rope_deltas is None:
|
|
position_ids, rope_deltas = self.get_rope_index(
|
|
input_ids, image_grid_thw, video_grid_thw, attention_mask
|
|
)
|
|
self.rope_deltas = rope_deltas
|
|
# then use the prev pre-calculated rope-deltas to get the correct position ids
|
|
else:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
delta = (
|
|
cache_position[0] + self.rope_deltas
|
|
if cache_position is not None
|
|
else 0
|
|
)
|
|
position_ids = torch.arange(seq_length, device=inputs_embeds.device)
|
|
position_ids = position_ids.view(1, -1).expand(batch_size, -1)
|
|
if cache_position is not None: # otherwise `deltas` is an int `0`
|
|
delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
|
|
position_ids = position_ids.add(delta)
|
|
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
|
|
|
|
outputs = self.model(
|
|
input_ids=None,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
logits = self.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
|
logits = logits.float()
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
|
shift_labels = shift_labels.view(-1)
|
|
# Enable model parallelism
|
|
shift_labels = shift_labels.to(shift_logits.device)
|
|
loss = loss_fct(shift_logits, shift_labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return Qwen2VLCausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
rope_deltas=self.rope_deltas,
|
|
)
|