210 lines
7.9 KiB
Python
210 lines
7.9 KiB
Python
from typing import Callable, Optional
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...cache_utils import Cache, DynamicCache
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, logging
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from ...utils.generic import check_model_inputs
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaForQuestionAnswering,
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LlamaForSequenceClassification,
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LlamaForTokenClassification,
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LlamaMLP,
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LlamaPreTrainedModel,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from ..mistral.modeling_mistral import MistralModel
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from .configuration_qwen2 import Qwen2Config
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logger = logging.get_logger(__name__)
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class Qwen2MLP(LlamaMLP):
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def __init__(self, config):
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super().__init__(config)
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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class Qwen2Attention(LlamaAttention):
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def __init__(self, config: Qwen2Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
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self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
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self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
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self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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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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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=self.sliding_window, # main diff with Llama
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Qwen2DecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: Qwen2Config, layer_idx: int):
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super().__init__()
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self.attention_type = config.layer_types[layer_idx]
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class Qwen2PreTrainedModel(LlamaPreTrainedModel):
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pass
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class Qwen2Model(MistralModel):
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def __init__(self, config: Qwen2Config):
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super().__init__(config)
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self.has_sliding_layers = "sliding_attention" in self.config.layer_types
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@check_model_inputs
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@auto_docstring
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutputWithPast:
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache()
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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# It may already have been prepared by e.g. `generate`
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if not isinstance(causal_mask_mapping := attention_mask, dict):
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# Prepare mask arguments
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mask_kwargs = {
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"config": self.config,
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"input_embeds": inputs_embeds,
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"attention_mask": attention_mask,
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"cache_position": cache_position,
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"past_key_values": past_key_values,
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"position_ids": position_ids,
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}
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# Create the masks
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causal_mask_mapping = {
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"full_attention": create_causal_mask(**mask_kwargs),
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}
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# The sliding window alternating layers are not always activated depending on the config
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if self.has_sliding_layers:
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causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
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hidden_states = inputs_embeds
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# create position embeddings to be shared across the decoder layers
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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for decoder_layer in self.layers[: self.config.num_hidden_layers]:
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hidden_states = decoder_layer(
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hidden_states,
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attention_mask=causal_mask_mapping[decoder_layer.attention_type],
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position_ids=position_ids,
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past_key_value=past_key_values,
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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 = self.norm(hidden_states)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=past_key_values if use_cache else None,
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)
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class Qwen2ForCausalLM(LlamaForCausalLM):
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pass
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class Qwen2ForSequenceClassification(LlamaForSequenceClassification):
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pass
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class Qwen2ForTokenClassification(LlamaForTokenClassification):
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pass
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class Qwen2ForQuestionAnswering(LlamaForQuestionAnswering):
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pass
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__all__ = [
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"Qwen2PreTrainedModel",
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"Qwen2Model",
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"Qwen2ForCausalLM",
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"Qwen2ForSequenceClassification",
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"Qwen2ForTokenClassification",
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"Qwen2ForQuestionAnswering",
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]
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