481 lines
20 KiB
Python
481 lines
20 KiB
Python
import inspect
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import math
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import warnings
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from functools import partial
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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import transformers
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from transformers.models.llama.modeling_llama import *
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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from .modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from .configuration_mplug_owl2 import LlamaConfig
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from .multiway import MultiwayNetwork
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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class LlamaAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
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self.k_proj = MultiwayNetwork(module_provider=partial(
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nn.Linear, in_features=self.hidden_size, out_features=self.num_key_value_heads * self.head_dim,
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bias=config.attention_bias)
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)
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self.v_proj = MultiwayNetwork(module_provider=partial(
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nn.Linear, in_features=self.hidden_size, out_features=self.num_key_value_heads * self.head_dim,
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bias=config.attention_bias)
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)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
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self._init_rope()
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def _init_rope(self):
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if self.config.rope_scaling is None:
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self.rotary_emb = LlamaRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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else:
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scaling_type = self.config.rope_scaling["type"]
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scaling_factor = self.config.rope_scaling["factor"]
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if scaling_type == "linear":
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self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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elif scaling_type == "dynamic":
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self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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else:
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raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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modality_indicators: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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padding_mask: Optional[torch.LongTensor] = None,
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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, modality_indicators)
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value_states = self.v_proj(hidden_states, modality_indicators)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len, position_ids=position_ids)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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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(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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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 LlamaDecoderLayer(nn.Module):
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def __init__(self, config: LlamaConfig, annoying_param):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = LlamaAttention(config=config)
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mlp_kwargs = {'config': config, "hidden_size": config.hidden_size,
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"intermediate_size": config.intermediate_size, "hidden_act": config.hidden_act}
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valid_params = set(inspect.signature(LlamaMLP.__init__).parameters.keys()) - {'self'}
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mlp_kwargs = {k: v for k, v in mlp_kwargs.items() if k in valid_params}
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self.mlp = LlamaMLP(**mlp_kwargs)
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self.input_layernorm = MultiwayNetwork(module_provider=partial(
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LlamaRMSNorm, hidden_size=config.hidden_size, eps=config.rms_norm_eps
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))
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self.post_attention_layernorm = MultiwayNetwork(module_provider=partial(
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LlamaRMSNorm, hidden_size=config.hidden_size, eps=config.rms_norm_eps
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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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modality_indicators: torch.Tensor = 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_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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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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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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"""
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states, modality_indicators)
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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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modality_indicators=modality_indicators,
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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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)
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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, modality_indicators)
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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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def model_forward(
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self,
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input_ids: torch.LongTensor = None,
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modality_indicators: torch.Tensor = 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[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
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seq_length_with_past = seq_length
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past_key_values_length = 0
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if past_key_values is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(
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(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
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)
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attention_mask = self._prepare_decoder_attention_mask(
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attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
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)
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hidden_states = inputs_embeds
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = () if use_cache else None
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for idx, decoder_layer in enumerate(self.layers):
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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past_key_value = past_key_values[idx] if past_key_values is not None else None
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if self.gradient_checkpointing and self.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, past_key_value, output_attentions)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(decoder_layer),
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hidden_states,
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modality_indicators,
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attention_mask,
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position_ids,
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)
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else:
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layer_outputs = decoder_layer(
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hidden_states,
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modality_indicators=modality_indicators,
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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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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def causal_model_forward(
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self,
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input_ids: torch.LongTensor = None,
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modality_indicators: torch.Tensor = 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[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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r"""
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Args:
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Returns:
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Example:
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```python
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>>> from transformers import AutoTokenizer, LlamaForCausalLM
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>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
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>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
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>>> prompt = "Hey, are you conscious? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = self.model(
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input_ids=input_ids,
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modality_indicators=modality_indicators,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = outputs[0]
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if self.config.pretraining_tp > 1:
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lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
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logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
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logits = torch.cat(logits, dim=-1)
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else:
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logits = self.lm_head(hidden_states)
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logits = logits.float()
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|
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
|
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# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
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|
shift_logits = shift_logits.view(-1, self.config.vocab_size)
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|
shift_labels = shift_labels.view(-1)
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|
# 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 CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
def replace_llama_modality_adaptive():
|
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transformers.models.llama.configuration_llama.LlamaConfig = LlamaConfig
|
|
transformers.models.llama.modeling_llama.LlamaAttention = LlamaAttention
|
|
transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
|
|
transformers.models.llama.modeling_llama.LlamaModel.forward = model_forward
|
|
transformers.models.llama.modeling_llama.LlamaForCausalLM.forward = causal_model_forward
|
|
|
|
|
|
if __name__ == "__main__":
|
|
replace_llama_modality_adaptive()
|
|
config = transformers.LlamaConfig.from_pretrained('/cpfs01/shared/public/test/vicuna-7b-v1.5/')
|
|
model = transformers.LlamaForCausalLM(config)
|
|
print(model)
|