1250 lines
47 KiB
Python
1250 lines
47 KiB
Python
# pylint: skip-file
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# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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PyTorch LLaMA model.
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Taken from https://github.com/epfml/landmark-attention/blob/main/llama/llama_mem.py and modified.
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"""
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import math
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from typing import List, Optional, Tuple, Union
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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 torch.nn import CrossEntropyLoss
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from transformers import LlamaTokenizer
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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)
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from transformers.models.llama.configuration_llama import LlamaConfig
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from transformers.models.llama.modeling_llama import (
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LLAMA_INPUTS_DOCSTRING,
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LLAMA_START_DOCSTRING,
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LlamaMLP,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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_expand_mask,
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_make_causal_mask,
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rotate_half,
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)
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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LOG = logging.getLogger("axolotl")
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_CONFIG_FOR_DOC = "LlamaConfig"
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MEM_TOKEN = "<landmark>" # nosec
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
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# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
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cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
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sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
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cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
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sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
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if q is None:
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q_embed = None
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else:
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class LandmarkGroupedSoftmaxFunction(torch.autograd.Function):
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"""
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Landmark grouped softmax function.
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"""
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# Note that forward, setup_context, and backward are @staticmethods
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@staticmethod
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def forward(ctx, x, dim, mem_cnt, resp_mem_idx):
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new_shape = list(x.shape)
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new_shape[dim] = mem_cnt # max_mem_cnt.item()
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max_by_group = x.new_zeros((*new_shape,))
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max_by_group.scatter_reduce_(
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src=x, index=resp_mem_idx, dim=dim, reduce="amax", include_self=False
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)
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maxes = torch.gather(max_by_group, dim, resp_mem_idx)
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# x_exp = torch.exp(x - torch.where(torch.isinf(maxes), 0, maxes))
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x_exp = torch.exp((x - maxes).to(torch.float32))
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cumsum_by_group = torch.zeros_like(max_by_group, dtype=x_exp.dtype)
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cumsum_by_group.scatter_add_(
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dim,
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resp_mem_idx,
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x_exp,
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)
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denom = torch.gather(cumsum_by_group, dim, resp_mem_idx)
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# probs = torch.where(denom < 0.5, 0, x_exp / denom)
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probs = x_exp / denom
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ctx.mem_cnt = mem_cnt
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ctx.dim = dim
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ctx.save_for_backward(resp_mem_idx, probs)
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return probs
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@staticmethod
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def backward(ctx, grad_probs):
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mem_cnt = ctx.mem_cnt
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dim = ctx.dim
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resp_mem_idx, probs = ctx.saved_tensors
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grad_x = grad_dim = grad_mem_cnt = grad_resp_mem_idx = None
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if ctx.needs_input_grad[0] or ctx.needs_input_grad[4]:
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grad_pair = grad_probs * probs
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new_shape = list(probs.shape)
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new_shape[dim] = mem_cnt # max_mem_cnt.item()
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cumsum_by_group = grad_pair.new_zeros((*new_shape,))
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cumsum_by_group.scatter_add_(dim, resp_mem_idx, grad_pair)
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if ctx.needs_input_grad[0]:
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grad_sum = torch.gather(cumsum_by_group, dim, resp_mem_idx)
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grad_x = grad_pair - probs * grad_sum
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assert not ctx.needs_input_grad[1]
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assert not ctx.needs_input_grad[2]
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assert not ctx.needs_input_grad[3]
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return grad_x, grad_dim, grad_mem_cnt, grad_resp_mem_idx
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def landmark_grouped_softmax(x, dim, is_mem, last_section_mask):
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last_and_rest_mask = last_section_mask # | mask
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full_access_mask = is_mem | last_and_rest_mask
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max_mem_cnt = 16
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mem_group_idx = torch.cumsum(is_mem, dim=dim)
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mem_bucket_id = max_mem_cnt - 1
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resp_mem_idx = torch.where(
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last_and_rest_mask,
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max_mem_cnt - 1,
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torch.where(is_mem, mem_bucket_id, mem_group_idx),
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)
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probs = LandmarkGroupedSoftmaxFunction.apply(x, dim, max_mem_cnt, resp_mem_idx)
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new_shape = list(x.shape)
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new_shape[dim] = max_mem_cnt
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group_prob = probs.new_zeros((*new_shape,))
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group_prob.scatter_(
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dim, torch.where(is_mem, mem_group_idx - 1, max_mem_cnt - 1), probs
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)
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probs = probs.mul(
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torch.where(
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full_access_mask,
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last_section_mask,
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torch.gather(group_prob, dim, resp_mem_idx),
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)
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)
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return probs
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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.max_position_embeddings = config.max_position_embeddings
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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(
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self.hidden_size, self.num_heads * self.head_dim, bias=False
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)
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self.k_proj = nn.Linear(
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self.hidden_size, self.num_heads * self.head_dim, bias=False
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)
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self.v_proj = nn.Linear(
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self.hidden_size, self.num_heads * self.head_dim, bias=False
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)
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self.o_proj = nn.Linear(
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self.num_heads * self.head_dim, self.hidden_size, bias=False
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)
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self.rotary_emb = LlamaRotaryEmbedding(
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self.head_dim, max_position_embeddings=self.max_position_embeddings
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)
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self.mem_freq = None
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self.top_k = None
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self.max_cache_size = None
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return (
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tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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.contiguous()
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)
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def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
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self.mem_freq = mem_freq
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self.top_k = top_k
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self.max_cache_size = max_cache_size
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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: bool = False,
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use_cache: bool = False,
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is_mem: Optional[torch.Tensor] = None,
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last_section_mask: Optional[torch.Tensor] = None,
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offload_cache_to_cpu: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = (
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self.q_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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key_states = (
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self.k_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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value_states = (
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self.v_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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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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if len(past_key_value) > 2:
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kv_seq_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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key_states_before_pos = key_states
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query_states, key_states = apply_rotary_pos_emb(
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query_states, key_states, cos, sin, position_ids
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)
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# [bsz, nh, t, hd]
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attn_prefix = None
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if past_key_value is not None:
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# reuse k, v, self_attention
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if self.mem_freq is None:
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cache_len = past_key_value[0].shape[2]
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if self.max_cache_size is not None:
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cache_len = min(cache_len, self.max_cache_size)
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if is_mem is not None:
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is_mem = torch.cat(
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(is_mem.new_zeros((1, 1, q_len, cache_len)), is_mem), dim=-1
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)
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last_section_mask = torch.cat(
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(
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last_section_mask.new_ones((1, 1, q_len, cache_len)),
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last_section_mask,
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),
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dim=-1,
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)
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past_key_states = torch.cat([past_key_value[0], key_states], dim=2)
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past_value_states = torch.cat([past_key_value[1], value_states], dim=2)
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key_states = past_key_states[:, :, -(q_len + cache_len) :]
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value_states = past_value_states[:, :, -(q_len + cache_len) :]
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expected_att_size = (bsz, self.num_heads, q_len, cache_len + q_len)
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else:
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orig_value_states = value_states
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incomplete_len = past_key_value[0].shape[2] % (self.mem_freq + 1)
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full_len = past_key_value[0].shape[2] - incomplete_len
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past_key_mem, past_key_incomplete = torch.split(
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past_key_value[0], (full_len, incomplete_len), dim=2
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)
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past_value_mem, past_value_incomplete = torch.split(
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past_key_value[1], (full_len, incomplete_len), dim=2
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)
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if offload_cache_to_cpu:
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past_key_value = (
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past_key_incomplete,
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past_value_incomplete,
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*past_key_value[2:],
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)
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if incomplete_len > 0:
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assert q_len + incomplete_len <= (self.mem_freq + 1)
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is_mem = torch.cat(
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(is_mem.new_zeros((1, 1, q_len, incomplete_len)), is_mem), dim=-1
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)
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last_section_mask = torch.cat(
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(
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last_section_mask.new_ones((1, 1, q_len, incomplete_len)),
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last_section_mask,
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),
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dim=-1,
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)
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if len(past_key_value) > 2:
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full_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
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past_key_incomplete_pos = torch.arange(
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full_len,
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full_len + incomplete_len,
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dtype=torch.long,
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device=position_ids.device,
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).unsqueeze(0)
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_, past_key_incomplete = apply_rotary_pos_emb(
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None, past_key_incomplete, cos, sin, past_key_incomplete_pos
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)
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key_states = torch.cat((past_key_incomplete, key_states), dim=2)
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value_states = torch.cat((past_value_incomplete, value_states), dim=2)
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past_key_mem = past_key_mem.view(
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bsz, self.num_heads, -1, self.mem_freq + 1, self.head_dim
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)
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past_value_mem = past_value_mem.view(
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bsz, self.num_heads, -1, self.mem_freq + 1, self.head_dim
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)
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if len(past_key_value) > 2:
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mem_key_nopos = torch.cat(
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(
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past_key_value[2],
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past_key_mem.select(dim=3, index=self.mem_freq),
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),
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dim=2,
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)
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past_key_mem_offload = past_key_value[3]
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past_key_mem = torch.cat(
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(
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past_key_mem_offload,
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past_key_mem.to(past_key_mem_offload.device),
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),
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dim=2,
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)
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past_value_mem = torch.cat(
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(
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past_key_value[4],
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past_value_mem.to(past_key_mem_offload.device),
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),
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dim=2,
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)
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else:
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mem_key_nopos = past_key_mem.select(dim=3, index=self.mem_freq)
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num_mems = past_key_mem.shape[2]
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top_k = min(self.top_k, num_mems)
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prefix_len = full_len - (top_k + 1) * (self.mem_freq + 1)
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mem_indices = torch.cat(
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(
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position_ids.new_zeros((max(0, num_mems - top_k),)),
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torch.arange(
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1,
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top_k + 1,
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device=query_states.device,
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dtype=position_ids.dtype,
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),
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),
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dim=0,
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)
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mem_pos = (mem_indices * (self.mem_freq + 1) + self.mem_freq).unsqueeze(
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0
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).expand(bsz, -1) + prefix_len
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_, mem_key = apply_rotary_pos_emb(
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None, mem_key_nopos, cos, sin, mem_pos
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)
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mem_attn_weights = torch.matmul(
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query_states, mem_key.transpose(2, 3)
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) / math.sqrt(self.head_dim)
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if offload_cache_to_cpu:
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aggregate = "max_over_tokens"
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else:
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aggregate = None
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if aggregate == "max_over_tokens":
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token_retrievers = 1
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head_retrievers = self.num_heads
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mem_attn_weights = torch.nn.functional.softmax(
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mem_attn_weights, dim=-1
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)
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mem_attn_weights = mem_attn_weights.amax(dim=2, keepdim=True)
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elif aggregate is None:
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token_retrievers = q_len
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head_retrievers = self.num_heads
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else:
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raise NotImplementedError()
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mem_selected_idx = (
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mem_attn_weights.topk(dim=-1, k=top_k)[1]
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.sort(dim=-1)[0]
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.view(bsz, head_retrievers, token_retrievers, top_k)
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)
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selected_indices = torch.arange(
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0,
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top_k * (self.mem_freq + 1),
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device=query_states.device,
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dtype=position_ids.dtype,
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)
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selected_indices = torch.where(
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mem_selected_idx >= num_mems - top_k, self.mem_freq + 1, 0
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).unsqueeze(-1) + selected_indices.view(
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1, 1, 1, top_k, self.mem_freq + 1
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)
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|
selected_indices = (
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|
selected_indices.view(
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bsz, head_retrievers, token_retrievers, -1
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).expand(bsz, self.num_heads, q_len, -1)
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+ prefix_len
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)
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|
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mem_selected_idx = mem_selected_idx.to(past_key_mem.device)
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|
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|
mem_selected_idx = mem_selected_idx.view(
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bsz, self.num_heads, token_retrievers, top_k, 1, 1
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).expand(
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bsz,
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self.num_heads,
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|
token_retrievers,
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top_k,
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self.mem_freq + 1,
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self.head_dim,
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)
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selected_keys = past_key_mem.unsqueeze(2).expand(
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bsz,
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self.num_heads,
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token_retrievers,
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-1,
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self.mem_freq + 1,
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self.head_dim,
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)
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selected_keys = selected_keys.take_along_dim(
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mem_selected_idx, dim=3
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).to(query_states.device)
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|
selected_values = (
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past_value_mem.unsqueeze(2)
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|
.expand(
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bsz,
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self.num_heads,
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|
token_retrievers,
|
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-1,
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self.mem_freq + 1,
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|
self.head_dim,
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|
)
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|
.take_along_dim(mem_selected_idx, dim=3)
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|
.to(query_states.device)
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)
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|
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selected_keys = selected_keys.view(
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bsz, self.num_heads, token_retrievers, -1, self.head_dim
|
|
).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
|
|
selected_keys = apply_rotary_pos_emb(
|
|
None, selected_keys.unsqueeze(1), cos, sin, selected_indices
|
|
)[1].squeeze(1)
|
|
selected_values = selected_values.view(
|
|
bsz, self.num_heads, token_retrievers, -1, self.head_dim
|
|
).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
|
|
attn_prefix = torch.matmul(
|
|
query_states.unsqueeze(3), selected_keys.transpose(3, 4)
|
|
).squeeze(3) / math.sqrt(self.head_dim)
|
|
is_mem_prefix = (
|
|
torch.cat(
|
|
(is_mem.new_zeros((self.mem_freq,)), is_mem.new_ones((1,)))
|
|
)
|
|
.unsqueeze(0)
|
|
.repeat((top_k, 1))
|
|
)
|
|
is_mem_prefix = is_mem_prefix.view(1, 1, 1, -1).expand(1, 1, q_len, -1)
|
|
is_mem = torch.cat((is_mem_prefix, is_mem), dim=-1)
|
|
last_section_mask = torch.cat(
|
|
(
|
|
last_section_mask.new_zeros(
|
|
(1, 1, q_len, top_k * (self.mem_freq + 1))
|
|
),
|
|
last_section_mask,
|
|
),
|
|
dim=-1,
|
|
)
|
|
expected_att_size = (bsz, self.num_heads, q_len, q_len + incomplete_len)
|
|
|
|
past_key_states = torch.cat(
|
|
[past_key_value[0], key_states_before_pos], dim=2
|
|
)
|
|
past_value_states = torch.cat(
|
|
[past_key_value[1], orig_value_states], dim=2
|
|
)
|
|
|
|
if offload_cache_to_cpu:
|
|
past_key_value = (
|
|
(
|
|
past_key_states,
|
|
past_value_states,
|
|
mem_key_nopos,
|
|
past_key_mem.to("cpu"),
|
|
past_value_mem.to("cpu"),
|
|
*past_key_value[5:],
|
|
)
|
|
if use_cache
|
|
else None
|
|
)
|
|
else:
|
|
past_key_value = (
|
|
(past_key_states, past_value_states) if use_cache else None
|
|
)
|
|
|
|
else:
|
|
if self.mem_freq is None:
|
|
past_key_states = key_states
|
|
else:
|
|
past_key_states = key_states_before_pos
|
|
past_value_states = value_states
|
|
expected_att_size = (bsz, self.num_heads, q_len, kv_seq_len)
|
|
past_key_value = (past_key_states, past_value_states) if use_cache else None
|
|
|
|
attn_weights = torch.matmul(
|
|
query_states, key_states.transpose(2, 3)
|
|
) / math.sqrt(self.head_dim)
|
|
if attn_weights.size() != expected_att_size:
|
|
raise ValueError(
|
|
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
raise ValueError(
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask[..., -attn_weights.shape[-1] :]
|
|
attn_weights = torch.max(
|
|
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
|
|
)
|
|
if attn_prefix is not None:
|
|
attn_weights = torch.cat((attn_prefix, attn_weights), dim=-1)
|
|
# upcast attention to fp32
|
|
if is_mem is None:
|
|
raise ValueError("Don't use this without landmarks")
|
|
|
|
attn_weights = landmark_grouped_softmax(
|
|
attn_weights,
|
|
dim=-1,
|
|
is_mem=is_mem.expand(-1, self.num_heads, -1, -1),
|
|
last_section_mask=last_section_mask,
|
|
).to(query_states.dtype)
|
|
|
|
if attn_prefix is not None:
|
|
attn_prefix, attn_weights = torch.split(
|
|
attn_weights,
|
|
(attn_prefix.shape[-1], attn_weights.shape[-1] - attn_prefix.shape[-1]),
|
|
dim=-1,
|
|
)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
if attn_prefix is not None:
|
|
attn_output += torch.matmul(
|
|
attn_prefix.unsqueeze(3), selected_values
|
|
).squeeze(3)
|
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
raise ValueError(
|
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}"
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2)
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
class LlamaDecoderLayer(nn.Module):
|
|
"""
|
|
Llama Decoder layer
|
|
"""
|
|
|
|
def __init__(self, config: LlamaConfig):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.self_attn = LlamaAttention(config=config)
|
|
self.mlp = LlamaMLP(
|
|
hidden_size=self.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
)
|
|
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = LlamaRMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
|
|
self.self_attn.set_mem_cache_args(mem_freq, top_k, max_cache_size)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
is_mem: Optional[torch.Tensor] = None,
|
|
last_section_mask: Optional[torch.Tensor] = None,
|
|
offload_cache_to_cpu: bool = False,
|
|
) -> Tuple[
|
|
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
|
|
]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
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
|
|
"""
|
|
|
|
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,
|
|
is_mem=is_mem,
|
|
last_section_mask=last_section_mask,
|
|
offload_cache_to_cpu=offload_cache_to_cpu,
|
|
)
|
|
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
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
|
LLAMA_START_DOCSTRING,
|
|
)
|
|
class LlamaModel(LlamaPreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
|
|
|
Args:
|
|
config: LlamaConfig
|
|
"""
|
|
|
|
def __init__(self, config: LlamaConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(
|
|
config.vocab_size, config.hidden_size, self.padding_idx
|
|
)
|
|
self.layers = nn.ModuleList(
|
|
[LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
self.mem_id = None
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
def set_mem_id(self, mem_id):
|
|
self.mem_id = mem_id
|
|
|
|
def set_mem_cache_args(self, mem_freq, top_k, max_cache_size):
|
|
for layer in self.layers:
|
|
layer.set_mem_cache_args(mem_freq, top_k, max_cache_size)
|
|
|
|
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
|
def _prepare_decoder_attention_mask(
|
|
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
|
|
):
|
|
# create causal mask
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
combined_attention_mask = None
|
|
if input_shape[-1] > 1:
|
|
combined_attention_mask = _make_causal_mask(
|
|
input_shape,
|
|
inputs_embeds.dtype,
|
|
device=inputs_embeds.device,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
expanded_attn_mask = _expand_mask(
|
|
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
|
).to(inputs_embeds.device)
|
|
combined_attention_mask = (
|
|
expanded_attn_mask
|
|
if combined_attention_mask is None
|
|
else expanded_attn_mask + combined_attention_mask
|
|
)
|
|
|
|
return combined_attention_mask
|
|
|
|
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
|
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,
|
|
offload_cache_to_cpu: Optional[bool] = None,
|
|
) -> 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
|
|
)
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
is_mem = None
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError(
|
|
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
|
|
)
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
if self.mem_id is not None:
|
|
with torch.no_grad():
|
|
is_mem = input_ids == self.mem_id
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
if self.mem_id is not None:
|
|
raise NotImplementedError
|
|
else:
|
|
raise ValueError(
|
|
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
|
|
)
|
|
|
|
seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
|
|
if past_key_values is not None:
|
|
if is_mem is not None:
|
|
pass
|
|
# raise NotImplementedError
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
|
if len(past_key_values[0]) > 2:
|
|
past_key_values_length += (
|
|
past_key_values[0][3].shape[2] * past_key_values[0][3].shape[3]
|
|
)
|
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
|
|
if position_ids is None:
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
position_ids = torch.arange(
|
|
past_key_values_length,
|
|
seq_length + past_key_values_length,
|
|
dtype=torch.long,
|
|
device=device,
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
else:
|
|
position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
# embed positions
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(
|
|
(batch_size, seq_length_with_past),
|
|
dtype=torch.bool,
|
|
device=inputs_embeds.device,
|
|
)
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
|
attention_mask,
|
|
(batch_size, seq_length),
|
|
inputs_embeds,
|
|
past_key_values_length,
|
|
)
|
|
|
|
last_section_mask = None
|
|
if is_mem is not None:
|
|
is_mem = is_mem.unsqueeze(1).unsqueeze(2)
|
|
current_len = input_ids.shape[1]
|
|
mem_ids = torch.where(
|
|
attention_mask[..., -current_len:] < -1,
|
|
0,
|
|
torch.cumsum(is_mem, -1) - is_mem.int(),
|
|
)
|
|
last_section_mask = torch.amax(mem_ids, -1, keepdim=True) == mem_ids
|
|
attention_mask[..., -current_len:].masked_fill_(
|
|
last_section_mask & is_mem,
|
|
torch.tensor(
|
|
torch.finfo(inputs_embeds.dtype).min, device=inputs_embeds.device
|
|
),
|
|
)
|
|
last_section_mask.logical_and_(attention_mask[..., -current_len:] > -1)
|
|
is_mem = is_mem.logical_and(attention_mask[..., -current_len:] > -1)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
LOG.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
past_key_value = (
|
|
past_key_values[idx] if past_key_values is not None else None
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(decoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
None,
|
|
output_attentions,
|
|
None,
|
|
is_mem,
|
|
last_section_mask,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
is_mem=is_mem,
|
|
last_section_mask=last_section_mask,
|
|
offload_cache_to_cpu=offload_cache_to_cpu,
|
|
)
|
|
|
|
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 LlamaForCausalLM(LlamaPreTrainedModel):
|
|
"""
|
|
Llama model with a causal language modeling head.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = LlamaModel(config)
|
|
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
self.mem_id = None
|
|
self.mem_freq = None
|
|
self.top_k = None
|
|
self.max_seq_len = None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(
|
|
output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
|
|
)
|
|
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,
|
|
offload_cache_to_cpu: Optional[bool] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
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 transformers import AutoTokenizer, LlamaForCausalLM
|
|
|
|
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
|
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
|
|
|
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # 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]
|
|
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
|
|
```"""
|
|
|
|
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
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
window_len = self.max_seq_len or input_ids.shape[1]
|
|
last_logits = None
|
|
for _, idx in enumerate(range(0, input_ids.shape[1], window_len)):
|
|
if idx >= 1:
|
|
if output_attentions or output_hidden_states:
|
|
raise NotImplementedError
|
|
if not use_cache:
|
|
raise NotImplementedError
|
|
outputs = self.model(
|
|
input_ids=input_ids[:, idx : idx + window_len],
|
|
attention_mask=attention_mask[
|
|
:, : idx + window_len + attention_mask.shape[1] - input_ids.shape[1]
|
|
]
|
|
if attention_mask is not None
|
|
else None,
|
|
position_ids=position_ids[:, idx : idx + window_len]
|
|
if position_ids is not None
|
|
else None,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds[:, idx : idx + window_len]
|
|
if inputs_embeds is not None
|
|
else None,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
offload_cache_to_cpu=offload_cache_to_cpu,
|
|
)
|
|
past_key_values = outputs.past_key_values
|
|
if last_logits is not None:
|
|
last_logits = torch.cat((last_logits, outputs[0]), dim=-2)
|
|
last_logits = outputs[0]
|
|
|
|
hidden_states = last_logits
|
|
logits = self.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# 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 CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def set_mem_id(self, mem_id):
|
|
self.mem_id = mem_id
|
|
self.model.set_mem_id(mem_id)
|
|
|
|
def set_mem_cache_args(self, max_seq_len, mem_freq, top_k, max_cache_size):
|
|
self.mem_freq = mem_freq
|
|
self.top_k = top_k
|
|
self.max_seq_len = max_seq_len
|
|
if self.max_seq_len is not None:
|
|
assert self.max_seq_len % (self.mem_freq + 1) == 0
|
|
self.model.set_mem_cache_args(mem_freq, top_k, max_cache_size)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
**kwargs,
|
|
):
|
|
total_len = input_ids.shape[1]
|
|
if past_key_values:
|
|
prev_len = input_ids.shape[1] - 1
|
|
else:
|
|
prev_len = 0
|
|
|
|
position_ids = kwargs.get("position_ids", None)
|
|
|
|
if self.mem_freq is not None:
|
|
if position_ids is not None:
|
|
raise NotImplementedError
|
|
# T = input_ids.shape[1]
|
|
|
|
prev_incomplete_len = prev_len % self.mem_freq
|
|
prev_complete_len = prev_len - prev_incomplete_len
|
|
incomplete_len = total_len % self.mem_freq
|
|
new_full_len = total_len - prev_complete_len - incomplete_len
|
|
|
|
prev_input, input_ids_with_mem, input_ids_without_mem = torch.split(
|
|
input_ids, (prev_complete_len, new_full_len, incomplete_len), dim=-1
|
|
)
|
|
|
|
bsz, _ = input_ids.size()
|
|
input_ids_with_mem = input_ids_with_mem.view(bsz, -1, self.mem_freq)
|
|
input_ids_with_mem = torch.cat(
|
|
(
|
|
input_ids_with_mem,
|
|
input_ids_with_mem.new_full(
|
|
(bsz, input_ids_with_mem.shape[1], 1), self.mem_id
|
|
),
|
|
),
|
|
dim=-1,
|
|
).view(bsz, -1)
|
|
input_ids = torch.cat(
|
|
(prev_input, input_ids_with_mem, input_ids_without_mem), dim=-1
|
|
)
|
|
if attention_mask is not None:
|
|
attention_mask_with_mem, attention_mask_without_mem = torch.split(
|
|
attention_mask,
|
|
(prev_complete_len + new_full_len, incomplete_len),
|
|
dim=-1,
|
|
)
|
|
attention_mask_with_mem = attention_mask_with_mem.view(
|
|
bsz, -1, self.mem_freq
|
|
)
|
|
attention_mask_with_mem = torch.cat(
|
|
(
|
|
attention_mask_with_mem,
|
|
attention_mask_with_mem.new_ones(
|
|
(bsz, attention_mask_with_mem.shape[1], 1)
|
|
),
|
|
),
|
|
dim=-1,
|
|
).view(bsz, -1)
|
|
attention_mask = torch.cat(
|
|
(attention_mask_with_mem, attention_mask_without_mem), dim=-1
|
|
)
|
|
|
|
input_ids = input_ids[:, prev_len:]
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
position_ids = position_ids[:, -input_ids.shape[1] :].unsqueeze(-1)
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if (
|
|
inputs_embeds is not None
|
|
and past_key_values is None
|
|
and self.mem_freq is None
|
|
):
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids}
|
|
|
|
model_inputs.update(
|
|
{
|
|
"position_ids": position_ids,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"attention_mask": attention_mask,
|
|
"offload_cache_to_cpu": kwargs.get("offload_cache_to_cpu"),
|
|
}
|
|
)
|
|
return model_inputs
|
|
|
|
@staticmethod
|
|
def _reorder_cache(past_key_values, beam_idx):
|
|
reordered_past = ()
|
|
for layer_past in past_key_values:
|
|
reordered_past += (
|
|
tuple(
|
|
past_state.index_select(0, beam_idx) for past_state in layer_past
|
|
),
|
|
)
|
|
return reordered_past
|
|
|
|
|
|
def add_mem_tokens(example, mem_freq, mem_id):
|
|
ids = example["input_ids"]
|
|
ret = []
|
|
prev_idx = 0
|
|
for t_idx in range(mem_freq, len(ids), mem_freq):
|
|
ret.extend(ids[prev_idx:t_idx])
|
|
ret.append(mem_id)
|
|
prev_idx = t_idx
|
|
ret.extend(ids[prev_idx:])
|
|
# drop attention_mask
|
|
return {"input_ids": ret}
|
|
|
|
|
|
def patch_llama_with_landmark_attn():
|
|
import transformers
|
|
|
|
transformers.models.llama.modeling_llama.LlamaForCausalLM = LlamaForCausalLM
|
|
transformers.models.llama.modeling_llama.LlamaModel = LlamaModel
|
|
transformers.models.llama.modeling_llama.LlamaAttention = LlamaAttention
|
|
transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
|
|
transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb
|
|
|
|
|
|
def set_model_mem_id(model: LlamaForCausalLM, tokenizer: LlamaTokenizer):
|
|
mem_id = tokenizer.convert_tokens_to_ids(MEM_TOKEN)
|
|
model.set_mem_id(mem_id)
|
|
|
|
|
|
def get_mem_id(tokenizer: LlamaTokenizer):
|
|
return tokenizer.convert_tokens_to_ids(MEM_TOKEN)
|