1118 lines
48 KiB
Python
1118 lines
48 KiB
Python
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# Copyright 2024 OrionStar Inc. team. All rights reserved.
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# Copied and adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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from transformers import AutoConfig, AutoModel
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from .configuration_orion import OrionConfig
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import numbers
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import importlib
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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.nn.functional as F
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from torch.nn.parameter import Parameter
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from torch.nn import init
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
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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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is_flash_attn_available,
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logging,
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replace_return_docstrings,
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)
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from .generation_utils import build_chat_input, TextIterStreamer
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from transformers.generation.utils import GenerationConfig
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from threading import Thread
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if is_flash_attn_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "OrionConfig"
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def _get_unpad_data(padding_mask):
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seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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# Copied from transformers.models.bart.modeling_bart._make_causal_mask
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def _make_causal_mask(
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input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
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):
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"""
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Make causal mask used for bi-directional self-attention.
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"""
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bsz, tgt_len = input_ids_shape
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mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
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mask_cond = torch.arange(mask.size(-1), device=device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(dtype)
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if past_key_values_length > 0:
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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# Copied from transformers.models.bart.modeling_bart._expand_mask
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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class OrionRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
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)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len].to(dtype=x.dtype),
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self.sin_cached[:seq_len].to(dtype=x.dtype),
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)
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class OrionLinearScalingRotaryEmbedding(OrionRotaryEmbedding):
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"""OrionRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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t = t / self.scaling_factor
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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class OrionDynamicNTKScalingRotaryEmbedding(OrionRotaryEmbedding):
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"""OrionRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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if seq_len > self.max_position_embeddings:
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base = self.base * (
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(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
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) ** (self.dim / (self.dim - 2))
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inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.gpt_neox.modeling_gpt_neox.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
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cos = cos[position_ids].unsqueeze(1) # [seq_len, dim] -> [batch_size, 1, seq_len, head_dim]
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sin = sin[position_ids].unsqueeze(1)
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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 OrionMLP(nn.Module):
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def __init__(self, config):
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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.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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if self.config.pretraining_tp > 1:
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slice = self.intermediate_size // self.config.pretraining_tp
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gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
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up_proj_slices = self.up_proj.weight.split(slice, dim=0)
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down_proj_slices = self.down_proj.weight.split(slice, dim=1)
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gate_proj = torch.cat(
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[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
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)
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up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
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intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
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down_proj = [
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F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
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]
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down_proj = sum(down_proj)
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else:
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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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 OrionAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: OrionConfig):
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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 = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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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 = OrionRotaryEmbedding(
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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 = OrionLinearScalingRotaryEmbedding(
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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 = OrionDynamicNTKScalingRotaryEmbedding(
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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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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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if self.config.pretraining_tp > 1:
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key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
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query_slices = self.q_proj.weight.split(
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(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
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)
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
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|
|
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
|
|
query_states = torch.cat(query_states, dim=-1)
|
||
|
|
|
||
|
|
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
|
|
key_states = torch.cat(key_states, dim=-1)
|
||
|
|
|
||
|
|
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
|
|
value_states = torch.cat(value_states, dim=-1)
|
||
|
|
|
||
|
|
else:
|
||
|
|
query_states = self.q_proj(hidden_states)
|
||
|
|
key_states = self.k_proj(hidden_states)
|
||
|
|
value_states = self.v_proj(hidden_states)
|
||
|
|
|
||
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
|
|
||
|
|
kv_seq_len = key_states.shape[-2]
|
||
|
|
if past_key_value is not None:
|
||
|
|
kv_seq_len += past_key_value[0].shape[-2]
|
||
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
|
|
||
|
|
if past_key_value is not None:
|
||
|
|
# reuse k, v, self_attention
|
||
|
|
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||
|
|
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||
|
|
|
||
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
||
|
|
|
||
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||
|
|
|
||
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||
|
|
|
||
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||
|
|
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
|
||
|
|
|
||
|
|
# upcast attention to fp32
|
||
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
||
|
|
|
||
|
|
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).contiguous()
|
||
|
|
|
||
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||
|
|
|
||
|
|
if self.config.pretraining_tp > 1:
|
||
|
|
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
||
|
|
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
||
|
|
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
||
|
|
else:
|
||
|
|
attn_output = self.o_proj(attn_output)
|
||
|
|
|
||
|
|
if not output_attentions:
|
||
|
|
attn_weights = None
|
||
|
|
|
||
|
|
return attn_output, attn_weights, past_key_value
|
||
|
|
|
||
|
|
|
||
|
|
class OrionFlashAttention2(OrionAttention):
|
||
|
|
"""
|
||
|
|
Orion flash attention module. This module inherits from `OrionAttention` as the weights of the module stays
|
||
|
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
||
|
|
flash attention and deal with padding tokens in case the input contains any of them.
|
||
|
|
"""
|
||
|
|
|
||
|
|
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: bool = False,
|
||
|
|
use_cache: bool = False,
|
||
|
|
padding_mask: Optional[torch.LongTensor] = None,
|
||
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
|
|
# OrionFlashAttention2 attention does not support output_attentions
|
||
|
|
output_attentions = False
|
||
|
|
|
||
|
|
bsz, q_len, _ = hidden_states.size()
|
||
|
|
|
||
|
|
query_states = self.q_proj(hidden_states)
|
||
|
|
key_states = self.k_proj(hidden_states)
|
||
|
|
value_states = self.v_proj(hidden_states)
|
||
|
|
|
||
|
|
# Flash attention requires the input to have the shape
|
||
|
|
# batch_size x seq_length x head_dime x hidden_dim
|
||
|
|
# therefore we just need to keep the original shape
|
||
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
|
|
||
|
|
kv_seq_len = key_states.shape[-2]
|
||
|
|
if past_key_value is not None:
|
||
|
|
kv_seq_len += past_key_value[0].shape[-2]
|
||
|
|
|
||
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||
|
|
|
||
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
|
|
||
|
|
if past_key_value is not None:
|
||
|
|
# reuse k, v, self_attention
|
||
|
|
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||
|
|
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||
|
|
|
||
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
||
|
|
|
||
|
|
query_states = query_states.transpose(1, 2)
|
||
|
|
key_states = key_states.transpose(1, 2)
|
||
|
|
value_states = value_states.transpose(1, 2)
|
||
|
|
|
||
|
|
# TODO: llama does not have dropout in the config??
|
||
|
|
# It is recommended to use dropout with FA according to the docs
|
||
|
|
# when training.
|
||
|
|
dropout_rate = 0.0 # if not self.training else self.attn_dropout
|
||
|
|
|
||
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||
|
|
# cast them back in float16 just to be sure everything works as expected.
|
||
|
|
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
||
|
|
# in fp32. (LlamaRMSNorm handles it correctly)
|
||
|
|
input_dtype = query_states.dtype
|
||
|
|
if input_dtype == torch.float32:
|
||
|
|
logger.warning_once(
|
||
|
|
"The input hidden states seems to be silently casted in float32, this might be related to"
|
||
|
|
" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
||
|
|
" float16."
|
||
|
|
)
|
||
|
|
|
||
|
|
query_states = query_states.to(torch.float16)
|
||
|
|
key_states = key_states.to(torch.float16)
|
||
|
|
value_states = value_states.to(torch.float16)
|
||
|
|
|
||
|
|
attn_output = self._flash_attention_forward(
|
||
|
|
query_states, key_states, value_states, padding_mask, q_len, dropout=dropout_rate
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
||
|
|
attn_output = self.o_proj(attn_output)
|
||
|
|
|
||
|
|
if not output_attentions:
|
||
|
|
attn_weights = None
|
||
|
|
|
||
|
|
return attn_output, attn_weights, past_key_value
|
||
|
|
|
||
|
|
def _flash_attention_forward(
|
||
|
|
self, query_states, key_states, value_states, padding_mask, query_length, dropout=0.0, softmax_scale=None
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||
|
|
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
query_states (`torch.Tensor`):
|
||
|
|
Input query states to be passed to Flash Attention API
|
||
|
|
key_states (`torch.Tensor`):
|
||
|
|
Input key states to be passed to Flash Attention API
|
||
|
|
value_states (`torch.Tensor`):
|
||
|
|
Input value states to be passed to Flash Attention API
|
||
|
|
padding_mask (`torch.Tensor`):
|
||
|
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||
|
|
position of padding tokens and 1 for the position of non-padding tokens.
|
||
|
|
dropout (`int`, *optional*):
|
||
|
|
Attention dropout
|
||
|
|
softmax_scale (`float`, *optional*):
|
||
|
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||
|
|
"""
|
||
|
|
# Contains at least one padding token in the sequence
|
||
|
|
if padding_mask is not None:
|
||
|
|
batch_size = query_states.shape[0]
|
||
|
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||
|
|
query_states, key_states, value_states, padding_mask, query_length
|
||
|
|
)
|
||
|
|
|
||
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||
|
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||
|
|
|
||
|
|
attn_output_unpad = flash_attn_varlen_func(
|
||
|
|
query_states,
|
||
|
|
key_states,
|
||
|
|
value_states,
|
||
|
|
cu_seqlens_q=cu_seqlens_q,
|
||
|
|
cu_seqlens_k=cu_seqlens_k,
|
||
|
|
max_seqlen_q=max_seqlen_in_batch_q,
|
||
|
|
max_seqlen_k=max_seqlen_in_batch_k,
|
||
|
|
dropout_p=dropout,
|
||
|
|
softmax_scale=softmax_scale,
|
||
|
|
causal=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
|
|
else:
|
||
|
|
attn_output = flash_attn_func(
|
||
|
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=True
|
||
|
|
)
|
||
|
|
|
||
|
|
return attn_output
|
||
|
|
|
||
|
|
def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length):
|
||
|
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
|
||
|
|
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||
|
|
|
||
|
|
key_layer = index_first_axis(
|
||
|
|
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
|
|
)
|
||
|
|
value_layer = index_first_axis(
|
||
|
|
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
|
|
)
|
||
|
|
if query_length == kv_seq_len:
|
||
|
|
query_layer = index_first_axis(
|
||
|
|
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||
|
|
)
|
||
|
|
cu_seqlens_q = cu_seqlens_k
|
||
|
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||
|
|
indices_q = indices_k
|
||
|
|
elif query_length == 1:
|
||
|
|
max_seqlen_in_batch_q = 1
|
||
|
|
cu_seqlens_q = torch.arange(
|
||
|
|
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||
|
|
) # There is a memcpy here, that is very bad.
|
||
|
|
indices_q = cu_seqlens_q[:-1]
|
||
|
|
query_layer = query_layer.squeeze(1)
|
||
|
|
else:
|
||
|
|
# The -q_len: slice assumes left padding.
|
||
|
|
padding_mask = padding_mask[:, -query_length:]
|
||
|
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask)
|
||
|
|
|
||
|
|
return (
|
||
|
|
query_layer,
|
||
|
|
key_layer,
|
||
|
|
value_layer,
|
||
|
|
indices_q,
|
||
|
|
(cu_seqlens_q, cu_seqlens_k),
|
||
|
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class OrionDecoderLayer(nn.Module):
|
||
|
|
def __init__(self, config: OrionConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.hidden_size = config.hidden_size
|
||
|
|
self.self_attn = (
|
||
|
|
OrionAttention(config=config)
|
||
|
|
if not getattr(config, "_flash_attn_2_enabled", False)
|
||
|
|
else OrionFlashAttention2(config=config)
|
||
|
|
)
|
||
|
|
self.mlp = OrionMLP(config)
|
||
|
|
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
|
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
|
|
||
|
|
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,
|
||
|
|
padding_mask: Optional[torch.LongTensor] = None,
|
||
|
|
) -> 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,
|
||
|
|
padding_mask=padding_mask,
|
||
|
|
)
|
||
|
|
hidden_states = residual + hidden_states
|
||
|
|
|
||
|
|
# Fully Connected
|
||
|
|
residual = hidden_states
|
||
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
|
|
hidden_states = self.mlp(hidden_states)
|
||
|
|
hidden_states = residual + hidden_states
|
||
|
|
|
||
|
|
outputs = (hidden_states,)
|
||
|
|
|
||
|
|
if output_attentions:
|
||
|
|
outputs += (self_attn_weights,)
|
||
|
|
|
||
|
|
if use_cache:
|
||
|
|
outputs += (present_key_value,)
|
||
|
|
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
class OrionPreTrainedModel(PreTrainedModel):
|
||
|
|
config_class = OrionConfig
|
||
|
|
base_model_prefix = "model"
|
||
|
|
supports_gradient_checkpointing = True
|
||
|
|
_no_split_modules = ["OrionDecoderLayer"]
|
||
|
|
_skip_keys_device_placement = "past_key_values"
|
||
|
|
_supports_flash_attn_2 = True
|
||
|
|
|
||
|
|
def _init_weights(self, module):
|
||
|
|
std = self.config.initializer_range
|
||
|
|
if isinstance(module, nn.Linear):
|
||
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
||
|
|
if module.bias is not None:
|
||
|
|
module.bias.data.zero_()
|
||
|
|
elif isinstance(module, nn.Embedding):
|
||
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
||
|
|
if module.padding_idx is not None:
|
||
|
|
module.weight.data[module.padding_idx].zero_()
|
||
|
|
|
||
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
||
|
|
if isinstance(module, OrionModel):
|
||
|
|
module.gradient_checkpointing = value
|
||
|
|
|
||
|
|
class OrionModel(OrionPreTrainedModel):
|
||
|
|
"""
|
||
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OrionDecoderLayer`]
|
||
|
|
|
||
|
|
Args:
|
||
|
|
config: OrionConfig
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, config: OrionConfig):
|
||
|
|
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([OrionDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||
|
|
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
# 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
|
||
|
|
|
||
|
|
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,
|
||
|
|
) -> 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
|
||
|
|
if input_ids is not None and inputs_embeds is not None:
|
||
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
||
|
|
elif input_ids is not None:
|
||
|
|
batch_size, seq_length = input_ids.shape
|
||
|
|
elif inputs_embeds is not None:
|
||
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
||
|
|
else:
|
||
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
seq_length_with_past = seq_length
|
||
|
|
past_key_values_length = 0
|
||
|
|
|
||
|
|
if past_key_values is not None:
|
||
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
||
|
|
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)
|
||
|
|
|
||
|
|
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
|
||
|
|
)
|
||
|
|
padding_mask = None
|
||
|
|
else:
|
||
|
|
if 0 in attention_mask:
|
||
|
|
padding_mask = attention_mask
|
||
|
|
else:
|
||
|
|
padding_mask = None
|
||
|
|
|
||
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
||
|
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = inputs_embeds
|
||
|
|
|
||
|
|
if self.gradient_checkpointing and self.training:
|
||
|
|
if use_cache:
|
||
|
|
logger.warning_once(
|
||
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
|
|
)
|
||
|
|
use_cache = False
|
||
|
|
|
||
|
|
# 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, past_key_value, output_attentions, padding_mask=padding_mask)
|
||
|
|
|
||
|
|
return custom_forward
|
||
|
|
|
||
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||
|
|
create_custom_forward(decoder_layer), hidden_states, attention_mask, position_ids
|
||
|
|
)
|
||
|
|
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,
|
||
|
|
padding_mask=padding_mask,
|
||
|
|
)
|
||
|
|
|
||
|
|
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 OrionForCausalLM(OrionPreTrainedModel):
|
||
|
|
model_type = "orion"
|
||
|
|
_tied_weights_keys = ["lm_head.weight"]
|
||
|
|
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
self.model = OrionModel(config)
|
||
|
|
self.vocab_size = config.vocab_size
|
||
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
|
|
||
|
|
# 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
|
||
|
|
|
||
|
|
@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,
|
||
|
|
) -> 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, OrionForCausalLM
|
||
|
|
|
||
|
|
>>> model = OrionForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
||
|
|
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
||
|
|
|
||
|
|
>>> prompt = "Hey, are you conscious? 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 conscious? Can you talk to me?\nI'm not conscious, 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)
|
||
|
|
outputs = self.model(
|
||
|
|
input_ids=input_ids,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
use_cache=use_cache,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = outputs[0]
|
||
|
|
if self.config.pretraining_tp > 1:
|
||
|
|
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
||
|
|
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
||
|
|
logits = torch.cat(logits, dim=-1)
|
||
|
|
else:
|
||
|
|
logits = self.lm_head(hidden_states)
|
||
|
|
logits = logits.float()
|
||
|
|
|
||
|
|
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 chat(self, tokenizer, messages: List[dict], streaming=False,generation_config: Optional[GenerationConfig]=None):
|
||
|
|
generation_config = generation_config or self.generation_config
|
||
|
|
input_tokens = build_chat_input(tokenizer,messages)
|
||
|
|
input_ids = torch.LongTensor([input_tokens]).to(self.device)
|
||
|
|
|
||
|
|
if streaming:
|
||
|
|
streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
|
||
|
|
Thread(target=self.generate, kwargs=dict(
|
||
|
|
inputs=input_ids, streamer=streamer,
|
||
|
|
generation_config=generation_config,
|
||
|
|
)).start()
|
||
|
|
return streamer
|
||
|
|
else:
|
||
|
|
outputs = self.generate(input_ids, generation_config=generation_config)
|
||
|
|
response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
|
||
|
|
return response
|
||
|
|
|
||
|
|
def prepare_inputs_for_generation(
|
||
|
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
||
|
|
):
|
||
|
|
if past_key_values:
|
||
|
|
input_ids = input_ids[:, -1:]
|
||
|
|
|
||
|
|
position_ids = kwargs.get("position_ids", None)
|
||
|
|
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)
|
||
|
|
if past_key_values:
|
||
|
|
position_ids = position_ids[:, -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:
|
||
|
|
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,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
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.to(past_state.device)) for past_state in layer_past),
|
||
|
|
)
|
||
|
|
return reordered_past
|
||
|
|
|
||
|
|
class OrionForSequenceClassification(OrionPreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
self.num_labels = config.num_labels
|
||
|
|
self.model = OrionModel(config)
|
||
|
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
||
|
|
|
||
|
|
# 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 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,
|
||
|
|
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
||
|
|
r"""
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||
|
|
"""
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
|
||
|
|
transformer_outputs = self.model(
|
||
|
|
input_ids,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
use_cache=use_cache,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
hidden_states = transformer_outputs[0]
|
||
|
|
logits = self.score(hidden_states)
|
||
|
|
|
||
|
|
if input_ids is not None:
|
||
|
|
batch_size = input_ids.shape[0]
|
||
|
|
else:
|
||
|
|
batch_size = inputs_embeds.shape[0]
|
||
|
|
|
||
|
|
if self.config.pad_token_id is None and batch_size != 1:
|
||
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
||
|
|
if self.config.pad_token_id is None:
|
||
|
|
sequence_lengths = -1
|
||
|
|
else:
|
||
|
|
if input_ids is not None:
|
||
|
|
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
|
||
|
|
logits.device
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
sequence_lengths = -1
|
||
|
|
|
||
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||
|
|
|
||
|
|
loss = None
|
||
|
|
if labels is not None:
|
||
|
|
labels = labels.to(logits.device)
|
||
|
|
if self.config.problem_type is None:
|
||
|
|
if self.num_labels == 1:
|
||
|
|
self.config.problem_type = "regression"
|
||
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||
|
|
self.config.problem_type = "single_label_classification"
|
||
|
|
else:
|
||
|
|
self.config.problem_type = "multi_label_classification"
|
||
|
|
|
||
|
|
if self.config.problem_type == "regression":
|
||
|
|
loss_fct = MSELoss()
|
||
|
|
if self.num_labels == 1:
|
||
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
||
|
|
else:
|
||
|
|
loss = loss_fct(pooled_logits, labels)
|
||
|
|
elif self.config.problem_type == "single_label_classification":
|
||
|
|
loss_fct = CrossEntropyLoss()
|
||
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
||
|
|
elif self.config.problem_type == "multi_label_classification":
|
||
|
|
loss_fct = BCEWithLogitsLoss()
|
||
|
|
loss = loss_fct(pooled_logits, labels)
|
||
|
|
if not return_dict:
|
||
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
||
|
|
return ((loss,) + output) if loss is not None else output
|
||
|
|
|
||
|
|
return SequenceClassifierOutputWithPast(
|
||
|
|
loss=loss,
|
||
|
|
logits=pooled_logits,
|
||
|
|
past_key_values=transformer_outputs.past_key_values,
|
||
|
|
hidden_states=transformer_outputs.hidden_states,
|
||
|
|
attentions=transformer_outputs.attentions,
|
||
|
|
)
|
||
|
|
|