This commit is contained in:
josc146
13
finetune/lora/v6/fla/models/abc/__init__.py
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13
finetune/lora/v6/fla/models/abc/__init__.py
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# -*- coding: utf-8 -*-
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from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
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from fla.models.abc.configuration_abc import ABCConfig
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from fla.models.abc.modeling_abc import ABCForCausalLM, ABCModel
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AutoConfig.register(ABCConfig.model_type, ABCConfig)
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AutoModel.register(ABCConfig, ABCModel)
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AutoModelForCausalLM.register(ABCConfig, ABCForCausalLM)
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__all__ = ['ABCConfig', 'ABCForCausalLM', 'ABCModel']
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74
finetune/lora/v6/fla/models/abc/configuration_abc.py
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74
finetune/lora/v6/fla/models/abc/configuration_abc.py
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# -*- coding: utf-8 -*-
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from typing import Optional
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from transformers.configuration_utils import PretrainedConfig
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class ABCConfig(PretrainedConfig):
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model_type = 'abc'
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keys_to_ignore_at_inference = ['past_key_values']
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def __init__(
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self,
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vocab_size: int = 32000,
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hidden_size: int = 2048,
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gate_low_rank_dim: int = 16,
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clamp_min: float = -32,
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clamp_max: float = 32,
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hidden_ratio: Optional[int] = 4,
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intermediate_size: Optional[int] = None,
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num_hidden_layers: int = 24,
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num_heads: int = 4,
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num_slots: Optional[int] = 64,
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use_short_conv: bool = True,
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conv_size: int = 4,
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share_conv_kernel: bool = True,
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exapnd_k: float = 0.5,
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exapnd_v: float = 1,
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hidden_act: str = "swish",
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max_position_embeddings: int = 2048,
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elementwise_affine: Optional[bool] = True,
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norm_eps: float = 1e-6,
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use_cache: bool = True,
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pad_token_id: int = None,
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bos_token_id: int = 1,
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eos_token_id: int = 2,
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initializer_range: float = 0.02,
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tie_word_embeddings: bool = False,
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fuse_norm: bool = True,
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fuse_cross_entropy: bool = True,
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**kwargs
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.gate_low_rank_dim = gate_low_rank_dim
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self.clamp_min = clamp_min
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self.clamp_max = clamp_max
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self.hidden_ratio = hidden_ratio
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_heads = num_heads
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self.num_slots = num_slots
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self.use_short_conv = use_short_conv
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self.conv_size = conv_size
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self.share_conv_kernel = share_conv_kernel
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self.expand_k = exapnd_k
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self.expand_v = exapnd_v
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self.hidden_act = hidden_act
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self.elementwise_affine = elementwise_affine
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self.norm_eps = norm_eps
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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self.fuse_cross_entropy = fuse_cross_entropy
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self.fuse_norm = fuse_norm
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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394
finetune/lora/v6/fla/models/abc/modeling_abc.py
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394
finetune/lora/v6/fla/models/abc/modeling_abc.py
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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import math
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import (BaseModelOutputWithPast,
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CausalLMOutputWithPast)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import logging
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from fla.layers.abc import ABCAttention
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from fla.models.abc.configuration_abc import ABCConfig
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from fla.models.utils import RecurrentCache
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from fla.modules import FusedCrossEntropyLoss, RMSNorm
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from fla.modules.activations import swiglu_linear
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logger = logging.get_logger(__name__)
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class ABCMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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hidden_ratio: Optional[int] = None,
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intermediate_size: Optional[int] = None,
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hidden_act: str = 'swish'
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) -> ABCMLP:
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super().__init__()
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self.hidden_size = hidden_size
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# the final number of params is `hidden_ratio * hidden_size^2`
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# `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio`
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if hidden_ratio is None:
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hidden_ratio = 4
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if intermediate_size is None:
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intermediate_size = int(hidden_size * hidden_ratio * 2 / 3)
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intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256)
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self.hidden_ratio = hidden_ratio
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self.intermediate_size = intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, 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[hidden_act]
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def forward(self, x):
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y = self.gate_proj(x)
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gate, y = y.chunk(2, -1)
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return swiglu_linear(gate, y, self.down_proj.weight, self.down_proj.bias)
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class ABCBlock(nn.Module):
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def __init__(self, config: ABCConfig, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
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self.attn = ABCAttention(
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hidden_size=config.hidden_size,
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expand_k=config.expand_k,
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expand_v=config.expand_v,
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num_heads=config.num_heads,
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num_slots=config.num_slots,
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use_short_conv=config.use_short_conv,
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conv_size=config.conv_size,
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share_conv_kernel=config.share_conv_kernel,
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gate_fn=config.hidden_act,
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elementwise_affine=config.elementwise_affine,
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norm_eps=config.norm_eps,
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clamp_min=config.clamp_min,
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clamp_max=config.clamp_max,
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fuse_norm=config.fuse_norm,
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layer_idx=layer_idx
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)
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self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
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self.mlp = ABCMLP(
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hidden_size=config.hidden_size,
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hidden_ratio=config.hidden_ratio,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[Tuple[List[torch.Tensor]]] = None,
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use_cache: Optional[bool] = False,
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output_attentions: Optional[bool] = False,
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**kwargs,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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residual = hidden_states
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hidden_states = self.attn_norm(hidden_states)
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hidden_states, attentions, past_key_values = self.attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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use_cache=use_cache,
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output_attentions=output_attentions
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)
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hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states, attentions, past_key_values)
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return outputs
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class ABCPreTrainedModel(PreTrainedModel):
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config_class = ABCConfig
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supports_gradient_checkpointing = True
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_no_split_modules = ['ABCBlock']
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def __init__(self, *inputs, **kwargs):
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super().__init__(*inputs, **kwargs)
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def _init_weights(
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self,
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module: nn.Module,
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rescale_prenorm_residual: bool = True,
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num_residuals_per_layer: int = 2,
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):
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if isinstance(module, (nn.Linear, nn.Conv1d)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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if rescale_prenorm_residual:
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# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
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# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
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# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
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# > -- GPT-2 :: https://openai.com/blog/better-language-models/
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#
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# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
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for name, p in module.named_parameters():
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if name in ["o_proj.weight", "down_proj.weight"]:
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# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
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# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
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# We need to reinit p since this code could be called multiple times
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# Having just p *= scale would repeatedly scale it down
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with torch.no_grad():
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p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers)
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class ABCModel(ABCPreTrainedModel):
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def __init__(self, config: ABCConfig):
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super().__init__(config)
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
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self.layers = nn.ModuleList([ABCBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
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self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
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self.gradient_checkpointing = False
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self.post_init()
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def get_input_embeddings(self):
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return self.embeddings
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def set_input_embeddings(self, value):
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self.embeddings = value
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None, # noqa
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inputs_embeds: Optional[torch.FloatTensor] = None,
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past_key_values: Optional[Tuple[List[torch.Tensor]]] = None,
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use_cache: Optional[bool] = None,
|
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None
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) -> Union[Tuple, BaseModelOutputWithPast]:
|
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if output_attentions:
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warnings.warn("`ABCModel` does not `output_attentions` now, setting it to `False`.")
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output_attentions = False
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
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elif input_ids is not None:
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batch_size = input_ids.shape[0]
|
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elif inputs_embeds is not None:
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batch_size = inputs_embeds.shape[0]
|
||||
else:
|
||||
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||||
|
||||
if inputs_embeds is None:
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inputs_embeds = self.embeddings(input_ids)
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hidden_states = inputs_embeds
|
||||
|
||||
if use_cache:
|
||||
if past_key_values is None:
|
||||
past_key_values = [layer.attn.init_state(batch_size) for layer in self.layers]
|
||||
if not isinstance(past_key_values, RecurrentCache):
|
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past_key_values = RecurrentCache.from_legacy_cache(past_key_values)
|
||||
|
||||
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
|
||||
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_attns = () if output_attentions else None
|
||||
for layer in self.layers:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
hidden_states, attentions, past_key_values = self._gradient_checkpointing_func(
|
||||
layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
past_key_values,
|
||||
use_cache,
|
||||
output_attentions
|
||||
)
|
||||
else:
|
||||
hidden_states, attentions, past_key_values = layer(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions
|
||||
)
|
||||
|
||||
if output_attentions:
|
||||
all_attns += (attentions,)
|
||||
|
||||
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 = None
|
||||
if use_cache:
|
||||
next_cache = past_key_values.to_legacy_cache()
|
||||
if not return_dict:
|
||||
return tuple(x for x in [hidden_states, next_cache, all_hidden_states, all_attns] if x is not None)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_attns
|
||||
)
|
||||
|
||||
|
||||
class ABCForCausalLM(ABCPreTrainedModel):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = ABCModel(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.embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.embeddings = 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
|
||||
|
||||
def generate(self, *args, **kwargs):
|
||||
try:
|
||||
return super().generate(*args, **kwargs)
|
||||
except AttributeError as exception:
|
||||
if 'past_key_values' in str(exception):
|
||||
raise AttributeError(
|
||||
f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, "
|
||||
f"which is not supported for {self.__class__.__name__}. "
|
||||
f"Try another generation strategy instead. "
|
||||
f"For the available generation strategies, check this doc: "
|
||||
f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
|
||||
)
|
||||
else:
|
||||
raise exception
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
past_key_values: Optional[Tuple[List[torch.Tensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
**kwargs
|
||||
):
|
||||
# only last token for `inputs_ids` if the `past_key_values` is passed along.
|
||||
if past_key_values is not None:
|
||||
if not isinstance(past_key_values, RecurrentCache):
|
||||
past_key_values = RecurrentCache.from_legacy_cache(past_key_values, input_ids.shape[1] - 1)
|
||||
input_ids = input_ids[:, -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['past_key_values'] = past_key_values
|
||||
return model_inputs
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
past_key_values: Optional[Tuple[List[torch.Tensor]]] = 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]:
|
||||
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
|
||||
|
||||
outputs = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
if self.config.fuse_cross_entropy:
|
||||
loss_fct = FusedCrossEntropyLoss(inplace_backward=True)
|
||||
else:
|
||||
loss_fct = nn.CrossEntropyLoss()
|
||||
# Enable model parallelism
|
||||
labels = labels.to(logits.device)
|
||||
labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1)
|
||||
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
|
||||
|
||||
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,
|
||||
)
|
||||
Reference in New Issue
Block a user