408 lines
17 KiB
Python
408 lines
17 KiB
Python
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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.hgrn import HGRNAttention
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from fla.models.hgrn.configuration_hgrn import HGRNConfig
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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 HGRNMLP(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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) -> HGRNMLP:
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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 HGRNBlock(nn.Module):
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def __init__(self, config: HGRNConfig, 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 = HGRNAttention(
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mode=config.attn_mode,
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hidden_size=config.hidden_size,
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num_heads=config.num_heads,
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expand_ratio=config.expand_ratio,
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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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elementwise_affine=config.elementwise_affine,
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norm_eps=config.norm_eps,
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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 = HGRNMLP(
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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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lower_bound: Optional[torch.Tensor] = 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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lower_bound=lower_bound
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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 HGRNPreTrainedModel(PreTrainedModel):
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config_class = HGRNConfig
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supports_gradient_checkpointing = True
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_no_split_modules = ['HGRNBlock']
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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 HGRNModel(HGRNPreTrainedModel):
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def __init__(self, config: HGRNConfig):
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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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if config.use_lower_bound:
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self.lower_bounds = nn.Parameter(torch.zeros(config.num_hidden_layers, config.hidden_size))
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self.layers = nn.ModuleList([HGRNBlock(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("`HGRNModel` 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]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.embeddings(input_ids)
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hidden_states = inputs_embeds
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if use_cache:
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if past_key_values is None:
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past_key_values = [layer.attn.init_state(batch_size) for layer in self.layers]
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if not isinstance(past_key_values, RecurrentCache):
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past_key_values = RecurrentCache.from_legacy_cache(past_key_values)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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all_hidden_states = () if output_hidden_states else None
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all_attns = () if output_attentions else None
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if self.config.use_lower_bound:
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lower_bounds = self.lower_bounds.softmax(0)
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lower_bounds = lower_bounds.cumsum(0) - lower_bounds[0]
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for i, layer in enumerate(self.layers):
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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lower_bound = lower_bounds[i] if self.config.use_lower_bound else None
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if self.gradient_checkpointing and self.training:
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hidden_states, attentions, past_key_values = self._gradient_checkpointing_func(
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layer.__call__,
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hidden_states,
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attention_mask,
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past_key_values,
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use_cache,
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output_attentions,
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lower_bound
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)
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else:
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hidden_states, attentions, past_key_values = layer(
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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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lower_bound=lower_bound
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)
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if output_attentions:
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all_attns += (attentions,)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = None
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if use_cache:
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next_cache = past_key_values.to_legacy_cache()
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if not return_dict:
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return tuple(x for x in [hidden_states, next_cache, all_hidden_states, all_attns] if x is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_attns
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)
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class HGRNForCausalLM(HGRNPreTrainedModel):
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_tied_weights_keys = ["lm_head.weight"]
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def __init__(self, config):
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super().__init__(config)
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self.model = HGRNModel(config)
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self.vocab_size = config.vocab_size
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.model.embeddings
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def set_input_embeddings(self, value):
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self.model.embeddings = value
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def get_output_embeddings(self):
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return self.lm_head
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def set_output_embeddings(self, new_embeddings):
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self.lm_head = new_embeddings
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def set_decoder(self, decoder):
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self.model = decoder
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def get_decoder(self):
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return self.model
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def generate(self, *args, **kwargs):
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try:
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return super().generate(*args, **kwargs)
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except AttributeError as exception:
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if 'past_key_values' in str(exception):
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raise AttributeError(
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f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, "
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f"which is not supported for {self.__class__.__name__}. "
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f"Try another generation strategy instead. "
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f"For the available generation strategies, check this doc: "
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f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
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)
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else:
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raise exception
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def prepare_inputs_for_generation(
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self,
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input_ids: torch.LongTensor = None,
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past_key_values: Optional[Tuple[List[torch.Tensor]]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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**kwargs
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):
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# only last token for `inputs_ids` if the `past_key_values` is passed along.
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if past_key_values is not None:
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if not isinstance(past_key_values, RecurrentCache):
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past_key_values = RecurrentCache.from_legacy_cache(past_key_values, input_ids.shape[1] - 1)
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input_ids, attention_mask = input_ids[:, -1:], attention_mask[:, -1:]
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# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
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if inputs_embeds is not None and past_key_values is None:
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model_inputs = {'inputs_embeds': inputs_embeds}
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else:
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# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
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# recompiles graphs as the stride of the inputs is a guard.
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# Ref: https://github.com/huggingface/transformers/pull/29114
|
||
|
|
# TODO: use `next_tokens` directly instead.
|
||
|
|
model_inputs = {'input_ids': input_ids.contiguous()}
|
||
|
|
|
||
|
|
model_inputs.update({
|
||
|
|
'past_key_values': past_key_values,
|
||
|
|
'use_cache': kwargs.get('use_cache'),
|
||
|
|
'attention_mask': attention_mask,
|
||
|
|
})
|
||
|
|
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,
|
||
|
|
)
|