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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from .configuration_plantgfm import PlantGFMConfig
from transformers import PreTrainedModel
from typing import Optional, Tuple, Union
from transformers.modeling_outputs import CausalLMOutput, SequenceClassifierOutput, BaseModelOutputWithNoAttention
def fftconv(u, k, D):
"""
We apply a convolution through the fourier domain (from the Convolution Theorem)
"""
seqlen = u.shape[-1]
fft_size = 2 * seqlen
k_f = torch.fft.rfft(k.to(torch.float32), n=fft_size) / fft_size
u_f = torch.fft.rfft(u.to(dtype=torch.float32), n=fft_size)
if len(u.shape) > 3: k_f = k_f.unsqueeze(1)
y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen]
out = y + u * D.unsqueeze(-1)
return out.to(dtype=u.dtype)
@torch.jit.script
def mul_sum(q, y):
return (q * y).sum(dim=1)
class HyenaSin(nn.Module):
"""The Sin activation function for the Hyena Filter function."""
def __init__(self, config):
super().__init__()
self.freq = nn.Parameter(config.activation_freq * torch.ones(1, config.filter_order)) if config.train_freq else config.activation_freq * torch.ones(1, config.filter_order)
def forward(self, x):
return torch.sin(self.freq * x)
class HyenaPositionalEmbedding(nn.Module):
def __init__(self, config):
"""Complex exponential positional embeddings for Hyena filters."""
super().__init__()
self.seq_len = config.max_seq_len
# The time embedding fed to the filteres is normalized so that t_f = 1
t = torch.linspace(0, 1, self.seq_len)[None, :, None] # 1, L, 1
if config.emb_dim > 1:
bands = (config.emb_dim - 1) // 2
# To compute the right embeddings we use the "proper" linspace
t_rescaled = torch.linspace(0, self.seq_len - 1, self.seq_len)[None, :, None]
w = 2 * math.pi * t_rescaled / self.seq_len # 1, L, 1
f = torch.linspace(1e-4, bands - 1, bands)[None, None]
z = torch.cat([t, torch.cos(-f * w), torch.sin(-f * w)], dim=-1)
self.register_buffer("z", z)
self.register_buffer("t", t)
def forward(self, L):
return self.z[:, :L], self.t[:, :L]
class HyenaExponentialModulation(nn.Module):
"""The window function applied to the output of the (MLP) filter function."""
def __init__(
self,
d_model,
fast_decay_pct=0.3,
slow_decay_pct=1.5,
target=1e-2,
modulate: bool=True,
shift: float = 0.05,
**kwargs
):
super().__init__()
self.modulate = modulate
self.shift = shift
max_decay = math.log(target) / fast_decay_pct
min_decay = math.log(target) / slow_decay_pct
deltas = torch.linspace(min_decay, max_decay, d_model)[None, None]
self.register_buffer("deltas", deltas)
def forward(self, t, x):
if self.modulate:
decay = torch.exp(-t * self.deltas.abs())
x = x * (decay + self.shift)
return x
class HyenaFilter(nn.Module):
def __init__(
self,
config,
**kwargs
):
"""
Implicit long filter with modulation.
Args:
d_model: number of channels in the input
emb_dim: dimension of the positional encoding (`emb_dim` - 1) // 2 is the number of bands
order: width of the FFN
num_inner_mlps: number of inner linear layers inside filter MLP
Note:
filter_dropout is not implemented
"""
super().__init__()
self.d_model = config.d_model * (config.hyena_order - 1)
self.use_bias = config.use_bias
self.bias = nn.Parameter(torch.randn(self.d_model))
self.dropout = nn.Dropout(config.hyena_filter_dropout)
act = HyenaSin(config)
self.emb_dim = config.emb_dim
assert self.emb_dim % 2 != 0 and self.emb_dim >= 3, "emb_dim must be odd and greater or equal to 3 (time, sine and cosine)"
self.seq_len = config.max_seq_len
self.pos_emb = HyenaPositionalEmbedding(config)
self.implicit_filter = nn.Sequential(
nn.Linear(self.emb_dim, config.filter_order),
act,
)
for i in range(config.num_inner_mlps):
self.implicit_filter.append(nn.Linear(config.filter_order, config.filter_order))
self.implicit_filter.append(act)
self.implicit_filter.append(nn.Linear(config.filter_order, config.d_model, bias=False))
self.modulation = HyenaExponentialModulation(config.d_model)
self.normalized = False
def filter(self, L, *args, **kwargs):
z, t = self.pos_emb(L)
h = self.implicit_filter(z.to(dtype=self.implicit_filter[0].weight.dtype))
h = self.modulation(t, h)
return h
def forward(self, x, L, k=None, bias=None, *args, **kwargs):
if k is None: k = self.filter(L)
# Ensure compatibility with filters that return a tuple
k = k[0] if type(k) is tuple else k
y = fftconv(x, k, bias)
return y
class HyenaOperator(nn.Module):
def __init__(
self,
config,
**filter_args,
):
r"""
Hyena operator described in the paper https://arxiv.org/pdf/2302.10866.pdf
Args:
d_model (int): Dimension of the input and output embeddings (width of the layer)
l_max: (int): Maximum input sequence length. Defaults to None
order: (int): Depth of the Hyena recurrence. Defaults to 2
dropout: (float): Dropout probability. Defaults to 0.0
filter_dropout: (float): Dropout probability for the filter. Defaults to 0.0
"""
super().__init__()
self.d_model = config.d_model
self.l_max = config.max_seq_len
self.order = config.hyena_order
inner_width = config.d_model * (self.order + 1)
self.dropout = nn.Dropout(config.hyena_dropout)
self.in_proj = nn.Linear(self.d_model, inner_width)
self.out_proj = nn.Linear(self.d_model, self.d_model)
self.short_filter = nn.Conv1d(
inner_width,
inner_width,
config.short_filter_order,
padding=2,
groups=inner_width
)
self.filter_fn = HyenaFilter(config)
def forward(self, u):
l = u.size(-2)
l_filter = min(l, self.l_max)
u = self.in_proj(u).transpose(1, 2)
uc = self.short_filter(u)[...,:l_filter]
*x, v = uc.split(self.d_model, dim=1)
k = self.filter_fn.filter(l_filter)[0]
k = k.transpose(0, 1).reshape(self.order - 1, self.d_model, l_filter)
bias = self.filter_fn.bias.reshape(self.order - 1, self.d_model)
for o, x_i in enumerate(reversed(x[1:])):
v = self.dropout(v * x_i)
v = self.filter_fn(v, l_filter, k=k[o], bias=bias[o])
y = (v * x[0]).transpose(1, 2)
y = self.out_proj(y)
return y
class PlantGFMRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
From Llama RMSNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight * hidden_states).to(input_dtype)
class PlantGFMSwiGLU(nn.Module):
def __init__(self, config):
"""
From Llama SwiGLU
"""
super().__init__()
in_features = config.d_model
hidden_features = config.d_inner
self.gate_proj = nn.Linear(in_features, hidden_features, bias=False)
self.down_proj = nn.Linear(hidden_features, in_features, bias=False)
self.up_proj = nn.Linear(in_features, hidden_features, bias=False)
def forward(self, x):
y = F.silu(self.gate_proj(x)) * self.up_proj(x)
y = self.down_proj(y)
return y
class PlantGFMBlock(nn.Module):
def __init__(self, config):
"""
Adapted from Llama Block, replace the Masked Multi-Head Attention (MHA) to Hyena Operator
"""
super().__init__()
self.input_layernorm = PlantGFMRMSNorm(hidden_size=config.d_model, eps=config.rms_norm_epsilon)
self.mixer = HyenaOperator(config)
self.post_attention_layernorm = PlantGFMRMSNorm(hidden_size=config.d_model, eps=config.rms_norm_epsilon)
self.mlp = PlantGFMSwiGLU(config)
def forward(self, hidden_states):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states.to(dtype=self.input_layernorm.weight.dtype))
hidden_states = self.mixer(hidden_states)
hidden_states = hidden_states + residual
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states.to(dtype=self.post_attention_layernorm.weight.dtype))
hidden_states = self.mlp(hidden_states)
hidden_states = hidden_states + residual
return hidden_states
class PlantGFMEmbeddings(nn.Module):
def __init__(self, config, padding_idx=None):
"""
If max_position_embeddings <= 0, there's no position embeddings
If word_embe_proj_dim is not None (e.g., OPT-350m), we embed to that dimension
the project up to embed_dim
"""
super().__init__()
vocab_size = config.vocab_size
if vocab_size % config.pad_vocab_size_multiple != 0:
vocab_size += config.pad_vocab_size_multiple - (vocab_size % config.pad_vocab_size_multiple)
self.word_embeddings = nn.Embedding(vocab_size, config.d_model, padding_idx=padding_idx)
def forward(self, input_ids):
"""
input_ids: (batch, seqlen)
"""
embeddings = self.word_embeddings(input_ids)
return embeddings
class PlantGFMBackbone(nn.Module):
def __init__(self, config) -> None:
super().__init__()
# note max_position_embeddings is 0 for Hyena, and therefore isn't used
self.embeddings = PlantGFMEmbeddings(config)
self.dropout = nn.Dropout(config.embed_dropout)
self.layers = nn.ModuleList([PlantGFMBlock(config) for _ in range(config.n_layer)])
self.rn_f = PlantGFMRMSNorm(hidden_size=config.d_model, eps=config.rms_norm_epsilon)
self.gradient_checkpointing = False
def forward(self, input_ids, inputs_embeds=None, output_hidden_states=False):
all_hidden_states = []
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.embeddings(input_ids)
if output_hidden_states:
all_hidden_states.append(hidden_states)
for layer in self.layers:
if self.gradient_checkpointing and self.training:
hidden_states = self._gradient_checkpointing_func(layer.__call__, hidden_states)
else:
hidden_states = layer(hidden_states)
if output_hidden_states:
all_hidden_states.append(hidden_states)
hidden_states = self.rn_f(hidden_states.to(dtype=self.rn_f.weight.dtype))
if output_hidden_states:
all_hidden_states.append(hidden_states)
return hidden_states, all_hidden_states
class PlantGFMPreTrainedModel(PreTrainedModel):
config_class = PlantGFMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PlantGFMBlock"]
_skip_keys_device_placement = "past_key_values"
_keys_to_ignore_on_load_missing = [r"freq"]
def _init_weights(self, module, initializer_range=0.02):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=initializer_range)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=initializer_range)
class PlantGFMModel(PlantGFMPreTrainedModel):
def __init__(self, config, **kwargs) -> None:
super().__init__(config, **kwargs)
self.backbone = PlantGFMBackbone(config)
self.config = config
# Initialize weights and apply final processing
self.post_init()
def forward(self, input_ids, inputs_embeds=None, output_hidden_states=None, return_dict=None):
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
hidden_states, all_hidden_states = self.backbone(input_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states)
if return_dict:
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states,
hidden_states=all_hidden_states if output_hidden_states else None)
elif output_hidden_states:
return hidden_states, all_hidden_states
else:
return hidden_states
class PlantGFMForCausalLM(PlantGFMPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.glm = PlantGFMModel(config)
vocab_size = config.vocab_size
if vocab_size % config.pad_vocab_size_multiple != 0:
vocab_size += config.pad_vocab_size_multiple - (vocab_size % config.pad_vocab_size_multiple)
self.vocab_size = vocab_size
self.lm_head = nn.Linear(config.d_model, vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.glm.backbone.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.glm.backbone.embeddings.word_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.glm = decoder
def get_decoder(self):
return self.glm
def forward(
self,
input_ids: torch.LongTensor = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutput]:
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.glm(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
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 = nn.CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.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 CausalLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
)
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor = None,
past=None,
**kwargs
):
if past:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids}
class PlantGFMForSequenceClassification(PlantGFMPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.num_labels = kwargs.get("num_labels", config.num_labels)
self.glm = PlantGFMModel(config)
self.score = nn.Linear(config.d_model, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.glm.backbone.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.glm.backbone.embeddings.word_embeddings = value
def forward(
self,
input_ids: torch.LongTensor = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
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.glm(
input_ids,
inputs_embeds=inputs_embeds,
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 = nn.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 = nn.CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = nn.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 SequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
) |