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modeling_gpt_neox.py
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modeling_gpt_neox.py
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from typing import Optional, Tuple, Union
import torch
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM, apply_rotary_pos_emb, logger
def gaudi_gpt_neox_attention_forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
position_ids: torch.LongTensor,
head_mask: Optional[torch.FloatTensor] = None,
layer_past: Optional[Tuple[torch.Tensor]] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
token_idx: Optional[torch.Tensor] = None,
):
"""
Copied from GPTNeoXAttention.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
The only differences are:
- add new args token_idx
- optimize KV cache
"""
has_layer_past = layer_past is not None
# Compute QKV
# Attention heads [batch, seq_len, hidden_size]
# --> [batch, seq_len, (np * 3 * head_size)]
qkv = self.query_key_value(hidden_states)
# [batch, seq_len, (num_heads * 3 * head_size)]
# --> [batch, seq_len, num_heads, 3 * head_size]
new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
qkv = qkv.view(*new_qkv_shape)
# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
# Compute rotary embeddings on rotary_ndims
query_rot = query[..., : self.rotary_ndims]
query_pass = query[..., self.rotary_ndims :]
key_rot = key[..., : self.rotary_ndims]
key_pass = key[..., self.rotary_ndims :]
# Compute token offset for rotary embeddings (when decoding)
seq_len = key.shape[-2]
if has_layer_past:
seq_len += layer_past[0].shape[-2]
cos, sin = self.rotary_emb(value, seq_len=seq_len)
query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
query = torch.cat((query, query_pass), dim=-1)
key = torch.cat((key, key_pass), dim=-1)
# Cache QKV values
if has_layer_past:
past_key = layer_past[0]
past_value = layer_past[1]
if token_idx is not None:
# HPU bug WA
past_key.index_add_(2, token_idx - 1, key - torch.index_select(past_key, 2, token_idx - 1))
past_value.index_add_(2, token_idx - 1, value - torch.index_select(past_value, 2, token_idx - 1))
key = past_key
value = past_value
else:
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
present = (key, value) if use_cache else None
# Compute attention
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
# Reshape outputs
attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size)
attn_output = self.dense(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs
def gaudi_gpt_neox_layer_forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
layer_past: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
token_idx: Optional[torch.Tensor] = None,
):
"""
Copied from GPTNeoxLayer.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
The only differences are:
- add new args token_idx
"""
attention_layer_outputs = self.attention(
self.input_layernorm(hidden_states),
attention_mask=attention_mask,
position_ids=position_ids,
layer_past=layer_past,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
token_idx=token_idx,
)
attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights)
outputs = attention_layer_outputs[1:]
if self.use_parallel_residual:
# pseudocode:
# x = x + attn(ln1(x)) + mlp(ln2(x))
mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
hidden_states = mlp_output + attn_output + hidden_states
else:
# pseudocode:
# x = x + attn(ln1(x))
# x = x + mlp(ln2(x))
attn_output = attn_output + hidden_states
mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
hidden_states = mlp_output + attn_output
if use_cache:
outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights)
else:
outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights)
return outputs
def gaudi_gpt_neox_model_forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
token_idx: Optional[torch.Tensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
"""
Copied from GPTNeoxModel.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
The only differences are:
- add new args token_idx
"""
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
use_cache = use_cache if use_cache is not None else self.config.use_cache
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:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * self.config.num_hidden_layers)
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, seq_length + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
# Attention mask.
if attention_mask is not None:
assert batch_size > 0, "batch_size has to be defined and > 0"
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.embed_in(input_ids)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for layer_past
return module(*inputs, use_cache, None, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer),
hidden_states,
attention_mask,
position_ids,
head_mask[i],
)
else:
outputs = layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask[i],
layer_past=layer_past,
use_cache=use_cache,
output_attentions=output_attentions,
token_idx=token_idx,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.final_layer_norm(hidden_states)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
class GaudiGPTNeoXForCausalLM(GPTNeoXForCausalLM):
"""
Inherits from GPTNeoXForCausalLM: https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt_neox/modeling_gpt_neox.py
The only differences are:
- add new args token_idx
- add token_idx into model_inputs
- from step2 when enable KV cache, slice next_input_ids from input_ids base on the token_idx
- from step2 when enable KV cache, slice next_position_ids from position_ids base on the token_idx
"""
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[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,
token_idx: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_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,
token_idx=token_idx,
)
hidden_states = outputs[0]
lm_logits = self.embed_out(hidden_states)
lm_loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# we are doing next-token prediction; shift prediction scores and input ids by one
shift_logits = lm_logits[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithPast(
loss=lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, token_idx=None, **kwargs
):
input_shape = input_ids.shape
# cut decoder_input_ids if past is used
if past_key_values and past_key_values[0] is not None:
if token_idx is not None:
input_ids = torch.index_select(input_ids, 1, token_idx - 1)
else:
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:
if token_idx is not None:
position_ids = torch.index_select(position_ids, 1, token_idx - 1)
else:
position_ids = position_ids[:, -1].unsqueeze(-1)
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"position_ids": position_ids,
"past_key_values": past_key_values,
"token_idx": token_idx,
}