pangu_2_6B / modeling_gptpangu.py

remove config class in model

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	"""PyTorch PanguAlpha GPT2 Model"""
	# from .configuration_gptpangu import GPTPanguConfig

	from typing import Tuple
	import math

	import torch
	from torch import nn

	from transformers.activations import ACT2FN
	from transformers.modeling_utils import PreTrainedModel
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast

	from transformers.utils import logging

	logger = logging.get_logger(__name__)


	class GPTPanguAttention(nn.Module):
	def __init__(self, config):
	super().__init__()

	max_positions = config.max_position_embeddings
	self.register_buffer(
	"bias",
	torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
	1, 1, max_positions, max_positions
	),
	)
	self.register_buffer("masked_bias", torch.tensor(-1e4))

	self.embed_dim = config.hidden_size
	self.num_heads = config.num_heads
	self.head_dim = self.embed_dim // self.num_heads
	if self.head_dim * self.num_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
	)

	self.scale_attn_weights = config.scale_attn_weights

	self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
	self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
	self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
	self.c_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)

	self.attn_dropout = nn.Dropout(config.attn_pdrop)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)


	def _attn(self, query, key, value, attention_mask=None, head_mask=None):
	attn_weights = torch.matmul(query, key.transpose(-1, -2))

	if self.scale_attn_weights:
	attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)

	query_length, key_length = query.size(-2), key.size(-2)
	causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool()
	attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))

	if attention_mask is not None:
	# Apply the attention mask
	attn_weights = attn_weights + attention_mask

	attn_weights = nn.functional.softmax(attn_weights, dim=-1)

	# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
	attn_weights = attn_weights.type(value.dtype)
	attn_weights = self.attn_dropout(attn_weights)

	# Mask heads if we want to
	if head_mask is not None:
	attn_weights = attn_weights * head_mask

	attn_output = torch.matmul(attn_weights, value)

	return attn_output, attn_weights

	def _split_heads(self, tensor, num_heads, attn_head_size):
	"""
	Splits hidden_size dim into attn_head_size and num_heads
	"""
	new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
	tensor = tensor.view(*new_shape)
	return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)

	def _merge_heads(self, tensor, num_heads, attn_head_size):
	"""
	Merges attn_head_size dim and num_attn_heads dim into hidden_size
	"""
	tensor = tensor.permute(0, 2, 1, 3).contiguous()
	new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
	return tensor.view(new_shape)

	def forward(
	self,
	hidden_states,
	layer_past=None,
	attention_mask=None,
	head_mask=None,
	custom_query=None,
	use_cache=False,
	output_attentions=False,
	):
	query = self.q_proj(custom_query) if custom_query is not None else self.q_proj(hidden_states)
	key = self.k_proj(hidden_states)
	value = self.v_proj(hidden_states)

	query = self._split_heads(query, self.num_heads, self.head_dim)
	key = self._split_heads(key, self.num_heads, self.head_dim)
	value = self._split_heads(value, self.num_heads, self.head_dim)

	if layer_past is not None:
	past_key, past_value = layer_past
	key = torch.cat((past_key, key), dim=-2)
	value = torch.cat((past_value, value), dim=-2)

	if use_cache is True:
	present = (key, value)
	else:
	present = None

	attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

	attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
	attn_output = self.c_proj(attn_output)
	attn_output = self.resid_dropout(attn_output)

	outputs = (attn_output, present)
	if output_attentions:
	outputs += (attn_weights,)

	return outputs # a, present, (attentions)


	class GPTPanguMLP(nn.Module):
	def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * hidden_size
	super().__init__()
	embed_dim = config.hidden_size
	self.c_fc = nn.Linear(embed_dim, intermediate_size)
	self.c_proj = nn.Linear(intermediate_size, embed_dim)
	self.act = ACT2FN[config.activation_function]
	self.dropout = nn.Dropout(config.resid_pdrop)

	def forward(self, hidden_states):
	hidden_states = self.c_fc(hidden_states)
	hidden_states = self.act(hidden_states)
	hidden_states = self.c_proj(hidden_states)
	hidden_states = self.dropout(hidden_states)
	return hidden_states


	class GPTPanguBlock(nn.Module):
	def __init__(self, config):
	super().__init__()
	hidden_size = config.hidden_size
	inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size

	self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	self.attn = GPTPanguAttention(config)
	self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	self.mlp = GPTPanguMLP(inner_dim, config)

	def forward(
	self,
	hidden_states,
	layer_past=None,
	attention_mask=None,
	head_mask=None,
	custom_query=None,
	use_cache=False,
	output_attentions=False,
	):
	residual = hidden_states
	hidden_states = self.ln_1(hidden_states)
	attn_outputs = self.attn(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask,
	custom_query=custom_query,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
	outputs = attn_outputs[1:]
	# residual connection
	hidden_states = attn_output + residual

	residual = hidden_states
	hidden_states = self.ln_2(hidden_states)
	feed_forward_hidden_states = self.mlp(hidden_states)
	# residual connection
	hidden_states = residual + feed_forward_hidden_states

	if use_cache:
	outputs = (hidden_states,) + outputs
	else:
	outputs = (hidden_states,) + outputs[1:]

	return outputs # hidden_states, present, (attentions, cross_attentions)


	class GPTPanguPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	# config_class = GPTPanguConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True

	def __init__(self, inputs, *kwargs):
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, (nn.Linear,)):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
	# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
	# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
	# > -- GPT-2 :: https://openai.com/blog/better-language-models/
	#
	# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
	for name, p in module.named_parameters():
	if "c_proj" in name and "weight" in name:
	# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
	p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.num_layers)))

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, GPTPanguModel):
	module.gradient_checkpointing = value


	class GPTPanguModel(GPTPanguPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.embed_dim = config.hidden_size

	self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
	self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
	self.wqe = nn.Embedding(config.max_position_embeddings, self.embed_dim)

	self.drop = nn.Dropout(config.embd_pdrop)
	self.h = nn.ModuleList([GPTPanguBlock(config) for _ in range(config.num_layers)])
	self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.wte

	def set_input_embeddings(self, new_embeddings):
	self.wte = new_embeddings

	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	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

	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()
	input_ids = input_ids.view(-1, input_shape[-1])
	batch_size = input_ids.shape[0]
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	batch_size = inputs_embeds.shape[0]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	device = input_ids.device if input_ids is not None else inputs_embeds.device

	if token_type_ids is not None:
	token_type_ids = token_type_ids.view(-1, input_shape[-1])
	if position_ids is not None:
	position_ids = position_ids.view(-1, input_shape[-1])

	if past_key_values is None:
	past_length = 0
	past_key_values = tuple([None] * len(self.h))
	else:
	past_length = past_key_values[0][0].size(-2)
	if position_ids is None:
	position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

	# GPT2Attention mask.
	if attention_mask is not None:
	if batch_size <= 0:
	raise ValueError("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 -10000.0 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) * -10000.0

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x num_heads x N x N
	# head_mask has shape n_layer x batch x num_heads x N x N
	head_mask = self.get_head_mask(head_mask, self.config.num_layers)

	if inputs_embeds is None:
	inputs_embeds = self.wte(input_ids)
	position_embeds = self.wpe(position_ids)
	hidden_states = inputs_embeds + position_embeds

	if token_type_ids is not None:
	token_type_embeds = self.wte(token_type_ids)
	hidden_states = hidden_states + token_type_embeds

	hidden_states = self.drop(hidden_states)

	output_shape = input_shape + (hidden_states.size(-1),)

	# top attention custom query
	last_layer_id = len(self.h) - 1
	query_embeds = self.wqe(position_ids)

	presents = () if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
	# Final LayerNorm before last query layer
	if i == last_layer_id:
	hidden_states = self.ln_f(hidden_states)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	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

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(*inputs, use_cache, output_attentions)

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states=hidden_states,
	layer_past=None,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	# custom query
	custom_query=query_embeds if i == last_layer_id else None,
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	# custom query
	custom_query=query_embeds if i == last_layer_id else None,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states = outputs[0]
	if use_cache is True:
	presents = presents + (outputs[1],)

	if output_attentions:
	all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

	hidden_states = hidden_states.view(*output_shape)
	# 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_self_attentions] if v is not None)

	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	)


	class GPTPanguForCausalLM(GPTPanguPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.transformer = GPTPanguModel(config)
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
	token_type_ids = kwargs.get("token_type_ids", None)
	# only last token for inputs_ids if past is defined in kwargs
	if past:
	input_ids = input_ids[:, -1].unsqueeze(-1)
	if token_type_ids is not None:
	token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

	attention_mask = kwargs.get("attention_mask", None)
	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:
	position_ids = position_ids[:, -1].unsqueeze(-1)
	else:
	position_ids = None
	return {
	"input_ids": input_ids,
	"past_key_values": past,
	"use_cache": kwargs.get("use_cache"),
	"position_ids": position_ids,
	"attention_mask": attention_mask,
	"token_type_ids": token_type_ids,
	}

	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
	``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
	``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	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]

	lm_logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = nn.CrossEntropyLoss()
	loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

	if not return_dict:
	output = (lm_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return CausalLMOutputWithPast(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)

	@staticmethod
	def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
	"""
	This function is used to re-order the :obj:`past_key_values` cache if
	:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
	called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
	"""
	return tuple(
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
	for layer_past in past
	)