Refact-1_6B-fim / modeling_gpt_refact.py

Update modeling_gpt_refact.py

a2b605b 7 months ago

No virus

24.9 kB

	import math
	import torch
	import torch.nn.functional as F
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from transformers.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	logging,
	)
	from typing import List, Optional, Tuple, Union

	from .configuration_gpt_refact import GPTRefactConfig

	logger = logging.get_logger(__name__)


	@torch.jit.script
	def upcast_masked_softmax(
	x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, softmax_dtype: torch.dtype
	):
	input_dtype = x.dtype
	x = x.to(softmax_dtype)
	x = torch.where(mask, x, mask_value)
	x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
	return x


	@torch.jit.script
	def upcast_softmax(x: torch.Tensor, softmax_dtype: torch.dtype):
	input_dtype = x.dtype
	x = x.to(softmax_dtype)
	x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
	return x


	@torch.jit.script
	def _get_slopes(attn_heads: int, dev: torch.device) -> torch.Tensor:
	"""
	## Get head-specific slope $m$ for each head
	* `n_heads` is the number of heads in the attention layer $n$
	The slope for first head is
	$$\frac{1}{2^{\frac{8}{n}}} = 2^{-\frac{8}{n}}$$
	The slopes for the rest of the heads are in a geometric series with a ratio same as above.
	For instance when the number of heads is $8$ the slopes are
	$$\frac{1}{2^1}, \frac{1}{2^2}, \dots, \frac{1}{2^8}$$
	"""

	# Get the closest power of 2 to `n_heads`.
	# If `n_heads` is not a power of 2, then we first calculate slopes to the closest (smaller) power of 2,
	# and then add the remaining slopes.
	n = 2 ** math.floor(math.log(attn_heads, 2))
	# $2^{-\frac{8}{n}}$
	m_0 = 2.0 ** (-8.0 / n)
	# $2^{-1\frac{8}{n}}, 2^{-2 \frac{8}{n}}, 2^{-3 \frac{8}{n}}, \dots$
	m = torch.pow(m_0, torch.arange(1, 1 + n, device=dev))

	# If `n_heads` is not a power of 2, then we add the remaining slopes.
	# We calculate the remaining slopes for $n * 2$ (avoiding slopes added previously).
	# And pick the slopes upto `n_heads`.
	if n < attn_heads:
	# $2^{-\frac{8}{2n}}$
	m_hat_0 = 2.0 ** (-4.0 / n)
	# $2^{-1\frac{8}{2n}}, 2^{-3 \frac{8}{2n}}, 2^{-5 \frac{8}{2n}}, \dots$
	# Note that we take steps by $2$ to avoid slopes added previously.
	m_hat = torch.pow(m_hat_0, torch.arange(1, 1 + 2 * (attn_heads - n), 2, device=dev))
	# Concatenate the slopes with the remaining slopes.
	m = torch.cat([m, m_hat])
	return m

	@torch.jit.script
	def get_alibi_biases(
	B: int,
	T: int,
	attn_heads: int,
	dev: torch.device,
	dtype: torch.dtype) -> torch.Tensor:
	"""
	## Calculate the attention biases matrix
	* `n_heads` is the number of heads in the attention layer
	* `mask` is the attention mask of shape `[seq_len_q, seq_len_k]`
	This returns a matrix of shape `[seq_len_q, seq_len_k, n_heads, ]` with ALiBi attention biases.
	"""

	# Get slopes $m$ for each head
	mask = torch.ones((T, T), device=dev, dtype=torch.bool)

	m = _get_slopes(attn_heads, dev).to(dtype)

	# Calculate distances $[0, 1, \dots, N]$
	# Here we calculate the distances using the mask.
	#
	# Since it's causal mask we can just use $[0, 1, \dots, N]$ too.
	# `distance = torch.arange(mask.shape[1], dtype=torch.long, device=mask.device)[None, :]`
	distance = mask.cumsum(dim=-1).to(dtype)

	# Multiply them pair-wise to get the AliBi bias matrix
	biases = distance[:, :, None] * m[None, None, :]
	biases = biases.permute(2, 0, 1)[None, :, :T, :T]
	return biases.contiguous()


	class Attention(nn.Module):

	def __init__(self, config, layer_idx=None):
	super().__init__()
	self.mask_value = None

	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_heads
	self.kv_attn_heads = 1

	self.scale_factor = self.head_dim ** -0.5

	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`:"
	f" {self.num_heads})."
	)

	self.layer_idx = layer_idx
	self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
	self.scale_attention_softmax_in_fp32 = (
	config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
	)
	self.attention_bias_in_fp32 = config.attention_bias_in_fp32

	self.q = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
	self.kv = nn.Linear(self.embed_dim, self.head_dim * 2, bias=False)
	self.c_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)

	def _get_mask_value(self, device, dtype):
	# torch.where expects a tensor. We use a cache to avoid recreating it every time.
	if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
	self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, device=device)
	return self.mask_value

	def _attn(self, query, key, value, attention_mask=None, alibi=None):
	dtype = query.dtype
	softmax_dtype = torch.float32 if self.attention_softmax_in_fp32 else dtype
	mask_value = self._get_mask_value(query.device, softmax_dtype)
	upcast = dtype != softmax_dtype

	query_shape = query.shape
	batch_size = query_shape[0]
	key_length = key.size(-1)

	# (batch_size, query_length, num_heads, head_dim) x (batch_size, head_dim, key_length)
	# -> (batch_size, query_length, num_heads, key_length)
	query_length = query_shape[1]
	attn_shape = (batch_size, query_length, self.num_heads, key_length)
	attn_view = (batch_size, query_length * self.num_heads, key_length)
	# No copy needed for MQA 2, or when layer_past is provided.
	query = query.reshape(batch_size, query_length * self.num_heads, self.head_dim)

	alibi = alibi.transpose(2, 1).reshape(alibi.shape[0], -1, alibi.shape[-1])
	initial_dtype = query.dtype
	new_dtype = torch.float32 if self.attention_bias_in_fp32 else initial_dtype
	attn_weights = alibi.baddbmm(
	batch1=query.to(new_dtype),
	batch2=key.to(new_dtype),
	beta=1,
	alpha=self.scale_factor
	).view(attn_shape).to(initial_dtype)

	if upcast:
	# Use a fused kernel to prevent a large overhead from casting and scaling.
	# Sub-optimal when the key length is not a multiple of 8.
	if attention_mask is None:
	attn_weights = upcast_softmax(attn_weights, softmax_dtype)
	else:
	attn_weights = upcast_masked_softmax(attn_weights, attention_mask, mask_value, softmax_dtype)
	else:
	if attention_mask is not None:
	# The fused kernel is very slow when the key length is not a multiple of 8, so we skip fusion.
	attn_weights = torch.where(attention_mask, attn_weights, mask_value)
	attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)

	attn_output = torch.bmm(attn_weights.view(attn_view), value).view(query_shape)

	return attn_output, attn_weights

	def forward(
	self,
	hidden_states: torch.Tensor,
	layer_past: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	alibi: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor, Optional[torch.Tensor]],
	Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
	]:
	query = self.q(hidden_states)
	kv = self.kv(hidden_states)
	key, value = kv.split(self.head_dim, dim=-1)

	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.transpose(-1, -2), value, attention_mask, alibi)
	attn_output = self.c_proj(attn_output)

	outputs = (attn_output, present)
	if output_attentions:
	attn_weights = attn_weights.transpose(1, 2)
	outputs += (attn_weights,)

	return outputs # a, present, (attentions)


	class MLP(nn.Module):

	def __init__(self, intermediate_size, config, multiple_of: int = 256):
	super().__init__()
	embed_dim = config.hidden_size
	hidden_dim = intermediate_size
	hidden_dim = int(2 * hidden_dim / 3)
	self.hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
	self.gate_up_proj = nn.Linear(embed_dim, self.hidden_dim * 2, bias=False)
	self.c_proj = nn.Linear(self.hidden_dim, embed_dim, bias=False)

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	up_proj = self.gate_up_proj(x)
	x1, x2 = torch.split(up_proj, self.hidden_dim, dim=-1)
	x = self.c_proj(F.silu(x1) * x2)
	return x


	class LayerNormNoBias(nn.Module):

	def __init__(self, shape: int, eps: float = 1e-5):
	super().__init__()
	self.shape = (shape,)
	self.eps = eps
	self.weight = nn.Parameter(torch.empty(self.shape))

	def forward(self, x: torch.Tensor) -> torch.Tensor:
	return F.layer_norm(x, self.shape, self.weight, None, self.eps)


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

	self.ln_1 = LayerNormNoBias(hidden_size, eps=config.layer_norm_epsilon)
	self.attn = Attention(config, layer_idx=layer_idx)
	self.ln_2 = LayerNormNoBias(hidden_size, eps=config.layer_norm_epsilon)
	self.mlp = MLP(self.inner_dim, config)

	def forward(
	self,
	hidden_states: Optional[Tuple[torch.Tensor]],
	layer_past: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	alibi: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
	]:
	hidden_states_norm = self.ln_1(hidden_states)
	attn_outputs = self.attn(
	hidden_states_norm,
	layer_past=layer_past,
	attention_mask=attention_mask,
	alibi=alibi,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
	outputs = attn_outputs[1:]
	# residual connection
	mix = attn_output + hidden_states

	norm_mix = self.ln_2(mix)
	feed_forward_hidden_states = self.mlp(norm_mix)
	# residual connection
	hidden_states = mix + 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 GPTRefactPreTrainedModel(PreTrainedModel):

	config_class = GPTRefactConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["GPTRefactBlock"]
	_skip_keys_device_placement = "past_key_values"

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

	def _init_weights(self, module):
	if isinstance(module, (MLP, Attention)):
	# 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
	module.c_proj.weight.data.normal_(
	mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
	)
	module.c_proj._is_hf_initialized = True
	elif 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, LayerNormNoBias):
	module.weight.data.fill_(1.0)


	class GPTRefactModel(GPTRefactPreTrainedModel):

	def __init__(self, config):
	super().__init__(config)
	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.multi_query = config.multi_query
	self.wte = nn.Embedding(config.vocab_size, self.embed_dim)

	self.h = nn.ModuleList([GPTRefactBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])

	self.max_positions = config.max_position_embeddings
	self.attention_bias_in_fp32 = config.attention_bias_in_fp32
	self.register_buffer(
	"bias", torch.tril(torch.ones((self.max_positions, self.max_positions), dtype=torch.bool)),
	persistent=False
	)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.wte

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
	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")

	if batch_size <= 0:
	raise ValueError("batch_size has to be defined and > 0")

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

	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)

	query_length = input_shape[-1]
	seq_length_with_past = past_length + query_length

	# Self-attention mask.
	key_length = past_length + query_length
	self_attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]
	if attention_mask is not None:
	self_attention_mask = self_attention_mask * attention_mask.view(batch_size, 1, -1).to(
	dtype=torch.bool, device=self_attention_mask.device
	)

	# MQA models: (batch_size, query_length, n_heads, key_length)
	attention_mask = self_attention_mask.unsqueeze(2)

	hidden_states = self.wte(input_ids) if inputs_embeds is None else inputs_embeds

	alibi_dtype = torch.float32 if self.attention_bias_in_fp32 else self.wte.weight.dtype
	alibi = get_alibi_biases(hidden_states.shape[0], seq_length_with_past,
	self.num_heads, device, alibi_dtype)[:, :, -query_length:, :]

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

	presents = [] if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, 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 past_key_value
	return module(*inputs, use_cache, output_attentions)

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states,
	None,
	attention_mask,
	alibi
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	alibi=alibi,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states = outputs[0]
	if use_cache:
	presents.append(outputs[1])

	if output_attentions:
	all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)

	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, all_cross_attentions]
	if v is not None
	)

	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	class GPTRefactForCausalLM(GPTRefactPreTrainedModel):

	_tied_weights_keys = ["lm_head.weight", "ln_f.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.transformer = GPTRefactModel(config)
	self.ln_f = LayerNormNoBias(self.transformer.embed_dim, eps=config.layer_norm_epsilon)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

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

	# gradient checkpointing support for lower versions of transformers
	import transformers
	from packaging import version

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

	v = version.parse(transformers.__version__)
	if v.major <= 4 and v.minor < 35:
	self._set_gradient_checkpointing = _set_gradient_checkpointing

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
	if inputs_embeds is not None and past_key_values is None:
	model_inputs = {"inputs_embeds": inputs_embeds}
	else:
	if past_key_values is not None:
	model_inputs = {"input_ids": input_ids[..., -1:]}
	else:
	model_inputs = {"input_ids": input_ids}

	model_inputs.update(
	{
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	}
	)
	return model_inputs

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
	r"""
	labels (`torch.Tensor` of shape `(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,
	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]

	x = self.ln_f(hidden_states)
	lm_logits = self.lm_head(x)

	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().to(shift_logits.device)
	# Flatten the tokens
	loss_fct = 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 CausalLMOutputWithCrossAttentions(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	cross_attentions=transformer_outputs.cross_attentions,
	)

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