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"""PyTorch InternLM2 model.""" |
|
import math |
|
import warnings |
|
from typing import List, Optional, Tuple, Union |
|
|
|
import torch |
|
import torch.utils.checkpoint |
|
from einops import rearrange |
|
from torch import nn |
|
from transformers.activations import ACT2FN |
|
from transformers.modeling_outputs import BaseModelOutputWithPast |
|
from transformers.modeling_utils import PreTrainedModel |
|
from transformers.utils import (add_start_docstrings, |
|
add_start_docstrings_to_model_forward, logging) |
|
|
|
try: |
|
from transformers.generation.streamers import BaseStreamer |
|
except: |
|
BaseStreamer = None |
|
|
|
from .build_mlp import PLoRA |
|
from .configuration_internlm_xcomposer2 import InternLMXcomposer2Config as InternLM2Config |
|
logger = logging.get_logger(__name__) |
|
|
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_CONFIG_FOR_DOC = 'InternLM2Config' |
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|
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def _make_causal_mask(input_ids_shape: torch.Size, |
|
dtype: torch.dtype, |
|
device: torch.device, |
|
past_key_values_length: int = 0): |
|
"""Make causal mask used for bi-directional self-attention.""" |
|
bsz, tgt_len = input_ids_shape |
|
mask = torch.full((tgt_len, tgt_len), |
|
torch.tensor(torch.finfo(dtype).min, device=device), |
|
device=device) |
|
mask_cond = torch.arange(mask.size(-1), device=device) |
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) |
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mask = mask.to(dtype) |
|
|
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if past_key_values_length > 0: |
|
mask = torch.cat([ |
|
torch.zeros( |
|
tgt_len, past_key_values_length, dtype=dtype, device=device), |
|
mask |
|
], |
|
dim=-1) |
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, |
|
tgt_len + past_key_values_length) |
|
|
|
|
|
|
|
def _expand_mask(mask: torch.Tensor, |
|
dtype: torch.dtype, |
|
tgt_len: Optional[int] = None): |
|
"""Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, |
|
src_seq_len]`.""" |
|
bsz, src_len = mask.size() |
|
tgt_len = tgt_len if tgt_len is not None else src_len |
|
|
|
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, |
|
src_len).to(dtype) |
|
|
|
inverted_mask = 1.0 - expanded_mask |
|
|
|
return inverted_mask.masked_fill( |
|
inverted_mask.to(torch.bool), |
|
torch.finfo(dtype).min) |
|
|
|
|
|
class InternLM2RMSNorm(nn.Module): |
|
|
|
def __init__(self, hidden_size, eps=1e-6): |
|
"""InternLM2RMSNorm is equivalent to T5LayerNorm.""" |
|
super().__init__() |
|
self.weight = nn.Parameter(torch.ones(hidden_size)) |
|
self.variance_epsilon = eps |
|
|
|
def forward(self, hidden_states): |
|
input_dtype = hidden_states.dtype |
|
hidden_states = hidden_states.to(torch.float32) |
|
variance = hidden_states.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 InternLM2RotaryEmbedding(nn.Module): |
|
|
|
def __init__(self, |
|
dim, |
|
max_position_embeddings=2048, |
|
base=10000, |
|
device=None): |
|
super().__init__() |
|
|
|
self.dim = dim |
|
self.max_position_embeddings = max_position_embeddings |
|
self.base = base |
|
inv_freq = 1.0 / ( |
|
self.base |
|
**(torch.arange(0, self.dim, 2).float().to(device) / self.dim)) |
|
self.register_buffer('inv_freq', inv_freq, persistent=False) |
|
|
|
|
|
self._set_cos_sin_cache( |
|
seq_len=max_position_embeddings, |
|
device=self.inv_freq.device, |
|
dtype=torch.get_default_dtype()) |
|
|
|
def _set_cos_sin_cache(self, seq_len, device, dtype): |
|
self.max_seq_len_cached = seq_len |
|
t = torch.arange( |
|
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
|
|
|
freqs = torch.einsum('i,j->ij', t, self.inv_freq) |
|
|
|
emb = torch.cat((freqs, freqs), dim=-1) |
|
self.register_buffer( |
|
'cos_cached', emb.cos().to(dtype), persistent=False) |
|
self.register_buffer( |
|
'sin_cached', emb.sin().to(dtype), persistent=False) |
|
|
|
def forward(self, x, seq_len=None): |
|
|
|
if seq_len > self.max_seq_len_cached: |
|
self._set_cos_sin_cache( |
|
seq_len=seq_len, device=x.device, dtype=x.dtype) |
|
|
|
return ( |
|
self.cos_cached[:seq_len].to(dtype=x.dtype), |
|
self.sin_cached[:seq_len].to(dtype=x.dtype), |
|
) |
|
|
|
|
|
class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding): |
|
"""InternLM2RotaryEmbedding extended with linear scaling. |
|
|
|
Credits to the Reddit user /u/kaiokendev |
|
""" |
|
|
|
def __init__(self, |
|
dim, |
|
max_position_embeddings=2048, |
|
base=10000, |
|
device=None, |
|
scaling_factor=1.0): |
|
self.scaling_factor = scaling_factor |
|
super().__init__(dim, max_position_embeddings, base, device) |
|
|
|
def _set_cos_sin_cache(self, seq_len, device, dtype): |
|
self.max_seq_len_cached = seq_len |
|
t = torch.arange( |
|
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
|
t = t / self.scaling_factor |
|
|
|
freqs = torch.einsum('i,j->ij', t, self.inv_freq) |
|
|
|
emb = torch.cat((freqs, freqs), dim=-1) |
|
self.register_buffer( |
|
'cos_cached', emb.cos().to(dtype), persistent=False) |
|
self.register_buffer( |
|
'sin_cached', emb.sin().to(dtype), persistent=False) |
|
|
|
|
|
class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding): |
|
"""InternLM2RotaryEmbedding extended with Dynamic NTK scaling. |
|
|
|
Credits to the Reddit users /u/bloc97 and /u/emozilla. |
|
""" |
|
|
|
def __init__(self, |
|
dim, |
|
max_position_embeddings=2048, |
|
base=10000, |
|
device=None, |
|
scaling_factor=1.0): |
|
self.scaling_factor = scaling_factor |
|
super().__init__(dim, max_position_embeddings, base, device) |
|
|
|
def _set_cos_sin_cache(self, seq_len, device, dtype): |
|
self.max_seq_len_cached = seq_len |
|
|
|
if seq_len > self.max_position_embeddings: |
|
base = self.base * ((self.scaling_factor * seq_len / |
|
self.max_position_embeddings) - |
|
(self.scaling_factor - 1))**( |
|
self.dim / (self.dim - 2)) |
|
inv_freq = 1.0 / ( |
|
base |
|
**(torch.arange(0, self.dim, 2).float().to(device) / self.dim)) |
|
self.register_buffer('inv_freq', inv_freq, persistent=False) |
|
|
|
t = torch.arange( |
|
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
|
|
|
freqs = torch.einsum('i,j->ij', t, self.inv_freq) |
|
|
|
emb = torch.cat((freqs, freqs), dim=-1) |
|
self.register_buffer( |
|
'cos_cached', emb.cos().to(dtype), persistent=False) |
|
self.register_buffer( |
|
'sin_cached', emb.sin().to(dtype), persistent=False) |
|
|
|
|
|
def rotate_half(x): |
|
"""Rotates half the hidden dims of the input.""" |
|
x1 = x[..., :x.shape[-1] // 2] |
|
x2 = x[..., x.shape[-1] // 2:] |
|
return torch.cat((-x2, x1), dim=-1) |
|
|
|
|
|
def apply_rotary_pos_emb(q, k, cos, sin, position_ids): |
|
|
|
cos = cos.squeeze(1).squeeze(0) |
|
sin = sin.squeeze(1).squeeze(0) |
|
cos = cos.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1) |
|
sin = sin.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1) |
|
if q.size(2) == 1: |
|
q_embed = (q * cos[:, :, -1:, :]) + ( |
|
rotate_half(q) * sin[:, :, -1:, :]) |
|
else: |
|
q_embed = (q * cos) + (rotate_half(q) * sin) |
|
|
|
if k.size(2) == 1: |
|
k_embed = (k * cos[:, :, -1:, :]) + ( |
|
rotate_half(k) * sin[:, :, -1:, :]) |
|
else: |
|
k_embed = (k * cos) + (rotate_half(k) * sin) |
|
|
|
return q_embed, k_embed |
|
|
|
|
|
class InternLM2MLP(nn.Module): |
|
|
|
def __init__(self, config): |
|
super().__init__() |
|
self.config = config |
|
self.hidden_size = config.hidden_size |
|
self.intermediate_size = config.intermediate_size |
|
|
|
self.w1 = PLoRA( |
|
self.hidden_size, |
|
self.intermediate_size, |
|
bias=False, |
|
lora_r=256, |
|
lora_alpha=256, |
|
lora_len=576) |
|
self.w3 = PLoRA( |
|
self.hidden_size, |
|
self.intermediate_size, |
|
bias=False, |
|
lora_r=256, |
|
lora_alpha=256, |
|
lora_len=576) |
|
self.w2 = PLoRA( |
|
self.intermediate_size, |
|
self.hidden_size, |
|
bias=False, |
|
lora_r=256, |
|
lora_alpha=256, |
|
lora_len=576) |
|
|
|
self.act_fn = ACT2FN[config.hidden_act] |
|
|
|
def forward(self, x, im_mask): |
|
down_proj = self.w2( |
|
self.act_fn(self.w1(x, im_mask)) * self.w3(x, im_mask), im_mask) |
|
|
|
return down_proj |
|
|
|
|
|
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: |
|
"""This is the equivalent of torch.repeat_interleave(x, dim=1, |
|
repeats=n_rep). |
|
|
|
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to |
|
(batch, num_attention_heads, seqlen, head_dim) |
|
""" |
|
batch, num_key_value_heads, slen, head_dim = hidden_states.shape |
|
if n_rep == 1: |
|
return hidden_states |
|
hidden_states = hidden_states[:, :, |
|
None, :, :].expand(batch, |
|
num_key_value_heads, |
|
n_rep, slen, head_dim) |
|
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, |
|
head_dim) |
|
|
|
|
|
class InternLM2Attention(nn.Module): |
|
"""Multi-headed attention from 'Attention Is All You Need' paper.""" |
|
|
|
def __init__(self, config: InternLM2Config): |
|
super().__init__() |
|
self.config = config |
|
self.hidden_size = config.hidden_size |
|
self.num_heads = config.num_attention_heads |
|
self.head_dim = self.hidden_size // self.num_heads |
|
self.num_key_value_heads = config.num_key_value_heads |
|
self.num_key_value_groups = self.num_heads // self.num_key_value_heads |
|
self.max_position_embeddings = config.max_position_embeddings |
|
self.is_causal = True |
|
|
|
if (self.head_dim * self.num_heads) != self.hidden_size: |
|
raise ValueError( |
|
f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}' |
|
f' and `num_heads`: {self.num_heads}).') |
|
|
|
self.wqkv = PLoRA( |
|
self.hidden_size, |
|
(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim, |
|
bias=config.bias, |
|
lora_r=256, |
|
lora_alpha=256, |
|
lora_len=576) |
|
|
|
self.wo = PLoRA( |
|
self.num_heads * self.head_dim, |
|
self.hidden_size, |
|
bias=config.bias, |
|
lora_r=256, |
|
lora_alpha=256, |
|
lora_len=576) |
|
self._init_rope() |
|
|
|
def _init_rope(self): |
|
if self.config.rope_scaling is None: |
|
self.rotary_emb = InternLM2RotaryEmbedding( |
|
self.head_dim, |
|
max_position_embeddings=self.max_position_embeddings, |
|
base=self.config.rope_theta, |
|
) |
|
else: |
|
scaling_type = self.config.rope_scaling['type'] |
|
scaling_factor = self.config.rope_scaling['factor'] |
|
if scaling_type == 'dynamic': |
|
self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding( |
|
self.head_dim, |
|
max_position_embeddings=self.max_position_embeddings, |
|
base=self.config.rope_theta, |
|
scaling_factor=scaling_factor) |
|
else: |
|
raise ValueError( |
|
"Currently we only support rotary embedding's type being 'dynamic'." |
|
) |
|
return self.rotary_emb |
|
|
|
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): |
|
return tensor.view(bsz, seq_len, self.num_heads, |
|
self.head_dim).transpose(1, 2).contiguous() |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
im_mask: Optional[Tuple[torch.Tensor]] = None, |
|
**kwargs, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], |
|
Optional[Tuple[torch.Tensor]]]: |
|
if 'padding_mask' in kwargs: |
|
warnings.warn( |
|
'Passing `padding_mask` is deprecated and will be removed in v4.37. ' |
|
'Please make sure use `attention_mask` instead.`') |
|
|
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
qkv_states = self.wqkv(hidden_states, im_mask) |
|
|
|
qkv_states = rearrange( |
|
qkv_states, |
|
'b q (h gs d) -> b q h gs d', |
|
gs=2 + self.num_key_value_groups, |
|
d=self.head_dim, |
|
) |
|
|
|
query_states = qkv_states[..., :self.num_key_value_groups, :] |
|
query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d') |
|
key_states = qkv_states[..., -2, :] |
|
value_states = qkv_states[..., -1, :] |
|
|
|
query_states = query_states.transpose(1, 2) |
|
key_states = key_states.transpose(1, 2) |
|
value_states = value_states.transpose(1, 2) |
|
|
|
kv_seq_len = key_states.shape[-2] |
|
if past_key_value is not None: |
|
kv_seq_len += past_key_value[0].shape[-2] |
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
|
query_states, key_states = apply_rotary_pos_emb( |
|
query_states, key_states, cos, sin, position_ids) |
|
|
|
if past_key_value is not None: |
|
|
|
key_states = torch.cat([past_key_value[0], key_states], dim=2) |
|
value_states = torch.cat([past_key_value[1], value_states], dim=2) |
|
|
|
past_key_value = (key_states, value_states) if use_cache else None |
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups) |
|
value_states = repeat_kv(value_states, self.num_key_value_groups) |
|
|
|
attn_weights = torch.matmul(query_states, key_states.transpose( |
|
2, 3)) / math.sqrt(self.head_dim) |
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
|
raise ValueError( |
|
f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is' |
|
f' {attn_weights.size()}') |
|
|
|
if attention_mask is not None: |
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
|
raise ValueError( |
|
f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}' |
|
) |
|
attn_weights = attn_weights + attention_mask |
|
|
|
|
|
attn_weights = nn.functional.softmax( |
|
attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
|
attn_output = torch.matmul(attn_weights, value_states) |
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): |
|
raise ValueError( |
|
f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is' |
|
f' {attn_output.size()}') |
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous() |
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) |
|
|
|
attn_output = self.wo(attn_output, im_mask) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
class InternLM2FlashAttention2(InternLM2Attention): |
|
"""InternLM2 flash attention module. |
|
|
|
This module inherits from `InternLM2Attention` as the weights of the module |
|
stays untouched. The only required change would be on the forward pass |
|
where it needs to correctly call the public API of flash attention and deal |
|
with padding tokens in case the input contains any of them. |
|
""" |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
im_mask: Optional[Tuple[torch.Tensor]] = None, |
|
**kwargs, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], |
|
Optional[Tuple[torch.Tensor]]]: |
|
|
|
if 'padding_mask' in kwargs: |
|
warnings.warn( |
|
'Passing `padding_mask` is deprecated and will be removed in v4.37. ' |
|
'Please make sure use `attention_mask` instead.`') |
|
|
|
|
|
attention_mask = kwargs.pop('padding_mask') |
|
|
|
output_attentions = False |
|
|
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
qkv_states = self.wqkv(hidden_states, im_mask) |
|
|
|
qkv_states = rearrange( |
|
qkv_states, |
|
'b q (h gs d) -> b q h gs d', |
|
gs=self.num_heads + 2 * self.num_key_value_heads, |
|
d=self.head_dim, |
|
q=q_len, |
|
) |
|
|
|
query_states = qkv_states[..., :self.num_key_value_groups, :] |
|
query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d') |
|
key_states = qkv_states[..., -2, :] |
|
value_states = qkv_states[..., -1, :] |
|
|
|
kv_seq_len = key_states.shape[-2] |
|
if past_key_value is not None: |
|
kv_seq_len += past_key_value[0].shape[-2] |
|
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
|
|
|
query_states, key_states = apply_rotary_pos_emb( |
|
query_states, key_states, cos, sin, position_ids) |
|
|
|
if past_key_value is not None: |
|
|
|
key_states = torch.cat([past_key_value[0], key_states], dim=2) |
|
value_states = torch.cat([past_key_value[1], value_states], dim=2) |
|
|
|
past_key_value = (key_states, value_states) if use_cache else None |
|
|
|
query_states = query_states.transpose(1, 2) |
|
key_states = key_states.transpose(1, 2) |
|
value_states = value_states.transpose(1, 2) |
|
|
|
dropout_rate = 0.0 if not self.training else self.attention_dropout |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
input_dtype = query_states.dtype |
|
if input_dtype == torch.float32: |
|
|
|
if hasattr(self.config, '_pre_quantization_dtype'): |
|
target_dtype = self.config._pre_quantization_dtype |
|
else: |
|
target_dtype = self.q_proj.weight.dtype |
|
|
|
logger.warning_once( |
|
f'The input hidden states seems to be silently casted in float32, this might be related to' |
|
f' the fact you have upcasted embedding or layer norm layers in float32. We will cast back ' |
|
f'the input in {target_dtype}.') |
|
|
|
query_states = query_states.to(target_dtype) |
|
key_states = key_states.to(target_dtype) |
|
value_states = value_states.to(target_dtype) |
|
|
|
attn_output = self._flash_attention_forward( |
|
query_states, |
|
key_states, |
|
value_states, |
|
attention_mask, |
|
q_len, |
|
dropout=dropout_rate) |
|
|
|
attn_output = attn_output.reshape(bsz, q_len, |
|
self.hidden_size).contiguous() |
|
attn_output = self.wo(attn_output, im_mask) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
class InternLM2DecoderLayer(nn.Module): |
|
|
|
def __init__(self, config: InternLM2Config): |
|
super().__init__() |
|
self.hidden_size = config.hidden_size |
|
self.attention = ( |
|
InternLM2Attention(config=config) |
|
if not getattr(config, '_flash_attn_2_enabled', False) else |
|
InternLM2FlashAttention2(config=config)) |
|
self.feed_forward = InternLM2MLP(config) |
|
self.attention_norm = InternLM2RMSNorm( |
|
config.hidden_size, eps=config.rms_norm_eps) |
|
self.ffn_norm = InternLM2RMSNorm( |
|
config.hidden_size, eps=config.rms_norm_eps) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: Optional[bool] = False, |
|
use_cache: Optional[bool] = False, |
|
im_mask: Optional[Tuple[torch.Tensor]] = None, |
|
**kwargs, |
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, |
|
torch.FloatTensor]]]: |
|
""" |
|
Args: |
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` |
|
attention_mask (`torch.FloatTensor`, *optional*): |
|
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, |
|
query_sequence_length, key_sequence_length)` if default attention is used. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding |
|
(see `past_key_values`). |
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
|
""" |
|
if 'padding_mask' in kwargs: |
|
warnings.warn( |
|
'Passing `padding_mask` is deprecated and will be removed in v4.37. ' |
|
'Please make sure use `attention_mask` instead.`') |
|
|
|
residual = hidden_states |
|
|
|
hidden_states = self.attention_norm(hidden_states) |
|
|
|
|
|
hidden_states, self_attn_weights, present_key_value = self.attention( |
|
hidden_states=hidden_states, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_value=past_key_value, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
im_mask=im_mask, |
|
**kwargs, |
|
) |
|
hidden_states = residual + hidden_states |
|
|
|
|
|
residual = hidden_states |
|
hidden_states = self.ffn_norm(hidden_states) |
|
hidden_states = self.feed_forward(hidden_states, im_mask) |
|
hidden_states = residual + hidden_states |
|
|
|
outputs = (hidden_states, ) |
|
|
|
if output_attentions: |
|
outputs += (self_attn_weights, ) |
|
|
|
if use_cache: |
|
outputs += (present_key_value, ) |
|
|
|
return outputs |
|
|
|
|
|
InternLM2_START_DOCSTRING = r""" |
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
|
etc.) |
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
|
and behavior. |
|
|
|
Parameters: |
|
config ([`InternLM2Config`]): |
|
Model configuration class with all the parameters of the model. Initializing with a config file does not |
|
load the weights associated with the model, only the configuration. Check out the |
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.', |
|
InternLM2_START_DOCSTRING, |
|
) |
|
class InternLM2PreTrainedModel(PreTrainedModel): |
|
config_class = InternLM2Config |
|
base_model_prefix = 'model' |
|
supports_gradient_checkpointing = True |
|
_no_split_modules = ['InternLM2DecoderLayer'] |
|
_skip_keys_device_placement = 'past_key_values' |
|
_supports_flash_attn_2 = True |
|
|
|
def _init_weights(self, module): |
|
std = self.config.initializer_range |
|
if isinstance(module, nn.Linear): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
|
|
|
|
InternLM2_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see |
|
`past_key_values`). |
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
|
information on the default strategy. |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.n_positions - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or |
|
when `config.use_cache=True`): |
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape |
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape |
|
`(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`. |
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't |
|
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` |
|
of shape `(batch_size, sequence_length)`. |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the |
|
model's internal embedding lookup matrix. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
|
`past_key_values`). |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.', |
|
InternLM2_START_DOCSTRING, |
|
) |
|
class InternLM2Model(InternLM2PreTrainedModel): |
|
"""Transformer decoder consisting of *config.num_hidden_layers* layers. |
|
Each layer is a [`InternLM2DecoderLayer`] |
|
|
|
Args: |
|
config: InternLM2Config |
|
""" |
|
|
|
_auto_class = 'AutoModel' |
|
|
|
def __init__(self, config: InternLM2Config): |
|
super().__init__(config) |
|
self.padding_idx = config.pad_token_id |
|
self.vocab_size = config.vocab_size |
|
|
|
self.tok_embeddings = nn.Embedding(config.vocab_size, |
|
config.hidden_size, |
|
self.padding_idx) |
|
self.layers = nn.ModuleList([ |
|
InternLM2DecoderLayer(config) |
|
for _ in range(config.num_hidden_layers) |
|
]) |
|
self.norm = InternLM2RMSNorm( |
|
config.hidden_size, eps=config.rms_norm_eps) |
|
|
|
self.gradient_checkpointing = False |
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.tok_embeddings |
|
|
|
def set_input_embeddings(self, value): |
|
self.tok_embeddings = value |
|
|
|
|
|
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, |
|
inputs_embeds, past_key_values_length): |
|
|
|
|
|
combined_attention_mask = None |
|
if input_shape[-1] > 1: |
|
combined_attention_mask = _make_causal_mask( |
|
input_shape, |
|
inputs_embeds.dtype, |
|
device=inputs_embeds.device, |
|
past_key_values_length=past_key_values_length, |
|
) |
|
|
|
if attention_mask is not None: |
|
|
|
expanded_attn_mask = _expand_mask( |
|
attention_mask, inputs_embeds.dtype, |
|
tgt_len=input_shape[-1]).to(inputs_embeds.device) |
|
combined_attention_mask = ( |
|
expanded_attn_mask if combined_attention_mask is None else |
|
expanded_attn_mask + combined_attention_mask) |
|
|
|
return combined_attention_mask |
|
|
|
@add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING) |
|
def forward(self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
**kwargs) -> Union[Tuple, BaseModelOutputWithPast]: |
|
|
|
im_mask = kwargs.get('im_mask', 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: |
|
batch_size, seq_length = input_ids.shape[:2] |
|
elif inputs_embeds is not None: |
|
batch_size, seq_length = inputs_embeds.shape[:2] |
|
else: |
|
raise ValueError( |
|
'You have to specify either input_ids or inputs_embeds') |
|
|
|
seq_length_with_past = seq_length |
|
past_key_values_length = 0 |
|
if past_key_values is not None: |
|
past_key_values_length = past_key_values[0][0].shape[2] |
|
seq_length_with_past = seq_length_with_past + past_key_values_length |
|
|
|
if position_ids is None: |
|
device = input_ids.device if input_ids is not None else inputs_embeds.device |
|
position_ids = torch.arange( |
|
past_key_values_length, |
|
seq_length + past_key_values_length, |
|
dtype=torch.long, |
|
device=device) |
|
position_ids = position_ids.unsqueeze(0) |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.tok_embeddings(input_ids) |
|
im_mask = torch.zeros(inputs_embeds.shape[:2]).to( |
|
inputs_embeds.device).bool() |
|
|
|
if attention_mask is None: |
|
attention_mask = torch.ones((batch_size, seq_length_with_past), |
|
dtype=torch.bool, |
|
device=inputs_embeds.device) |
|
attention_mask = self._prepare_decoder_attention_mask( |
|
attention_mask, (batch_size, seq_length), inputs_embeds, |
|
past_key_values_length) |
|
|
|
|
|
hidden_states = inputs_embeds |
|
|
|
if self.gradient_checkpointing and self.training: |
|
if use_cache: |
|
logger.warning_once( |
|
'`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...' |
|
) |
|
use_cache = False |
|
|
|
|
|
all_hidden_states = () if output_hidden_states else None |
|
all_self_attns = () if output_attentions else None |
|
next_decoder_cache = () if use_cache else None |
|
|
|
for idx, decoder_layer in enumerate(self.layers): |
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states, ) |
|
|
|
past_key_value = past_key_values[ |
|
idx] if past_key_values is not None else None |
|
|
|
if self.gradient_checkpointing and self.training: |
|
|
|
def create_custom_forward(module): |
|
|
|
def custom_forward(*inputs): |
|
|
|
return module(*inputs, output_attentions, None, |
|
im_mask) |
|
|
|
return custom_forward |
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint( |
|
create_custom_forward(decoder_layer), |
|
hidden_states, |
|
attention_mask, |
|
position_ids, |
|
None, |
|
) |
|
else: |
|
layer_outputs = decoder_layer( |
|
hidden_states, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_value=past_key_value, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
im_mask=im_mask, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
|
|
if use_cache: |
|
next_decoder_cache += ( |
|
layer_outputs[2 if output_attentions else 1], ) |
|
|
|
if output_attentions: |
|
all_self_attns += (layer_outputs[1], ) |
|
|
|
hidden_states = self.norm(hidden_states) |
|
|
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states, ) |
|
|
|
next_cache = next_decoder_cache if use_cache else None |
|
if not return_dict: |
|
return tuple( |
|
v for v in |
|
[hidden_states, next_cache, all_hidden_states, all_self_attns] |
|
if v is not None) |
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attns, |
|
) |
|
|