ChatLaw2-MoE / modeling_internlm.py
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# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch InternLM2 model."""
import math
import torch.distributed as dist
import queue
import inspect
import threading
import warnings
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
from torch import nn
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
import copy
try:
from transformers.generation.streamers import BaseStreamer
except: # noqa # pylint: disable=bare-except
BaseStreamer = None
from .configuration_internlm import InternLM2Config
from transformers.cache_utils import Cache, DynamicCache
from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
from transformers.modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
from transformers.models.auto import (
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from transformers.utils import ExplicitEnum, ModelOutput, is_accelerate_available, logging
from transformers.generation.beam_constraints import DisjunctiveConstraint, PhrasalConstraint
from transformers.generation.beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
from transformers.generation.configuration_utils import GenerationConfig
from transformers.generation.logits_process import (
LogitsProcessorList
)
from transformers.generation.stopping_criteria import (
StoppingCriteriaList
)
from transformers.generation.utils import (
GenerationMode,
GenerateOutput,
GenerateDecoderOnlyOutput,
GenerateEncoderDecoderOutput
)
from transformers.generation.stopping_criteria import (
StoppingCriteriaList,
validate_stopping_criteria,
)
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "InternLM2Config"
GenerateNonBeamOutput = Union[GenerateDecoderOnlyOutput, GenerateEncoderDecoderOutput]
flash_attn_func, flash_attn_varlen_func = None, None
pad_input, index_first_axis, unpad_input = None, None, None
def _import_flash_attn():
global flash_attn_func, flash_attn_varlen_func
global pad_input, index_first_axis, unpad_input
try:
from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
except ImportError:
raise ImportError("flash_attn is not installed.")
def cosine_similarity_loss_parallel(opinion, layers_to_include):
total_loss = 0.0
for layer_idx in layers_to_include:
current_layer = opinion[layer_idx]
batch_size, seq_len, num_experts, hidden_dim = current_layer.shape
norm_hidden_states = F.normalize(current_layer, p=2, dim=-1)
cos_sim_matrix = torch.matmul(norm_hidden_states, norm_hidden_states.transpose(-2, -1))
upper_tri_cos_sim = torch.triu(cos_sim_matrix, diagonal=1)
total_loss += upper_tri_cos_sim.sum()
normalization_factor = len(layers_to_include) * batch_size * seq_len * num_experts * (num_experts - 1) / 2
return total_loss / normalization_factor
def load_balancing_loss_func(gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2) -> float:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
Logits from the `gate`, should be a tuple of tensors. Shape: [batch_size, seqeunce_length, num_experts].
num_experts (`int`, *optional*):
Number of experts
Returns:
The auxiliary loss.
"""
if gate_logits is None:
return 0
if isinstance(gate_logits, tuple):
# cat along the layers?
compute_device = gate_logits[0].device
gate_logits = torch.cat([gate.to(compute_device) for gate in gate_logits], dim=0)
routing_weights, selected_experts = torch.topk(gate_logits, top_k, dim=-1)
routing_weights = routing_weights.softmax(dim=-1)
# cast the expert indices to int64, otherwise one-hot encoding will fail
if selected_experts.dtype != torch.int64:
selected_experts = selected_experts.to(torch.int64)
if len(selected_experts.shape) == 2:
selected_experts = selected_experts.unsqueeze(2)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
# For a given token, determine if it was routed to a given expert.
expert_mask = torch.max(expert_mask, axis=-2).values
# cast to float32 otherwise mean will fail
expert_mask = expert_mask.to(torch.float32)
tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)
router_prob_per_group_and_expert = torch.mean(routing_weights, axis=-1)
return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert.unsqueeze(-1)) * (num_experts**2)
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
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)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.bart.modeling_bart._expand_mask
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)
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
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)
# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
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)
# Build here to make `torch.jit.trace` work.
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)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
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):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
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)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
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)
# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
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)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
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)
# Copied from transformers.model.llama.modeling_llama.rotate_half
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)
# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors."""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class InternLM2BLockSparseTop2MLP(nn.Module):
def __init__(self, config: InternLM2Config):
super().__init__()
self.intermediate_size = config.intermediate_size
self.hidden_dim = config.hidden_size
self.w1 = nn.Linear(self.hidden_dim, self.intermediate_size, bias=False)
self.w3 = nn.Linear(self.hidden_dim, self.intermediate_size, bias=False)
self.w2 = nn.Linear(self.intermediate_size, self.hidden_dim, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
current_hidden_states = self.w2(current_hidden_states)
return current_hidden_states
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.num_experts = config.num_local_experts
self.top_k = config.num_experts_per_tok
self.gate = nn.Linear(self.hidden_size, self.num_experts, bias=False)
self.experts = nn.ModuleList([InternLM2BLockSparseTop2MLP(config) for _ in range(self.num_experts)])
def forward(self, hidden_states: torch.Tensor, output_expert_opinion: Optional[bool] = False, output_selected_expert: Optional[List] = None):
batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits = self.gate(hidden_states)
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
topk_weight, topk_idx = torch.topk(routing_weights, self.top_k, dim=-1, sorted=False)
topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
topk_weight = topk_weight.to(hidden_states.dtype)
hidden_states = hidden_states.repeat_interleave(self.top_k, dim=0)
y = torch.empty_like(hidden_states)
flat_topk_idx = topk_idx.view(-1)
for i in range(self.num_experts):
expert = self.experts[i]
y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i]).to(dtype=y.dtype)
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
final_hidden_states = y.reshape(batch_size, sequence_length, hidden_dim)
if output_selected_expert is not None:
output_selected_expert.append(flat_topk_idx)
return final_hidden_states, router_logits, None
# Copied from transformers.model.llama.modeling_llama.repeat_kv
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)
# Modified from transformers.model.llama.modeling_llama.LlamaAttention
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 = nn.Linear(
self.hidden_size,
(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
bias=config.bias,
)
self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
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,
)
elif scaling_type == "linear":
self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
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' or 'linear'.")
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,
**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)
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:
# reuse k, v, self_attention
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
# upcast attention to fp32
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)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
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,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# InternLM2FlashAttention2 attention does not support output_attentions
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.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
output_attentions = False
bsz, q_len, _ = hidden_states.size()
qkv_states = self.wqkv(hidden_states)
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:
# reuse k, v, self_attention
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)
attn_output = self._flash_attention_forward(
query_states, key_states, value_states, attention_mask, q_len
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.wo(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def _flash_attention_forward(
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be passed to Flash Attention API
key_states (`torch.Tensor`):
Input key states to be passed to Flash Attention API
value_states (`torch.Tensor`):
Input value states to be passed to Flash Attention API
attention_mask (`torch.Tensor`):
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
position of padding tokens and 1 for the position of non-padding tokens.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
# Contains at least one padding token in the sequence
causal = self.is_causal and query_length != 1
if attention_mask is not None:
batch_size = query_states.shape[0]
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
query_states, key_states, value_states, attention_mask, query_length
)
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
attn_output_unpad = flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_in_batch_q,
max_seqlen_k=max_seqlen_in_batch_k,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
else:
attn_output = flash_attn_func(
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
)
return attn_output
def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
key_layer = index_first_axis(
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
value_layer = index_first_axis(
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
if query_length == kv_seq_len:
query_layer = index_first_axis(
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
)
cu_seqlens_q = cu_seqlens_k
max_seqlen_in_batch_q = max_seqlen_in_batch_k
indices_q = indices_k
elif query_length == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(
batch_size + 1, dtype=torch.int32, device=query_layer.device
) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_layer = query_layer.squeeze(1)
else:
# The -q_len: slice assumes left padding.
attention_mask = attention_mask[:, -query_length:]
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
return (
query_layer,
key_layer,
value_layer,
indices_q.to(torch.int64),
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
INTERNLM2_ATTENTION_CLASSES = {
"eager": InternLM2Attention,
"flash_attention_2": InternLM2FlashAttention2,
}
# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
class InternLM2DecoderLayer(nn.Module):
def __init__(self, config: InternLM2Config):
super().__init__()
self.hidden_size = config.hidden_size
self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](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,
output_router_logits: Optional[bool] = False,
output_expert_opinion: Optional[bool] = False,
output_selected_expert: Optional[List] = None,
use_cache: Optional[bool] = False,
**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)
# Self Attention
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,
**kwargs,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.ffn_norm(hidden_states)
hidden_states, router_logits, tok_expert_opinion = self.feed_forward(hidden_states, output_selected_expert=output_selected_expert)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
if output_expert_opinion:
outputs += (tok_expert_opinion,)
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.
"""
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
@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"
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.
"""
# Modified from transformers.model.llama.modeling_llama.LlamaModel
@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.config = config
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
# Initialize weights and apply final processing
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):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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,
output_router_logits: Optional[bool] = None,
output_expert_opinion: Optional[bool] = None,
output_selected_expert: Optional[List] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
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 self.config.attn_implementation == "flash_attention_2":
_import_flash_attn()
# retrieve input_ids and inputs_embeds
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)
if self.config.attn_implementation == "flash_attention_2":
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
else:
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
)
# embed positions
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
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
all_expert_opinion = () if output_expert_opinion 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):
# None for past_key_value
return module(*inputs, output_attentions, None)
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,
output_selected_expert=output_selected_expert
)
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],)
if output_router_logits:
all_router_logits += (layer_outputs[-2 if output_expert_opinion else -1],)
if output_expert_opinion:
all_expert_opinion += (layer_outputs[-1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
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,
)
# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
class InternLM2ForCausalLM(InternLM2PreTrainedModel):
_auto_class = "AutoModelForCausalLM"
_tied_weights_keys = ["output.weight"]
def __init__(self, config):
super().__init__(config)
self.model = InternLM2Model(config)
self.vocab_size = config.vocab_size
self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.tok_embeddings
def set_input_embeddings(self, value):
self.model.tok_embeddings = value
def get_output_embeddings(self):
return self.output
def set_output_embeddings(self, new_embeddings):
self.output = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
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,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
output_expert_opinion: Optional[bool] = None,
output_selected_expert: Optional[List] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, InternLM2ForCausalLM
>>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_selected_expert=output_selected_expert,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.output(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 = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.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)
aux_loss = None
cor_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok
)
if labels is not None:
loss += self.router_aux_loss_coef * aux_loss
if output_expert_opinion:
opinion = outputs.all_expert_opinion
cor_loss= cosine_similarity_loss_parallel(opinion, list(range(int(self.num_hidden_layers * 2 / 3), self.num_hidden_layers)))
cor_loss = torch.abs(cor_loss)
loss += self.cor_loss_coef * cor_loss
if not return_dict:
output = (logits,) + outputs[1:]
if output_router_logits:
output = (aux_loss,) + output
return (loss,) + output if loss is not None else output
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=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, inputs_embeds=None, **kwargs
):
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
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:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction="", prefix=""):
if tokenizer.add_bos_token:
prompt = ""
else:
prompt = tokenizer.bos_token
if meta_instruction:
prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
for record in history:
prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n""" + prefix
return tokenizer([prompt], return_tensors="pt")
@torch.no_grad()
def chat(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = [],
streamer: Optional[BaseStreamer] = None,
max_new_tokens: int = 1024,
do_sample: bool = True,
temperature: float = 0.8,
top_p: float = 0.8,
meta_instruction: str = "You are a",
prefix: str = "",
infer_kwargs: dict = {},
**kwargs,
):
inputs = self.build_inputs(tokenizer, query, history, meta_instruction, prefix)
inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
# also add end-of-assistant token in eos token id to avoid unnecessary generation
eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]] + kwargs.pop("custom_eos_token_id", [])
outputs = self.generate(
**inputs,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
eos_token_id=eos_token_id,
infer_kwargs=infer_kwargs,
**kwargs,
)
outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
response = tokenizer.decode(outputs, skip_special_tokens=True)
response = response.split("<|im_end|>")[0]
history = history + [(query, response)]
return response, history
@torch.no_grad()
def stream_chat(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = [],
max_new_tokens: int = 1024,
do_sample: bool = True,
temperature: float = 0.8,
top_p: float = 0.8,
**kwargs,
):
"""
Return a generator in format: (response, history)
Eg.
('你好,有什么可以帮助您的吗', [('你好', '你好,有什么可以帮助您的吗')])
('你好,有什么可以帮助您的吗?', [('你好', '你好,有什么可以帮助您的吗?')])
"""
if BaseStreamer is None:
raise ModuleNotFoundError(
"The version of `transformers` is too low. Please make sure "
"that you have installed `transformers>=4.28.0`."
)
response_queue = queue.Queue(maxsize=20)
class ChatStreamer(BaseStreamer):
def __init__(self, tokenizer) -> None:
super().__init__()
self.tokenizer = tokenizer
self.queue = response_queue
self.query = query
self.history = history
self.response = ""
self.cache = []
self.received_inputs = False
self.queue.put((self.response, history + [(self.query, self.response)]))
def put(self, value):
if len(value.shape) > 1 and value.shape[0] > 1:
raise ValueError("ChatStreamer only supports batch size 1")
elif len(value.shape) > 1:
value = value[0]
if not self.received_inputs:
# The first received value is input_ids, ignore here
self.received_inputs = True
return
self.cache.extend(value.tolist())
token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
if token.strip() != "<|im_end|>":
self.response = self.response + token
history = self.history + [(self.query, self.response)]
self.queue.put((self.response, history))
self.cache = []
else:
self.end()
def end(self):
self.queue.put(None)
def stream_producer():
return self.chat(
tokenizer=tokenizer,
query=query,
streamer=ChatStreamer(tokenizer=tokenizer),
history=history,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
**kwargs,
)
def consumer():
producer = threading.Thread(target=stream_producer)
producer.start()
while True:
res = response_queue.get()
if res is None:
return
yield res
return consumer()
def greedy_search(
self,
input_ids: torch.LongTensor,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
streamer: Optional["BaseStreamer"] = None,
infer_kwargs: dict = {},
**model_kwargs,
) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be
used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.greedy_search`] directly. Use generate()
instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForCausalLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... StoppingCriteriaList,
... MaxLengthCriteria,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "It might be possible to"
>>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... MinLengthLogitsProcessor(10, eos_token_id=model.generation_config.eos_token_id),
... ]
... )
>>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
>>> outputs = model.greedy_search(
... input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["It might be possible to get a better understanding of the nature of the problem, but it's not"]
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# keep track of which sequences are already finished
unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
# prepare model inputs
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
outputs = self(
**model_inputs,
**infer_kwargs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_tokens_scores = logits_processor(input_ids, next_token_logits)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_tokens_scores,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# argmax
next_tokens = torch.argmax(next_tokens_scores, dim=-1)
# finished sentences should have their next token be a padding token
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
if streamer is not None:
streamer.put(next_tokens.cpu())
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if eos_token was found in one sentence, set sentence to finished
if eos_token_id_tensor is not None:
unfinished_sequences = unfinished_sequences.mul(
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
)
# stop when each sentence is finished
if unfinished_sequences.max() == 0:
this_peer_finished = True
# stop if we exceed the maximum length
if stopping_criteria(input_ids, scores):
this_peer_finished = True
if this_peer_finished and not synced_gpus:
break
if streamer is not None:
streamer.end()
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return GenerateEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
past_key_values=model_kwargs.get("past_key_values"),
)
else:
return GenerateDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
past_key_values=model_kwargs.get("past_key_values"),
)
else:
return input_ids
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
synced_gpus: Optional[bool] = None,
assistant_model: Optional["PreTrainedModel"] = None,
streamer: Optional["BaseStreamer"] = None,
negative_prompt_ids: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
infer_kwargs: dict = {},
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head.
<Tip warning={true}>
Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).
</Tip>
Parameters:
inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
logits_processor (`LogitsProcessorList`, *optional*):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
stopping_criteria (`StoppingCriteriaList`, *optional*):
Custom stopping criteria that complement the default stopping criteria built from arguments and a
generation config. If a stopping criteria is passed that is already created with the arguments or a
generation config an error is thrown. If your stopping criteria depends on the `scores` input, make
sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is
intended for advanced users.
prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
If provided, this function constraints the beam search to allowed tokens only at each step. If not
provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
`input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
Retrieval](https://arxiv.org/abs/2010.00904).
synced_gpus (`bool`, *optional*):
Whether to continue running the while loop until max_length. Unless overridden this flag will be set to
`True` under DeepSpeed ZeRO Stage 3 multiple GPUs environment to avoid hanging if one GPU finished
generating before other GPUs. Otherwise it'll be set to `False`.
assistant_model (`PreTrainedModel`, *optional*):
An assistant model that can be used to accelerate generation. The assistant model must have the exact
same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistent model
is much faster than running generation with the model you're calling generate from. As such, the
assistant model should be much smaller.
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
negative_prompt_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
The negative prompt needed for some processors such as CFG. The batch size must match the input batch
size. This is an experimental feature, subject to breaking API changes in future versions.
negative_prompt_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Attention_mask for `negative_prompt_ids`.
kwargs (`Dict[str, Any]`, *optional*):
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
[`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.GenerateDecoderOnlyOutput`],
- [`~generation.GenerateBeamDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.GenerateEncoderDecoderOutput`],
- [`~generation.GenerateBeamEncoderDecoderOutput`]
"""
if synced_gpus is None:
if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
synced_gpus = True
else:
synced_gpus = False
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
# three conditions must be met
# 1) the generation config must have been created from the model config (`_from_model_config` field);
# 2) the generation config must have seen no modification since its creation (the hash is the same);
# 3) the user must have set generation parameters in the model config.
if (
self.generation_config._from_model_config
and self.generation_config._original_object_hash == hash(self.generation_config)
and self.config._has_non_default_generation_parameters()
):
new_generation_config = GenerationConfig.from_model_config(self.config)
if new_generation_config != self.generation_config:
warnings.warn(
"You have modified the pretrained model configuration to control generation. This is a"
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use and modify the model generation configuration (see"
" https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )"
)
self.generation_config = new_generation_config
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
generation_config.validate()
self._validate_model_kwargs(model_kwargs.copy())
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask", None) is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
eos_token_id = generation_config.eos_token_id
if isinstance(eos_token_id, list):
eos_token_id = eos_token_id[0]
logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
generation_config.pad_token_id = eos_token_id
# 3. Define model inputs
# inputs_tensor has to be defined
# model_input_name is defined if model-specific keyword input is passed
# otherwise model_input_name is None
# all model-specific keyword inputs are removed from `model_kwargs`
inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
inputs, generation_config.bos_token_id, model_kwargs
)
batch_size = inputs_tensor.shape[0]
# 4. Define other model kwargs
model_kwargs["output_attentions"] = generation_config.output_attentions
model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
# decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
# generating the first new token or not, and we only want to use the embeddings for the first new token)
if not self.config.is_encoder_decoder and model_input_name == "inputs_embeds":
model_kwargs["use_cache"] = True
else:
model_kwargs["use_cache"] = generation_config.use_cache
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
requires_attention_mask = "encoder_outputs" not in model_kwargs
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
)
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
# If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
# Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
if (
generation_config.pad_token_id is not None
and len(inputs_tensor.shape) == 2
and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
# if model is encoder decoder encoder_outputs are created
# and added to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, model_kwargs, model_input_name
)
# 5. Prepare `input_ids` which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
batch_size=batch_size,
model_input_name=model_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=generation_config.decoder_start_token_id,
bos_token_id=generation_config.bos_token_id,
device=inputs_tensor.device,
)
else:
input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
if streamer is not None:
streamer.put(input_ids.cpu())
# 6. Prepare `max_length` depending on other stopping criteria.
input_ids_length = input_ids.shape[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if generation_config.max_new_tokens is not None:
if not has_default_max_length and generation_config.max_length is not None:
logger.warning(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
generation_config.max_length = generation_config.max_new_tokens + input_ids_length
self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
# 7. determine generation mode
generation_mode = self._get_generation_mode(generation_config, assistant_model)
if streamer is not None and (generation_config.num_beams > 1):
raise ValueError(
"`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
)
if self.device.type != input_ids.device.type:
warnings.warn(
"You are calling .generate() with the `input_ids` being on a device type different"
f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
" Please make sure that you have put `input_ids` to the"
f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
" running `.generate()`.",
UserWarning,
)
# 8. prepare distribution pre_processing samplers
prepared_logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_length,
encoder_input_ids=inputs_tensor,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
model_kwargs=model_kwargs,
negative_prompt_ids=negative_prompt_ids,
negative_prompt_attention_mask=negative_prompt_attention_mask,
)
# 9. prepare stopping criteria
prepared_stopping_criteria = self._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
# 10. go into different generation modes
if generation_mode == GenerationMode.ASSISTED_GENERATION:
if generation_config.num_return_sequences > 1:
raise ValueError(
"num_return_sequences has to be 1 when doing assisted generate, "
f"but is {generation_config.num_return_sequences}."
)
if batch_size > 1:
raise ValueError("assisted generate is only supported for batch_size = 1")
if not model_kwargs["use_cache"]:
raise ValueError("assisted generate requires `use_cache=True`")
# 11. Get the candidate generator, given the parameterization
candidate_generator = self._get_candidate_generator(
generation_config=generation_config,
input_ids=input_ids,
inputs_tensor=inputs_tensor,
assistant_model=assistant_model,
logits_processor=logits_processor,
model_kwargs=model_kwargs,
)
# 12. run assisted generate
return self.assisted_decoding(
input_ids,
candidate_generator=candidate_generator,
do_sample=generation_config.do_sample,
logits_processor=prepared_logits_processor,
logits_warper=self._get_logits_warper(generation_config) if generation_config.do_sample else None,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
)
if generation_mode == GenerationMode.GREEDY_SEARCH:
# 11. run greedy search
return self.greedy_search(
input_ids,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
infer_kwargs=infer_kwargs,
**model_kwargs,
)
elif generation_mode == GenerationMode.CONTRASTIVE_SEARCH:
if not model_kwargs["use_cache"]:
raise ValueError("Contrastive search requires `use_cache=True`")
return self.contrastive_search(
input_ids,
top_k=generation_config.top_k,
penalty_alpha=generation_config.penalty_alpha,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
sequential=generation_config.low_memory,
**model_kwargs,
)
elif generation_mode == GenerationMode.SAMPLE:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
# 12. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run sample
return self.sample(
input_ids,
logits_processor=prepared_logits_processor,
logits_warper=logits_warper,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs
)
elif generation_mode == GenerationMode.BEAM_SEARCH:
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.beam_search(
input_ids,
beam_scorer,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
elif generation_mode == GenerationMode.BEAM_SAMPLE:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
# 12. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 13. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 14. run beam sample
return self.beam_sample(
input_ids,
beam_scorer,
logits_processor=prepared_logits_processor,
logits_warper=logits_warper,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
elif generation_mode == GenerationMode.GROUP_BEAM_SEARCH:
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
num_beam_groups=generation_config.num_beam_groups,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.group_beam_search(
input_ids,
beam_scorer,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
elif generation_mode == GenerationMode.CONSTRAINED_BEAM_SEARCH:
final_constraints = []
if generation_config.constraints is not None:
final_constraints = generation_config.constraints
if generation_config.force_words_ids is not None:
def typeerror():
raise ValueError(
"`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]` "
f"of positive integers, but is {generation_config.force_words_ids}."
)
if (
not isinstance(generation_config.force_words_ids, list)
or len(generation_config.force_words_ids) == 0
):
typeerror()
for word_ids in generation_config.force_words_ids:
if isinstance(word_ids[0], list):
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any(not isinstance(token_ids, list) for token_ids in word_ids):
typeerror()
if any(
any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
for token_ids in word_ids
):
typeerror()
constraint = DisjunctiveConstraint(word_ids)
else:
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
typeerror()
constraint = PhrasalConstraint(word_ids)
final_constraints.append(constraint)
# 11. prepare beam search scorer
constrained_beam_scorer = ConstrainedBeamSearchScorer(
constraints=final_constraints,
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.constrained_beam_search(
input_ids,
constrained_beam_scorer=constrained_beam_scorer,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
@add_start_docstrings(
"""
The InternLM2 Model transformer with a sequence classification head on top (linear layer).
[`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
as other causal models (e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
InternLM2_START_DOCSTRING,
)
class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = InternLM2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.tok_embeddings
def set_input_embeddings(self, value):
self.model.tok_embeddings = value
@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,
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,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
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.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
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]
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).int().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 = 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 = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = 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 SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)