diff --git "a/modeling_internlm.py" "b/modeling_internlm.py"
new file mode 100644--- /dev/null
+++ "b/modeling_internlm.py"
@@ -0,0 +1,2357 @@
+# 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.
+
+
+
+ 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).
+
+
+
+
+ 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.
+
+
+
+ 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).
+
+
+
+ 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,
+ )