1612 lines
72 KiB
Python
1612 lines
72 KiB
Python
# coding=utf-8
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# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch Mixtral model."""
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import inspect
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import math
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...modeling_attn_mask_utils import (
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_prepare_4d_causal_attention_mask,
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_prepare_4d_causal_attention_mask_for_sdpa,
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)
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from ...modeling_outputs import (
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MoeCausalLMOutputWithPast,
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MoeModelOutputWithPast,
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SequenceClassifierOutputWithPast,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import is_torch_greater_or_equal_than_1_13
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from ...utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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)
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from ...utils.import_utils import is_torch_fx_available
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from .configuration_mixtral import MixtralConfig
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
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# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
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# It means that the function will not be traced through and simply appear as a node in the graph.
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if is_torch_fx_available():
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if not is_torch_greater_or_equal_than_1_13:
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import torch.fx
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_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "MixtralConfig"
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def load_balancing_loss_func(
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gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
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) -> float:
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r"""
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Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
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See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
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function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
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experts is too unbalanced.
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Args:
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gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
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Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
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shape [batch_size X sequence_length, num_experts].
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attention_mask (`torch.Tensor`, None):
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The attention_mask used in forward function
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shape [batch_size X sequence_length] if not None.
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num_experts (`int`, *optional*):
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Number of experts
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Returns:
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The auxiliary loss.
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"""
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if gate_logits is None or not isinstance(gate_logits, tuple):
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return 0
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if isinstance(gate_logits, tuple):
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compute_device = gate_logits[0].device
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concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
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routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
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_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
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expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
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if attention_mask is None:
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# Compute the percentage of tokens routed to each experts
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tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
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# Compute the average probability of routing to these experts
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router_prob_per_expert = torch.mean(routing_weights, dim=0)
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else:
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batch_size, sequence_length = attention_mask.shape
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num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
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# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
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expert_attention_mask = (
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attention_mask[None, :, :, None, None]
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.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
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.reshape(-1, top_k, num_experts)
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.to(compute_device)
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)
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# Compute the percentage of tokens routed to each experts
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tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
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expert_attention_mask, dim=0
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)
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# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
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router_per_expert_attention_mask = (
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attention_mask[None, :, :, None]
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.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
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.reshape(-1, num_experts)
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.to(compute_device)
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)
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# Compute the average probability of routing to these experts
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router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
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router_per_expert_attention_mask, dim=0
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)
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overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
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return overall_loss * num_experts
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mixtral
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class MixtralRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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MixtralRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Mixtral
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class MixtralRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
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)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len].to(dtype=x.dtype),
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self.sin_cached[:seq_len].to(dtype=x.dtype),
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)
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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# Copied from transformers.models.llama.modeling_llama.repeat_kv
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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# Copied from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Mixtral
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class MixtralAttention(nn.Module):
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"""
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Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
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and "Generating Long Sequences with Sparse Transformers".
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"""
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def __init__(self, config: MixtralConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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self.attention_dropout = config.attention_dropout
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.rotary_emb = MixtralRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
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)
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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if self.layer_idx is None:
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raise ValueError(
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f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
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"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
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"with a layer index."
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)
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Mixtral
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class MixtralFlashAttention2(MixtralAttention):
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"""
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Mixtral flash attention module. This module inherits from `MixtralAttention` 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.
|
|
"""
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
|
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
|
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
|
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs,
|
|
):
|
|
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")
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
if self.layer_idx is None:
|
|
raise ValueError(
|
|
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
|
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
|
"with a layer index."
|
|
)
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
|
|
# Because the input can be padded, the absolute sequence length depends on the max position id.
|
|
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
|
|
cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
|
|
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
|
|
|
use_sliding_windows = (
|
|
_flash_supports_window_size
|
|
and getattr(self.config, "sliding_window", None) is not None
|
|
and kv_seq_len > self.config.sliding_window
|
|
)
|
|
|
|
if not _flash_supports_window_size:
|
|
logger.warning_once(
|
|
"The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
|
|
" make sure to upgrade flash-attn library."
|
|
)
|
|
|
|
if past_key_value is not None:
|
|
# Activate slicing cache only if the config has a value `sliding_windows` attribute
|
|
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
|
|
if (
|
|
getattr(self.config, "sliding_window", None) is not None
|
|
and kv_seq_len > self.config.sliding_window
|
|
and cache_has_contents
|
|
):
|
|
slicing_tokens = 1 - self.config.sliding_window
|
|
|
|
past_key = past_key_value[self.layer_idx][0]
|
|
past_value = past_key_value[self.layer_idx][1]
|
|
|
|
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
|
|
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
|
|
|
|
if past_key.shape[-2] != self.config.sliding_window - 1:
|
|
raise ValueError(
|
|
f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
|
|
f" {past_key.shape}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask[:, slicing_tokens:]
|
|
attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
|
|
|
|
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
dropout_rate = 0.0 if not self.training else self.attention_dropout
|
|
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
|
# cast them back in float16 just to be sure everything works as expected.
|
|
input_dtype = query_states.dtype
|
|
if input_dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = torch.get_autocast_gpu_dtype()
|
|
# Handle the case where the model is quantized
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
|
target_dtype = self.config._pre_quantization_dtype
|
|
else:
|
|
target_dtype = self.q_proj.weight.dtype
|
|
|
|
logger.warning_once(
|
|
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
|
f" {target_dtype}."
|
|
)
|
|
|
|
query_states = query_states.to(target_dtype)
|
|
key_states = key_states.to(target_dtype)
|
|
value_states = value_states.to(target_dtype)
|
|
|
|
# Reashape to the expected shape for Flash Attention
|
|
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,
|
|
dropout=dropout_rate,
|
|
use_sliding_windows=use_sliding_windows,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
|
attn_output = self.o_proj(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,
|
|
use_sliding_windows=False,
|
|
):
|
|
"""
|
|
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 (`float`):
|
|
Attention dropout
|
|
softmax_scale (`float`, *optional*):
|
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
|
use_sliding_windows (`bool`, *optional*):
|
|
Whether to activate sliding window attention.
|
|
"""
|
|
if not self._flash_attn_uses_top_left_mask:
|
|
causal = self.is_causal
|
|
else:
|
|
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
|
causal = self.is_causal and query_length != 1
|
|
|
|
# Contains at least one padding token in the sequence
|
|
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._upad_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
|
|
|
|
if not use_sliding_windows:
|
|
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,
|
|
)
|
|
else:
|
|
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,
|
|
window_size=(self.config.sliding_window, self.config.sliding_window),
|
|
)
|
|
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
|
else:
|
|
if not use_sliding_windows:
|
|
attn_output = flash_attn_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
)
|
|
else:
|
|
attn_output = flash_attn_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
window_size=(self.config.sliding_window, self.config.sliding_window),
|
|
)
|
|
|
|
return attn_output
|
|
|
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
|
batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
|
|
|
|
# On the first iteration we need to properly re-create the padding mask
|
|
# by slicing it on the proper place
|
|
if kv_seq_len != attention_mask.shape[-1]:
|
|
attention_mask_num_tokens = attention_mask.shape[-1]
|
|
attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
|
|
|
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
|
|
|
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
|
|
value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
|
|
|
|
if query_length == kv_seq_len:
|
|
query_layer = index_first_axis(
|
|
query_layer.reshape(batch_size * kv_seq_len, 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,
|
|
(cu_seqlens_q, cu_seqlens_k),
|
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Mixtral
|
|
class MixtralSdpaAttention(MixtralAttention):
|
|
"""
|
|
Mixtral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
|
`MixtralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
|
SDPA API.
|
|
"""
|
|
|
|
# Adapted from MixtralAttention.forward
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
if output_attentions:
|
|
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
|
logger.warning_once(
|
|
"MixtralModel is using MixtralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
|
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
|
)
|
|
return super().forward(
|
|
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,
|
|
)
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
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:
|
|
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
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()}"
|
|
)
|
|
|
|
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
|
if query_states.device.type == "cuda" and attention_mask is not None:
|
|
query_states = query_states.contiguous()
|
|
key_states = key_states.contiguous()
|
|
value_states = value_states.contiguous()
|
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=attention_mask,
|
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
|
is_causal=self.is_causal and attention_mask is None and q_len > 1,
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
return attn_output, None, past_key_value
|
|
|
|
|
|
MIXTRAL_ATTENTION_CLASSES = {
|
|
"eager": MixtralAttention,
|
|
"flash_attention_2": MixtralFlashAttention2,
|
|
"sdpa": MixtralSdpaAttention,
|
|
}
|
|
|
|
|
|
class MixtralBlockSparseTop2MLP(nn.Module):
|
|
def __init__(self, config: MixtralConfig):
|
|
super().__init__()
|
|
self.ffn_dim = config.intermediate_size
|
|
self.hidden_dim = config.hidden_size
|
|
|
|
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
|
self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
|
|
self.w3 = nn.Linear(self.hidden_dim, self.ffn_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 MixtralBLockSparseTop2MLP(MixtralBlockSparseTop2MLP):
|
|
def __init__(self, *args, **kwargs):
|
|
logger.warning_once(
|
|
"MixtralBLockSparseTop2MLP is deprecated by MixtralBlockSparseTop2MLP and will be removed in v4.40."
|
|
)
|
|
super().__init__(*args, **kwargs)
|
|
|
|
|
|
class MixtralSparseMoeBlock(nn.Module):
|
|
"""
|
|
This implementation is
|
|
strictly equivalent to standard MoE with full capacity (no
|
|
dropped tokens). It's faster since it formulates MoE operations
|
|
in terms of block-sparse operations to accomodate imbalanced
|
|
assignments of tokens to experts, whereas standard MoE either
|
|
(1) drop tokens at the cost of reduced performance or (2) set
|
|
capacity factor to number of experts and thus waste computation
|
|
and memory on padding.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.hidden_dim = config.hidden_size
|
|
self.ffn_dim = config.intermediate_size
|
|
self.num_experts = config.num_local_experts
|
|
self.top_k = config.num_experts_per_tok
|
|
|
|
# gating
|
|
self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
|
|
|
|
self.experts = nn.ModuleList([MixtralBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
|
|
|
|
# Jitter parameters
|
|
self.jitter_noise = config.router_jitter_noise
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
""" """
|
|
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
|
if self.training and self.jitter_noise > 0:
|
|
hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
|
|
hidden_states = hidden_states.view(-1, hidden_dim)
|
|
# router_logits: (batch * sequence_length, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
|
|
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
|
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
|
# we cast back to the input dtype
|
|
routing_weights = routing_weights.to(hidden_states.dtype)
|
|
|
|
final_hidden_states = torch.zeros(
|
|
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
|
)
|
|
|
|
# One hot encode the selected experts to create an expert mask
|
|
# this will be used to easily index which expert is going to be sollicitated
|
|
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
|
|
|
# Loop over all available experts in the model and perform the computation on each expert
|
|
for expert_idx in range(self.num_experts):
|
|
expert_layer = self.experts[expert_idx]
|
|
idx, top_x = torch.where(expert_mask[expert_idx])
|
|
|
|
# Index the correct hidden states and compute the expert hidden state for
|
|
# the current expert. We need to make sure to multiply the output hidden
|
|
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
|
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
|
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
|
|
|
# However `index_add_` only support torch tensors for indexing so we'll use
|
|
# the `top_x` tensor here.
|
|
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
|
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
|
return final_hidden_states, router_logits
|
|
|
|
|
|
class MixtralDecoderLayer(nn.Module):
|
|
def __init__(self, config: MixtralConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = MIXTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
|
|
|
self.block_sparse_moe = MixtralSparseMoeBlock(config)
|
|
self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = MixtralRMSNorm(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,
|
|
use_cache: Optional[bool] = False,
|
|
**kwargs,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
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.`"
|
|
)
|
|
"""
|
|
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, sequence_length)` where padding elements are indicated by 0.
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
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_router_logits (`bool`, *optional*):
|
|
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
|
|
should not be returned during inference.
|
|
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`).
|
|
"""
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
|
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,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states, router_logits = self.block_sparse_moe(hidden_states)
|
|
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,)
|
|
|
|
return outputs
|
|
|
|
|
|
MIXTRAL_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 ([`MixtralConfig`]):
|
|
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
|
load the weights associated with the model, only the configuration. Check out the
|
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Mixtral Model outputting raw hidden-states without any specific head on top.",
|
|
MIXTRAL_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->Mixtral
|
|
class MixtralPreTrainedModel(PreTrainedModel):
|
|
config_class = MixtralConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["MixtralDecoderLayer"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn_2 = True
|
|
_supports_sdpa = True
|
|
_supports_cache_class = True
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.initializer_range
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
|
|
|
|
MIXTRAL_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 `decoder_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, encoder_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 `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_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.
|
|
output_router_logits (`bool`, *optional*):
|
|
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
|
|
should not be returned during inference.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Mixtral Model outputting raw hidden-states without any specific head on top.",
|
|
MIXTRAL_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->MIXTRAL,Mistral->Mixtral
|
|
class MixtralModel(MixtralPreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MixtralDecoderLayer`]
|
|
|
|
Args:
|
|
config: MixtralConfig
|
|
"""
|
|
|
|
def __init__(self, config: MixtralConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
|
self.layers = nn.ModuleList(
|
|
[MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self._attn_implementation = config._attn_implementation
|
|
self.norm = MixtralRMSNorm(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.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
# Ignore copy
|
|
@add_start_docstrings_to_model_forward(MIXTRAL_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,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MoeModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
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
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
past_key_values_length = 0
|
|
|
|
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
|
|
|
|
if use_cache:
|
|
use_legacy_cache = not isinstance(past_key_values, Cache)
|
|
if use_legacy_cache:
|
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
|
past_key_values_length = past_key_values.get_usable_length(seq_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).view(-1, seq_length)
|
|
else:
|
|
position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
|
|
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
|
|
if is_padding_right:
|
|
raise ValueError(
|
|
"You are attempting to perform batched generation with padding_side='right'"
|
|
" this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "
|
|
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
|
)
|
|
|
|
if self._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
|
|
elif self._attn_implementation == "sdpa" and not output_attentions:
|
|
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
|
# the manual implementation that requires a 4D causal mask in all cases.
|
|
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
|
attention_mask,
|
|
(batch_size, seq_length),
|
|
inputs_embeds,
|
|
past_key_values_length,
|
|
sliding_window=self.config.sliding_window,
|
|
)
|
|
else:
|
|
# 4d mask is passed through the layers
|
|
attention_mask = _prepare_4d_causal_attention_mask(
|
|
attention_mask,
|
|
(batch_size, seq_length),
|
|
inputs_embeds,
|
|
past_key_values_length,
|
|
sliding_window=self.config.sliding_window,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# 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
|
|
next_decoder_cache = None
|
|
|
|
for decoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
output_router_logits,
|
|
use_cache,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
output_router_logits=output_router_logits,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
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[-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 = None
|
|
if use_cache:
|
|
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
|
|
if v is not None
|
|
)
|
|
return MoeModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
router_logits=all_router_logits,
|
|
)
|
|
|
|
|
|
class MixtralForCausalLM(MixtralPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = MixtralModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.router_aux_loss_coef = config.router_aux_loss_coef
|
|
self.num_experts = config.num_local_experts
|
|
self.num_experts_per_tok = config.num_experts_per_tok
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
|
# Ignore copy
|
|
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,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
|
|
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, MixtralForCausalLM
|
|
|
|
>>> model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
|
|
|
|
>>> 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_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
|
|
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_router_logits=output_router_logits,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
logits = self.lm_head(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
|
|
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,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
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
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
output_router_logits=False,
|
|
**kwargs,
|
|
):
|
|
# Omit tokens covered by past_key_values
|
|
if past_key_values is not None:
|
|
if isinstance(past_key_values, Cache):
|
|
cache_length = past_key_values.get_seq_length()
|
|
past_length = past_key_values.seen_tokens
|
|
max_cache_length = past_key_values.get_max_length()
|
|
else:
|
|
cache_length = past_length = past_key_values[0][0].shape[2]
|
|
max_cache_length = None
|
|
|
|
# Keep only the unprocessed tokens:
|
|
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
|
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
|
# input)
|
|
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
|
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
|
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
|
# input_ids based on the past_length.
|
|
elif past_length < input_ids.shape[1]:
|
|
input_ids = input_ids[:, past_length:]
|
|
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
|
|
|
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
|
if (
|
|
max_cache_length is not None
|
|
and attention_mask is not None
|
|
and cache_length + input_ids.shape[1] > max_cache_length
|
|
):
|
|
attention_mask = attention_mask[:, -max_cache_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,
|
|
"output_router_logits": output_router_logits,
|
|
}
|
|
)
|
|
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
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The Mixtral Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`MixtralForSequenceClassification`] 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).
|
|
""",
|
|
MIXTRAL_START_DOCSTRING,
|
|
)
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mixtral, LLAMA->MIXTRAL
|
|
class MixtralForSequenceClassification(MixtralPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = MixtralModel(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.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
@add_start_docstrings_to_model_forward(MIXTRAL_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:
|
|
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
|
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
|
sequence_lengths = sequence_lengths.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,
|
|
)
|