1238 lines
53 KiB
Python
1238 lines
53 KiB
Python
# coding=utf-8
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# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
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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 GPT-J model."""
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import warnings
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from typing import Optional, Union
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import torch
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import torch.fx
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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 ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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add_start_docstrings,
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auto_docstring,
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is_torch_flex_attn_available,
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is_torch_fx_proxy,
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logging,
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)
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from ...utils.model_parallel_utils import assert_device_map, get_device_map
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from .configuration_gptj import GPTJConfig
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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from ...integrations.flex_attention import make_flex_block_causal_mask
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if is_flash_attn_available():
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from ...modeling_flash_attention_utils import _flash_attention_forward
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logger = logging.get_logger(__name__)
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def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim))
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sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float()
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return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
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@torch.fx.wrap
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def get_embed_positions(embed_positions, position_ids):
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return embed_positions.to(position_ids.device).repeat(position_ids.shape[0], 1, 1)
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def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
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x1 = x[:, :, :, ::2]
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x2 = x[:, :, :, 1::2]
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x = torch.stack((-x2, x1), dim=-1)
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return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')
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def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
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sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
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cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
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return (tensor * cos) + (rotate_every_two(tensor) * sin)
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class GPTJAttention(nn.Module):
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def __init__(self, config, layer_idx=None):
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super().__init__()
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self.config = config
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max_positions = config.max_position_embeddings
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self.attn_dropout = nn.Dropout(config.attn_pdrop)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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self.is_causal = True
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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.embed_dim = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_attention_heads
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if self.head_dim * self.num_attention_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
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f" `num_attention_heads`: {self.num_attention_heads})."
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)
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self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.rotary_dim = config.rotary_dim
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pos_embd_dim = self.rotary_dim or self.embed_dim
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self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
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def _split_heads(self, tensor, num_attention_heads, attn_head_size, rotary):
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"""
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Splits hidden dim into attn_head_size and num_attention_heads
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"""
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new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
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tensor = tensor.view(new_shape)
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if rotary:
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return tensor
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if len(tensor.shape) == 5:
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return tensor.permute(0, 1, 3, 2, 4) # (batch, blocks, head, block_length, head_features)
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elif len(tensor.shape) == 4:
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return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
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else:
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raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
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def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
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"""
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Merges attn_head_size dim and num_attn_heads dim into hidden dim
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"""
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if len(tensor.shape) == 5:
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tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
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elif len(tensor.shape) == 4:
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tensor = tensor.permute(0, 2, 1, 3).contiguous()
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else:
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raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
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new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
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return tensor.view(new_shape)
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def _attn(
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self,
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query,
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key,
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value,
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attention_mask=None,
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head_mask=None,
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):
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# Keep the attention weights computation in fp32 to avoid overflow issues
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query = query.to(torch.float32)
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key = key.to(torch.float32)
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attn_weights = torch.matmul(query, key.transpose(-1, -2))
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attn_weights = attn_weights / self.scale_attn
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = attn_weights.to(value.dtype)
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attn_weights = self.attn_dropout(attn_weights)
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# Mask heads if we want to
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if head_mask is not None:
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attn_weights = attn_weights * head_mask
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attn_output = torch.matmul(attn_weights, value)
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return attn_output, attn_weights
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def _get_embed_positions(self, position_ids):
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embed_positions = self.embed_positions
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if embed_positions.device != position_ids.device:
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embed_positions = embed_positions.to(position_ids.device)
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self.embed_positions = embed_positions
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return embed_positions.repeat(position_ids.shape[0], 1, 1)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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layer_past: Optional[Cache] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = False,
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output_attentions: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[
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tuple[torch.Tensor, tuple[torch.Tensor]],
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Optional[tuple[torch.Tensor, tuple[torch.Tensor], tuple[torch.Tensor, ...]]],
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]:
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query = self.q_proj(hidden_states)
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key = self.k_proj(hidden_states)
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value = self.v_proj(hidden_states)
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query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
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key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
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value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
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if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
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# The logic to conditionally copy to GPU could not be traced, so we do this
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# every time in the torch.fx case
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embed_positions = get_embed_positions(self.embed_positions, position_ids)
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else:
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embed_positions = self._get_embed_positions(position_ids)
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repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
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sincos = torch.gather(embed_positions, 1, repeated_position_ids)
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sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
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if self.rotary_dim is not None:
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k_rot = key[:, :, :, : self.rotary_dim]
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k_pass = key[:, :, :, self.rotary_dim :]
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q_rot = query[:, :, :, : self.rotary_dim]
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q_pass = query[:, :, :, self.rotary_dim :]
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k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
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q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
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key = torch.cat([k_rot, k_pass], dim=-1)
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query = torch.cat([q_rot, q_pass], dim=-1)
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else:
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key = apply_rotary_pos_emb(key, sin, cos)
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query = apply_rotary_pos_emb(query, sin, cos)
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key = key.permute(0, 2, 1, 3)
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query = query.permute(0, 2, 1, 3)
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if layer_past is not None:
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"partial_rotation_size": self.rotary_dim,
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"cache_position": cache_position,
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}
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key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
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# compute self-attention: V x Softmax(QK^T)
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attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
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attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
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attn_output = self.out_proj(attn_output)
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attn_output = self.resid_dropout(attn_output)
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return attn_output, attn_weights
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class GPTJFlashAttention2(GPTJAttention):
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"""
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GPTJ flash attention module. This module inherits from `GPTJAttention` as the weights of the module stays
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untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
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flash attention and deal with padding tokens in case the input contains any of them.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, 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.
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# 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).
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self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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layer_past: Optional[Cache] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = False,
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output_attentions: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[
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tuple[torch.Tensor, tuple[torch.Tensor]],
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Optional[tuple[torch.Tensor, tuple[torch.Tensor], tuple[torch.Tensor, ...]]],
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]:
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query = self.q_proj(hidden_states)
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key = self.k_proj(hidden_states)
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value = self.v_proj(hidden_states)
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query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
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key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
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value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
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if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
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# The logic to conditionally copy to GPU could not be traced, so we do this
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# every time in the torch.fx case
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embed_positions = get_embed_positions(self.embed_positions, position_ids)
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else:
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embed_positions = self._get_embed_positions(position_ids)
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repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
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sincos = torch.gather(embed_positions, 1, repeated_position_ids)
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sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
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if self.rotary_dim is not None:
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k_rot = key[:, :, :, : self.rotary_dim]
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k_pass = key[:, :, :, self.rotary_dim :]
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q_rot = query[:, :, :, : self.rotary_dim]
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q_pass = query[:, :, :, self.rotary_dim :]
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k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
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q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
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key = torch.cat([k_rot, k_pass], dim=-1)
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query = torch.cat([q_rot, q_pass], dim=-1)
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else:
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key = apply_rotary_pos_emb(key, sin, cos)
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query = apply_rotary_pos_emb(query, sin, cos)
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# tanspose to have the desired shape
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# before transpose: batch_size x seq_length x num_attention_heads x head_dim
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# after transpose: batch_size x num_attention_heads x seq_length x head_dim
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key = key.permute(0, 2, 1, 3)
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query = query.permute(0, 2, 1, 3)
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# value: batch_size x num_attention_heads x seq_length x head_dim
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if layer_past is not None:
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"partial_rotation_size": self.rotary_dim,
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"cache_position": cache_position,
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}
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key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
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# The Flash attention requires the input to have the shape
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# batch_size x seq_length x head_dim x hidden_dim
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# therefore we need to keep the original shape for query and key, and reshape value
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# to have the correct shape.
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key = key.permute(0, 2, 1, 3).contiguous()
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query = query.permute(0, 2, 1, 3).contiguous()
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value = value.permute(0, 2, 1, 3).contiguous()
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in the correct dtype just to be sure everything works as expected.
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# This might slowdown training & inference so it is recommended to not cast the LayerNorms
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# in fp32. (LlamaRMSNorm handles it correctly)
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input_dtype = query.dtype
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device_type = query.device.type if query.device.type != "mps" else "cpu"
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = (
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torch.get_autocast_dtype(device_type)
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if hasattr(torch, "get_autocast_dtype")
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else torch.get_autocast_gpu_dtype()
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)
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query = query.to(target_dtype)
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key = key.to(target_dtype)
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value = value.to(target_dtype)
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attention_dropout = self.config.attn_pdrop if self.training else 0.0 # attn_pdrop in gptj
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query_length = query.shape[1]
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# Compute attention
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attn_weights = _flash_attention_forward(
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query,
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key,
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value,
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attention_mask,
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query_length,
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dropout=attention_dropout,
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is_causal=self.is_causal,
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use_top_left_mask=self._flash_attn_uses_top_left_mask,
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)
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# Reshape outputs
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attn_output = attn_weights.reshape(
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attn_weights.shape[0], attn_weights.shape[1], attn_weights.shape[2] * attn_weights.shape[3]
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)
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attn_output = self.out_proj(attn_output)
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attn_output = self.resid_dropout(attn_output)
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return attn_output, attn_weights
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GPTJ_ATTENTION_CLASSES = {
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"eager": GPTJAttention,
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"flash_attention_2": GPTJFlashAttention2,
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}
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class GPTJMLP(nn.Module):
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def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
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super().__init__()
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embed_dim = config.n_embd
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self.fc_in = nn.Linear(embed_dim, intermediate_size)
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self.fc_out = nn.Linear(intermediate_size, embed_dim)
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self.act = ACT2FN[config.activation_function]
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self.dropout = nn.Dropout(config.resid_pdrop)
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def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
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hidden_states = self.fc_in(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.fc_out(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class GPTJBlock(GradientCheckpointingLayer):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
|
|
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
|
self.attn = GPTJ_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
|
self.mlp = GPTJMLP(inner_dim, config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Optional[torch.FloatTensor],
|
|
layer_past: Optional[Cache] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = False,
|
|
output_attentions: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple[torch.Tensor], Optional[tuple[torch.Tensor, tuple[torch.FloatTensor, ...]]]]:
|
|
residual = hidden_states
|
|
hidden_states = self.ln_1(hidden_states)
|
|
attn_outputs, attn_weights = self.attn(
|
|
hidden_states=hidden_states,
|
|
layer_past=layer_past,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
feed_forward_hidden_states = self.mlp(hidden_states)
|
|
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
|
|
|
return hidden_states, attn_weights
|
|
|
|
|
|
@auto_docstring
|
|
class GPTJPreTrainedModel(PreTrainedModel):
|
|
config: GPTJConfig
|
|
base_model_prefix = "transformer"
|
|
is_parallelizable = True
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["GPTJBlock"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn = True
|
|
_can_compile_fullgraph = True
|
|
_supports_param_buffer_assignment = False
|
|
|
|
def __init__(self, *inputs, **kwargs):
|
|
super().__init__(*inputs, **kwargs)
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights."""
|
|
if isinstance(module, (nn.Linear,)):
|
|
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
PARALLELIZE_DOCSTRING = r"""
|
|
This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute
|
|
attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks
|
|
across all devices.
|
|
|
|
Args:
|
|
device_map (`dict[int, list]`, *optional*):
|
|
A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
|
|
automatically mapped to the first device (for esoteric reasons). That means that the first device should
|
|
have fewer attention modules mapped to it than other devices. For reference, the GPT-J models have the
|
|
following number of attention modules:
|
|
|
|
- gpt-j-6B: 28
|
|
|
|
Example:
|
|
|
|
```python
|
|
# Here is an example of a device map on a machine with 4 GPUs using gpt-j-6B, which has a total of 28 attention modules:
|
|
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
|
|
device_map = {
|
|
0: [0, 1, 2, 3, 4, 5, 6],
|
|
1: [7, 8, 9, 10, 11, 12, 13],
|
|
2: [14, 15, 16, 17, 18, 19, 20],
|
|
3: [21, 22, 23, 24, 25, 26, 27],
|
|
}
|
|
model.parallelize(device_map)
|
|
```
|
|
"""
|
|
|
|
DEPARALLELIZE_DOCSTRING = r"""
|
|
Moves the model to CPU from a model parallel state.
|
|
|
|
Example:
|
|
|
|
```python
|
|
# On a 4 GPU machine with gpt-j-6B:
|
|
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
|
|
device_map = {
|
|
0: [0, 1, 2, 3, 4, 5, 6],
|
|
1: [7, 8, 9, 10, 11, 12, 13],
|
|
2: [14, 15, 16, 17, 18, 19, 20],
|
|
3: [21, 22, 23, 24, 25, 26, 27],
|
|
}
|
|
model.parallelize(device_map) # Splits the model across several devices
|
|
model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
|
|
```
|
|
"""
|
|
|
|
|
|
@auto_docstring
|
|
class GPTJModel(GPTJPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.embed_dim = config.n_embd
|
|
self.vocab_size = config.vocab_size
|
|
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
|
|
self.drop = nn.Dropout(config.embd_pdrop)
|
|
self.h = nn.ModuleList([GPTJBlock(config, layer_idx=i) for i in range(config.n_layer)])
|
|
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
|
|
|
@add_start_docstrings(PARALLELIZE_DOCSTRING)
|
|
def parallelize(self, device_map=None):
|
|
warnings.warn(
|
|
"`GPTJModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your"
|
|
" model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
|
|
" `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1,"
|
|
" ...}",
|
|
FutureWarning,
|
|
)
|
|
# Check validity of device_map
|
|
self.device_map = (
|
|
get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
|
|
)
|
|
assert_device_map(self.device_map, len(self.h))
|
|
self.model_parallel = True
|
|
self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
|
|
self.last_device = "cuda:" + str(max(self.device_map.keys()))
|
|
self.wte = self.wte.to(self.first_device)
|
|
# Load onto devices
|
|
for k, v in self.device_map.items():
|
|
for block in v:
|
|
cuda_device = "cuda:" + str(k)
|
|
self.h[block] = self.h[block].to(cuda_device)
|
|
# ln_f to last
|
|
self.ln_f = self.ln_f.to(self.last_device)
|
|
|
|
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
|
|
def deparallelize(self):
|
|
warnings.warn(
|
|
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
|
|
FutureWarning,
|
|
)
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
self.first_device = "cpu"
|
|
self.last_device = "cpu"
|
|
self.wte = self.wte.to("cpu")
|
|
for index in range(len(self.h)):
|
|
self.h[index] = self.h[index].to("cpu")
|
|
self.ln_f = self.ln_f.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.wte
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.wte = new_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor]]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPast]:
|
|
r"""
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *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.
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or 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
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.wte(input_ids)
|
|
|
|
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
|
|
if not isinstance(past_key_values, (type(None), Cache)):
|
|
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
seq_length = inputs_embeds.shape[1]
|
|
if cache_position is None:
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x num_attention_heads x N x N
|
|
# head_mask has shape n_layer x batch x num_attention_heads x N x N
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
hidden_states = inputs_embeds
|
|
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids.view(-1, seq_length)
|
|
token_type_embeds = self.wte(token_type_ids)
|
|
hidden_states = hidden_states + token_type_embeds
|
|
|
|
hidden_states = self.drop(hidden_states)
|
|
output_shape = (-1, seq_length, hidden_states.size(-1))
|
|
|
|
all_self_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
for i, block in enumerate(self.h):
|
|
# Model parallel
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(hidden_states.device)
|
|
|
|
# Ensure layer_past is on same device as hidden_states (might not be correct)
|
|
if past_key_values is not None:
|
|
for layer in past_key_values.layers:
|
|
layer.keys = layer.keys.to(hidden_states.device)
|
|
layer.values = layer.values.to(hidden_states.device)
|
|
|
|
# Ensure that attention_mask is always on the same device as hidden_states
|
|
if causal_mask is not None:
|
|
causal_mask = causal_mask.to(hidden_states.device)
|
|
if isinstance(head_mask, torch.Tensor):
|
|
head_mask = head_mask.to(hidden_states.device)
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
outputs = block(
|
|
hidden_states,
|
|
layer_past=past_key_values,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask[i],
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[1],)
|
|
|
|
# Model Parallel: If it's the last layer for that device, put things on the next device
|
|
if self.model_parallel:
|
|
for k, v in self.device_map.items():
|
|
if i == v[-1] and "cuda:" + str(k) != self.last_device:
|
|
hidden_states = hidden_states.to("cuda:" + str(k + 1))
|
|
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
hidden_states = hidden_states.view(output_shape)
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attentions] if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, "BlockMask"],
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool = False,
|
|
):
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and (attention_mask == 0.0).any():
|
|
return attention_mask
|
|
return None
|
|
if self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask)
|
|
return attention_mask
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype = input_tensor.dtype
|
|
sequence_length = input_tensor.shape[1]
|
|
if using_compilable_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type in ["cuda", "xpu", "npu"]
|
|
and not output_attentions
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
|
|
`(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache,
|
|
to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The GPT-J Model transformer with a language modeling head on top.
|
|
"""
|
|
)
|
|
class GPTJForCausalLM(GPTJPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = GPTJModel(config)
|
|
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings(PARALLELIZE_DOCSTRING)
|
|
def parallelize(self, device_map=None):
|
|
warnings.warn(
|
|
"`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
|
|
" your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
|
|
" `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':"
|
|
" 0, 'transformer.h.1': 1, ...}",
|
|
FutureWarning,
|
|
)
|
|
self.device_map = (
|
|
get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
|
|
if device_map is None
|
|
else device_map
|
|
)
|
|
assert_device_map(self.device_map, len(self.transformer.h))
|
|
self.transformer.parallelize(self.device_map)
|
|
self.lm_head = self.lm_head.to(self.transformer.first_device)
|
|
self.model_parallel = True
|
|
|
|
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
|
|
def deparallelize(self):
|
|
warnings.warn(
|
|
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
|
|
FutureWarning,
|
|
)
|
|
self.transformer.deparallelize()
|
|
self.transformer = self.transformer.to("cpu")
|
|
self.lm_head = self.lm_head.to("cpu")
|
|
self.model_parallel = False
|
|
torch.cuda.empty_cache()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor]]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Union[tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *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.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
# Set device for model parallelism
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(self.transformer.first_device)
|
|
hidden_states = hidden_states.to(self.lm_head.weight.device)
|
|
|
|
# make sure sampling in fp16 works correctly and
|
|
# compute loss in fp32 to match with mesh-tf version
|
|
# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
|
|
lm_logits = self.lm_head(hidden_states).to(torch.float32)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(lm_logits.device)
|
|
# Flatten the tokens
|
|
loss = self.loss_function(
|
|
lm_logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
loss = loss.to(hidden_states.dtype)
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The GPT-J Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
|
(e.g. GPT, GPT-2, GPT-Neo) 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).
|
|
"""
|
|
)
|
|
class GPTJForSequenceClassification(GPTJPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = GPTJModel(config)
|
|
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[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"""
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *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.
|
|
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.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
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:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(pooled_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,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class GPTJForQuestionAnswering(GPTJPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = GPTJModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *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.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"GPTJForCausalLM",
|
|
"GPTJForQuestionAnswering",
|
|
"GPTJForSequenceClassification",
|
|
"GPTJModel",
|
|
"GPTJPreTrainedModel",
|
|
]
|