812 lines
36 KiB
Python
812 lines
36 KiB
Python
# coding=utf-8
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# Copyright 2022 Salesforce authors, 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 CodeGen model."""
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, StaticCache
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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_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_utils import PreTrainedModel
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from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
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from .configuration_codegen import CodeGenConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "Salesforce/codegen-2B-mono"
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_CONFIG_FOR_DOC = "CodeGenConfig"
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# Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions
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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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# Copied from transformers.models.gptj.modeling_gptj.rotate_every_two
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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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# Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb
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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 CodeGenAttention(nn.Module):
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def __init__(self, config, layer_idx=None):
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super().__init__()
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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.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.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, 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, x, n_head, dim_head, mp_num):
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reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
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reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
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return reshaped
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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 n_ctx
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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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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights += causal_mask
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attn_weights = attn_weights / self.scale_attn
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attn_weights = nn.Softmax(dim=-1)(attn_weights)
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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 forward(
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self,
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hidden_states: Optional[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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qkv = self.qkv_proj(hidden_states)
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# TODO(enijkamp): factor out number of logical TPU-v4 cores or make forward pass agnostic
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mp_num = 4
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qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
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local_dim = self.head_dim * self.num_attention_heads // mp_num
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query, value, key = torch.split(qkv_split, local_dim, dim=-1)
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query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
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key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
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value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
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value = value.permute(0, 2, 1, 3)
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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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sincos = embed_positions[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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# Note that this cast is quite ugly, but is not implemented before ROPE as k_rot in the original codebase is always in fp32.
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# Reference: https://github.com/salesforce/CodeGen/blob/f210c3bb1216c975ad858cd4132c0fdeabf4bfc2/codegen1/jaxformer/hf/codegen/modeling_codegen.py#L38
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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.to(hidden_states.dtype), 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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outputs = (attn_output, layer_past)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs # a, present, (attentions)
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# Copied from transformers.models.gptj.modeling_gptj.GPTJMLP with GPTJ->CodeGen
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class CodeGenMLP(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)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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# Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->CodeGen
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class CodeGenBlock(nn.Module):
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# Ignore copy
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def __init__(self, config, layer_idx=None):
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super().__init__()
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inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
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self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.attn = CodeGenAttention(config, layer_idx)
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self.mlp = CodeGenMLP(inner_dim, config)
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def forward(
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self,
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hidden_states: Optional[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[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_outputs = self.attn(
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hidden_states=hidden_states,
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layer_past=layer_past,
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attention_mask=attention_mask,
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position_ids=position_ids,
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head_mask=head_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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cache_position=cache_position,
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)
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attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
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outputs = attn_outputs[1:]
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feed_forward_hidden_states = self.mlp(hidden_states)
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hidden_states = attn_output + feed_forward_hidden_states + residual
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if use_cache:
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outputs = (hidden_states,) + outputs
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else:
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outputs = (hidden_states,) + outputs[1:]
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return outputs # hidden_states, present, (attentions)
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class CodeGenPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = CodeGenConfig
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base_model_prefix = "transformer"
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supports_gradient_checkpointing = True
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_no_split_modules = ["CodeGenBlock"]
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_skip_keys_device_placement = "past_key_values"
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_supports_cache_class = True
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_supports_quantized_cache = True
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_supports_static_cache = True
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def __init__(self, *inputs, **kwargs):
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super().__init__(*inputs, **kwargs)
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def _init_weights(self, module):
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"""Initialize the weights."""
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if isinstance(module, (nn.Linear,)):
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# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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CODEGEN_START_DOCSTRING = r"""
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This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
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behavior.
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Parameters:
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config ([`CodeGenConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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CODEGEN_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `({0})`):
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Indices of input sequence tokens in the vocabulary.
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Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
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1]`:
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- 0 corresponds to a *sentence A* token,
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- 1 corresponds to a *sentence B* token.
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[What are token type IDs?](../glossary#token-type-ids)
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position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.n_positions - 1]`.
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[What are position IDs?](../glossary#position-ids)
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head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_dim)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
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is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
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model's internal embedding lookup matrix.
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past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
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Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
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blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
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returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
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Two formats are allowed:
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- a [`~cache_utils.Cache`] instance, see our
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[kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
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- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
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shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
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cache format.
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The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
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legacy cache format will be returned.
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If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
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have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
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of shape `(batch_size, sequence_length)`.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
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Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
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this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
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the complete sequence length.
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"""
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@add_start_docstrings(
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"The bare CodeGen Model transformer outputting raw hidden-states without any specific head on top.",
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CODEGEN_START_DOCSTRING,
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)
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class CodeGenModel(CodeGenPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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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([CodeGenBlock(config, layer_idx=i) for i in range(config.n_layer)])
|
|
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
|
self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.wte
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.wte = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(CODEGEN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPast,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
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]:
|
|
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)
|
|
|
|
# kept for BC (non `Cache` `past_key_values` inputs)
|
|
return_legacy_cache = False
|
|
if use_cache and not isinstance(past_key_values, Cache):
|
|
return_legacy_cache = True
|
|
if past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
else:
|
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
|
logger.warning_once(
|
|
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
|
|
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
|
|
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
|
|
)
|
|
|
|
seq_length = inputs_embeds.shape[1]
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(past_seen_tokens, past_seen_tokens + 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))
|
|
|
|
next_decoder_cache = None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
for i, block in enumerate(self.h):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
outputs = self._gradient_checkpointing_func(
|
|
block.__call__,
|
|
hidden_states,
|
|
None,
|
|
causal_mask,
|
|
position_ids,
|
|
head_mask[i],
|
|
use_cache,
|
|
output_attentions,
|
|
cache_position,
|
|
)
|
|
else:
|
|
outputs = block(
|
|
hidden_states=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 use_cache is True:
|
|
next_decoder_cache = outputs[1]
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 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,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
if return_legacy_cache:
|
|
next_cache = next_cache.to_legacy_cache()
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: torch.Tensor,
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool,
|
|
):
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and 0.0 in attention_mask:
|
|
return attention_mask
|
|
return None
|
|
|
|
# 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_static_cache = isinstance(past_key_values, StaticCache)
|
|
|
|
# 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_static_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, device = input_tensor.dtype, input_tensor.device
|
|
sequence_length = input_tensor.shape[1]
|
|
if using_static_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,
|
|
device=device,
|
|
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 == "cuda"
|
|
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
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
device: torch.device,
|
|
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.
|
|
device (`torch.device`):
|
|
The device to plcae the 4D attention mask on.
|
|
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=device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=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, :]
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The CodeGen Model transformer with a language modeling head on top.
|
|
""",
|
|
CODEGEN_START_DOCSTRING,
|
|
)
|
|
class CodeGenForCausalLM(CodeGenPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = CodeGenModel(config)
|
|
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(CODEGEN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=CausalLMOutputWithPast,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
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,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
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]
|
|
|
|
# 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)
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
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,
|
|
)
|
|
|
|
@staticmethod
|
|
def _reorder_cache(
|
|
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
|
|
) -> Tuple[Tuple[torch.Tensor]]:
|
|
"""
|
|
This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
|
|
[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
|
beam_idx at every generation step.
|
|
"""
|
|
return tuple(
|
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
|
|
for layer_past in past_key_values
|
|
)
|