1399 lines
62 KiB
Python
1399 lines
62 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from <path_to_diff_file.py>.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the diff. If any change should be done, please apply the change to the
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# diff.py file directly.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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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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from typing import List, 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 BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, HybridCache
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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)
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from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa
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from .configuration_gemma2 import CodeXEmbedConfig
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from transformers import AutoTokenizer, AutoModel
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import torch
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import logging
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import numpy as np
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from typing import List, Dict
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if is_flash_attn_2_available():
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from transformers.modeling_flash_attention_utils import _flash_attention_forward
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logger = logging.getLogger(__name__)
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# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
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def _prepare_4d_causal_attention_mask_with_cache_position(
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attention_mask: torch.Tensor,
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sequence_length: int,
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target_length: int,
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dtype: torch.dtype,
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device: torch.device,
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min_dtype: float,
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cache_position: torch.Tensor,
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batch_size: int,
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):
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"""
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Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
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`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
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Args:
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attention_mask (`torch.Tensor`):
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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)`.
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sequence_length (`int`):
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The sequence length being processed.
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target_length (`int`):
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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.
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dtype (`torch.dtype`):
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The dtype to use for the 4D attention mask.
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device (`torch.device`):
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The device to plcae the 4D attention mask on.
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min_dtype (`float`):
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The minimum value representable with the dtype `dtype`.
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cache_position (`torch.Tensor`):
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Indices depicting the position of the input sequence tokens in the sequence.
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batch_size (`torch.Tensor`):
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Batch size.
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"""
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if attention_mask is not None and attention_mask.dim() == 4:
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# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
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causal_mask = attention_mask
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else:
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causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
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if sequence_length != 1:
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causal_mask = torch.triu(causal_mask, diagonal=1)
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causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
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causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
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if attention_mask is not None:
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causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
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mask_length = attention_mask.shape[-1]
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padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
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padding_mask = padding_mask == 0
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causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
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padding_mask, min_dtype
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)
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return causal_mask
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class Gemma2RMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.zeros(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float())
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# Llama does x.to(float16) * w whilst Gemma2 is (x * w).to(float16)
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# See https://github.com/huggingface/transformers/pull/29402
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output = output * (1.0 + self.weight.float())
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return output.type_as(x)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.eps}"
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class Gemma2RotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
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self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
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@torch.no_grad()
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def forward(self, x, position_ids, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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self.inv_freq.to(x.device)
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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# Force float32 since bfloat16 loses precision on long contexts
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# See https://github.com/huggingface/transformers/pull/29285
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device_type = x.device.type
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device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False):
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos()
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sin = emb.sin()
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class Gemma2MLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_activation]
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def forward(self, x):
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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class Gemma2Attention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: CodeXEmbedConfig, layer_idx: Optional[int] = None, is_causal: bool=False):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.attention_dropout = config.attention_dropout
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = config.head_dim
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = is_causal
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self.scaling = config.query_pre_attn_scalar**-0.5
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if self.hidden_size % self.num_heads != 0:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
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self.rotary_emb = Gemma2RotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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self.sliding_window = config.sliding_window if not bool(layer_idx % 2) else None
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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cos, sin = self.rotary_emb(value_states, position_ids)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"sliding_window": self.sliding_window,
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"cache_position": cache_position,
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}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
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if self.config.attn_logit_softcapping is not None:
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attn_weights = attn_weights / self.config.attn_logit_softcapping
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attn_weights = torch.tanh(attn_weights)
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attn_weights = attn_weights * self.config.attn_logit_softcapping
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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_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.view(bsz, q_len, -1)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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class Gemma2FlashAttention2(Gemma2Attention):
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"""
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Gemma2 flash attention module. This module inherits from `Gemma2Attention` 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 alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
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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 = not is_flash_attn_greater_or_equal_2_10()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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output_attentions = False
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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# 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 just need to keep the original shape
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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cos, sin = self.rotary_emb(value_states, position_ids)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"sliding_window": self.sliding_window,
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"cache_position": cache_position,
|
|
}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
if attention_mask is not None:
|
|
seq_len = attention_mask.shape[1]
|
|
key_states = key_states[:, :, :seq_len]
|
|
value_states = value_states[:, :, :seq_len]
|
|
|
|
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
|
# to be able to avoid many of these transpose/reshape/view.
|
|
query_states = query_states.transpose(1, 2)
|
|
key_states = key_states.transpose(1, 2)
|
|
value_states = value_states.transpose(1, 2)
|
|
|
|
dropout_rate = self.attention_dropout if self.training else 0.0
|
|
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
|
# cast them back in the correct dtype just to be sure everything works as expected.
|
|
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
|
# in fp32. (Gemma2RMSNorm handles it correctly)
|
|
|
|
input_dtype = query_states.dtype
|
|
if input_dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = torch.get_autocast_gpu_dtype()
|
|
# Handle the case where the model is quantized
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
|
target_dtype = self.config._pre_quantization_dtype
|
|
else:
|
|
target_dtype = self.q_proj.weight.dtype
|
|
|
|
logger.warning_once(
|
|
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
|
f" {target_dtype}."
|
|
)
|
|
|
|
query_states = query_states.to(target_dtype)
|
|
key_states = key_states.to(target_dtype)
|
|
value_states = value_states.to(target_dtype)
|
|
|
|
attn_output = _flash_attention_forward(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
q_len,
|
|
dropout=dropout_rate,
|
|
softmax_scale=self.scaling,
|
|
is_causal=self.is_causal,
|
|
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
|
softcap=self.config.attn_logit_softcapping if is_flash_attn_greater_or_equal("2.6.0") else None,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
class Gemma2SdpaAttention(Gemma2Attention):
|
|
"""
|
|
Gemma2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
|
`Gemma2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
|
SDPA API.
|
|
"""
|
|
|
|
# Adapted from Gemma2Attention.forward
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
if output_attentions:
|
|
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
|
logger.warning_once(
|
|
"Gemma2Model is using Gemma2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
|
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
|
)
|
|
return super().forward(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
cos, sin = self.rotary_emb(value_states, position_ids)
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
|
cache_kwargs = {
|
|
"sin": sin,
|
|
"cos": cos,
|
|
"sliding_window": self.sliding_window,
|
|
"cache_position": cache_position,
|
|
}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
causal_mask = attention_mask
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
|
|
|
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
|
if query_states.device.type == "cuda" and causal_mask is not None:
|
|
query_states = query_states.contiguous()
|
|
key_states = key_states.contiguous()
|
|
value_states = value_states.contiguous()
|
|
|
|
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
|
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
|
# We pass our own mask, so is_causal must be False
|
|
is_causal = True if causal_mask is None and q_len > 1 else False
|
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=causal_mask,
|
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
is_causal=is_causal,
|
|
scale=self.scaling,
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.view(bsz, q_len, -1)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
return attn_output, None, past_key_value
|
|
|
|
|
|
GEMMA2_ATTENTION_CLASSES = {
|
|
"eager": Gemma2Attention,
|
|
"flash_attention_2": Gemma2FlashAttention2,
|
|
"sdpa": Gemma2SdpaAttention,
|
|
}
|
|
|
|
|
|
class Gemma2DecoderLayer(nn.Module):
|
|
def __init__(self, config: CodeXEmbedConfig, layer_idx: int, is_causal: bool):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = GEMMA2_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx, is_causal=is_causal)
|
|
|
|
self.mlp = Gemma2MLP(config)
|
|
self.input_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
self.is_sliding = not bool(layer_idx % 2)
|
|
self.pre_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.sliding_window = config.sliding_window
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
if self.is_sliding and attention_mask is not None: # efficient SDPA and no padding
|
|
# Flash-attn is a 2D tensor
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
attention_mask = attention_mask[:, -self.sliding_window :]
|
|
else:
|
|
min_dtype = torch.finfo(attention_mask.dtype).min
|
|
sliding_window_mask = torch.tril(
|
|
torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
|
|
)
|
|
attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
|
|
if attention_mask.shape[-1] <= 1: # when decoding
|
|
attention_mask = attention_mask[:, :, :, -self.sliding_window :]
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.pre_feedforward_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = self.post_feedforward_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
GEMMA2_START_DOCSTRING = r"""
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`CodeXEmbedConfig`]):
|
|
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
|
load the weights associated with the model, only the configuration. Check out the
|
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Gemma2 Model outputting raw hidden-states without any specific head on top.",
|
|
GEMMA2_START_DOCSTRING,
|
|
)
|
|
class Gemma2PreTrainedModel(PreTrainedModel):
|
|
config_class = CodeXEmbedConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["Gemma2DecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn_2 = True
|
|
_supports_sdpa = True
|
|
_supports_cache_class = True
|
|
_supports_quantized_cache = False
|
|
_supports_static_cache = True
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.initializer_range
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
|
|
|
|
_CONFIG_FOR_DOC = "CodeXEmbedConfig"
|
|
|
|
|
|
GEMMA2_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
|
`past_key_values`).
|
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
|
information on the default strategy.
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.n_positions - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
|
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
|
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
|
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
|
|
|
Two formats are allowed:
|
|
- a [`~cache_utils.Cache`] instance;
|
|
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
|
cache format.
|
|
|
|
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
|
legacy cache format will be returned.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
|
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
|
of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
|
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
|
the complete sequence length.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Gemma2 Model outputting raw hidden-states without any specific head on top.",
|
|
GEMMA2_START_DOCSTRING,
|
|
)
|
|
class Gemma2Model(Gemma2PreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Gemma2DecoderLayer`]
|
|
|
|
Args:
|
|
config: CodeXEmbedConfig
|
|
"""
|
|
|
|
def __init__(self, config: CodeXEmbedConfig, **kwargs):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
|
self.is_causal = getattr(kwargs, 'is_causal', False)
|
|
self.layers = nn.ModuleList(
|
|
[Gemma2DecoderLayer(config, layer_idx, self.is_causal) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
@add_start_docstrings_to_model_forward(GEMMA2_INPUTS_DOCSTRING)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
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 cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training and 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.embed_tokens(input_ids)
|
|
|
|
if cache_position is None:
|
|
cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
if self.is_causal:
|
|
causal_mask = self._update_attention_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
else:
|
|
causal_mask = _prepare_4d_attention_mask_for_sdpa(
|
|
attention_mask, inputs_embeds.dtype
|
|
)
|
|
|
|
# embed positions
|
|
hidden_states = inputs_embeds
|
|
|
|
# normalized
|
|
# Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
|
|
# See https://github.com/huggingface/transformers/pull/29402
|
|
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
|
|
hidden_states = hidden_states * normalizer
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
|
|
for decoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
hidden_states,
|
|
causal_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
cache_position,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = past_key_values if use_cache else None
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
def _update_attention_mask(
|
|
self,
|
|
attention_mask: torch.Tensor,
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool,
|
|
):
|
|
# Flash Attention currently doesn't support static cache but Gemma2 work only with static cache.
|
|
# So we will pass in attention mask as is in any case, not only when ther's padding. Then we'll use its shape
|
|
# to cut out keys/values trailing 0 used in static cache. This workaround should be compile compatible
|
|
# as it doesn't cause dynamic control issues.
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
return attention_mask
|
|
|
|
dtype, device = input_tensor.dtype, input_tensor.device
|
|
min_dtype = torch.finfo(dtype).min
|
|
sequence_length = input_tensor.shape[1]
|
|
if isinstance(past_key_values, HybridCache):
|
|
target_length = past_key_values.get_max_length()
|
|
else:
|
|
target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1]
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
device=device,
|
|
min_dtype=min_dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
return causal_mask
|
|
|
|
|
|
class Gemma2ForCausalLM(Gemma2PreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = Gemma2Model(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@add_start_docstrings_to_model_forward(GEMMA2_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, GemmaForCausalLM
|
|
|
|
>>> model = GemmaForCausalLM.from_pretrained("google/gemma-2-9b")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
|
|
|
|
>>> prompt = "What is your favorite condiment?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"What is your favorite condiment?"
|
|
```"""
|
|
if self.training and self.config._attn_implementation != "eager":
|
|
logger.warning_once(
|
|
"It is strongly recommended to train Gemma2 models with the `eager` attention implementation "
|
|
f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
|
|
)
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
logits = self.lm_head(hidden_states)
|
|
if self.config.final_logit_softcapping is not None:
|
|
logits = logits / self.config.final_logit_softcapping
|
|
logits = torch.tanh(logits)
|
|
logits = logits * self.config.final_logit_softcapping
|
|
|
|
logits = logits.float()
|
|
loss = None
|
|
if labels is not None:
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
|
shift_labels = shift_labels.view(-1)
|
|
# Enable model parallelism
|
|
shift_labels = shift_labels.to(shift_logits.device)
|
|
loss = loss_fct(shift_logits, shift_labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
use_cache=True,
|
|
**kwargs,
|
|
):
|
|
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
|
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
|
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
|
if past_key_values is not None:
|
|
if inputs_embeds is not None: # Exception 1
|
|
input_ids = input_ids[:, -cache_position.shape[0] :]
|
|
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
|
|
input_ids = input_ids[:, cache_position]
|
|
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past_key_values:
|
|
position_ids = position_ids[:, -input_ids.shape[1] :]
|
|
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
|
|
# `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
|
|
# during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
|
|
# batch size = 1 case, `position_ids` is already contiguous but with varying stride
|
|
# which retriggers a capture.
|
|
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if inputs_embeds is not None and cache_position[0] == 0:
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
# The clone here is for the same reason as for `position_ids`.
|
|
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format)}
|
|
|
|
if isinstance(past_key_values, HybridCache) and attention_mask.ndim == 2:
|
|
if inputs_embeds is not None:
|
|
batch_size, sequence_length = inputs_embeds.shape
|
|
device = inputs_embeds.device
|
|
else:
|
|
batch_size, sequence_length = input_ids.shape
|
|
device = input_ids.device
|
|
|
|
dtype = self.lm_head.weight.dtype
|
|
min_dtype = torch.finfo(dtype).min
|
|
|
|
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=past_key_values.get_max_length(),
|
|
dtype=dtype,
|
|
device=device,
|
|
min_dtype=min_dtype,
|
|
cache_position=cache_position,
|
|
batch_size=batch_size,
|
|
)
|
|
|
|
model_inputs.update(
|
|
{
|
|
"position_ids": position_ids,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": use_cache,
|
|
"attention_mask": attention_mask,
|
|
}
|
|
)
|
|
return model_inputs
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The Gemma2 Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`Gemma2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
|
(e.g. GPT-2) do.
|
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a
|
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
|
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
|
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
|
each row of the batch).
|
|
""",
|
|
GEMMA2_START_DOCSTRING,
|
|
)
|
|
class Gemma2ForSequenceClassification(Gemma2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = Gemma2Model(config)
|
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
@add_start_docstrings_to_model_forward(GEMMA2_INPUTS_DOCSTRING)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size = input_ids.shape[0]
|
|
else:
|
|
batch_size = inputs_embeds.shape[0]
|
|
|
|
if self.config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
|
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
|
sequence_lengths = sequence_lengths.to(logits.device)
|
|
else:
|
|
sequence_lengths = -1
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(logits.device)
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The Gemma2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
|
|
output) e.g. for Named-Entity-Recognition (NER) tasks.
|
|
""",
|
|
GEMMA2_START_DOCSTRING,
|
|
)
|
|
class Gemma2ForTokenClassification(Gemma2PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = Gemma2Model(config)
|
|
if getattr(config, "classifier_dropout", None) is not None:
|
|
classifier_dropout = config.classifier_dropout
|
|
elif getattr(config, "hidden_dropout", None) is not None:
|
|
classifier_dropout = config.hidden_dropout
|
|
else:
|
|
classifier_dropout = 0.1
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
@add_start_docstrings_to_model_forward(GEMMA2_INPUTS_DOCSTRING)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.score(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def get_detailed_instruct(task_description: str, query: str) -> str:
|
|
return f'Instruct: {task_description}\nQuery: {query}'
|
|
|
|
class CodeXEmbedModel2B(PreTrainedModel):
|
|
config_class = CodeXEmbedConfig
|
|
base_model_prefix = 'model'
|
|
def __init__(self, config, **kwargs):
|
|
super().__init__(config)
|
|
self.model = Gemma2Model.from_pretrained(config._name_or_path, trust_remote_code=True, is_causal=False, device_map="auto")
|
|
self.tokenizer = AutoTokenizer.from_pretrained(config._name_or_path, trust_remote_code=True, device_map="auto")
|
|
|
|
if not self.tokenizer.pad_token:
|
|
self.tokenizer.pad_token = self.tokenizer.eos_token
|
|
self.tokenizer.padding_side = 'right'
|
|
|
|
def forward(self, **kwargs):
|
|
return self.model(**kwargs)
|
|
|
|
def last_token_pool(self, model_output, attention_mask):
|
|
last_hidden_states = model_output.last_hidden_state
|
|
sequence_lengths = attention_mask.sum(dim=1) - 1
|
|
batch_size = last_hidden_states.shape[0]
|
|
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
|
|
|
|
def encode_text(self, texts: List[str], max_length: int) -> np.ndarray:
|
|
logging.info(f"Encoding {len(texts)} texts...")
|
|
|
|
# Tokenize all texts
|
|
encoded_input = self.tokenizer(
|
|
texts,
|
|
padding=True,
|
|
truncation=True,
|
|
max_length=max_length,
|
|
return_tensors="pt"
|
|
).to('cuda')
|
|
|
|
# Generate embeddings
|
|
with torch.no_grad():
|
|
model_output = self.model(**encoded_input)
|
|
embeddings = self.last_token_pool(model_output, encoded_input['attention_mask'])
|
|
|
|
if embeddings is None:
|
|
logging.error("Embeddings are None.")
|
|
else:
|
|
logging.info(f"Encoded {len(embeddings)} embeddings.")
|
|
|
|
return embeddings.cpu()
|
|
|
|
def encode_queries(self, queries: List[str], max_length: int, instruction: str, **kwargs) -> np.ndarray:
|
|
all_queries = [get_detailed_instruct(instruction, query) for query in queries]
|
|
return self.encode_text(all_queries, max_length)
|
|
|
|
def encode_corpus(self, corpus: List[str], max_length: int,
|
|
**kwargs) -> np.ndarray:
|
|
return self.encode_text(corpus, max_length)
|
|
|
|
## AutoModel Register
|
|
AutoModel.register(CodeXEmbedConfig, CodeXEmbedModel2B)
|
|
|
|
## Register for auto class
|
|
CodeXEmbedModel2B.register_for_auto_class("AutoModel")
|