1326 lines
61 KiB
Python
1326 lines
61 KiB
Python
# coding=utf-8
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# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch OLMo model."""
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import math
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import 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 ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import ALL_LAYERNORM_LAYERS
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from ...utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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)
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from .configuration_olmo import OlmoConfig
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "OlmoConfig"
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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class OlmoLayerNorm(nn.Module):
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"""LayerNorm but with no learnable weight or bias."""
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def __init__(self, hidden_size: int) -> None:
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super().__init__()
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self.normalized_shape = (hidden_size,)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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orig_dtype = hidden_states.dtype
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return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to(
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orig_dtype
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)
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ALL_LAYERNORM_LAYERS.append(OlmoLayerNorm)
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# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Olmo
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class OlmoRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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super().__init__()
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self.scaling_factor = scaling_factor
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# For BC we register cos and sin cached
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self.max_seq_len_cached = max_position_embeddings
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
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t = t / self.scaling_factor
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
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self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
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@property
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def sin_cached(self):
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logger.warning_once(
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"The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
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"the forward method of RoPE from now on instead. It is not used in the `OlmoAttention` class"
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)
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return self._sin_cached
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@property
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def cos_cached(self):
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logger.warning_once(
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"The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
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"the forward method of RoPE from now on instead. It is not used in the `OlmoAttention` class"
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)
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return self._cos_cached
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@torch.no_grad()
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def forward(self, x, position_ids):
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# x: [bs, num_attention_heads, seq_len, head_size]
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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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# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Olmo
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class OlmoLinearScalingRotaryEmbedding(OlmoRotaryEmbedding):
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"""OlmoRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
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def forward(self, x, position_ids):
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# difference to the original RoPE: a scaling factor is aplied to the position ids
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position_ids = position_ids.float() / self.scaling_factor
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cos, sin = super().forward(x, position_ids)
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return cos, sin
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# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Olmo
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class OlmoDynamicNTKScalingRotaryEmbedding(OlmoRotaryEmbedding):
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"""OlmoRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
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def forward(self, x, position_ids):
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# difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
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seq_len = torch.max(position_ids) + 1
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if seq_len > self.max_position_embeddings:
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base = self.base * (
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(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
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) ** (self.dim / (self.dim - 2))
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inv_freq = 1.0 / (
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base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
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cos, sin = super().forward(x, position_ids)
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return cos, sin
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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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 OlmoMLP(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_act]
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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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# Copied from transformers.models.llama.modeling_llama.repeat_kv
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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class OlmoAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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# Copied from transformers.models.llama.modeling_llama.LlamaAttention.__init__ with Llama->Olmo
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def __init__(self, config: OlmoConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.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 = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=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.hidden_size, self.hidden_size, bias=config.attention_bias)
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self._init_rope()
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# Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->Olmo
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def _init_rope(self):
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if self.config.rope_scaling is None:
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self.rotary_emb = OlmoRotaryEmbedding(
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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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else:
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scaling_type = self.config.rope_scaling["type"]
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scaling_factor = self.config.rope_scaling["factor"]
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if scaling_type == "linear":
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self.rotary_emb = OlmoLinearScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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elif scaling_type == "dynamic":
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self.rotary_emb = OlmoDynamicNTKScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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else:
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raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
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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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**kwargs,
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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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if self.config.clip_qkv is not None:
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query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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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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past_key_value = getattr(self, "past_key_value", past_key_value)
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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 = {"sin": sin, "cos": cos, "cache_position": cache_position}
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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)) / math.sqrt(self.head_dim)
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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.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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class OlmoFlashAttention2(OlmoAttention):
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"""
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||
OLMo flash attention module. This module inherits from `OlmoAttention` as the weights of the module stays
|
||
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
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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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||
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||
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
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||
def __init__(self, *args, **kwargs):
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||
super().__init__(*args, **kwargs)
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||
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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()
|
||
|
||
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,
|
||
past_key_value: Optional[Cache] = None,
|
||
output_attentions: bool = False,
|
||
use_cache: bool = False,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
**kwargs,
|
||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
output_attentions = False
|
||
|
||
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)
|
||
|
||
if self.config.clip_qkv is not None:
|
||
query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
|
||
# Flash attention requires the input to have the shape
|
||
# batch_size x seq_length x head_dim x hidden_dim
|
||
# therefore we just need to keep the original shape
|
||
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)
|
||
|
||
past_key_value = getattr(self, "past_key_value", past_key_value)
|
||
|
||
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, "cache_position": cache_position}
|
||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
||
# 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. (OlmoRMSNorm 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 = self._flash_attention_forward(
|
||
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
||
)
|
||
|
||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
if not output_attentions:
|
||
attn_weights = None
|
||
|
||
return attn_output, attn_weights, past_key_value
|
||
|
||
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward with Llama->Olmo
|
||
def _flash_attention_forward(
|
||
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
||
):
|
||
"""
|
||
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||
|
||
Args:
|
||
query_states (`torch.Tensor`):
|
||
Input query states to be passed to Flash Attention API
|
||
key_states (`torch.Tensor`):
|
||
Input key states to be passed to Flash Attention API
|
||
value_states (`torch.Tensor`):
|
||
Input value states to be passed to Flash Attention API
|
||
attention_mask (`torch.Tensor`):
|
||
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||
position of padding tokens and 1 for the position of non-padding tokens.
|
||
dropout (`float`):
|
||
Attention dropout
|
||
softmax_scale (`float`, *optional*):
|
||
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||
"""
|
||
if not self._flash_attn_uses_top_left_mask:
|
||
causal = self.is_causal
|
||
else:
|
||
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in OlmoFlashAttention2 __init__.
|
||
causal = self.is_causal and query_length != 1
|
||
|
||
# Contains at least one padding token in the sequence
|
||
if attention_mask is not None:
|
||
batch_size = query_states.shape[0]
|
||
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||
query_states, key_states, value_states, attention_mask, query_length
|
||
)
|
||
|
||
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||
|
||
attn_output_unpad = flash_attn_varlen_func(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
cu_seqlens_q=cu_seqlens_q,
|
||
cu_seqlens_k=cu_seqlens_k,
|
||
max_seqlen_q=max_seqlen_in_batch_q,
|
||
max_seqlen_k=max_seqlen_in_batch_k,
|
||
dropout_p=dropout,
|
||
softmax_scale=softmax_scale,
|
||
causal=causal,
|
||
)
|
||
|
||
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
else:
|
||
attn_output = flash_attn_func(
|
||
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
||
)
|
||
|
||
return attn_output
|
||
|
||
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
||
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
||
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
||
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||
|
||
key_layer = index_first_axis(
|
||
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
)
|
||
value_layer = index_first_axis(
|
||
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
)
|
||
if query_length == kv_seq_len:
|
||
query_layer = index_first_axis(
|
||
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||
)
|
||
cu_seqlens_q = cu_seqlens_k
|
||
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||
indices_q = indices_k
|
||
elif query_length == 1:
|
||
max_seqlen_in_batch_q = 1
|
||
cu_seqlens_q = torch.arange(
|
||
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||
) # There is a memcpy here, that is very bad.
|
||
indices_q = cu_seqlens_q[:-1]
|
||
query_layer = query_layer.squeeze(1)
|
||
else:
|
||
# The -q_len: slice assumes left padding.
|
||
attention_mask = attention_mask[:, -query_length:]
|
||
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
||
|
||
return (
|
||
query_layer,
|
||
key_layer,
|
||
value_layer,
|
||
indices_q,
|
||
(cu_seqlens_q, cu_seqlens_k),
|
||
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||
)
|
||
|
||
|
||
class OlmoSdpaAttention(OlmoAttention):
|
||
"""
|
||
OLMo attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
||
`OlmoAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
||
SDPA API.
|
||
"""
|
||
|
||
# Adapted from OlmoAttention.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(
|
||
"OlmoModel is using OlmoSdpaAttention, 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)
|
||
|
||
if self.config.clip_qkv is not None:
|
||
query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
|
||
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)
|
||
|
||
# In case static cache is used, it is an instance attribute.
|
||
past_key_value = getattr(self, "past_key_value", past_key_value)
|
||
|
||
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, "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 and cache_position is not None:
|
||
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()
|
||
|
||
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=causal_mask is None and q_len > 1,
|
||
)
|
||
|
||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
||
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
return attn_output, None, past_key_value
|
||
|
||
|
||
OLMO_ATTENTION_CLASSES = {
|
||
"eager": OlmoAttention,
|
||
"flash_attention_2": OlmoFlashAttention2,
|
||
"sdpa": OlmoSdpaAttention,
|
||
}
|
||
|
||
|
||
class OlmoDecoderLayer(nn.Module):
|
||
def __init__(self, config: OlmoConfig, layer_idx: int):
|
||
super().__init__()
|
||
self.hidden_size = config.hidden_size
|
||
|
||
self.self_attn = OLMO_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
||
|
||
self.mlp = OlmoMLP(config)
|
||
self.input_layernorm = OlmoLayerNorm(config.hidden_size)
|
||
self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size)
|
||
|
||
# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer.forward
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||
output_attentions: Optional[bool] = False,
|
||
use_cache: Optional[bool] = False,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
**kwargs,
|
||
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
"""
|
||
Args:
|
||
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||
attention_mask (`torch.FloatTensor`, *optional*):
|
||
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
||
query_sequence_length, key_sequence_length)` if default attention is used.
|
||
output_attentions (`bool`, *optional*):
|
||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
returned tensors for more detail.
|
||
use_cache (`bool`, *optional*):
|
||
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||
(see `past_key_values`).
|
||
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||
"""
|
||
if "padding_mask" in kwargs:
|
||
warnings.warn(
|
||
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||
)
|
||
|
||
residual = hidden_states
|
||
|
||
hidden_states = self.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,
|
||
**kwargs,
|
||
)
|
||
hidden_states = residual + hidden_states
|
||
|
||
# Fully Connected
|
||
residual = hidden_states
|
||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
hidden_states = self.mlp(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
|
||
|
||
|
||
OLMO_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 ([`OlmoConfig`]):
|
||
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 Olmo Model outputting raw hidden-states without any specific head on top.",
|
||
OLMO_START_DOCSTRING,
|
||
)
|
||
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->Olmo
|
||
class OlmoPreTrainedModel(PreTrainedModel):
|
||
config_class = OlmoConfig
|
||
base_model_prefix = "model"
|
||
supports_gradient_checkpointing = True
|
||
_no_split_modules = ["OlmoDecoderLayer"]
|
||
_skip_keys_device_placement = ["past_key_values"]
|
||
_supports_flash_attn_2 = True
|
||
_supports_sdpa = True
|
||
_supports_cache_class = True
|
||
|
||
def _init_weights(self, module):
|
||
std = self.config.initializer_range
|
||
if isinstance(module, nn.Linear):
|
||
module.weight.data.normal_(mean=0.0, std=std)
|
||
if module.bias is not None:
|
||
module.bias.data.zero_()
|
||
elif isinstance(module, nn.Embedding):
|
||
module.weight.data.normal_(mean=0.0, std=std)
|
||
if module.padding_idx is not None:
|
||
module.weight.data[module.padding_idx].zero_()
|
||
|
||
def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
|
||
if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
|
||
raise ValueError(
|
||
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
|
||
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
|
||
)
|
||
|
||
for layer in self.model.layers:
|
||
device = layer.input_layernorm.weight.device
|
||
if hasattr(self.config, "_pre_quantization_dtype"):
|
||
dtype = self.config._pre_quantization_dtype
|
||
else:
|
||
dtype = layer.self_attn.o_proj.weight.dtype
|
||
layer.self_attn.past_key_value = cache_cls(
|
||
self.config, max_batch_size, max_cache_len, device=device, dtype=dtype
|
||
)
|
||
|
||
def _reset_cache(self):
|
||
for layer in self.model.layers:
|
||
layer.self_attn.past_key_value = None
|
||
|
||
|
||
OLMO_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 Olmo Model outputting raw hidden-states without any specific head on top.",
|
||
OLMO_START_DOCSTRING,
|
||
)
|
||
class OlmoModel(OlmoPreTrainedModel):
|
||
"""
|
||
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OlmoDecoderLayer`]
|
||
|
||
Args:
|
||
config: OlmoConfig
|
||
"""
|
||
|
||
def __init__(self, config: OlmoConfig):
|
||
super().__init__(config)
|
||
self.padding_idx = config.pad_token_id
|
||
self.vocab_size = config.vocab_size
|
||
|
||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||
self.layers = nn.ModuleList(
|
||
[OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||
)
|
||
self.norm = OlmoLayerNorm(config.hidden_size)
|
||
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(OLMO_INPUTS_DOCSTRING)
|
||
# Copied from transformers.models.llama.modeling_llama.LlamaModel.forward
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
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)
|
||
|
||
past_seen_tokens = 0
|
||
if use_cache: # kept for BC (cache positions)
|
||
if not isinstance(past_key_values, StaticCache):
|
||
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
||
past_seen_tokens = past_key_values.get_seq_length()
|
||
|
||
if cache_position is None:
|
||
if isinstance(past_key_values, StaticCache):
|
||
raise ValueError("cache_position is a required argument when using StaticCache.")
|
||
cache_position = torch.arange(
|
||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], 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_seen_tokens)
|
||
|
||
# embed positions
|
||
hidden_states = inputs_embeds
|
||
|
||
# decoder layers
|
||
all_hidden_states = () if output_hidden_states else None
|
||
all_self_attns = () if output_attentions else None
|
||
next_decoder_cache = None
|
||
|
||
for decoder_layer in self.layers:
|
||
if output_hidden_states:
|
||
all_hidden_states += (hidden_states,)
|
||
|
||
if self.gradient_checkpointing and self.training:
|
||
layer_outputs = self._gradient_checkpointing_func(
|
||
decoder_layer.__call__,
|
||
hidden_states,
|
||
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 use_cache:
|
||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||
|
||
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 = None
|
||
if use_cache:
|
||
next_cache = (
|
||
next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
|
||
)
|
||
if not return_dict:
|
||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] 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,
|
||
)
|
||
|
||
# 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_seen_tokens: int,
|
||
):
|
||
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
||
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
||
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
||
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
||
|
||
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
|
||
|
||
if self.config._attn_implementation == "sdpa":
|
||
# 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.
|
||
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
||
attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
|
||
):
|
||
return None
|
||
|
||
dtype, device = input_tensor.dtype, input_tensor.device
|
||
min_dtype = torch.finfo(dtype).min
|
||
sequence_length = input_tensor.shape[1]
|
||
if hasattr(getattr(self.layers[0], "self_attn", {}), "past_key_value"): # static cache
|
||
target_length = self.config.max_position_embeddings
|
||
else: # dynamic cache
|
||
target_length = (
|
||
attention_mask.shape[-1]
|
||
if isinstance(attention_mask, torch.Tensor)
|
||
else past_seen_tokens + sequence_length + 1
|
||
)
|
||
|
||
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(input_tensor.shape[0], 1, -1, -1)
|
||
if attention_mask is not None:
|
||
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
||
if attention_mask.dim() == 2:
|
||
mask_length = attention_mask.shape[-1]
|
||
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
|
||
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
|
||
elif attention_mask.dim() == 4:
|
||
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
|
||
# cache. In that case, the 4D attention mask attends to the newest tokens only.
|
||
if attention_mask.shape[-2] < cache_position[0] + sequence_length:
|
||
offset = cache_position[0]
|
||
else:
|
||
offset = 0
|
||
mask_shape = attention_mask.shape
|
||
mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
|
||
causal_mask[
|
||
: mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]
|
||
] = mask_slice
|
||
|
||
if (
|
||
self.config._attn_implementation == "sdpa"
|
||
and attention_mask is not None
|
||
and attention_mask.device.type == "cuda"
|
||
):
|
||
# 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
|
||
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
||
|
||
return causal_mask
|
||
|
||
|
||
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->OLMO,Llama->Olmo
|
||
class OlmoForCausalLM(OlmoPreTrainedModel):
|
||
_tied_weights_keys = ["lm_head.weight"]
|
||
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.model = OlmoModel(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(OLMO_INPUTS_DOCSTRING)
|
||
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
||
# Ignore copy
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
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, OlmoForCausalLM
|
||
|
||
>>> model = OlmoForCausalLM.from_pretrained("allenai/OLMo-1B-hf")
|
||
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")
|
||
|
||
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||
|
||
>>> # Generate
|
||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m'
|
||
```
|
||
"""
|
||
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)
|
||
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, **kwargs
|
||
):
|
||
# With static cache, the `past_key_values` is None
|
||
# TODO joao: standardize interface for the different Cache classes and remove of this if
|
||
has_static_cache = False
|
||
if past_key_values is None:
|
||
past_key_values = getattr(getattr(self.model.layers[0], "self_attn", {}), "past_key_value", None)
|
||
has_static_cache = past_key_values is not None
|
||
|
||
past_length = 0
|
||
if past_key_values is not None:
|
||
if isinstance(past_key_values, Cache):
|
||
past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
|
||
max_cache_length = (
|
||
torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
|
||
if past_key_values.get_max_length() is not None
|
||
else None
|
||
)
|
||
cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
|
||
# TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
|
||
else:
|
||
cache_length = past_length = past_key_values[0][0].shape[2]
|
||
max_cache_length = None
|
||
|
||
# Keep only the unprocessed tokens:
|
||
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
||
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
||
# input)
|
||
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
||
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
||
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
||
# input_ids based on the past_length.
|
||
elif past_length < input_ids.shape[1]:
|
||
input_ids = input_ids[:, past_length:]
|
||
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
||
|
||
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
||
if (
|
||
max_cache_length is not None
|
||
and attention_mask is not None
|
||
and cache_length + input_ids.shape[1] > max_cache_length
|
||
):
|
||
attention_mask = attention_mask[:, -max_cache_length:]
|
||
|
||
position_ids = kwargs.get("position_ids", None)
|
||
if attention_mask is not None and position_ids is None:
|
||
# create position_ids on the fly for batch generation
|
||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||
if past_key_values:
|
||
position_ids = position_ids[:, -input_ids.shape[1] :]
|
||
|
||
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
||
if inputs_embeds is not None and past_key_values is None:
|
||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||
else:
|
||
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
|
||
# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
|
||
# TODO: use `next_tokens` directly instead.
|
||
model_inputs = {"input_ids": input_ids.contiguous()}
|
||
|
||
input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
|
||
if cache_position is None:
|
||
cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
|
||
else:
|
||
cache_position = cache_position[-input_length:]
|
||
|
||
if has_static_cache:
|
||
past_key_values = None
|
||
|
||
model_inputs.update(
|
||
{
|
||
"position_ids": position_ids,
|
||
"cache_position": cache_position,
|
||
"past_key_values": past_key_values,
|
||
"use_cache": kwargs.get("use_cache"),
|
||
"attention_mask": attention_mask,
|
||
}
|
||
)
|
||
return model_inputs
|
||
|
||
@staticmethod
|
||
def _reorder_cache(past_key_values, beam_idx):
|
||
reordered_past = ()
|
||
for layer_past in past_key_values:
|
||
reordered_past += (
|
||
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
||
)
|
||
return reordered_past
|