851 lines
37 KiB
Python
851 lines
37 KiB
Python
# coding=utf-8
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# Copyright 2024 The Dream team, HKUNLP Group 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 and Qwen 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 and Qwen used by the Meta AI and Qwen 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 Dream model."""
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import math
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from typing import List, Optional, Tuple, Union
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import os
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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 transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache
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from transformers.modeling_outputs import (
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BaseModelOutput,
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MaskedLMOutput,
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)
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
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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_2_10,
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logging,
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)
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from transformers import PretrainedConfig
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from .configuration_dream import DreamConfig
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from .generation_utils import DreamGenerationMixin, DreamGenerationConfig
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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.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "Dream-7B"
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_CONFIG_FOR_DOC = "DreamConfig"
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Dream
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class DreamRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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DreamRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Dream
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class DreamRotaryEmbedding(nn.Module):
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def __init__(
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self,
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dim=None,
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max_position_embeddings=2048,
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base=10000,
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device=None,
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scaling_factor=1.0,
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rope_type="default",
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config: Optional[DreamConfig] = None,
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):
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super().__init__()
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# TODO (joao): remove the `if` below, only used for BC
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self.rope_kwargs = {}
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if config is None:
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logger.warning_once(
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"`DreamRotaryEmbedding` can now be fully parameterized by passing the model config through the "
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"`config` argument. All other arguments will be removed in v4.46"
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)
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self.rope_kwargs = {
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"rope_type": rope_type,
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"factor": scaling_factor,
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"dim": dim,
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"base": base,
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"max_position_embeddings": max_position_embeddings,
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}
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self.rope_type = rope_type
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self.max_seq_len_cached = max_position_embeddings
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self.original_max_seq_len = max_position_embeddings
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else:
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# BC: "rope_type" was originally "type"
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if config.rope_scaling is not None:
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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def reset_parameters(self):
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, self.inv_freq.device, **self.rope_kwargs)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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def _dynamic_frequency_update(self, position_ids, device):
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"""
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dynamic RoPE layers should recompute `inv_freq` in the following situations:
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1 - growing beyond the cached sequence length (allow scaling)
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2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
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"""
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seq_len = torch.max(position_ids) + 1
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if seq_len > self.max_seq_len_cached: # growth
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inv_freq, self.attention_scaling = self.rope_init_fn(
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self.config, device, seq_len=seq_len, **self.rope_kwargs
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
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self.max_seq_len_cached = seq_len
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if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
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self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
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self.max_seq_len_cached = self.original_max_seq_len
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@torch.no_grad()
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def forward(self, x, position_ids):
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if "dynamic" in self.rope_type:
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self._dynamic_frequency_update(position_ids, device=x.device)
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# Core RoPE block
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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 (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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# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
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cos = cos * self.attention_scaling
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sin = sin * self.attention_scaling
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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.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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# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Dream
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class DreamMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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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, hidden_state):
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return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
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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 DreamAttention(nn.Module):
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"""
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Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
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and "Generating Long Sequences with Sparse Transformers".
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"""
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def __init__(self, config: DreamConfig, 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 `layer_idx` is not recommended and will "
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"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = False
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self.attention_dropout = config.attention_dropout
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.rotary_emb = DreamRotaryEmbedding(config=self.config)
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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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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
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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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if position_embeddings is None:
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logger.warning_once(
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"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
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"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
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"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
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"removed and `position_embeddings` will be mandatory."
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)
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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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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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if 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 DreamSdpaAttention(DreamAttention):
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"""
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Dream attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
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`DreamAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
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SDPA API.
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"""
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# Adapted from DreamAttention.forward
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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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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if output_attentions:
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# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
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logger.warning_once(
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"DreamModel is using DreamSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
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'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.'
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)
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return super().forward(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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if position_embeddings is None:
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logger.warning_once(
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"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
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"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
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"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
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"removed and `position_embeddings` will be mandatory."
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)
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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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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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
# causal_mask = attention_mask
|
|
# if attention_mask is not None: # no matter the length, we just slice it
|
|
# causal_mask = attention_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 attention_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.
|
|
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
|
# is_causal = 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=attention_mask if isinstance(attention_mask, torch.Tensor) else None,
|
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
is_causal=False, # hard coded
|
|
)
|
|
|
|
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
|
|
|
|
|
|
class DreamDecoderLayer(nn.Module):
|
|
def __init__(self, config: DreamConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
if config.sliding_window and config._attn_implementation != "flash_attention_2":
|
|
logger.warning_once(
|
|
f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
|
|
"unexpected results may be encountered."
|
|
)
|
|
|
|
# self.self_attn = Dream_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
|
self.self_attn = DreamSdpaAttention(config, layer_idx)
|
|
|
|
self.mlp = DreamMLP(config)
|
|
self.input_layernorm = DreamRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = DreamRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
|
|
**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, sequence_length)` where padding elements are indicated by 0.
|
|
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
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
|
with `head_dim` being the embedding dimension of each attention head.
|
|
kwargs (`dict`, *optional*):
|
|
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
|
into the model
|
|
"""
|
|
|
|
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,
|
|
position_embeddings=position_embeddings,
|
|
)
|
|
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
|
|
|
|
class DreamPreTrainedModel(PreTrainedModel):
|
|
config_class = DreamConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["DreamDecoderLayer"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn_2 = True
|
|
_supports_sdpa = True
|
|
_supports_cache_class = True
|
|
_supports_quantized_cache = True
|
|
_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_()
|
|
|
|
@classmethod
|
|
def from_pretrained(
|
|
cls,
|
|
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
|
|
*model_args,
|
|
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
|
|
cache_dir: Optional[Union[str, os.PathLike]] = None,
|
|
ignore_mismatched_sizes: bool = False,
|
|
force_download: bool = False,
|
|
local_files_only: bool = False,
|
|
token: Optional[Union[str, bool]] = None,
|
|
revision: str = "main",
|
|
use_safetensors: Optional[bool] = None,
|
|
weights_only: bool = True,
|
|
**kwargs,
|
|
):
|
|
_model = super().from_pretrained(
|
|
pretrained_model_name_or_path,
|
|
*model_args,
|
|
config=config,
|
|
cache_dir=cache_dir,
|
|
ignore_mismatched_sizes=ignore_mismatched_sizes,
|
|
force_download=force_download,
|
|
local_files_only=local_files_only,
|
|
token=token,
|
|
revision=revision,
|
|
use_safetensors=use_safetensors,
|
|
weights_only=weights_only,
|
|
**kwargs,
|
|
)
|
|
# NOTE(Lin): we need to override the generation config
|
|
# because the generation config loaded in `from_pretrained`
|
|
# does not include all the attributes of DreamGenerationConfig
|
|
resume_download = kwargs.get("resume_download", None)
|
|
proxies = kwargs.get("proxies", None)
|
|
subfolder = kwargs.get("subfolder", "")
|
|
from_auto_class = kwargs.get("_from_auto", False)
|
|
from_pipeline = kwargs.get("_from_pipeline", None)
|
|
_model.generation_config = DreamGenerationConfig.from_pretrained(
|
|
pretrained_model_name_or_path,
|
|
cache_dir=cache_dir,
|
|
force_download=force_download,
|
|
resume_download=resume_download,
|
|
proxies=proxies,
|
|
local_files_only=local_files_only,
|
|
token=token,
|
|
revision=revision,
|
|
subfolder=subfolder,
|
|
_from_auto=from_auto_class,
|
|
_from_pipeline=from_pipeline,
|
|
)
|
|
return _model
|
|
|
|
class DreamBaseModel(DreamPreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DreamDecoderLayer`]
|
|
|
|
Args:
|
|
config: DreamConfig
|
|
"""
|
|
|
|
def __init__(self, config: DreamConfig):
|
|
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(
|
|
[DreamDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self._attn_implementation = config._attn_implementation
|
|
self.norm = DreamRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = DreamRotaryEmbedding(config=config)
|
|
|
|
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
|
|
|
|
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, BaseModelOutput]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
# decoder layers
|
|
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,
|
|
attention_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
cache_position,
|
|
position_embeddings,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
)
|
|
|
|
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,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
class DreamModel(DreamGenerationMixin, DreamPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = DreamBaseModel(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 reset_rope_parameters(self):
|
|
self.model.rotary_emb.reset_parameters()
|
|
for layer in self.model.layers:
|
|
layer.self_attn.rotary_emb.reset_parameters()
|
|
|
|
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
|
|
|
|
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,
|
|
num_logits_to_keep: int = 0,
|
|
**loss_kwargs,
|
|
) -> Union[Tuple, MaskedLMOutput]:
|
|
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
|
|
|
|
if isinstance(attention_mask, str) and attention_mask == "full" or attention_mask == None:
|
|
# whether attention_mask is full
|
|
pass
|
|
|
|
elif isinstance(attention_mask, torch.Tensor):
|
|
if not torch.any(attention_mask == 0.0):
|
|
attention_mask = 'full'
|
|
elif attention_mask.dim() == 2:
|
|
# [B, L] → [B, 1, L, L]
|
|
attention_mask = torch.logical_and(
|
|
attention_mask.unsqueeze(1).unsqueeze(-2),
|
|
attention_mask.unsqueeze(1).unsqueeze(-1),
|
|
)
|
|
attention_mask = attention_mask.to(torch.bool)
|
|
|
|
elif attention_mask.dim() in (3, 4):
|
|
# already extended/broadcasted form
|
|
if attention_mask.dtype != torch.bool:
|
|
attention_mask = attention_mask.to(torch.bool)
|
|
|
|
else:
|
|
raise ValueError(f"Unexpected attention_mask shape: {attention_mask.shape}")
|
|
|
|
else:
|
|
raise TypeError(f"Unsupported attention_mask type: {type(attention_mask)}")
|
|
|
|
# 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]
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|