1009 lines
38 KiB
Python
1009 lines
38 KiB
Python
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#
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# For licensing see accompanying LICENSE file.
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# Copyright (C) 2024 Apple Inc. All Rights Reserved.
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#
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import Tensor, nn
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from torch.nn import CrossEntropyLoss
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from torch.nn import functional as F
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from transformers import PreTrainedModel
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache, StaticCache
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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)
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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# this import has to be relative, otherwise, when setting trust_remote_code=True
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# huggingface transformers won't be able to load the module correctly
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from .configuration_openelm import OpenELMConfig, make_divisible
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class OpenELMRMSNorm(nn.Module):
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def __init__(self, num_features: int, eps: float = 1e-6):
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"""
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Initialize the OpenELMRMSNorm normalization layer.
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Args:
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dim (int): The dimension of the input tensor.
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eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
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Attributes:
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eps (float): A small value added to the denominator for numerical stability.
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weight (nn.Parameter): Learnable scaling parameter.
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"""
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(num_features))
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self.num_features = num_features
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def _norm(self, x: Tensor) -> Tensor:
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"""
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Apply the OpenELMRMSNorm normalization to the input tensor.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The normalized tensor.
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"""
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x: Tensor) -> Tensor:
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"""
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Forward pass through the OpenELMRMSNorm layer.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The output tensor after applying OpenELMRMSNorm.
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"""
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output = self._norm(x.float()).type_as(x)
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return output * self.weight
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def extra_repr(self) -> str:
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return (
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super().extra_repr() + f"num_features={self.num_features}, eps={self.eps}"
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)
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class OpenELMPreTrainedModel(PreTrainedModel):
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config_class = OpenELMConfig
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base_model_prefix = "transformer"
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supports_gradient_checkpointing = True
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_no_split_modules = ["OpenELMDecoderLayer"]
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_skip_keys_device_placement = "past_key_values"
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def __init__(self, *inputs, **kwargs) -> None:
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super().__init__(*inputs, **kwargs)
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def _init_weights(self, module: nn.Module) -> None:
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"""Initialize the weights."""
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if isinstance(module, nn.Linear):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, OpenELMRMSNorm):
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module.weight.data.fill_(1.0)
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def _rotate_half(x: Tensor) -> Tensor:
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x1, x2 = x.chunk(2, dim=-1)
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return torch.cat((-x2, x1), dim=-1)
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def _apply_rotary_pos_emb(x: Tensor, pos_sin: Tensor, pos_cos: Tensor) -> Tensor:
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return (x * pos_cos) + (_rotate_half(x) * pos_sin)
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class OpenELMRotaryEmbedding(torch.nn.Module):
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"""
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The rotary position embeddings (aka RoPE) from `RoFormer <https://arxiv.org/abs/2104.09864>`_.
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RoPE encodes the position information of tokens using a rotation matrix, and is able to capture
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explicit relative positional dependencies.
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Args:
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model_dim: The dimensionality of the model's hidden state.
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max_seq_length: Maximum sequence length.
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freq_constant: A constant used for computing frequencies.
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"""
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def __init__(
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self, model_dim: int, max_seq_length: int, freq_constant: int = 10000
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) -> None:
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inv_freq = 1.0 / (
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freq_constant
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** (torch.arange(0, model_dim, 2, dtype=torch.float32) / model_dim)
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)
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super().__init__()
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self.model_dim = model_dim
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self.freq_constant = freq_constant
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self.max_seq_length = max_seq_length
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self._cached_cos = None
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self._cached_sin = None
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self._cached_seq_length = max_seq_length
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self._compute_sin_cos_embeddings(max_seq_length)
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def extra_repr(self) -> str:
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return f"\tmodel_dim={self.model_dim}, max_seq_length={self.max_seq_length}, freq_constant={self.freq_constant}"
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def _compute_sin_cos_embeddings(
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self,
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key_len: int,
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key_device: torch.device = torch.device("cpu"),
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key_dtype: torch.dtype = torch.float32,
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) -> None:
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"""
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Compute sine and cos embeddings.
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Args:
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key_len: Number of tokens in the key embeddings in the transformer model.
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device: Device where the key embeddings are stored.
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key_dtype: Data type of the key embeddings.
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Returns:
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None
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...note:
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We recalculate the sine and cosine embeddings if any of the following conditions are met:
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1. The number of tokens in key embeddings are greater than the cached sequence length.
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2. Sine and cosine caches are empty.
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3. The device and data type of sine and cosine embeddings does not match with the key embeddings.
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"""
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if (
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key_len > self._cached_seq_length
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or self._cached_cos is None
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or (self._cached_cos is not None and self._cached_cos.device != key_device)
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or (self._cached_cos is not None and self._cached_cos.dtype != key_dtype)
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or self._cached_sin is None
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or (self._cached_sin is not None and self._cached_sin.device != key_device)
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or (self._cached_sin is not None and self._cached_sin.dtype != key_dtype)
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):
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self._cached_seq_length = max(key_len, self._cached_seq_length)
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# The shape of 'pos_index' is [number of key tokens]
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pos_index = torch.arange(
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self._cached_seq_length,
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dtype=torch.float32,
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device=self.inv_freq.device,
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)
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# The shape of 'pos_index_theta' is [number of key tokens, model dimension]
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pos_index_theta = torch.einsum("i,j->ij", pos_index, self.inv_freq)
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# The shape of 'emb' is [number of key tokens, model dimension]
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emb = torch.cat((pos_index_theta, pos_index_theta), dim=-1)
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# the shape of cos and sin embeddings is [number of key tokens, model_dim]
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cos_emb = emb.cos().to(dtype=key_dtype, device=key_device)
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sin_emb = emb.sin().to(dtype=key_dtype, device=key_device)
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# the shape of cached cos and sin embeddings is [1, 1, number of key tokens, model_dim]
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self._cached_cos = cos_emb[None, None, :, :]
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self._cached_sin = sin_emb[None, None, :, :]
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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The forward function of RoPE embeddings.
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Args:
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query: Query embeddings in the transformer model. The shape of query embeddings is
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[Batch, number of query heads, number of query tokens, model dimension].
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key: Key embeddings in the transformer model. The shape of key embeddings is
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[Batch, number of key heads, number of key tokens, model dimension].
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Returns:
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A tuple containing the query and key embeddings with positional information. The shape of the returned query
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and key embeddings is the same as the input query and key embeddings respectively.
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...note:
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The RoPE embedding computation is done in full-precision. After the computation, input query and key tensors
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are casted to original input datatype.
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"""
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dim = key.shape[-1]
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key_len = key.shape[2]
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query_len = query.shape[2]
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assert dim == self.model_dim
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assert key.device == query.device
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assert key.dtype == query.dtype
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# In the context of self-attention, the lengths of keys and queries are equal.
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# However, in generation tasks, such as predicting the next token in a sequence, the lengths of keys and queries
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# can differ. For instance, when employing key-value (KV) caching for sequence prediction, the keys
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# represent embeddings of previous tokens and the current token, while the query corresponds
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# to the embedding of the current token only.
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assert (
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key_len >= query_len
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), "Number of keys has to be greater than or equal to number of queries."
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query_float = query.float()
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key_float = key.float()
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self._compute_sin_cos_embeddings(
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key_len, key_device=key_float.device, key_dtype=key_float.dtype
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)
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query_float = _apply_rotary_pos_emb(
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x=query_float,
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pos_sin=self._cached_sin[..., key_len - query_len : key_len, :],
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pos_cos=self._cached_cos[..., key_len - query_len : key_len, :],
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)
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key_float = _apply_rotary_pos_emb(
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x=key_float,
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pos_sin=self._cached_sin[..., :key_len, :],
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pos_cos=self._cached_cos[..., :key_len, :],
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)
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return query_float.type_as(query), key_float.type_as(key)
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class OpenELMMultiHeadCausalAttention(nn.Module):
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def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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head_dim = config.head_dim
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q_heads = config.num_query_heads[layer_idx]
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k_heads = config.num_kv_heads[layer_idx]
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v_heads = config.num_kv_heads[layer_idx]
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self.qkv_proj = nn.Linear(
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in_features=config.model_dim,
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out_features=(q_heads + k_heads + v_heads) * head_dim,
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bias=False,
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)
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self.pos_embedding = OpenELMRotaryEmbedding(
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model_dim=config.head_dim,
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max_seq_length=config.rope_max_length,
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freq_constant=config.rope_freq_constant,
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)
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if config.normalize_qk_projections:
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self.q_norm = OpenELMRMSNorm(
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num_features=config.head_dim,
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)
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self.k_norm = OpenELMRMSNorm(
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num_features=config.head_dim,
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)
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else:
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self.q_norm = None
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self.k_norm = None
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self.out_proj = nn.Linear(
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in_features=q_heads * head_dim,
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out_features=config.model_dim,
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bias=False,
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)
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self.head_dim = config.head_dim
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self.num_q_heads = q_heads
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self.num_k_heads = k_heads
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self.num_v_heads = v_heads
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self.transformer_dim = config.model_dim
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self.num_groups = self.num_q_heads // self.num_k_heads
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def extra_repr(self) -> str:
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return (
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super().extra_repr()
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+ f"query_heads={self.num_q_heads}, key_heads={self.num_k_heads}, value_heads={self.num_v_heads}"
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)
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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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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""
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Forward pass of multi-head self-attention.
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Args:
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hidden_states: Input tensor of the shape [batch size, sequence length, model dimension].
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past_key_value: Tensor storing the cached keys and values.
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output_attentions: output attention weights.
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use_cache: Specifies whether to use kv-cache for generation.
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cache_position: used for updating the kv-cache.
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Returns:
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The output of the same shape as the input, optionally with a tensor containing cached keys and values.
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"""
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# scaled_dot_product_attention does not return attention weights, set output_attentions to False
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output_attentions = False
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batch_size, seq_length, d_model = hidden_states.size()
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# [B, S, d] --> [B, S, (q_h + k_h + v_h) * h]
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qkv = self.qkv_proj(hidden_states)
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# [B, S, (q_h + k_h + v_h) * h] --> [B, S, (q_h + k_h + v_h), h]
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qkv = qkv.reshape(
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batch_size,
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seq_length,
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self.num_q_heads + self.num_k_heads + self.num_v_heads,
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self.head_dim,
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)
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# [B, S, (q_h + k_h + v_h), h] --> [B, (q_h + k_h + v_h), S, h]
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qkv = qkv.transpose(1, 2)
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# [B, (q_h + k_h + v_h), S, h] --> [B, q_h, S h], [B, k_h, S, h], [B, v_h, S, h]
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|
|
queries, keys, values = qkv.split(
|
||
|
|
[self.num_q_heads, self.num_k_heads, self.num_v_heads], dim=1
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.q_norm is not None:
|
||
|
|
queries = self.q_norm(queries)
|
||
|
|
|
||
|
|
if self.k_norm is not None:
|
||
|
|
keys = self.k_norm(keys)
|
||
|
|
|
||
|
|
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; position_ids needed for the static cache
|
||
|
|
# cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||
|
|
cache_kwargs = {"cache_position": cache_position}
|
||
|
|
keys, values = past_key_value.update(
|
||
|
|
keys, values, self.layer_idx, cache_kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
# Add positional embedding
|
||
|
|
queries, keys = self.pos_embedding(queries, keys)
|
||
|
|
|
||
|
|
if self.num_groups != 1:
|
||
|
|
# GQA
|
||
|
|
# [B, k_h, S, h] --> [B, q_h, S, h]
|
||
|
|
keys = keys.repeat_interleave(self.num_groups, dim=1)
|
||
|
|
# [B, v_h, S, h] --> [B, q_h, S, h]
|
||
|
|
values = values.repeat_interleave(self.num_groups, dim=1)
|
||
|
|
|
||
|
|
causal_mask = attention_mask
|
||
|
|
if attention_mask is not None and cache_position is not None:
|
||
|
|
causal_mask = causal_mask[:, :, cache_position, : keys.shape[-2]]
|
||
|
|
|
||
|
|
attn_output = F.scaled_dot_product_attention(
|
||
|
|
queries,
|
||
|
|
keys,
|
||
|
|
values,
|
||
|
|
attn_mask=causal_mask,
|
||
|
|
dropout_p=0,
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
||
|
|
attn_output = attn_output.reshape(
|
||
|
|
batch_size, seq_length, self.num_q_heads * self.head_dim
|
||
|
|
)
|
||
|
|
attn_output = self.out_proj(attn_output)
|
||
|
|
if not output_attentions:
|
||
|
|
attn_weights = None
|
||
|
|
return attn_output, attn_weights, past_key_value
|
||
|
|
|
||
|
|
|
||
|
|
class OpenELMFeedForwardNetwork(nn.Module):
|
||
|
|
def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
|
||
|
|
super().__init__()
|
||
|
|
ffn_multiplier = config.ffn_multipliers[layer_idx]
|
||
|
|
intermediate_dim = int(
|
||
|
|
make_divisible(
|
||
|
|
ffn_multiplier * config.model_dim,
|
||
|
|
divisor=config.ffn_dim_divisor,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
if config.ffn_with_glu:
|
||
|
|
# FFN with Gated linear unit, as described in https://arxiv.org/abs/2002.05202v1.
|
||
|
|
self.proj_1 = nn.Linear(
|
||
|
|
in_features=config.model_dim,
|
||
|
|
out_features=2 * intermediate_dim,
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
self.proj_2 = nn.Linear(
|
||
|
|
in_features=intermediate_dim,
|
||
|
|
out_features=config.model_dim,
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
self.ffn_with_glu = True
|
||
|
|
else:
|
||
|
|
# Standard FFN, as described in https://arxiv.org/abs/1706.03762
|
||
|
|
self.proj_1 = nn.Linear(
|
||
|
|
in_features=config.model_dim,
|
||
|
|
out_features=intermediate_dim,
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
self.proj_2 = nn.Linear(
|
||
|
|
in_features=intermediate_dim,
|
||
|
|
out_features=config.model_dim,
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
self.ffn_with_glu = False
|
||
|
|
|
||
|
|
self.act = ACT2FN[config.activation_fn_name]
|
||
|
|
|
||
|
|
def extra_repr(self) -> str:
|
||
|
|
return super().extra_repr() + f"(ffn_with_glu) : {self.ffn_with_glu}"
|
||
|
|
|
||
|
|
def forward(self, x: Tensor) -> Tensor:
|
||
|
|
"""Forward function of FFN layer.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: Input tensor of the shape [batch size, sequence length, model dimension].
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tensor of the same shape as the input.
|
||
|
|
"""
|
||
|
|
if self.ffn_with_glu:
|
||
|
|
y_12 = self.proj_1(x)
|
||
|
|
y_1, y_2 = y_12.chunk(2, dim=-1)
|
||
|
|
y = self.act(y_1) * y_2
|
||
|
|
return self.proj_2(y)
|
||
|
|
else:
|
||
|
|
return self.proj_2(self.act(self.proj_1(x)))
|
||
|
|
|
||
|
|
|
||
|
|
class OpenELMDecoderLayer(nn.Module):
|
||
|
|
def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
|
||
|
|
super().__init__()
|
||
|
|
self.attn = OpenELMMultiHeadCausalAttention(config=config, layer_idx=layer_idx)
|
||
|
|
self.ffn = OpenELMFeedForwardNetwork(config=config, layer_idx=layer_idx)
|
||
|
|
self.ffn_norm = OpenELMRMSNorm(
|
||
|
|
num_features=config.model_dim,
|
||
|
|
)
|
||
|
|
self.attn_norm = OpenELMRMSNorm(
|
||
|
|
num_features=config.model_dim,
|
||
|
|
)
|
||
|
|
|
||
|
|
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
|
||
|
|
"""
|
||
|
|
residual = hidden_states
|
||
|
|
hidden_states = self.attn_norm(hidden_states)
|
||
|
|
|
||
|
|
# Self Attention
|
||
|
|
hidden_states, self_attn_weights, present_key_value = self.attn(
|
||
|
|
hidden_states=hidden_states,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
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.ffn_norm(hidden_states)
|
||
|
|
hidden_states = self.ffn(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 OpenELMModel(OpenELMPreTrainedModel):
|
||
|
|
config_class = OpenELMConfig
|
||
|
|
|
||
|
|
def __init__(self, config: OpenELMConfig):
|
||
|
|
super().__init__(config)
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.token_embeddings = nn.Embedding(
|
||
|
|
embedding_dim=config.model_dim,
|
||
|
|
num_embeddings=config.vocab_size,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.layers = nn.ModuleList(
|
||
|
|
OpenELMDecoderLayer(config=config, layer_idx=layer_idx)
|
||
|
|
for layer_idx in range(config.num_transformer_layers)
|
||
|
|
)
|
||
|
|
self.norm = OpenELMRMSNorm(num_features=config.model_dim)
|
||
|
|
if config.share_input_output_layers:
|
||
|
|
self.classifier = None
|
||
|
|
else:
|
||
|
|
self.classifier = nn.Linear(
|
||
|
|
in_features=config.model_dim,
|
||
|
|
out_features=config.vocab_size,
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
self.num_transformer_layers = config.num_transformer_layers
|
||
|
|
self.gradient_checkpointing = False
|
||
|
|
|
||
|
|
# Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
|
||
|
|
# NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_context_length`.
|
||
|
|
causal_mask = torch.full(
|
||
|
|
(config.max_context_length, config.max_context_length),
|
||
|
|
fill_value=True,
|
||
|
|
dtype=torch.bool,
|
||
|
|
)
|
||
|
|
self.register_buffer(
|
||
|
|
"causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
|
||
|
|
)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
self.reset_parameters(config=config)
|
||
|
|
|
||
|
|
def get_input_embeddings(self):
|
||
|
|
return self.token_embeddings
|
||
|
|
|
||
|
|
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
||
|
|
self.token_embeddings = new_embeddings
|
||
|
|
|
||
|
|
def reset_parameters(self, config: OpenELMConfig) -> None:
|
||
|
|
"""Initialize the layers in Language Model
|
||
|
|
|
||
|
|
The initialization scheme is followed, following `OPT <https://arxiv.org/pdf/2205.01068.pdf>`_.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
use_megatron_std: Use standard deviation as described in Megatron-LM.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
None
|
||
|
|
"""
|
||
|
|
for module in self.modules():
|
||
|
|
if isinstance(module, nn.Linear):
|
||
|
|
std = module.in_features**-0.5
|
||
|
|
torch.nn.init.normal_(module.weight, mean=0.0, std=std)
|
||
|
|
if module.bias is not None:
|
||
|
|
torch.nn.init.zeros_(module.bias)
|
||
|
|
elif isinstance(module, nn.Embedding):
|
||
|
|
std = module.embedding_dim**-0.5
|
||
|
|
torch.nn.init.normal_(module.weight, mean=0.0, std=std)
|
||
|
|
elif isinstance(module, OpenELMRMSNorm):
|
||
|
|
if module.weight is not None:
|
||
|
|
torch.nn.init.ones_(module.weight)
|
||
|
|
if hasattr(module, "bias") and module.bias is not None:
|
||
|
|
torch.nn.init.zeros_(module.bias)
|
||
|
|
|
||
|
|
model_dim = config.model_dim
|
||
|
|
n_layers = config.num_transformer_layers
|
||
|
|
std = (model_dim**-0.5) * ((2 * n_layers) ** -0.5)
|
||
|
|
for param_name, param in self.named_parameters():
|
||
|
|
if param_name.endswith("out_proj.weight") or param_name.endswith(
|
||
|
|
"ffn.proj_2.weight"
|
||
|
|
):
|
||
|
|
torch.nn.init.normal_(param, mean=0.0, std=std)
|
||
|
|
|
||
|
|
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.token_embeddings(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:
|
||
|
|
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)
|
||
|
|
|
||
|
|
# 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,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _update_causal_mask(self, attention_mask, input_tensor):
|
||
|
|
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
|
||
|
|
|
||
|
|
batch_size, seq_length = input_tensor.shape[:2]
|
||
|
|
dtype = input_tensor.dtype
|
||
|
|
device = input_tensor.device
|
||
|
|
|
||
|
|
# support going beyond cached `max_position_embedding`
|
||
|
|
if seq_length > self.causal_mask.shape[-1]:
|
||
|
|
causal_mask = torch.full(
|
||
|
|
(2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]),
|
||
|
|
fill_value=1,
|
||
|
|
)
|
||
|
|
self.register_buffer(
|
||
|
|
"causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
|
||
|
|
)
|
||
|
|
|
||
|
|
# We use the current dtype to avoid any overflows
|
||
|
|
min_dtype = torch.finfo(dtype).min
|
||
|
|
causal_mask = (
|
||
|
|
self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype)
|
||
|
|
* min_dtype
|
||
|
|
)
|
||
|
|
|
||
|
|
causal_mask = causal_mask.to(dtype=dtype, device=device)
|
||
|
|
if attention_mask is not None and 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
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.config._attn_implementation == "sdpa" and attention_mask is not None:
|
||
|
|
# For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
|
||
|
|
is_tracing = (
|
||
|
|
torch.jit.is_tracing()
|
||
|
|
or isinstance(input_tensor, torch.fx.Proxy)
|
||
|
|
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
|
||
|
|
)
|
||
|
|
if not is_tracing and torch.any(attention_mask != 1):
|
||
|
|
# Attend to all tokens in masked rows from 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 = causal_mask.mul(
|
||
|
|
~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)
|
||
|
|
).to(dtype)
|
||
|
|
|
||
|
|
return causal_mask
|
||
|
|
|
||
|
|
|
||
|
|
class OpenELMForCausalLM(OpenELMPreTrainedModel):
|
||
|
|
_tied_weights_keys = ["lm_head.weight"]
|
||
|
|
|
||
|
|
def __init__(self, config: OpenELMConfig):
|
||
|
|
super().__init__(config)
|
||
|
|
self.transformer = OpenELMModel(config)
|
||
|
|
self.vocab_size = config.vocab_size
|
||
|
|
if config.share_input_output_layers:
|
||
|
|
self.lm_head = None
|
||
|
|
else:
|
||
|
|
self.lm_head = nn.Linear(config.model_dim, config.vocab_size, bias=False)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def get_input_embeddings(self):
|
||
|
|
return self.transformer.token_embeddings
|
||
|
|
|
||
|
|
def set_input_embeddings(self, value):
|
||
|
|
self.transformer.token_embeddings = 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.transformer = decoder
|
||
|
|
|
||
|
|
def get_decoder(self):
|
||
|
|
return self.transformer
|
||
|
|
|
||
|
|
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]:
|
||
|
|
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.transformer(
|
||
|
|
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]
|
||
|
|
if self.lm_head is None:
|
||
|
|
# shared
|
||
|
|
logits = F.linear(
|
||
|
|
hidden_states, weight=self.transformer.token_embeddings.weight
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
logits = self.lm_head(hidden_states)
|
||
|
|
logits = logits[:, : self.config.vocab_size]
|
||
|
|
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,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
past_length = 0
|
||
|
|
if past_key_values is not None:
|
||
|
|
if isinstance(past_key_values, Cache):
|
||
|
|
cache_length = past_key_values.get_seq_length()
|
||
|
|
past_length = past_key_values.seen_tokens
|
||
|
|
max_cache_length = past_key_values.get_max_length()
|
||
|
|
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 self.generation_config.cache_implementation == "static":
|
||
|
|
# generation with static cache
|
||
|
|
cache_position = kwargs.get("cache_position", None)
|
||
|
|
if cache_position is None:
|
||
|
|
past_length = 0
|
||
|
|
else:
|
||
|
|
past_length = cache_position[-1] + 1
|
||
|
|
input_ids = input_ids[:, past_length:]
|
||
|
|
position_ids = position_ids[:, past_length:]
|
||
|
|
|
||
|
|
# we should only keep a `cache_position` in generate, and do +=1.
|
||
|
|
# same goes for position ids. Could also help with continued generation.
|
||
|
|
cache_position = torch.arange(
|
||
|
|
past_length,
|
||
|
|
past_length + position_ids.shape[-1],
|
||
|
|
device=position_ids.device,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 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
|
||
|
|
# We could use `next_tokens` directly instead.
|
||
|
|
model_inputs = {"input_ids": input_ids.contiguous()}
|
||
|
|
|
||
|
|
model_inputs.update(
|
||
|
|
{
|
||
|
|
"position_ids": position_ids.contiguous(),
|
||
|
|
"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
|