1481 lines
60 KiB
Python
1481 lines
60 KiB
Python
# coding=utf-8
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# Copyright 2023 University of Wisconsin-Madison and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch MRA model."""
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import math
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from pathlib import Path
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from torch.utils.cpp_extension import load
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutputWithCrossAttentions,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_ninja_available,
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is_torch_cuda_available,
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logging,
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)
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from .configuration_mra import MraConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "uw-madison/mra-base-512-4"
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_CONFIG_FOR_DOC = "MraConfig"
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_TOKENIZER_FOR_DOC = "AutoTokenizer"
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from ..deprecated._archive_maps import MRA_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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mra_cuda_kernel = None
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def load_cuda_kernels():
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global mra_cuda_kernel
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src_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "mra"
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def append_root(files):
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return [src_folder / file for file in files]
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src_files = append_root(["cuda_kernel.cu", "cuda_launch.cu", "torch_extension.cpp"])
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mra_cuda_kernel = load("cuda_kernel", src_files, verbose=True)
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def sparse_max(sparse_qk_prod, indices, query_num_block, key_num_block):
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"""
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Computes maximum values for softmax stability.
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"""
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if len(sparse_qk_prod.size()) != 4:
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raise ValueError("sparse_qk_prod must be a 4-dimensional tensor.")
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if len(indices.size()) != 2:
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raise ValueError("indices must be a 2-dimensional tensor.")
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if sparse_qk_prod.size(2) != 32:
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raise ValueError("The size of the second dimension of sparse_qk_prod must be 32.")
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if sparse_qk_prod.size(3) != 32:
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raise ValueError("The size of the third dimension of sparse_qk_prod must be 32.")
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index_vals = sparse_qk_prod.max(dim=-2).values.transpose(-1, -2)
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index_vals = index_vals.contiguous()
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indices = indices.int()
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indices = indices.contiguous()
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max_vals, max_vals_scatter = mra_cuda_kernel.index_max(index_vals, indices, query_num_block, key_num_block)
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max_vals_scatter = max_vals_scatter.transpose(-1, -2)[:, :, None, :]
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return max_vals, max_vals_scatter
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def sparse_mask(mask, indices, block_size=32):
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"""
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Converts attention mask to a sparse mask for high resolution logits.
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"""
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if len(mask.size()) != 2:
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raise ValueError("mask must be a 2-dimensional tensor.")
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if len(indices.size()) != 2:
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raise ValueError("indices must be a 2-dimensional tensor.")
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if mask.shape[0] != indices.shape[0]:
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raise ValueError("mask and indices must have the same size in the zero-th dimension.")
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batch_size, seq_len = mask.shape
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num_block = seq_len // block_size
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batch_idx = torch.arange(indices.size(0), dtype=torch.long, device=indices.device)
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mask = mask.reshape(batch_size, num_block, block_size)
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mask = mask[batch_idx[:, None], (indices % num_block).long(), :]
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return mask
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def mm_to_sparse(dense_query, dense_key, indices, block_size=32):
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"""
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Performs Sampled Dense Matrix Multiplication.
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"""
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batch_size, query_size, dim = dense_query.size()
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_, key_size, dim = dense_key.size()
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if query_size % block_size != 0:
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raise ValueError("query_size (size of first dimension of dense_query) must be divisible by block_size.")
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if key_size % block_size != 0:
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raise ValueError("key_size (size of first dimension of dense_key) must be divisible by block_size.")
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dense_query = dense_query.reshape(batch_size, query_size // block_size, block_size, dim).transpose(-1, -2)
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dense_key = dense_key.reshape(batch_size, key_size // block_size, block_size, dim).transpose(-1, -2)
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if len(dense_query.size()) != 4:
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raise ValueError("dense_query must be a 4-dimensional tensor.")
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if len(dense_key.size()) != 4:
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raise ValueError("dense_key must be a 4-dimensional tensor.")
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if len(indices.size()) != 2:
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raise ValueError("indices must be a 2-dimensional tensor.")
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if dense_query.size(3) != 32:
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raise ValueError("The third dimension of dense_query must be 32.")
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if dense_key.size(3) != 32:
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raise ValueError("The third dimension of dense_key must be 32.")
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dense_query = dense_query.contiguous()
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dense_key = dense_key.contiguous()
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indices = indices.int()
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indices = indices.contiguous()
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return mra_cuda_kernel.mm_to_sparse(dense_query, dense_key, indices.int())
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def sparse_dense_mm(sparse_query, indices, dense_key, query_num_block, block_size=32):
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"""
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Performs matrix multiplication of a sparse matrix with a dense matrix.
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"""
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batch_size, key_size, dim = dense_key.size()
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if key_size % block_size != 0:
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raise ValueError("key_size (size of first dimension of dense_key) must be divisible by block_size.")
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if sparse_query.size(2) != block_size:
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raise ValueError("The size of the second dimension of sparse_query must be equal to the block_size.")
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if sparse_query.size(3) != block_size:
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raise ValueError("The size of the third dimension of sparse_query must be equal to the block_size.")
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dense_key = dense_key.reshape(batch_size, key_size // block_size, block_size, dim).transpose(-1, -2)
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if len(sparse_query.size()) != 4:
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raise ValueError("sparse_query must be a 4-dimensional tensor.")
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if len(dense_key.size()) != 4:
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raise ValueError("dense_key must be a 4-dimensional tensor.")
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if len(indices.size()) != 2:
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raise ValueError("indices must be a 2-dimensional tensor.")
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if dense_key.size(3) != 32:
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raise ValueError("The size of the third dimension of dense_key must be 32.")
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sparse_query = sparse_query.contiguous()
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indices = indices.int()
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indices = indices.contiguous()
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dense_key = dense_key.contiguous()
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dense_qk_prod = mra_cuda_kernel.sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
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dense_qk_prod = dense_qk_prod.transpose(-1, -2).reshape(batch_size, query_num_block * block_size, dim)
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return dense_qk_prod
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def transpose_indices(indices, dim_1_block, dim_2_block):
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return ((indices % dim_2_block) * dim_1_block + torch.div(indices, dim_2_block, rounding_mode="floor")).long()
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class MraSampledDenseMatMul(torch.autograd.Function):
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@staticmethod
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def forward(ctx, dense_query, dense_key, indices, block_size):
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sparse_qk_prod = mm_to_sparse(dense_query, dense_key, indices, block_size)
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ctx.save_for_backward(dense_query, dense_key, indices)
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ctx.block_size = block_size
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return sparse_qk_prod
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@staticmethod
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def backward(ctx, grad):
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dense_query, dense_key, indices = ctx.saved_tensors
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block_size = ctx.block_size
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query_num_block = dense_query.size(1) // block_size
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key_num_block = dense_key.size(1) // block_size
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indices_T = transpose_indices(indices, query_num_block, key_num_block)
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grad_key = sparse_dense_mm(grad.transpose(-1, -2), indices_T, dense_query, key_num_block)
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grad_query = sparse_dense_mm(grad, indices, dense_key, query_num_block)
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return grad_query, grad_key, None, None
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@staticmethod
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def operator_call(dense_query, dense_key, indices, block_size=32):
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return MraSampledDenseMatMul.apply(dense_query, dense_key, indices, block_size)
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class MraSparseDenseMatMul(torch.autograd.Function):
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@staticmethod
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def forward(ctx, sparse_query, indices, dense_key, query_num_block):
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sparse_qk_prod = sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
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ctx.save_for_backward(sparse_query, indices, dense_key)
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ctx.query_num_block = query_num_block
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return sparse_qk_prod
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@staticmethod
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def backward(ctx, grad):
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sparse_query, indices, dense_key = ctx.saved_tensors
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query_num_block = ctx.query_num_block
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key_num_block = dense_key.size(1) // sparse_query.size(-1)
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indices_T = transpose_indices(indices, query_num_block, key_num_block)
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grad_key = sparse_dense_mm(sparse_query.transpose(-1, -2), indices_T, grad, key_num_block)
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grad_query = mm_to_sparse(grad, dense_key, indices)
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return grad_query, None, grad_key, None
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@staticmethod
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def operator_call(sparse_query, indices, dense_key, query_num_block):
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return MraSparseDenseMatMul.apply(sparse_query, indices, dense_key, query_num_block)
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class MraReduceSum:
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@staticmethod
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def operator_call(sparse_query, indices, query_num_block, key_num_block):
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batch_size, num_block, block_size, _ = sparse_query.size()
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if len(sparse_query.size()) != 4:
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raise ValueError("sparse_query must be a 4-dimensional tensor.")
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if len(indices.size()) != 2:
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raise ValueError("indices must be a 2-dimensional tensor.")
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_, _, block_size, _ = sparse_query.size()
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batch_size, num_block = indices.size()
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sparse_query = sparse_query.sum(dim=2).reshape(batch_size * num_block, block_size)
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batch_idx = torch.arange(indices.size(0), dtype=torch.long, device=indices.device)
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global_idxes = (
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torch.div(indices, key_num_block, rounding_mode="floor").long() + batch_idx[:, None] * query_num_block
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).reshape(batch_size * num_block)
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temp = torch.zeros(
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(batch_size * query_num_block, block_size), dtype=sparse_query.dtype, device=sparse_query.device
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)
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output = temp.index_add(0, global_idxes, sparse_query).reshape(batch_size, query_num_block, block_size)
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output = output.reshape(batch_size, query_num_block * block_size)
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return output
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def get_low_resolution_logit(query, key, block_size, mask=None, value=None):
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"""
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Compute low resolution approximation.
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"""
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batch_size, seq_len, head_dim = query.size()
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num_block_per_row = seq_len // block_size
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value_hat = None
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if mask is not None:
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token_count = mask.reshape(batch_size, num_block_per_row, block_size).sum(dim=-1)
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query_hat = query.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
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token_count[:, :, None] + 1e-6
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)
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key_hat = key.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
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token_count[:, :, None] + 1e-6
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)
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if value is not None:
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value_hat = value.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
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token_count[:, :, None] + 1e-6
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)
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else:
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token_count = block_size * torch.ones(batch_size, num_block_per_row, dtype=torch.float, device=query.device)
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query_hat = query.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
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key_hat = key.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
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if value is not None:
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value_hat = value.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
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low_resolution_logit = torch.matmul(query_hat, key_hat.transpose(-1, -2)) / math.sqrt(head_dim)
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low_resolution_logit_row_max = low_resolution_logit.max(dim=-1, keepdims=True).values
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if mask is not None:
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low_resolution_logit = (
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low_resolution_logit - 1e4 * ((token_count[:, None, :] * token_count[:, :, None]) < 0.5).float()
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)
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return low_resolution_logit, token_count, low_resolution_logit_row_max, value_hat
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def get_block_idxes(
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low_resolution_logit, num_blocks, approx_mode, initial_prior_first_n_blocks, initial_prior_diagonal_n_blocks
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):
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"""
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Compute the indices of the subset of components to be used in the approximation.
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"""
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batch_size, total_blocks_per_row, _ = low_resolution_logit.shape
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if initial_prior_diagonal_n_blocks > 0:
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offset = initial_prior_diagonal_n_blocks // 2
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temp_mask = torch.ones(total_blocks_per_row, total_blocks_per_row, device=low_resolution_logit.device)
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diagonal_mask = torch.tril(torch.triu(temp_mask, diagonal=-offset), diagonal=offset)
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low_resolution_logit = low_resolution_logit + diagonal_mask[None, :, :] * 5e3
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if initial_prior_first_n_blocks > 0:
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low_resolution_logit[:, :initial_prior_first_n_blocks, :] = (
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low_resolution_logit[:, :initial_prior_first_n_blocks, :] + 5e3
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)
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low_resolution_logit[:, :, :initial_prior_first_n_blocks] = (
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low_resolution_logit[:, :, :initial_prior_first_n_blocks] + 5e3
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)
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top_k_vals = torch.topk(
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low_resolution_logit.reshape(batch_size, -1), num_blocks, dim=-1, largest=True, sorted=False
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)
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indices = top_k_vals.indices
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if approx_mode == "full":
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threshold = top_k_vals.values.min(dim=-1).values
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high_resolution_mask = (low_resolution_logit >= threshold[:, None, None]).float()
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elif approx_mode == "sparse":
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high_resolution_mask = None
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else:
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raise ValueError(f"{approx_mode} is not a valid approx_model value.")
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return indices, high_resolution_mask
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def mra2_attention(
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query,
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key,
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value,
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mask,
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num_blocks,
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approx_mode,
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block_size=32,
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initial_prior_first_n_blocks=0,
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initial_prior_diagonal_n_blocks=0,
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):
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"""
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Use Mra to approximate self-attention.
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"""
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if mra_cuda_kernel is None:
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return torch.zeros_like(query).requires_grad_()
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batch_size, num_head, seq_len, head_dim = query.size()
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meta_batch = batch_size * num_head
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if seq_len % block_size != 0:
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raise ValueError("sequence length must be divisible by the block_size.")
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num_block_per_row = seq_len // block_size
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query = query.reshape(meta_batch, seq_len, head_dim)
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key = key.reshape(meta_batch, seq_len, head_dim)
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value = value.reshape(meta_batch, seq_len, head_dim)
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if mask is not None:
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query = query * mask[:, :, None]
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key = key * mask[:, :, None]
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value = value * mask[:, :, None]
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if approx_mode == "full":
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low_resolution_logit, token_count, low_resolution_logit_row_max, value_hat = get_low_resolution_logit(
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query, key, block_size, mask, value
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)
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elif approx_mode == "sparse":
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with torch.no_grad():
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low_resolution_logit, token_count, low_resolution_logit_row_max, _ = get_low_resolution_logit(
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query, key, block_size, mask
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)
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else:
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raise Exception('approx_mode must be "full" or "sparse"')
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with torch.no_grad():
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low_resolution_logit_normalized = low_resolution_logit - low_resolution_logit_row_max
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indices, high_resolution_mask = get_block_idxes(
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low_resolution_logit_normalized,
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num_blocks,
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approx_mode,
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initial_prior_first_n_blocks,
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initial_prior_diagonal_n_blocks,
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)
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high_resolution_logit = MraSampledDenseMatMul.operator_call(
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query, key, indices, block_size=block_size
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) / math.sqrt(head_dim)
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max_vals, max_vals_scatter = sparse_max(high_resolution_logit, indices, num_block_per_row, num_block_per_row)
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high_resolution_logit = high_resolution_logit - max_vals_scatter
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if mask is not None:
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high_resolution_logit = high_resolution_logit - 1e4 * (1 - sparse_mask(mask, indices)[:, :, :, None])
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high_resolution_attn = torch.exp(high_resolution_logit)
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high_resolution_attn_out = MraSparseDenseMatMul.operator_call(
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high_resolution_attn, indices, value, num_block_per_row
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)
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high_resolution_normalizer = MraReduceSum.operator_call(
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high_resolution_attn, indices, num_block_per_row, num_block_per_row
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)
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if approx_mode == "full":
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low_resolution_attn = (
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torch.exp(low_resolution_logit - low_resolution_logit_row_max - 1e4 * high_resolution_mask)
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* token_count[:, None, :]
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)
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low_resolution_attn_out = (
|
|
torch.matmul(low_resolution_attn, value_hat)[:, :, None, :]
|
|
.repeat(1, 1, block_size, 1)
|
|
.reshape(meta_batch, seq_len, head_dim)
|
|
)
|
|
low_resolution_normalizer = (
|
|
low_resolution_attn.sum(dim=-1)[:, :, None].repeat(1, 1, block_size).reshape(meta_batch, seq_len)
|
|
)
|
|
|
|
log_correction = low_resolution_logit_row_max.repeat(1, 1, block_size).reshape(meta_batch, seq_len) - max_vals
|
|
if mask is not None:
|
|
log_correction = log_correction * mask
|
|
|
|
low_resolution_corr = torch.exp(log_correction * (log_correction <= 0).float())
|
|
low_resolution_attn_out = low_resolution_attn_out * low_resolution_corr[:, :, None]
|
|
low_resolution_normalizer = low_resolution_normalizer * low_resolution_corr
|
|
|
|
high_resolution_corr = torch.exp(-log_correction * (log_correction > 0).float())
|
|
high_resolution_attn_out = high_resolution_attn_out * high_resolution_corr[:, :, None]
|
|
high_resolution_normalizer = high_resolution_normalizer * high_resolution_corr
|
|
|
|
context_layer = (high_resolution_attn_out + low_resolution_attn_out) / (
|
|
high_resolution_normalizer[:, :, None] + low_resolution_normalizer[:, :, None] + 1e-6
|
|
)
|
|
|
|
elif approx_mode == "sparse":
|
|
context_layer = high_resolution_attn_out / (high_resolution_normalizer[:, :, None] + 1e-6)
|
|
else:
|
|
raise Exception('config.approx_mode must be "full" or "sparse"')
|
|
|
|
if mask is not None:
|
|
context_layer = context_layer * mask[:, :, None]
|
|
|
|
context_layer = context_layer.reshape(batch_size, num_head, seq_len, head_dim)
|
|
|
|
return context_layer
|
|
|
|
|
|
class MraEmbeddings(nn.Module):
|
|
"""Construct the embeddings from word, position and token_type embeddings."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
|
# any TensorFlow checkpoint file
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2)
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
self.register_buffer(
|
|
"token_type_ids",
|
|
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
|
|
persistent=False,
|
|
)
|
|
|
|
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
if position_ids is None:
|
|
position_ids = self.position_ids[:, :seq_length]
|
|
|
|
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
|
|
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
|
|
# issue #5664
|
|
if token_type_ids is None:
|
|
if hasattr(self, "token_type_ids"):
|
|
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
class MraSelfAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
|
|
f"heads ({config.num_attention_heads})"
|
|
)
|
|
|
|
kernel_loaded = mra_cuda_kernel is not None
|
|
if is_torch_cuda_available() and is_ninja_available() and not kernel_loaded:
|
|
try:
|
|
load_cuda_kernels()
|
|
except Exception as e:
|
|
logger.warning(f"Could not load the custom kernel for multi-scale deformable attention: {e}")
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
self.position_embedding_type = (
|
|
position_embedding_type if position_embedding_type is not None else config.position_embedding_type
|
|
)
|
|
|
|
self.num_block = (config.max_position_embeddings // 32) * config.block_per_row
|
|
self.num_block = min(self.num_block, int((config.max_position_embeddings // 32) ** 2))
|
|
|
|
self.approx_mode = config.approx_mode
|
|
self.initial_prior_first_n_blocks = config.initial_prior_first_n_blocks
|
|
self.initial_prior_diagonal_n_blocks = config.initial_prior_diagonal_n_blocks
|
|
|
|
def transpose_for_scores(self, layer):
|
|
new_layer_shape = layer.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
layer = layer.view(*new_layer_shape)
|
|
return layer.permute(0, 2, 1, 3)
|
|
|
|
def forward(self, hidden_states, attention_mask=None):
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
batch_size, num_heads, seq_len, head_dim = query_layer.size()
|
|
|
|
# revert changes made by get_extended_attention_mask
|
|
attention_mask = 1.0 + attention_mask / 10000.0
|
|
attention_mask = (
|
|
attention_mask.squeeze().repeat(1, num_heads, 1).reshape(batch_size * num_heads, seq_len).int()
|
|
)
|
|
|
|
# The CUDA kernels are most efficient with inputs whose size is a multiple of a GPU's warp size (32). Inputs
|
|
# smaller than this are padded with zeros.
|
|
gpu_warp_size = 32
|
|
|
|
if head_dim < gpu_warp_size:
|
|
pad_size = batch_size, num_heads, seq_len, gpu_warp_size - head_dim
|
|
|
|
query_layer = torch.cat([query_layer, torch.zeros(pad_size, device=query_layer.device)], dim=-1)
|
|
key_layer = torch.cat([key_layer, torch.zeros(pad_size, device=key_layer.device)], dim=-1)
|
|
value_layer = torch.cat([value_layer, torch.zeros(pad_size, device=value_layer.device)], dim=-1)
|
|
|
|
context_layer = mra2_attention(
|
|
query_layer.float(),
|
|
key_layer.float(),
|
|
value_layer.float(),
|
|
attention_mask.float(),
|
|
self.num_block,
|
|
approx_mode=self.approx_mode,
|
|
initial_prior_first_n_blocks=self.initial_prior_first_n_blocks,
|
|
initial_prior_diagonal_n_blocks=self.initial_prior_diagonal_n_blocks,
|
|
)
|
|
|
|
if head_dim < gpu_warp_size:
|
|
context_layer = context_layer[:, :, :, :head_dim]
|
|
|
|
context_layer = context_layer.reshape(batch_size, num_heads, seq_len, head_dim)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(*new_context_layer_shape)
|
|
|
|
outputs = (context_layer,)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
|
|
class MraSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class MraAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
self.self = MraSelfAttention(config, position_embedding_type=position_embedding_type)
|
|
self.output = MraSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(self, hidden_states, attention_mask=None):
|
|
self_outputs = self.self(hidden_states, attention_mask)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
|
|
class MraIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOutput
|
|
class MraOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class MraLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = MraAttention(config)
|
|
self.add_cross_attention = config.add_cross_attention
|
|
self.intermediate = MraIntermediate(config)
|
|
self.output = MraOutput(config)
|
|
|
|
def forward(self, hidden_states, attention_mask=None):
|
|
self_attention_outputs = self.attention(hidden_states, attention_mask)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class MraEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([MraLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(hidden_states, attention_mask)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
|
|
return BaseModelOutputWithCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform
|
|
class MraPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Mra
|
|
class MraLMPredictionHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = MraPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
|
|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Mra
|
|
class MraOnlyMLMHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.predictions = MraLMPredictionHead(config)
|
|
|
|
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
# Copied from transformers.models.yoso.modeling_yoso.YosoPreTrainedModel with Yoso->Mra,yoso->mra
|
|
class MraPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = MraConfig
|
|
base_model_prefix = "mra"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
MRA_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
|
|
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`MraConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
MRA_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare MRA Model transformer outputting raw hidden-states without any specific head on top.",
|
|
MRA_START_DOCSTRING,
|
|
)
|
|
class MraModel(MraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = MraEmbeddings(config)
|
|
self.encoder = MraEncoder(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithCrossAttentions,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithCrossAttentions]:
|
|
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 input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
if not return_dict:
|
|
return (sequence_output,) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("""MRA Model with a `language modeling` head on top.""", MRA_START_DOCSTRING)
|
|
class MraForMaskedLM(MraPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.mra = MraModel(config)
|
|
self.cls = MraOnlyMLMHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.cls.predictions.decoder = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MaskedLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MaskedLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[1:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.yoso.modeling_yoso.YosoClassificationHead with Yoso->Mra
|
|
class MraClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
self.config = config
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
x = self.dropout(x)
|
|
x = self.dense(x)
|
|
x = ACT2FN[self.config.hidden_act](x)
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""MRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
|
|
the pooled output) e.g. for GLUE tasks.""",
|
|
MRA_START_DOCSTRING,
|
|
)
|
|
class MraForSequenceClassification(MraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.mra = MraModel(config)
|
|
self.classifier = MraClassificationHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=SequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""MRA Model with a multiple choice classification head on top (a linear layer on top of
|
|
the pooled output and a softmax) e.g. for RocStories/SWAG tasks.""",
|
|
MRA_START_DOCSTRING,
|
|
)
|
|
class MraForMultipleChoice(MraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.mra = MraModel(config)
|
|
self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, MultipleChoiceModelOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.mra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_state = outputs[0] # (bs * num_choices, seq_len, dim)
|
|
pooled_output = hidden_state[:, 0] # (bs * num_choices, dim)
|
|
pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim)
|
|
pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""MRA Model with a token classification head on top (a linear layer on top of
|
|
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.""",
|
|
MRA_START_DOCSTRING,
|
|
)
|
|
class MraForTokenClassification(MraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.mra = MraModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=TokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# Only keep active parts of the loss
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1) == 1
|
|
active_logits = logits.view(-1, self.num_labels)
|
|
active_labels = torch.where(
|
|
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
|
|
)
|
|
loss = loss_fct(active_logits, active_labels)
|
|
else:
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""MRA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
|
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).""",
|
|
MRA_START_DOCSTRING,
|
|
)
|
|
class MraForQuestionAnswering(MraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
config.num_labels = 2
|
|
self.num_labels = config.num_labels
|
|
|
|
self.mra = MraModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=QuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1)
|
|
end_logits = end_logits.squeeze(-1)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[1:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|