dmlc--dgl
Zihao Ye
650f6ee1e0
* gat * upd * upd sage * upd * upd * upd * upd * upd * add gmmconv * upd ggnn * upd * upd * upd * upd * add citation examples * add README * fix cheb * improve doc * formula * upd * trigger * lint * lint * upd * add test for transform * add test * check * upd * improve doc * shape check * upd * densechebconv, currently not correct (?) * fix cheb * fix * upd * upd sgc-reddit * upd * trigger
154 行
3.7 KiB
Python
154 行
3.7 KiB
Python
"""This file defines the unified tensor framework interface required by DGL
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unit testing, other than the ones used in the framework itself.
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"""
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###############################################################################
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# Tensor, data type and context interfaces
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def cuda():
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"""Context object for CUDA."""
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pass
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def is_cuda_available():
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"""Check whether CUDA is available."""
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pass
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###############################################################################
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# Tensor functions on feature data
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# --------------------------------
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# These functions are performance critical, so it's better to have efficient
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# implementation in each framework.
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def array_equal(a, b):
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"""Check whether the two tensors are *exactly* equal."""
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pass
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def allclose(a, b, rtol=1e-4, atol=1e-4):
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"""Check whether the two tensors are numerically close to each other."""
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pass
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def randn(shape):
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"""Generate a tensor with elements from standard normal distribution."""
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pass
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def attach_grad(x):
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"""Flag the tensor *in-place* to have its gradient computed in backward
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pass.
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If the flag is already set, reset the gradient buffer as well.
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"""
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pass
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def backward(x, head_gradient=None):
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"""Invoke backward computation with an optional head gradient.
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Returns nothing."""
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pass
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def grad(x):
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"""Fetches the gradient from the tensor after backward computation."""
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pass
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def is_no_grad(x):
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"""Check whether a tensor has its gradient computed."""
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pass
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def full(shape, fill_value, dtype, ctx):
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pass
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def narrow_row_set(x, start, stop, new):
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"""Set a slice of the given tensor to a new value."""
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pass
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def sparse_to_numpy(x):
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"""Convert a sparse tensor to a numpy array."""
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pass
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def clone(x):
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pass
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def reduce_sum(x):
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"""Sums all the elements into a single scalar."""
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pass
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def softmax(x, dim):
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"""Softmax Operation on Tensors"""
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pass
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def spmm(x, y):
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"""Sparse dense matrix multiply"""
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pass
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def add(a, b):
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"""Compute a + b"""
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pass
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def sub(a, b):
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"""Compute a - b"""
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pass
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def mul(a, b):
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"""Compute a * b"""
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pass
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def div(a, b):
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"""Compute a / b"""
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pass
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def sum(x, dim):
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"""Computes the sum of array elements over given axes"""
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pass
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def max(x, dim):
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"""Computes the max of array elements over given axes"""
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pass
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def min(x, dim):
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"""Computes the min of array elements over given axes"""
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pass
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def prod(x, dim):
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"""Computes the prod of array elements over given axes"""
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pass
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def matmul(a, b):
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"""Compute Matrix Multiplication between a and b"""
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pass
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###############################################################################
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# Tensor functions used *only* on index tensor
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# ----------------
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# These operators are light-weighted, so it is acceptable to fallback to
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# numpy operators if currently missing in the framework. Ideally in the future,
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# DGL should contain all the operations on index, so this set of operators
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# should be gradually removed.
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###############################################################################
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# Other interfaces
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# ----------------
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# These are not related to tensors. Some of them are temporary workarounds that
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# should be included in DGL in the future.
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class record_grad(object):
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"""Context manager that records the gradients"""
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def __init__(self):
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pass
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def __enter__(self):
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pass
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def __exit__(self, exc_type, exc_value, exc_traceback):
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pass
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class no_grad(object):
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"""Context manager that explicitly disables gradient computation"""
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def __init__(self):
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pass
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def __enter__(self):
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pass
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def __exit__(self, exc_type, exc_value, exc_traceback):
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pass
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