chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from .sparse import *
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from .tensor import *
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import mxnet as mx
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import numpy as np
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from mxnet import nd
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from ..._sparse_ops import (
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_bwd_segment_cmp,
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_csrmask,
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_csrmm,
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_csrsum,
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_gsddmm,
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_gspmm,
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_scatter_add,
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_segment_reduce,
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)
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from ...base import ALL, dgl_warning, is_all
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from ...heterograph_index import create_unitgraph_from_csr
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from .tensor import (
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asnumpy,
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context,
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copy_to,
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to_backend_ctx,
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zerocopy_from_numpy,
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)
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__all__ = [
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"gspmm",
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"gsddmm",
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"edge_softmax",
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"segment_reduce",
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"scatter_add",
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"csrmm",
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"csrsum",
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"csrmask",
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]
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def _scatter_nd(index, src, n_rows):
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"""Similar to PyTorch's scatter nd on first dimension."""
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assert index.shape == src.shape
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dgl_warning("MXNet do not support scatter_add, fallback to numpy.")
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ctx = context(src)
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index = asnumpy(index)
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src = asnumpy(src)
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shp = index.shape
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ndim = src.ndim
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offsets = []
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stride = 1
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for i in reversed(range(1, ndim)):
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di = shp[i]
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offset_i = np.arange(di, dtype=index.dtype)
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offsets.append(
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(stride * offset_i).reshape(
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(1,) * i + (di,) + (1,) * (ndim - 1 - i)
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)
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)
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stride *= di
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if ndim > 1:
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new_idx = index * stride + sum(offsets)
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else:
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new_idx = index
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src = src.reshape(-1)
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new_idx = new_idx.reshape(-1)
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rst = np.zeros((stride * n_rows,), dtype=src.dtype)
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np.add.at(rst, new_idx, src)
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rst = rst.reshape(n_rows, *shp[1:])
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rst = copy_to(zerocopy_from_numpy(rst), ctx)
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return rst
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def _gather_nd(index, src):
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"""Similar to PyTorch's gather nd on first dimension."""
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ctx = context(src)
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shp = index.shape
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ndim = src.ndim
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offsets = []
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stride = 1
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for i in reversed(range(1, ndim)):
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di = shp[i]
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offset_i = nd.arange(di, dtype=index.dtype)
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offsets.append(
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(stride * offset_i).reshape(
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(1,) * i + (di,) + (1,) * (ndim - 1 - i)
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)
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)
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stride *= di
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if ndim > 1:
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new_idx = index * stride + copy_to(sum(offsets), ctx)
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else:
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new_idx = index
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src = src.reshape(-1)
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new_idx = new_idx.reshape(-1)
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rst = nd.take(src, new_idx).reshape(shp)
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return rst
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def _reduce_grad(grad, shape):
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"""Reduce gradient on the broadcast dimension
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If there is broadcast in forward pass, gradients need to be reduced on
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broadcast dimension. This function checks the input tensor shape and
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gradient shape and perform the reduction.
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Parameters
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----------
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grad: Tensor
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Gradient tensor
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shape: tuple
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Shape of input tensor
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Returns
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-------
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Tensor
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"""
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grad_shape = grad.shape[1:]
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in_shape = shape[1:]
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if in_shape == grad_shape:
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# no need to reduce
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return grad
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num_to_squeeze = len(grad_shape) - len(in_shape)
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# pad inshape
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in_shape = (1,) * num_to_squeeze + in_shape
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# pad in_shape
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in_shape = (1,) * num_to_squeeze + in_shape
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reduce_idx = np.nonzero(np.asarray(grad_shape) - np.asarray(in_shape))[0]
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reduce_idx += 1 # skip batch dim
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grad = grad.sum(axis=tuple(reduce_idx), keepdims=True)
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return grad.reshape(shape)
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def _need_reduce_last_dim(ufeat, efeat):
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"""Indicates whether to reduce the last dimension on edges
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in the backward pass of spmm,
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if so, use dot instead of mul."""
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ushp = ufeat.shape
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eshp = efeat.shape
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return ushp[1:-1] == eshp[1:-1] and eshp[-1] == 1 and ushp[-1] > 1
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def _muldiv(op, x):
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return 1.0 / x if op == "div" else x
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def _addsub(op, x):
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return -x if op == "sub" else x
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def _expand(x, shape):
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return x.broadcast_to((x.shape[0], *shape))
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class GSpMM(mx.autograd.Function):
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def __init__(self, gidx, op, reduce_op):
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super(GSpMM, self).__init__()
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self.gidx = gidx
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self.op = op
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self.reduce_op = reduce_op
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def forward(self, X, Y):
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out, (argX, argY) = _gspmm(self.gidx, self.op, self.reduce_op, X, Y)
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self.save_for_backward(X, Y, argX, argY)
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return out
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def backward(self, dZ):
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ctx = context(dZ)
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X, Y, argX, argY = self.saved_tensors
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gidx, op, reduce_op = self.gidx, self.op, self.reduce_op
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if op != "copy_rhs":
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g_rev = gidx.reverse()
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if reduce_op == "sum":
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if op in ["mul", "div"]:
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dX = _gspmm(g_rev, "mul", "sum", dZ, _muldiv(op, Y))[0]
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elif op in ["add", "sub"]:
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dX = _gspmm(g_rev, "copy_lhs", "sum", dZ, Y)[0]
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elif op == "copy_lhs":
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dX = _gspmm(g_rev, "copy_lhs", "sum", dZ, None)[0]
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else:
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if op in ["mul", "div"]:
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dX = _scatter_nd(
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argX,
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_muldiv(op, _gather_nd(argY, _expand(Y, dZ.shape[1:])))
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* dZ,
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X.shape[0],
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)
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elif op in ["add", "sub", "copy_lhs"]:
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dX = _scatter_nd(argX, dZ, X.shape[0])
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dX = _reduce_grad(dX, X.shape)
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else:
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dX = nd.zeros_like(X)
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if op != "copy_lhs":
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if reduce_op == "sum":
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if op == "mul" and _need_reduce_last_dim(X, Y):
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dY = _gsddmm(gidx, "dot", X, dZ)
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elif op in ["mul", "div"]:
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dY = _gsddmm(gidx, "mul", X, dZ)
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if op == "div":
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dY = -dY / (Y**2)
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elif op in ["add", "sub", "copy_rhs"]:
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dY = _gsddmm(gidx, "copy_rhs", X, _addsub(op, dZ))
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else:
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if op in ["mul", "div"]:
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dY = _scatter_nd(
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argY,
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_gather_nd(argX, _expand(X, dZ.shape[1:])) * dZ,
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Y.shape[0],
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)
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if op == "div":
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dY = -dY / (Y**2)
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elif op in ["add", "sub", "copy_rhs"]:
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dY = _scatter_nd(argY, _addsub(op, dZ), Y.shape[0])
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dY = _reduce_grad(dY, Y.shape)
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else:
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dY = nd.zeros_like(Y)
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self.saved_tensors = None
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return dX, dY
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def gspmm(gidx, op, reduce_op, lhs_data, rhs_data):
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func = GSpMM(gidx, op, reduce_op)
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ctx = to_backend_ctx(gidx.ctx)
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# XXX(minjie): There is a bug in MXNet's autograd system when one of the inputs
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# does not require gradient. Although it still invokes the backward function,
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# it does not set the gradient value to the correct buffer, resulting all the
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# input gradients to be zero. Fix this by enforcing all the inputs to require
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# gradients.
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if lhs_data is None:
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lhs_data = nd.zeros((1,), ctx=ctx)
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lhs_data.attach_grad()
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if rhs_data is None:
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rhs_data = nd.zeros((1,), ctx=ctx)
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rhs_data.attach_grad()
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return func(lhs_data, rhs_data)
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class GSDDMM(mx.autograd.Function):
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def __init__(self, gidx, op, lhs_target, rhs_target):
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super(GSDDMM, self).__init__()
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self.gidx = gidx
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self.op = op
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self.lhs_target = lhs_target
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self.rhs_target = rhs_target
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def forward(self, X, Y):
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out = _gsddmm(
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self.gidx, self.op, X, Y, self.lhs_target, self.rhs_target
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)
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self.save_for_backward(X, Y)
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return out
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def backward(self, dZ):
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ctx = context(dZ)
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X, Y = self.saved_tensors
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gidx, op = self.gidx, self.op
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lhs_target, rhs_target = self.lhs_target, self.rhs_target
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if op != "copy_rhs":
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if lhs_target in ["u", "v"]:
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_gidx = gidx if self.lhs_target == "v" else gidx.reverse()
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if op in ["add", "sub", "copy_lhs"]:
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dX = _gspmm(_gidx, "copy_rhs", "sum", None, dZ)[0]
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else: # mul, div, dot
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if rhs_target == lhs_target:
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dX = _gspmm(_gidx, "copy_rhs", "sum", None, dZ)[
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0
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] * _muldiv(op, Y)
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elif self.rhs_target == "e":
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dX = _gspmm(
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_gidx, "copy_rhs", "sum", None, dZ * _muldiv(op, Y)
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)[0]
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else: # rhs_target = !lhs_target
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dX = _gspmm(_gidx, "mul", "sum", _muldiv(op, Y), dZ)[0]
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else: # lhs_target == 'e'
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if op in ["add", "sub", "copy_lhs"]:
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dX = dZ
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else: # mul, div, dot
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dX = _gsddmm(
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gidx, "mul", dZ, _muldiv(op, Y), "e", rhs_target
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)
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dX = _reduce_grad(dX, X.shape)
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else:
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dX = nd.zeros_like(X)
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if op != "copy_lhs":
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if self.rhs_target in ["u", "v"]:
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_gidx = gidx if rhs_target == "v" else gidx.reverse()
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if op in ["add", "sub", "copy_rhs"]:
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dY = _gspmm(
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_gidx, "copy_rhs", "sum", None, _addsub(op, dZ)
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)[0]
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else: # mul, div, dot
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if lhs_target == rhs_target:
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dY = _gspmm(_gidx, "copy_rhs", "sum", None, dZ)[0] * X
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elif self.lhs_target == "e":
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dY = _gspmm(_gidx, "copy_rhs", "sum", None, dZ * X)[0]
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else: # rhs_target = !lhs_target
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dY = _gspmm(_gidx, "mul", "sum", X, dZ)[0]
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if op == "div":
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dY = -dY / (Y**2)
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else:
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if op in ["add", "sub", "copy_rhs"]:
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dY = _addsub(op, dZ)
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else: # mul, div, dot
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dY = _gsddmm(gidx, "mul", dZ, X, "e", lhs_target)
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if op == "div":
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dY = -dY / (Y**2)
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dY = _reduce_grad(dY, Y.shape)
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else:
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dY = nd.zeros_like(Y)
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self.saved_tensors = None
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return dX, dY
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def gsddmm(gidx, op, lhs_data, rhs_data, lhs_target="u", rhs_target="v"):
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func = GSDDMM(gidx, op, lhs_target, rhs_target)
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ctx = to_backend_ctx(gidx.ctx)
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if lhs_data is None:
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lhs_data = nd.zeros((1,), ctx=ctx)
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if rhs_data is None:
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rhs_data = nd.zeros((1,), ctx=ctx)
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return func(lhs_data, rhs_data)
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class EdgeSoftmax(mx.autograd.Function):
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def __init__(self, gidx, eids, norm_by):
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super(EdgeSoftmax, self).__init__()
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if not is_all(eids):
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gidx = gidx.edge_subgraph([eids], True).graph
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if norm_by == "src":
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gidx = gidx.reverse()
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self.gidx = gidx
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def forward(self, score):
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"""Forward function.
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Pseudo-code:
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.. code:: python
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score = dgl.EData(g, score)
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score_max = score.dst_max() # of type dgl.NData
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score = score - score_max # edge_sub_dst, ret dgl.EData
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score_sum = score.dst_sum() # of type dgl.NData
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out = score / score_sum # edge_div_dst, ret dgl.EData
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return out.data
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"""
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gidx = self.gidx
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score_max = _gspmm(gidx, "copy_rhs", "max", None, score)[0]
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score = mx.nd.exp(_gsddmm(gidx, "sub", score, score_max, "e", "v"))
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score_sum = _gspmm(gidx, "copy_rhs", "sum", None, score)[0]
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out = _gsddmm(gidx, "div", score, score_sum, "e", "v")
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self.save_for_backward(out)
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return out
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def backward(self, grad_out):
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"""Backward function.
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Pseudo-code:
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.. code:: python
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g, out = ctx.backward_cache
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grad_out = dgl.EData(g, grad_out)
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out = dgl.EData(g, out)
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sds = out * grad_out # type dgl.EData
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sds_sum = sds.dst_sum() # type dgl.NData
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grad_score = sds - sds * sds_sum # multiple expressions
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"""
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(out,) = self.saved_tensors
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gidx = self.gidx
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sds = out * grad_out
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accum = gspmm(gidx, "copy_rhs", "sum", None, sds)
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grad_score = sds - gsddmm(gidx, "mul", out, accum, "e", "v")
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self.save_tensors = None
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return grad_score
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def edge_softmax(gidx, logits, eids=ALL, norm_by="dst"):
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softmax_op = EdgeSoftmax(gidx, eids, norm_by)
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return softmax_op(logits)
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class SegmentReduce(mx.autograd.Function):
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def __init__(self, op, offsets):
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super(SegmentReduce, self).__init__()
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self.op = op
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self.offsets = offsets
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def forward(self, x):
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y, arg = _segment_reduce(self.op, x, self.offsets)
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self.save_for_backward(arg)
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return y
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def backward(self, dy):
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(arg,) = self.saved_tensors
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offsets = self.offsets
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m = offsets[-1].asscalar()
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if self.op == "sum":
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offsets_np = asnumpy(offsets[1:])
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indices_np = np.zeros((m + 1,), dtype=offsets_np.dtype)
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np.add.at(indices_np, offsets_np, np.ones_like(offsets_np))
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indices_np = np.cumsum(indices_np, -1)[:-1]
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indices = zerocopy_from_numpy(indices_np)
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dx = dy[indices]
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else:
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dx = _bwd_segment_cmp(dy, arg, m)
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return dx
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def segment_reduce(op, x, offsets):
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segment_reduce_op = SegmentReduce(op, offsets)
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return segment_reduce_op(x)
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class ScatterAdd(mx.autograd.Function):
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def __init__(self, idx, m):
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super(ScatterAdd, self).__init__()
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self.idx = idx
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self.m = m
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def forward(self, x):
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y = _scatter_add(x, self.idx, self.m)
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return y
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def backward(self, dy):
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return dy[self.idx]
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def scatter_add(x, idx, m):
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scatter_add_op = ScatterAdd(idx, m)
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return scatter_add_op(x)
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class CSRMM(mx.autograd.Function):
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def __init__(self, gidxA, gidxB, num_vtypes):
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super().__init__()
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self.gidxA = gidxA
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self.gidxB = gidxB
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self.num_vtypes = num_vtypes
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def forward(self, A_weights, B_weights):
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gidxC, C_weights = _csrmm(
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self.gidxA, A_weights, self.gidxB, B_weights, self.num_vtypes
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)
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(
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nrows,
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ncols,
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||||
C_indptr,
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C_indices,
|
||||
C_eids,
|
||||
) = gidxC.adjacency_matrix_tensors(0, False, "csr")
|
||||
# Note: the returned C_indptr, C_indices and C_eids tensors MUST be the same
|
||||
# as the underlying tensors of the created graph gidxC.
|
||||
self.backward_cache = gidxC
|
||||
self.save_for_backward(A_weights, B_weights)
|
||||
nrows = nd.array([nrows], dtype="int64")
|
||||
ncols = nd.array([ncols], dtype="int64")
|
||||
return nrows, ncols, C_indptr, C_indices, C_eids, C_weights
|
||||
|
||||
def backward(
|
||||
self, dnrows, dncols, dC_indptr, dC_indices, dC_eids, dC_weights
|
||||
):
|
||||
# Only the last argument is meaningful.
|
||||
gidxC = self.backward_cache
|
||||
A_weights, B_weights = self.saved_tensors
|
||||
dgidxA, dA_weights = _csrmm(
|
||||
gidxC,
|
||||
dC_weights,
|
||||
self.gidxB.reverse(),
|
||||
B_weights,
|
||||
self.gidxA.number_of_ntypes(),
|
||||
)
|
||||
dgidxB, dB_weights = _csrmm(
|
||||
self.gidxA.reverse(),
|
||||
A_weights,
|
||||
gidxC,
|
||||
dC_weights,
|
||||
self.gidxB.number_of_ntypes(),
|
||||
)
|
||||
dA_weights = _csrmask(dgidxA, dA_weights, self.gidxA)
|
||||
dB_weights = _csrmask(dgidxB, dB_weights, self.gidxB)
|
||||
return dA_weights, dB_weights
|
||||
|
||||
|
||||
def csrmm(gidxA, A_weights, gidxB, B_weights, num_vtypes):
|
||||
op = CSRMM(gidxA, gidxB, num_vtypes)
|
||||
nrows, ncols, C_indptr, C_indices, C_eids, C_weights = op(
|
||||
A_weights, B_weights
|
||||
)
|
||||
gidxC = create_unitgraph_from_csr(
|
||||
num_vtypes,
|
||||
nrows.asscalar(),
|
||||
ncols.asscalar(),
|
||||
C_indptr,
|
||||
C_indices,
|
||||
C_eids,
|
||||
["coo", "csr", "csc"],
|
||||
)
|
||||
return gidxC, C_weights
|
||||
|
||||
|
||||
class CSRSum(mx.autograd.Function):
|
||||
def __init__(self, gidxs):
|
||||
super().__init__()
|
||||
self.gidxs = gidxs
|
||||
|
||||
def forward(self, *weights):
|
||||
gidxC, C_weights = _csrsum(self.gidxs, weights)
|
||||
(
|
||||
nrows,
|
||||
ncols,
|
||||
C_indptr,
|
||||
C_indices,
|
||||
C_eids,
|
||||
) = gidxC.adjacency_matrix_tensors(0, False, "csr")
|
||||
# Note: the returned C_indptr, C_indices and C_eids tensors MUST be the same
|
||||
# as the underlying tensors of the created graph gidxC.
|
||||
self.backward_cache = gidxC
|
||||
nrows = nd.array([nrows], dtype="int64")
|
||||
ncols = nd.array([ncols], dtype="int64")
|
||||
return nrows, ncols, C_indptr, C_indices, C_eids, C_weights
|
||||
|
||||
def backward(
|
||||
self, dnrows, dncols, dC_indptr, dC_indices, dC_eids, dC_weights
|
||||
):
|
||||
# Only the last argument is meaningful.
|
||||
gidxC = self.backward_cache
|
||||
return tuple(csrmask(gidxC, dC_weights, gidx) for gidx in self.gidxs)
|
||||
|
||||
|
||||
def csrsum(gidxs, weights):
|
||||
op = CSRSum(gidxs)
|
||||
nrows, ncols, C_indptr, C_indices, C_eids, C_weights = op(*weights)
|
||||
num_vtypes = gidxs[0].number_of_ntypes()
|
||||
gidxC = create_unitgraph_from_csr(
|
||||
num_vtypes,
|
||||
nrows.asscalar(),
|
||||
ncols.asscalar(),
|
||||
C_indptr,
|
||||
C_indices,
|
||||
C_eids,
|
||||
["coo", "csr", "csc"],
|
||||
)
|
||||
return gidxC, C_weights
|
||||
|
||||
|
||||
class CSRMask(mx.autograd.Function):
|
||||
def __init__(self, gidxA, gidxB):
|
||||
super().__init__()
|
||||
self.gidxA = gidxA
|
||||
self.gidxB = gidxB
|
||||
|
||||
def forward(self, A_weights):
|
||||
return _csrmask(self.gidxA, A_weights, self.gidxB)
|
||||
|
||||
def backward(self, dB_weights):
|
||||
return _csrmask(self.gidxB, dB_weights, self.gidxA)
|
||||
|
||||
|
||||
def csrmask(gidxA, A_weights, gidxB):
|
||||
op = CSRMask(gidxA, gidxB)
|
||||
return op(A_weights)
|
||||
@@ -0,0 +1 @@
|
||||
"""Sparse optimizer is not supported for mxnet"""
|
||||
@@ -0,0 +1,573 @@
|
||||
from __future__ import absolute_import
|
||||
|
||||
import builtins
|
||||
import numbers
|
||||
import os
|
||||
|
||||
import mxnet as mx
|
||||
import mxnet.ndarray as nd
|
||||
import numpy as np
|
||||
|
||||
from ... import ndarray as dglnd
|
||||
from ...function.base import TargetCode
|
||||
from ...utils import version
|
||||
|
||||
if version.parse(mx.__version__) < version.parse("1.6.0"):
|
||||
raise RuntimeError("DGL requires MXNet >= 1.6")
|
||||
|
||||
# After MXNet 1.5, empty tensors aren't supprted by default.
|
||||
# After we turn on the numpy compatible flag, MXNet supports empty NDArray.
|
||||
mx.set_np_shape(bool(os.environ.get("DGL_MXNET_SET_NP_SHAPE", True)))
|
||||
|
||||
|
||||
def data_type_dict():
|
||||
return {
|
||||
"float16": np.float16,
|
||||
"float32": np.float32,
|
||||
"float64": np.float64,
|
||||
"uint8": np.uint8,
|
||||
"int8": np.int8,
|
||||
"int16": np.int16,
|
||||
"int32": np.int32,
|
||||
"int64": np.int64,
|
||||
"bool": np.bool_,
|
||||
} # mxnet does not support bool
|
||||
|
||||
|
||||
def cpu():
|
||||
return mx.cpu()
|
||||
|
||||
|
||||
def tensor(data, dtype=None):
|
||||
if dtype == np.bool_:
|
||||
# mxnet doesn't support bool
|
||||
dtype = np.int32
|
||||
if isinstance(data, nd.NDArray):
|
||||
if dtype is None or data.dtype == dtype:
|
||||
return data
|
||||
else:
|
||||
return data.astype(dtype)
|
||||
else:
|
||||
if isinstance(data, numbers.Number):
|
||||
data = [data]
|
||||
if dtype is None:
|
||||
if isinstance(data, np.ndarray):
|
||||
dtype = np.int32 if data.dtype == np.bool_ else data.dtype
|
||||
elif len(data) == 0:
|
||||
dtype = np.int64
|
||||
else:
|
||||
dtype = (
|
||||
np.int64
|
||||
if isinstance(data[0], numbers.Integral)
|
||||
else np.float32
|
||||
)
|
||||
return nd.array(data, dtype=dtype)
|
||||
|
||||
|
||||
def as_scalar(data):
|
||||
if data.size != 1:
|
||||
raise ValueError("The current array is not a scalar")
|
||||
if data.shape != (1,):
|
||||
data = data.expand_dims(axis=0)
|
||||
return data.asscalar()
|
||||
|
||||
|
||||
def get_preferred_sparse_format():
|
||||
"""Get the preferred sparse matrix format supported by the backend.
|
||||
|
||||
Different backends have their preferred backend. This info is useful when
|
||||
constructing a sparse matrix.
|
||||
"""
|
||||
return "csr"
|
||||
|
||||
|
||||
def sparse_matrix(data, index, shape, force_format=False):
|
||||
fmt = index[0]
|
||||
if fmt == "coo":
|
||||
if force_format:
|
||||
raise TypeError(
|
||||
"MXNet backend only supports CSR format,"
|
||||
" but COO format is forced."
|
||||
)
|
||||
coord = index[1]
|
||||
# generate convert idx
|
||||
# FIXME: cannot use int64
|
||||
tmp_data = nd.arange(
|
||||
len(coord[0]), dtype=data.dtype, ctx=coord[0].context
|
||||
)
|
||||
tmp_spmat = nd.sparse.csr_matrix(
|
||||
(tmp_data, (coord[0], coord[1])), tuple(shape), ctx=data.context
|
||||
)
|
||||
convert_idx = nd.cast(tmp_spmat.data, dtype="int64")
|
||||
# shuffle the data
|
||||
data = data[convert_idx]
|
||||
spmat = nd.sparse.csr_matrix(
|
||||
(data, tmp_spmat.indices, tmp_spmat.indptr),
|
||||
tuple(shape),
|
||||
ctx=data.context,
|
||||
)
|
||||
return spmat, convert_idx
|
||||
elif fmt == "csr":
|
||||
indices = index[1]
|
||||
indptr = index[2]
|
||||
spmat = nd.sparse.csr_matrix(
|
||||
(data, indices, indptr), tuple(shape), ctx=data.context
|
||||
)
|
||||
# No conversion is required.
|
||||
return spmat, None
|
||||
else:
|
||||
raise TypeError("Invalid format: %s." % fmt)
|
||||
|
||||
|
||||
def sparse_matrix_indices(spmat):
|
||||
return ("csr", spmat.indices, spmat.indptr)
|
||||
|
||||
|
||||
def is_tensor(obj):
|
||||
return isinstance(obj, nd.NDArray)
|
||||
|
||||
|
||||
def shape(input):
|
||||
# NOTE: the input cannot be a symbol
|
||||
return input.shape
|
||||
|
||||
|
||||
def dtype(input):
|
||||
# NOTE: the input cannot be a symbol
|
||||
return input.dtype
|
||||
|
||||
|
||||
def ndim(input):
|
||||
return input.ndim
|
||||
|
||||
|
||||
def context(input):
|
||||
return input.context
|
||||
|
||||
|
||||
def device_type(ctx):
|
||||
return ctx.device_type
|
||||
|
||||
|
||||
def device_id(ctx):
|
||||
return ctx.device_id
|
||||
|
||||
|
||||
def to_backend_ctx(dglctx):
|
||||
dev_type = dglctx.device_type
|
||||
if dev_type == 1:
|
||||
return mx.cpu()
|
||||
elif dev_type == 2:
|
||||
return mx.gpu(dglctx.device_id)
|
||||
else:
|
||||
raise ValueError("Unsupported DGL device context:", dglctx)
|
||||
|
||||
|
||||
def astype(input, ty):
|
||||
if ty == np.bool_:
|
||||
ty = np.int32
|
||||
return input.astype(ty)
|
||||
|
||||
|
||||
def asnumpy(input):
|
||||
return input.asnumpy()
|
||||
|
||||
|
||||
def copy_to(input, ctx, **kwargs):
|
||||
return input.as_in_context(ctx)
|
||||
|
||||
|
||||
def is_pinned(input):
|
||||
return input.context == mx.cpu_pinned()
|
||||
|
||||
|
||||
def sum(input, dim, keepdims=False):
|
||||
if len(input) == 0:
|
||||
return nd.array([0.0], dtype=input.dtype, ctx=input.context)
|
||||
return nd.sum(input, axis=dim, keepdims=keepdims)
|
||||
|
||||
|
||||
def floor_div(in1, in2):
|
||||
return in1 / in2
|
||||
|
||||
|
||||
def reduce_sum(input):
|
||||
return input.sum()
|
||||
|
||||
|
||||
def cumsum(input, dim):
|
||||
return nd.cumsum(input, axis=dim)
|
||||
|
||||
|
||||
def mean(input, dim):
|
||||
return nd.mean(input, axis=dim)
|
||||
|
||||
|
||||
def reduce_mean(input):
|
||||
return input.mean()
|
||||
|
||||
|
||||
def max(input, dim):
|
||||
return nd.max(input, axis=dim)
|
||||
|
||||
|
||||
def reduce_max(input):
|
||||
return input.max()
|
||||
|
||||
|
||||
def min(input, dim):
|
||||
return nd.min(input, axis=dim)
|
||||
|
||||
|
||||
def reduce_min(input):
|
||||
return input.min()
|
||||
|
||||
|
||||
def topk(input, k, dim, descending=True):
|
||||
return nd.topk(
|
||||
input, axis=dim, k=k, ret_typ="value", is_ascend=not descending
|
||||
)
|
||||
|
||||
|
||||
def argtopk(input, k, dim, descending=True):
|
||||
idx = nd.argsort(input, dim, is_ascend=not descending)
|
||||
return nd.slice_axis(input, dim, 0, k)
|
||||
|
||||
|
||||
def argsort(input, dim, descending):
|
||||
idx = nd.argsort(input, dim, is_ascend=not descending)
|
||||
idx = nd.cast(idx, dtype="int64")
|
||||
return idx
|
||||
|
||||
|
||||
def exp(input):
|
||||
return nd.exp(input)
|
||||
|
||||
|
||||
def inverse(input):
|
||||
return nd.linalg_inverse(input)
|
||||
|
||||
|
||||
def sqrt(input):
|
||||
return nd.sqrt(input)
|
||||
|
||||
|
||||
def softmax(input, dim=-1):
|
||||
return nd.softmax(input, axis=dim)
|
||||
|
||||
|
||||
def cat(seq, dim):
|
||||
return nd.concat(*seq, dim=dim)
|
||||
|
||||
|
||||
def stack(seq, dim):
|
||||
return nd.stack(*seq, axis=dim)
|
||||
|
||||
|
||||
def split(x, sizes_or_sections, dim):
|
||||
if isinstance(sizes_or_sections, list) and len(sizes_or_sections) == 1:
|
||||
assert len(x) == sizes_or_sections[0]
|
||||
return [x]
|
||||
|
||||
if isinstance(sizes_or_sections, (np.ndarray, list)):
|
||||
sizes_or_sections1 = tuple(np.cumsum(sizes_or_sections)[:-1])
|
||||
return nd.split_v2(x, sizes_or_sections1, axis=dim)
|
||||
|
||||
|
||||
def repeat(input, repeats, dim):
|
||||
if isinstance(repeats, nd.NDArray):
|
||||
return nd.array(
|
||||
np.repeat(input.asnumpy(), repeats.asnumpy(), axis=dim),
|
||||
ctx=input.context,
|
||||
dtype=input.dtype,
|
||||
)
|
||||
else:
|
||||
return nd.repeat(input, repeats, axis=dim)
|
||||
|
||||
|
||||
def gather_row(data, row_index):
|
||||
# MXNet workaround for empty row index
|
||||
if len(row_index) == 0:
|
||||
if data.shape[0] == 0:
|
||||
return data
|
||||
else:
|
||||
return data[0:0]
|
||||
|
||||
if isinstance(row_index, nd.NDArray):
|
||||
return nd.take(data, row_index)
|
||||
else:
|
||||
return data[
|
||||
row_index,
|
||||
]
|
||||
|
||||
|
||||
def slice_axis(data, axis, begin, end):
|
||||
dim = data.shape[axis]
|
||||
if begin < 0:
|
||||
begin += dim
|
||||
if end <= 0:
|
||||
end += dim
|
||||
return nd.slice_axis(data, axis, begin, end)
|
||||
|
||||
|
||||
def take(data, indices, dim):
|
||||
return nd.take(data, indices, dim)
|
||||
|
||||
|
||||
def narrow_row(data, start, stop):
|
||||
return data[start:stop]
|
||||
|
||||
|
||||
def index_add_inplace(data, row_idx, value):
|
||||
raise NotImplementedError("MXNet doesn't support inplace index_add")
|
||||
|
||||
|
||||
def scatter_row(data, row_index, value):
|
||||
return mx.nd.contrib.index_copy(data, row_index, value)
|
||||
|
||||
|
||||
def scatter_row_inplace(data, row_index, value):
|
||||
data[row_index] = value
|
||||
|
||||
|
||||
def squeeze(input, dim):
|
||||
return nd.squeeze(input, axis=dim)
|
||||
|
||||
|
||||
def unsqueeze(input, dim):
|
||||
return nd.expand_dims(input, axis=dim)
|
||||
|
||||
|
||||
def reshape(input, shape):
|
||||
# NOTE: the input cannot be a symbol
|
||||
return nd.reshape(input, shape)
|
||||
|
||||
|
||||
def swapaxes(input, axis1, axis2):
|
||||
return nd.swapaxes(input, axis1, axis2)
|
||||
|
||||
|
||||
def empty(shape, dtype, ctx):
|
||||
return nd.empty(shape, dtype=dtype, ctx=ctx)
|
||||
|
||||
|
||||
def zeros(shape, dtype, ctx):
|
||||
return nd.zeros(shape, dtype=dtype, ctx=ctx)
|
||||
|
||||
|
||||
def zeros_like(input):
|
||||
return nd.zeros_like(input)
|
||||
|
||||
|
||||
def ones(shape, dtype, ctx):
|
||||
return nd.ones(shape, dtype=dtype, ctx=ctx)
|
||||
|
||||
|
||||
def uniform(shape, dtype, ctx, low, high):
|
||||
return nd.random.uniform(low, high, ctx=ctx, dtype=dtype, shape=shape)
|
||||
|
||||
|
||||
def randint(shape, dtype, ctx, low, high):
|
||||
return nd.random.randint(low, high, ctx=ctx, dtype=dtype, shape=shape)
|
||||
|
||||
|
||||
def pad_packed_tensor(input, lengths, value, l_min=None):
|
||||
old_shape = input.shape
|
||||
if isinstance(lengths, nd.NDArray):
|
||||
lengths = list(lengths.asnumpy())
|
||||
max_len = builtins.max(lengths)
|
||||
|
||||
if l_min is not None:
|
||||
max_len = builtins.max(max_len, l_min)
|
||||
|
||||
batch_size = len(lengths)
|
||||
ctx = input.context
|
||||
dtype = input.dtype
|
||||
x = nd.full(
|
||||
(batch_size * max_len, *old_shape[1:]), value, ctx=ctx, dtype=dtype
|
||||
)
|
||||
index = []
|
||||
for i, l in enumerate(lengths):
|
||||
index.extend(range(i * max_len, i * max_len + l))
|
||||
index = nd.array(index, ctx=ctx)
|
||||
return scatter_row(x, index, input).reshape(
|
||||
batch_size, max_len, *old_shape[1:]
|
||||
)
|
||||
|
||||
|
||||
def pack_padded_tensor(input, lengths):
|
||||
batch_size, max_len = input.shape[:2]
|
||||
ctx = input.context
|
||||
index = []
|
||||
for i, l in enumerate(lengths):
|
||||
index.extend(range(i * max_len, i * max_len + l))
|
||||
index = nd.array(index, ctx=ctx)
|
||||
return gather_row(input.reshape(batch_size * max_len, -1), index)
|
||||
|
||||
|
||||
def boolean_mask(input, mask):
|
||||
return mx.contrib.nd.boolean_mask(input, mask)
|
||||
|
||||
|
||||
def equal(x, y):
|
||||
return x == y
|
||||
|
||||
|
||||
def allclose(x, y, rtol=1e-4, atol=1e-4):
|
||||
return np.allclose(x.asnumpy(), y.asnumpy(), rtol=rtol, atol=atol)
|
||||
|
||||
|
||||
def logical_not(input):
|
||||
return nd.logical_not(input)
|
||||
|
||||
|
||||
def logical_and(input1, input2):
|
||||
return nd.logical_and(input1, input2)
|
||||
|
||||
|
||||
def clone(input):
|
||||
return input.copy()
|
||||
|
||||
|
||||
def clamp(data, min_val, max_val):
|
||||
return nd.clip(data, min_val, max_val)
|
||||
|
||||
|
||||
def replace_inf_with_zero(x):
|
||||
return nd.where(nd.abs(x) == np.inf, nd.zeros_like(x), x)
|
||||
|
||||
|
||||
def count_nonzero(input):
|
||||
# TODO: fallback to numpy is unfortunate
|
||||
tmp = input.asnumpy()
|
||||
return np.count_nonzero(tmp)
|
||||
|
||||
|
||||
def unique(input, return_inverse=False, return_counts=False):
|
||||
# TODO: fallback to numpy is unfortunate
|
||||
tmp = input.asnumpy()
|
||||
if return_inverse and return_counts:
|
||||
tmp, inv, count = np.unique(
|
||||
tmp, return_inverse=True, return_counts=True
|
||||
)
|
||||
tmp = nd.array(tmp, ctx=input.context, dtype=input.dtype)
|
||||
inv = nd.array(inv, ctx=input.context)
|
||||
count = nd.array(count, ctx=input.context)
|
||||
return tmp, inv, count
|
||||
elif return_inverse or return_counts:
|
||||
tmp, tmp2 = np.unique(
|
||||
tmp, return_inverse=return_inverse, return_counts=return_counts
|
||||
)
|
||||
tmp = nd.array(tmp, ctx=input.context, dtype=input.dtype)
|
||||
tmp2 = nd.array(tmp2, ctx=input.context)
|
||||
return tmp, tmp2
|
||||
else:
|
||||
tmp = np.unique(tmp)
|
||||
return nd.array(tmp, ctx=input.context, dtype=input.dtype)
|
||||
|
||||
|
||||
def full_1d(length, fill_value, dtype, ctx):
|
||||
return nd.full((length,), fill_value, dtype=dtype, ctx=ctx)
|
||||
|
||||
|
||||
def nonzero_1d(input):
|
||||
# TODO: fallback to numpy is unfortunate
|
||||
tmp = input.asnumpy()
|
||||
tmp = np.nonzero(tmp)[0]
|
||||
r = nd.array(tmp, ctx=input.context, dtype=tmp.dtype)
|
||||
return r
|
||||
|
||||
|
||||
def sort_1d(input):
|
||||
# TODO: this isn't an ideal implementation.
|
||||
val = nd.sort(input, axis=None, is_ascend=True)
|
||||
idx = nd.argsort(input, is_ascend=True)
|
||||
idx = nd.cast(idx, dtype="int64")
|
||||
return val, idx
|
||||
|
||||
|
||||
def arange(start, stop, dtype=np.int64, ctx=None):
|
||||
if start >= stop:
|
||||
return nd.array([], dtype=dtype, ctx=ctx)
|
||||
else:
|
||||
return nd.arange(start, stop, dtype=dtype, ctx=ctx)
|
||||
|
||||
|
||||
def rand_shuffle(arr):
|
||||
return mx.nd.random.shuffle(arr)
|
||||
|
||||
|
||||
def zerocopy_to_dlpack(arr):
|
||||
return arr.to_dlpack_for_read()
|
||||
|
||||
|
||||
def zerocopy_from_dlpack(dlpack_arr):
|
||||
return nd.from_dlpack(dlpack_arr)
|
||||
|
||||
|
||||
def zerocopy_to_numpy(arr):
|
||||
# NOTE: not zerocopy
|
||||
return arr.asnumpy()
|
||||
|
||||
|
||||
def zerocopy_from_numpy(np_data):
|
||||
np_data = np.asarray(np_data, order="C")
|
||||
return mx.nd.from_numpy(np_data, zero_copy=True)
|
||||
|
||||
|
||||
def zerocopy_to_dgl_ndarray(arr):
|
||||
arr.to_dlpack_for_read()
|
||||
return dglnd.from_dlpack(arr.to_dlpack_for_read())
|
||||
|
||||
|
||||
def zerocopy_to_dgl_ndarray_for_write(arr):
|
||||
return dglnd.from_dlpack(arr.to_dlpack_for_write())
|
||||
|
||||
|
||||
def zerocopy_from_dgl_ndarray(arr):
|
||||
return nd.from_dlpack(arr.to_dlpack())
|
||||
|
||||
|
||||
def sync():
|
||||
"""Synchronize computation.
|
||||
|
||||
In DL frameworks such as MXNet and TensorFlow, the computation in operators
|
||||
are done asynchronously. This is to synchronize computation and makes sure
|
||||
that all computation is complete after this function call.
|
||||
"""
|
||||
mx.nd.waitall()
|
||||
|
||||
|
||||
def attach_grad(tensor):
|
||||
tensor.attach_grad()
|
||||
return tensor
|
||||
|
||||
|
||||
def backward(x, head_gradient=None):
|
||||
x.backward(head_gradient)
|
||||
|
||||
|
||||
def grad(x):
|
||||
return x.grad
|
||||
|
||||
|
||||
def is_no_grad(x):
|
||||
return (x != 0).sum() == 0
|
||||
|
||||
|
||||
def is_recording():
|
||||
return mx.autograd.is_recording()
|
||||
|
||||
|
||||
record_grad = mx.autograd.record
|
||||
|
||||
|
||||
class no_grad(object):
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __enter__(self):
|
||||
pass
|
||||
|
||||
def __exit__(self, exc_type, exc_value, exc_traceback):
|
||||
pass
|
||||
Reference in New Issue
Block a user