【Paddle Toolkit Development Competition No.4】 Paddle 适配 torch-scatter

jointContribution/paddle_scatter/__init__.py

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+from .composite import scatter_log_softmax
+from .composite import scatter_logsumexp
+from .composite import scatter_softmax
+from .composite import scatter_std
+from .scatter import scatter
+from .scatter import scatter_add
+from .scatter import scatter_max
+from .scatter import scatter_mean
+from .scatter import scatter_min
+from .scatter import scatter_mul
+from .scatter import scatter_sum
+from .segment_coo import gather_coo
+from .segment_coo import segment_add_coo
+from .segment_coo import segment_coo
+from .segment_coo import segment_max_coo
+from .segment_coo import segment_mean_coo
+from .segment_coo import segment_min_coo
+from .segment_coo import segment_sum_coo
+from .segment_csr import gather_csr
+from .segment_csr import segment_add_csr
+from .segment_csr import segment_csr
+from .segment_csr import segment_max_csr
+from .segment_csr import segment_mean_csr
+from .segment_csr import segment_min_csr
+from .segment_csr import segment_sum_csr
+
+__all__ = [
+    "scatter_sum",
+    "scatter_add",
+    "scatter_mul",
+    "scatter_mean",
+    "scatter_min",
+    "scatter_max",
+    "scatter",
+    "segment_sum_csr",
+    "segment_add_csr",
+    "segment_mean_csr",
+    "segment_min_csr",
+    "segment_max_csr",
+    "segment_csr",
+    "gather_csr",
+    "segment_sum_coo",
+    "segment_add_coo",
+    "segment_mean_coo",
+    "segment_min_coo",
+    "segment_max_coo",
+    "segment_coo",
+    "gather_coo",
+    "scatter_std",
+    "scatter_logsumexp",
+    "scatter_softmax",
+    "scatter_log_softmax",
+]

jointContribution/paddle_scatter/composite/__init__.py

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+from .logsumexp import scatter_logsumexp
+from .softmax import scatter_log_softmax
+from .softmax import scatter_softmax
+from .std import scatter_std
+
+__all__ = [
+    "scatter_std",
+    "scatter_logsumexp",
+    "scatter_softmax",
+    "scatter_log_softmax",
+]

jointContribution/paddle_scatter/composite/logsumexp.py

@@ -0,0 +1,82 @@
+from typing import Optional
+
+import paddle
+from paddle_scatter import scatter_max
+from paddle_scatter import scatter_sum
+from paddle_scatter.utils import broadcast
+
+
+def scatter_logsumexp(
+    src: paddle.Tensor,
+    index: paddle.Tensor,
+    dim: int = -1,
+    out: Optional[paddle.Tensor] = None,
+    dim_size: Optional[int] = None,
+    eps: float = 1e-12,
+) -> paddle.Tensor:
+    r"""Reduces all values from the `src` tensor into `out` at the
+    indices specified in the `index` tensor along a given axis`dim`,
+    the reduction method is logsumexp. (If dtype of `src` is int, output is still int.)
+
+    Args:
+        src (paddle.Tensor): The source tensor.
+        index (paddle.Tensor): The indices of elements to scatter. The dimension
+            of index should either be 1-D or :math:`i+1`-D. See Notes for more
+            details.
+        dim (int, optional): The axis along which to index. Default is -1.
+        out (paddle.Tensor|None, optional): The destination tensor. Default is None.
+        dim_size (int|None, optional): If `out` is not given, automatically create output
+            with size `dim_size` at dimension `dim`. If `dim_size` is not given,
+            a minimal sized output tensor according to `index.max() + 1` is returned.
+            Default is None.
+        eps (float, optional): Eplison factor added to the sum of exponent values during
+            computation in case they are zero. Default is 1e-12.
+
+    Returns:
+        paddle.Tensor, the reduced tensor by logsumexp reduction method.
+    """
+    if not paddle.is_floating_point(src):
+        raise ValueError(
+            "`scatter_logsumexp` can only be computed over "
+            "tensors with floating point data types."
+        )
+
+    index = broadcast(index, src, dim)
+    eps = paddle.full([], eps, dtype=src.dtype)
+
+    if out is not None:
+        dim_size = out.shape[dim]
+    else:
+        if dim_size is None:
+            dim_size = int(index.max()) + 1
+
+    size = src.shape
+    size[dim] = dim_size
+    max_value_per_index = paddle.full(
+        size,
+        fill_value=float("-inf"),
+        dtype=src.dtype,
+    )
+    scatter_max(src, index, dim, max_value_per_index, dim_size=dim_size)[0]
+    max_per_src_element = max_value_per_index.take_along_axis(indices=index, axis=dim)
+    recentered_score = src - max_per_src_element
+    recentered_score.masked_fill_(paddle.isnan(recentered_score), float("-inf"))
+
+    orig_out: Optional[paddle.Tensor] = None
+    if out is not None:
+        orig_out = out.clone()
+        res = out.subtract(max_value_per_index).exp()
+
+        sum_per_index = scatter_sum(recentered_score.exp(), index, dim, res, dim_size)
+    else:
+        sum_per_index = scatter_sum(recentered_score.exp(), index, dim, None, dim_size)
+
+    res = sum_per_index.add(eps).log().add(max_value_per_index)
+
+    if orig_out is None:
+        return res.nan_to_num_(neginf=0.0)
+
+    mask = ~res.isfinite()
+    res[mask] = orig_out[mask]
+    paddle.assign(res, out)
+    return out

jointContribution/paddle_scatter/composite/softmax.py

@@ -0,0 +1,99 @@
+from typing import Optional
+
+import paddle
+from paddle_scatter import scatter_max
+from paddle_scatter import scatter_sum
+from paddle_scatter.utils import broadcast
+
+
+def scatter_softmax(
+    src: paddle.Tensor,
+    index: paddle.Tensor,
+    dim: int = -1,
+    dim_size: Optional[int] = None,
+) -> paddle.Tensor:
+    r"""Reduces all values from the `src` tensor into `out` at the
+    indices specified in the `index` tensor along a given axis`dim`,
+    the reduction method is softmax. (If dtype of `src` is int, output is still int.)
+
+    Args:
+        src (paddle.Tensor): The source tensor.
+        index (paddle.Tensor): The indices of elements to scatter. The dimension
+            of index should either be 1-D or :math:`i+1`-D. See Notes for more
+            details.
+        dim (int, optional): The axis along which to index. Default is -1.
+        dim_size (int|None, optional): If `out` is not given, automatically create output
+            with size `dim_size` at dimension `dim`. If `dim_size` is not given,
+            a minimal sized output tensor according to `index.max() + 1` is returned.
+            Default is None.
+
+    Returns:
+        paddle.Tensor, the reduced tensor by softmax reduction method.
+    """
+    if not paddle.is_floating_point(src):
+        raise ValueError(
+            "`scatter_softmax` can only be computed over tensors "
+            "with floating point data types."
+        )
+
+    index = broadcast(index, src, dim)
+
+    max_value_per_index = scatter_max(src, index, dim=dim, dim_size=dim_size)[0]
+    max_per_src_element = max_value_per_index.take_along_axis(indices=index, axis=dim)
+
+    recentered_scores = src - max_per_src_element
+    recentered_scores_exp = recentered_scores.exp()
+
+    sum_per_index = scatter_sum(recentered_scores_exp, index, dim, dim_size=dim_size)
+    normalizing_constants = sum_per_index.take_along_axis(indices=index, axis=dim)
+
+    return recentered_scores_exp.divide(normalizing_constants)
+
+
+def scatter_log_softmax(
+    src: paddle.Tensor,
+    index: paddle.Tensor,
+    dim: int = -1,
+    eps: float = 1e-12,
+    dim_size: Optional[int] = None,
+) -> paddle.Tensor:
+    r"""Reduces all values from the `src` tensor into `out` at the
+    indices specified in the `index` tensor along a given axis`dim`,
+    the reduction method is log_softmax. (If dtype of `src` is int, output is still int.)
+
+    Args:
+        src (paddle.Tensor): The source tensor.
+        index (paddle.Tensor): The indices of elements to scatter. The dimension
+            of index should either be 1-D or :math:`i+1`-D. See Notes for more
+            details.
+        dim (int, optional): The axis along which to index. Default is -1.
+        eps (float, optional): Eplison factor added to the normalizing constants during
+            computation in case they are zero. Default is 1e-12.
+        dim_size (int|None, optional): If `out` is not given, automatically create output
+            with size `dim_size` at dimension `dim`. If `dim_size` is not given,
+            a minimal sized output tensor according to `index.max() + 1` is returned.
+            Default is None.
+
+    Returns:
+        paddle.Tensor, the reduced tensor by log_softmax reduction method.
+    """
+    if not paddle.is_floating_point(src):
+        raise ValueError(
+            "`scatter_log_softmax` can only be computed over "
+            "tensors with floating point data types."
+        )
+
+    index = broadcast(index, src, dim)
+    eps = paddle.full([], eps, dtype=src.dtype)
+
+    max_value_per_index = scatter_max(src, index, dim=dim, dim_size=dim_size)[0]
+    max_per_src_element = max_value_per_index.take_along_axis(indices=index, axis=dim)
+
+    recentered_scores = src - max_per_src_element
+
+    sum_per_index = scatter_sum(recentered_scores.exp(), index, dim, dim_size=dim_size)
+    normalizing_constants = (
+        sum_per_index.add(eps).log().take_along_axis(indices=index, axis=dim)
+    )
+
+    return recentered_scores.subtract(normalizing_constants)

jointContribution/paddle_scatter/composite/std.py

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+from typing import Optional
+
+import paddle
+from paddle_scatter import scatter_sum
+from paddle_scatter.utils import broadcast
+
+
+def scatter_std(
+    src: paddle.Tensor,
+    index: paddle.Tensor,
+    dim: int = -1,
+    out: Optional[paddle.Tensor] = None,
+    dim_size: Optional[int] = None,
+    unbiased: bool = True,
+) -> paddle.Tensor:
+    r"""Reduces all values from the `src` tensor into `out` at the
+    indices specified in the `index` tensor along a given axis`dim`,
+    the reduction method is std. (If dtype of `src` is int, output is still int.)
+
+    Args:
+        src (paddle.Tensor): The source tensor.
+        index (paddle.Tensor): The indices of elements to scatter. The dimension
+            of index should either be 1-D or :math:`i+1`-D. See Notes for more
+            details.
+        dim (int, optional): The axis along which to index. Default is -1.
+        out (paddle.Tensor|None, optional): The destination tensor. Default is None.
+        dim_size (int|None, optional): If `out` is not given, automatically create output
+            with size `dim_size` at dimension `dim`. If `dim_size` is not given,
+            a minimal sized output tensor according to `index.max() + 1` is returned.
+            Default is None.
+        unbiased (bool, optional): Indicate whether to calculate biased std (divide by n)
+            or unbiased std (divide by n-1). Default is True.
+
+    Returns:
+        paddle.Tensor, the reduced tensor by std reduction method.
+    """
+    if out is not None:
+        dim_size = out.shape[dim]
+
+    if dim < 0:
+        dim = src.dim() + dim
+
+    count_dim = dim
+    if index.dim() <= dim:
+        count_dim = index.dim() - 1
+
+    ones = paddle.ones(index.shape, dtype=src.dtype)
+    count = scatter_sum(ones, index, count_dim, dim_size=dim_size)
+
+    index = broadcast(index, src, dim)
+    tmp = scatter_sum(src, index, dim, dim_size=dim_size)
+    count = broadcast(count, tmp, dim).clip(1)
+    mean = tmp.divide(count)
+
+    var = src - mean.take_along_axis(indices=index, axis=dim)
+    var = var * var
+    res = scatter_sum(var, index, dim, out, dim_size)
+
+    if unbiased:
+        count = count.subtract(paddle.full([], 1, dtype=src.dtype)).clip(1)
+    res = res.divide(count + 1e-6).sqrt()
+
+    if out is not None:
+        paddle.assign(res, out)
+        return out
+    else:
+        return res