fix l class ut

.gitignore

@@ -166,4 +166,5 @@ xiyouji.txt
 *.jit
 flagged/
 
-huggingface_transformers/
+huggingface_transformers/
+diffusers/

mindnlp/core/__init__.py

@@ -26,6 +26,8 @@
     Union as _Union,
 )
 
+from mindspore.runtime import Stream
+
 strided = None
 contiguous_format = None
 preserve_format = None

mindnlp/core/_tensor.py

@@ -538,11 +538,34 @@ def new_full(self, size, fill_value, *, dtype=None, device=None, requires_grad=F
     StubTensor.new_full = new_full
 
     def new_zeros(self, *size, dtype=None, device=None, requires_grad=False, layout=None, pin_memory=False):
-        return ops.zeros(*size, dtype=dtype if dtype is not None else self.dtype)
+        if isinstance(size[0], (tuple, list)):
+            size = size[0]
+
+        new_size = ()
+        for s in size:
+            if isinstance(s, Tensor):
+                s = s.item()
+            new_size += (s,)
+        return ops.zeros(*new_size, dtype=dtype if dtype is not None else self.dtype)
 
     Tensor.new_zeros = new_zeros
     StubTensor.new_zeros = new_zeros
 
+    def new_ones(self, *size, dtype=None, device=None, requires_grad=False, layout=None, pin_memory=False, **kwargs):
+        size = kwargs.get('size', size)
+        if isinstance(size[0], (tuple, list)):
+            size = size[0]
+
+        new_size = ()
+        for s in size:
+            if isinstance(s, Tensor):
+                s = s.item()
+            new_size += (s,)
+        return ops.ones(*new_size, dtype=dtype if dtype is not None else self.dtype)
+
+    Tensor.new_ones = new_ones
+    StubTensor.new_ones = new_ones
+
     Tensor.sum = ops.sum
     StubTensor.sum = ops.sum
 
@@ -598,6 +621,12 @@ def __contains__(self, item):
     Tensor.mean = ops.mean
     StubTensor.mean = ops.mean
 
+    Tensor.amax = ops.amax
+    StubTensor.amax = ops.amax
+
+    Tensor.as_strided = ops.as_strided
+    StubTensor.as_strided = ops.as_strided
+
 def _rebuild_from_type_v2(func, new_type, args, state):
     ret = func(*args)
     return ret

mindnlp/core/fft/__init__.py

@@ -2,7 +2,7 @@
 from mindspore import ops
 from mindspore.ops._primitive_cache import _get_cache_prim
 from ..configs import use_pyboost
-from ..ops import narrow
+from ..ops import narrow, roll
 from ..nn import functional as F
 
 def rfft(input, n=None, dim=-1, norm="backward"):
@@ -35,4 +35,13 @@ def fftn(input, s=None, dim=None, norm=None):
 def fft(input, s=None, dim=-1, norm=None):
     return ops.fft(input, s, dim, norm)
 
-__all__ = ['fft', 'fftn', 'irfft', 'rfft']
+def fftshift(x, dim=None):
+    return ops.fftshift(x, dim)
+
+def ifftn(input, s=None, dim=None, norm=None, *, out=None):
+    return ops.ifftn(input, s, dim, norm)
+
+def ifftshift(input, dim=None):
+    return ops.ifftshift(input, dim)
+
+__all__ = ['fft', 'fftn', 'irfft', 'rfft']

mindnlp/core/mps/__init__.py

@@ -0,0 +1,11 @@
+def is_available():
+    return False
+
+def empty_cache():
+    pass
+
+def device_count():
+    return 0
+
+def manual_seed(*args, **kwargs):
+    pass

mindnlp/core/nn/modules/__init__.py

@@ -6,7 +6,7 @@
 from .normalization import LayerNorm, GroupNorm, RMSNorm
 from .dropout import Dropout, Dropout2d
 from .activation import *
-from .conv import Conv3d, Conv2d, Conv1d, ConvTranspose2d, ConvTranspose1d
+from .conv import Conv3d, Conv2d, Conv1d, ConvTranspose2d, ConvTranspose1d, ConvTranspose3d
 from .padding import ZeroPad2d, ConstantPad2d, ConstantPad1d, ConstantPad3d
 from .batchnorm import BatchNorm2d, BatchNorm1d, SyncBatchNorm
 from .pooling import AdaptiveAvgPool2d, AvgPool1d, MaxPool2d, MaxPool1d, AdaptiveAvgPool1d, AvgPool2d

mindnlp/core/nn/modules/conv.py

@@ -666,101 +666,101 @@ def forward(self, input: Tensor, output_size: Optional[list[int]] = None) -> Ten
             self.dilation,
         )
 
-# class ConvTranspose3d(_ConvTransposeNd):
-#     r"""Applies a 3D transposed convolution operator over an input image composed of several input
-#     planes.
-#     The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
-#     and sums over the outputs from all input feature planes.
-
-#     This module can be seen as the gradient of Conv3d with respect to its input.
-#     It is also known as a fractionally-strided convolution or
-#     a deconvolution (although it is not an actual deconvolution operation).
-
-#     | :attr:`stride` controls the stride for the cross-correlation.
-#     | If :attr:`padding` is non-zero, then the input is implicitly zero-padded on both sides
-#       for :attr:`padding` number of points.
-#     | If :attr:`output_padding` is non-zero, then the output is implicitly zero-padded on one side
-#       for :attr:`output_padding` number of points.
-#     | :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm.
-#       It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does.
-#     | :attr:`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels`
-#       must both be divisible by `groups`.
-#     |       At groups=1, all inputs are convolved to all outputs.
-#     |       At groups=2, the operation becomes equivalent to having two conv layers
-#                  side by side, each seeing half the input channels,
-#                  and producing half the output channels, and both subsequently concatenated.
-#             At groups=`in_channels`, each input channel is convolved with its own set of filters
-#                  (of size `out_channels // in_channels`).
-
-#     The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding`
-#     can either be:
-
-#         - a single ``int`` -- in which case the same value is used for the depth, height and width dimensions
-#         - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
-#           the second `int` for the height dimension and the third `int` for the width dimension
-
-#     .. note::
-
-#          Depending of the size of your kernel, several (of the last)
-#          columns of the input might be lost, because it is a valid `cross-correlation`_,
-#          and not a full `cross-correlation`_.
-#          It is up to the user to add proper padding.
-
-#     Args:
-#         in_channels (int): Number of channels in the input image
-#         out_channels (int): Number of channels produced by the convolution
-#         kernel_size (int or tuple): Size of the convolving kernel
-#         stride (int or tuple, optional): Stride of the convolution
-#         padding (int or tuple, optional): Zero-padding added to both sides of the input
-#         output_padding (int or tuple, optional): Zero-padding added to one side of the output
-#         groups (int, optional): Number of blocked connections from input channels to output channels
-#         bias (bool, optional): If True, adds a learnable bias to the output
-#         dilation (int or tuple, optional): Spacing between kernel elements
-
-#     Shape:
-#         - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
-#         - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where
-#           :math:`D_{out} = (D_{in} - 1) * stride[0] - 2 * padding[0] + kernel\_size[0] + output\_padding[0]`
-#           :math:`H_{out} = (H_{in} - 1) * stride[1] - 2 * padding[1] + kernel\_size[1] + output\_padding[1]`
-#           :math:`W_{out} = (W_{in} - 1) * stride[2] - 2 * padding[2] + kernel\_size[2] + output\_padding[2]`
-
-#     Attributes:
-#         weight (Tensor): the learnable weights of the module of shape
-#                          (in_channels, out_channels, kernel_size[0], kernel_size[1], kernel_size[2])
-#         bias (Tensor):   the learnable bias of the module of shape (out_channels)
-
-#     Examples::
-
-#         >>> # With square kernels and equal stride
-#         >>> m = nn.ConvTranspose3d(16, 33, 3, stride=2)
-#         >>> # non-square kernels and unequal stride and with padding
-#         >>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
-#         >>> input = autograd.Variable(core.randn(20, 16, 10, 50, 100))
-#         >>> output = m(input)
-
-#     .. _cross-correlation:
-#         https://en.wikipedia.org/wiki/Cross-correlation
-
-#     .. _link:
-#         https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
-#     """
-
-#     def __init__(self, in_channels, out_channels, kernel_size, stride=1,
-#                  padding=0, output_padding=0, groups=1, bias=True, dilation=1):
-#         kernel_size = _triple(kernel_size)
-#         stride = _triple(stride)
-#         padding = _triple(padding)
-#         dilation = _triple(dilation)
-#         output_padding = _triple(output_padding)
-#         super(ConvTranspose3d, self).__init__(
-#             in_channels, out_channels, kernel_size, stride, padding, dilation,
-#             True, output_padding, groups, bias)
-
-#     def forward(self, input, output_size=None):
-#         output_padding = self._output_padding(input, output_size)
-#         return F.conv_transpose3d(
-#             input, self.weight, self.bias, self.stride, self.padding,
-#             output_padding, self.groups, self.dilation)
+class ConvTranspose3d(_ConvTransposeNd):
+    r"""Applies a 3D transposed convolution operator over an input image composed of several input
+    planes.
+    The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
+    and sums over the outputs from all input feature planes.
+
+    This module can be seen as the gradient of Conv3d with respect to its input.
+    It is also known as a fractionally-strided convolution or
+    a deconvolution (although it is not an actual deconvolution operation).
+
+    | :attr:`stride` controls the stride for the cross-correlation.
+    | If :attr:`padding` is non-zero, then the input is implicitly zero-padded on both sides
+      for :attr:`padding` number of points.
+    | If :attr:`output_padding` is non-zero, then the output is implicitly zero-padded on one side
+      for :attr:`output_padding` number of points.
+    | :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm.
+      It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does.
+    | :attr:`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels`
+      must both be divisible by `groups`.
+    |       At groups=1, all inputs are convolved to all outputs.
+    |       At groups=2, the operation becomes equivalent to having two conv layers
+                 side by side, each seeing half the input channels,
+                 and producing half the output channels, and both subsequently concatenated.
+            At groups=`in_channels`, each input channel is convolved with its own set of filters
+                 (of size `out_channels // in_channels`).
+
+    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding`
+    can either be:
+
+        - a single ``int`` -- in which case the same value is used for the depth, height and width dimensions
+        - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
+          the second `int` for the height dimension and the third `int` for the width dimension
+
+    .. note::
+
+         Depending of the size of your kernel, several (of the last)
+         columns of the input might be lost, because it is a valid `cross-correlation`_,
+         and not a full `cross-correlation`_.
+         It is up to the user to add proper padding.
+
+    Args:
+        in_channels (int): Number of channels in the input image
+        out_channels (int): Number of channels produced by the convolution
+        kernel_size (int or tuple): Size of the convolving kernel
+        stride (int or tuple, optional): Stride of the convolution
+        padding (int or tuple, optional): Zero-padding added to both sides of the input
+        output_padding (int or tuple, optional): Zero-padding added to one side of the output
+        groups (int, optional): Number of blocked connections from input channels to output channels
+        bias (bool, optional): If True, adds a learnable bias to the output
+        dilation (int or tuple, optional): Spacing between kernel elements
+
+    Shape:
+        - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
+        - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where
+          :math:`D_{out} = (D_{in} - 1) * stride[0] - 2 * padding[0] + kernel\_size[0] + output\_padding[0]`
+          :math:`H_{out} = (H_{in} - 1) * stride[1] - 2 * padding[1] + kernel\_size[1] + output\_padding[1]`
+          :math:`W_{out} = (W_{in} - 1) * stride[2] - 2 * padding[2] + kernel\_size[2] + output\_padding[2]`
+
+    Attributes:
+        weight (Tensor): the learnable weights of the module of shape
+                         (in_channels, out_channels, kernel_size[0], kernel_size[1], kernel_size[2])
+        bias (Tensor):   the learnable bias of the module of shape (out_channels)
+
+    Examples::
+
+        >>> # With square kernels and equal stride
+        >>> m = nn.ConvTranspose3d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
+        >>> input = autograd.Variable(core.randn(20, 16, 10, 50, 100))
+        >>> output = m(input)
+
+    .. _cross-correlation:
+        https://en.wikipedia.org/wiki/Cross-correlation
+
+    .. _link:
+        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, output_padding=0, groups=1, bias=True, dilation=1):
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        output_padding = _triple(output_padding)
+        super(ConvTranspose3d, self).__init__(
+            in_channels, out_channels, kernel_size, stride, padding, dilation,
+            True, output_padding, groups, bias)
+
+    def forward(self, input, output_size=None):
+        output_padding = self._output_padding(input, output_size)
+        return F.conv_transpose3d(
+            input, self.weight, self.bias, self.stride, self.padding,
+            output_padding, self.groups, self.dilation)
 
 
 # TODO: Conv2dLocal

mindnlp/core/ops/array.py

@@ -120,6 +120,7 @@ def masked_select(input, mask, *, out=None):
 # narrow
 has_narrow = hasattr(mindspore.mint, 'narrow')
 def narrow(input, dim, start, length):
+    length = length.item() if isinstance(length, mindspore.Tensor) else length
     if use_pyboost() and has_narrow:
         return mindspore.mint.narrow(input, dim, start, length)
     return ops.narrow(input, dim, start, length)

mindnlp/core/ops/creation.py

@@ -191,7 +191,7 @@ def empty_like(input, *, dtype=None, layout=None, device=None, requires_grad=Fal
 
 # full
 has_full = hasattr(mindspore.mint, 'full')
-def full(size, fill_value, *, dtype=None, device=None):
+def full(size, fill_value, *, dtype=None, device=None, **kwargs):
     new_size = ()
     for s in size:
         if isinstance(s, mindspore.Tensor):

mindnlp/core/ops/other.py

@@ -152,8 +152,8 @@ def clone(input):
 # cumsum
 has_cumsum = hasattr(mindspore.mint, "cumsum")
 
-
-def cumsum(input, dim, dtype=None, out=None):
+def cumsum(input, dim=None, dtype=None, out=None, **kwargs):
+    dim = kwargs.pop('axis', dim)
     input_dtype = input.dtype
     if input_dtype == mindspore.int64:
         input = input.to(mindspore.int32)