[TOPI] Add ops compute

topi/python/topi/math.py

@@ -2,6 +2,42 @@
 from __future__ import absolute_import as _abs
 import tvm
 
+@tvm.tag_scope(tag='ewise')
+def identity(x):
+    """Take identity of input x.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    # pylint: disable=unnecessary-lambda
+    return tvm.compute(x.shape, lambda *i: x(*i))
+
+
+@tvm.tag_scope(tag='ewise')
+def negation(x):
+    """Take negation of input x.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    # pylint: disable=unnecessary-lambda
+    return tvm.compute(x.shape, lambda *i: -x(*i))
+
+
 @tvm.tag_scope(tag="ewise")
 def exp(x):
     """Take exponential of input x.

topi/python/topi/nn/__init__.py

@@ -2,7 +2,12 @@
 """Neural network operators"""
 from __future__ import absolute_import as _abs
 
-from .ewise import *
-from .mapping import *
-from .conv import *
+from .batch_norm import *
+from .convolution import *
+from .elemwise import *
 from .dilate import *
+from .flatten import *
+from .fully_connected import *
+from .mapping import *
+from .pooling import *
+from .softmax import *

topi/python/topi/nn/batch_norm.py

@@ -0,0 +1,55 @@
+"""TVM operator batch normalization compute."""
+from __future__ import absolute_import
+import tvm
+
+@tvm.tag_scope(tag='batch_norm')
+def batch_norm(data, gamma, beta, moving_mean, moving_var, eps, fix_gamma):
+    """Batch normalization operator in NCHW layout.
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    gamma : tvm.Tensor
+        1-D with shape [channel]
+
+    beta : tvm.Tensor
+        1-D with shape [channel]
+
+    moving_mean : tvm.Tensor
+        1-D with shape [channel]
+
+    moving_var : tvm.Tensor
+        1-D with shape [channel]
+
+    eps : float
+        Epsilon to prevent div 0.
+
+    fix_gamma : boolean
+        Fix gamma while training
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    mean : tvm.Tensor
+        1-D with shape [channel]
+
+    var : tvm.Tensor
+        1-D with shape [channel]
+    """
+    assert len(data.shape) == 4, "only support 4-dim batch norm"
+    batch, channel, height, width = data.shape
+    if fix_gamma:
+        out = tvm.compute((batch, channel, height, width), \
+            lambda b, c, h, w: (data[b, c, h, w] - moving_mean[c]) / \
+            tvm.intrin.sqrt(moving_var[c] + eps) + beta[c])
+    else:
+        out = tvm.compute((batch, channel, height, width), \
+            lambda b, c, h, w: (data[b, c, h, w] - moving_mean[c]) / \
+            tvm.intrin.sqrt(moving_var[c] + eps) * gamma[c] + beta[c])
+    mean = tvm.compute((C, ), lambda c: moving_mean[c])
+    var = tvm.compute((C, ), lambda c: moving_var[c])
+    return [out, mean, var]

topi/python/topi/nn/conv.py → topi/python/topi/nn/convolution.py

@@ -89,7 +89,7 @@ def conv2d_hwcn(Input, Filter, stride, padding):
 
     Returns
     -------
-    Output : tvm.Tensor
+    output : tvm.Tensor
         4-D with shape [out_height, out_width, out_channel, batch]
     """
     assert isinstance(stride, int) or len(stride) == 2

topi/python/topi/nn/flatten.py

@@ -0,0 +1,32 @@
+"""TVM operator flatten compute."""
+from __future__ import absolute_import
+import tvm
+
+@tvm.tag_scope(tag='flatten')
+def flatten(data):
+    """Flattens the input array into a 2-D array by collapsing the higher dimensions.
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        Input array.
+
+    Returns
+    -------
+    output : tvm.Tensor
+        2-D array with collapsed higher dimensions.
+    """
+    ishape = data.shape
+    dim = 1
+    for i in range(1, len(ishape)):
+        dim = dim * ishape[i]
+    oshape = [ishape[0], dim]
+
+    def unwrap(idx, shape):
+        index = []
+        for s in reversed(shape):
+            index.append(idx % s)
+            idx = idx / s
+        return list(reversed(index))
+
+    return tvm.compute(oshape, lambda i, j: data(i, *unwrap(j, ishape[1:])))

topi/python/topi/nn/fully_connected.py

@@ -0,0 +1,62 @@
+"""TVM operator fully connected compute."""
+from __future__ import absolute_import
+import tvm
+
+
+@tvm.tag_scope(tag='fully_connected')
+def fully_connected(data, weight):
+    """Matrix multiplication
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        2-D with shape [batch, in_dim]
+
+    weight : tvm.Tensor
+        2-D with shape [out_dim, in_dim]
+
+    Returns
+    -------
+    output : tvm.Tensor
+        2-D with shape [batch, out_dim]
+    """
+    assert len(data.shape) == 2 and len(weight.shape) == 2, \
+        "only support 2-dim fully_connected"
+    batch, in_dim = data.shape
+    out_dim, _ = weight.shape
+    k = tvm.reduce_axis((0, in_dim), name='k')
+    return tvm.compute((batch, out_dim), lambda i, j: \
+        tvm.sum(data[i][k] * weight[j][k], axis=k))
+
+
+@tvm.tag_scope(tag='fully_connected_with_bias')
+def fully_connected_with_bias(data, weight, bias):
+    """Applies a linear transformation: :math:`Y = XW^T + b`.
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        2-D with shape [batch, in_dim]
+
+    weight : tvm.Tensor
+        2-D with shape [out_dim, in_dim]
+
+    bias : tvm.Tensor
+        1-D with shape [out_dim]
+
+    Returns
+    -------
+    output : tvm.Tensor
+        2-D with shape [batch, out_dim]
+    """
+    assert len(data.shape) == 2 and len(weight.shape) == 2, \
+        "only support 2-dim fully_connected"
+    assert len(data.shape) == 2 and len(weight.shape) == 2 and len(bias.shape) == 1, \
+        "only support 2-dim fully_connected"
+    batch, in_dim = data.shape
+    out_dim, _ = weight.shape
+    k = tvm.reduce_axis((0, in_dim), name='k')
+    matmul = tvm.compute((batch, out_dim), lambda i, j: \
+        tvm.sum(data[i, k] * weight[j, k], axis=k))
+    return tvm.compute((batch, out_dim), lambda i, j: \
+        matmul[i, j] + bias[j])

topi/python/topi/nn/pooling.py

@@ -0,0 +1,77 @@
+"""TVM operator pooling compute."""
+from __future__ import absolute_import
+import tvm
+
+@tvm.tag_scope(tag='max_pool')
+def max_pool(data, kernel, stride, pad):
+    """Perform max pooling on the data
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        4-D with shape [batch, channel, in_height, in_width]
+
+    kernel : list/tuple of two ints
+        Kernel size, or [kernel_height, kernel_width]
+
+    stride : list/tuple of two ints
+        Stride size, or [stride_height, stride_width]
+
+    pad : list/tuple of two ints
+        Pad size, or [pad_height, pad_width]
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, channel, out_height, out_width]
+    """
+    assert len(data.shape) == 4, "only support 4-dim pooling"
+    assert len(stride.shape) == 2, "only support 2-dim stride"
+    assert len(pad.shape) == 2, "only support 2-dim pad"
+    kernel_height, kernel_width = kernel
+    stride_height, stride_width = stride
+    pad_height, pad_width = pad
+    batch, channel, height, width = data.shape
+    padded_height = height + 2*pad_height
+    padded_width = width + 2*pad_width
+    out_height = (height + 2*pad_height - kernl_height) / stride_height + 1
+    out_width = (width + 2*pad_width - kernel_width) / stride_width + 1
+
+    dheight = tvm.reduce_axis((0, kernel_height))
+    dwidth = tvm.reduce_axis((0, kernel_width))
+
+    temp = tvm.compute((batch, channel, padded_height, padded_width), lambda i, c, h, w: \
+        tvm.select(
+            tvm.make.Or(tvm.make.Or((h < pad_height), (h >= height + pad_height)),
+                        tvm.make.Or((w < pad_width), (w >= width + pad_width))),
+            tvm.min_value('float32'),
+            data[i, c, h - pad_height, w - pad_width]), name='temp')
+
+    return tvm.compute((batch, channel, out_height, out_width), lambda i, c, h, w: \
+        tvm.max(temp[i, c, h*stride_height+dheight, w*stride_width+dwidth], axis=[dheight, dwidth]))
+
+
+@tvm.tag_scope(tag='global_avg_pool')
+def global_avg_pool(data):
+    """Perform global average pooling on the data
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        4-D with shape [batch, channel, in_height, in_width]
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, channel, 1, 1]
+    """
+    assert len(data.shape) == 4, "only support 4-dim pooling"
+    batch, channel, height, width = data.shape
+
+    dheight = tvm.reduce_axis((0, height))
+    dwidth = tvm.reduce_axis((0, width))
+
+    tsum = tvm.compute((batch, channel, 1, 1), lambda n, c, h, w: \
+        tvm.sum(data[n, c, dheight, dwidth], axis=[dheight, dwidth]))
+    return tvm.compute((batch, channel, 1, 1), lambda n, c, h, w: \
+        tsum[n, c, h, w] / (height*width))

topi/python/topi/nn/softmax.py

@@ -0,0 +1,27 @@
+"""TVM operator softmax compute."""
+from __future__ import absolute_import
+import tvm
+
+@tvm.tag_scope(tag='softmax')
+def softmax(x):
+    """Perform softmax activation on the data
+
+    Parameters
+    ----------
+    data : tvm.Tensor
+        2-D input data
+
+    Returns
+    -------
+    output : tvm.Tensor
+        2-D output with same shape
+    """
+    assert len(x.shape) == 2, "only support 2-dim softmax"
+    m, n = x.shape
+    k = tvm.reduce_axis((0, n), name='k')
+    max_elem = tvm.compute((m, ), lambda i: \
+        tvm.max(x[i, k]), axis=k)
+    expsum = tvm.compute((m, ), lambda i: \
+        tvm.sum(tvm.exp(x[i, k] - max_elem[i]), axis=k))
+    return tvm.compute(x.shape, lambda i, j: \
+        tvm.exp(x[i, j] - max_elem[i]) / expsum[i])