chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,110 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=unused-variable, invalid-name
+"""1D convolution in python"""
+
+import numpy as np
+
+from tvm.topi.nn.utils import get_pad_tuple1d
+
+
+def dilate_np(x, dilation):
+    """1D dilation using numpy
+
+    Parameters
+    ----------
+    x : numpy.ndarray
+        Array to dilate with shape [batch, in_channel, in_width]
+
+    dilation : int
+        dilation rate of output
+
+    Returns
+    -------
+    out : numpy.ndarray
+        Dilated output with shape [batch, in_channel, (in_width - 1) * dilation + 1]
+    """
+    irange = range(len(x) - 1)
+    for d in range(dilation - 1):
+        indices = [(d + 1) * (i + 1) for i in irange]
+        x = np.insert(x, indices, 0)
+    return x
+
+
+def group_conv1d_ncw_python(a_np, w_np, stride, padding, dilation, groups):
+    "Grouped version of `conv1d_ncw_python`, see that for documentation"
+    a_slices = np.array_split(a_np, groups, axis=1)
+    w_slices = np.array_split(w_np, groups, axis=0)
+    b_slices = [
+        conv1d_ncw_python(a_slice, w_slice, stride, padding, dilation)
+        for a_slice, w_slice in zip(a_slices, w_slices)
+    ]
+    return np.concatenate(b_slices, axis=1)
+
+
+def conv1d_ncw_python(a_np, w_np, stride, padding, dilation):
+    """1D convolution operator in NCW layout
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        3-D with shape [batch, in_channel, in_width]
+
+    w_np : numpy.ndarray
+        3-D with shape [num_filter, in_channel, filter_width]
+
+    stride : int
+        Stride size
+
+    padding : int, tuple, or str
+        Single int for padding size or tuple of (left, right) padding
+        or a string in ['VALID', 'SAME']
+
+    dilation : int
+        Dilation rate of the kernel
+
+    groups : int
+        Number of groups in the convolution
+
+    Returns
+    -------
+    b_np : numpy.ndarray
+        3-D with shape [batch, out_channel, out_width]
+    """
+    batch, in_c, in_w = a_np.shape
+    out_c, _, filter_w = w_np.shape
+    if isinstance(stride, tuple | list):
+        stride = stride[0]
+    if isinstance(dilation, tuple | list):
+        dilation = dilation[0]
+
+    dilated_filter_w = (filter_w - 1) * dilation + 1
+    pad_left, pad_right = get_pad_tuple1d(padding, (dilated_filter_w,))
+    out_w = ((in_w - dilated_filter_w + pad_left + pad_right) // stride) + 1
+
+    padded_a_np = np.zeros((batch, in_c, in_w + pad_left + pad_right))
+    padded_a_np[:, :, pad_left : (in_w + pad_left)] = a_np
+
+    b_np = np.zeros((batch, out_c, out_w))
+    for n in range(batch):
+        for f in range(out_c):
+            for c in range(in_c):
+                out = np.convolve(
+                    padded_a_np[n, c], np.flip(dilate_np(w_np[f, c], dilation)), mode="valid"
+                )
+                b_np[n, f] += out[::stride]
+    return b_np