FrozenGene commented on a change in pull request #6477:
URL: https://github.com/apache/incubator-tvm/pull/6477#discussion_r502164406
##########
File path: include/tvm/topi/nn.h
##########
@@ -459,6 +460,178 @@ inline tvm::te::Tensor group_conv2d_ngchw(const
tvm::te::Tensor& I, const tvm::t
return tvm::te::compute(output_shape, l, name, tag);
}
+/*!
+ * \brief Divide spatial dimensions of the input into a grid of blocks.
+ *
+ * \param data The input tensor.
+ * \param block_shape The size of the spatial block.
+ * \param pad_before The zero-padding size before each spatial dimension.
+ * \param pad_after The zero-padding size after each spatial dimension.
+ * \param name The name of the operation.
+ * \param tag The tag to mark the operation.
+ *
+ * \return A Tensor whose op member is the space_to_batch_nd operation
+ */
+inline tvm::te::Tensor space_to_batch_nd(const tvm::te::Tensor& data,
+ const tvm::Array<Integer>&
block_shape,
+ const tvm::Array<tvm::PrimExpr>&
pad_before,
+ const tvm::Array<tvm::PrimExpr>&
pad_after,
+ std::string name =
"space_to_batch_nd",
+ std::string tag = kInjective) {
+ tvm::te::Tensor padded_t;
+ CHECK_EQ(pad_before.size(), pad_after.size());
+ CHECK_EQ(block_shape.size(), pad_before.size())
+ << "Paddings must be provided for each spatial dimension";
+ tvm::Array<tvm::PrimExpr> pad_before_int32;
+ tvm::Array<tvm::PrimExpr> pad_after_int32;
+
+ // pad size for batch dimension is 0
+ pad_before_int32.push_back(tvm::cast(tvm::DataType::Int(32), 0));
+ pad_after_int32.push_back(tvm::cast(tvm::DataType::Int(32), 0));
+ // insert pad sizes given for spatial dimensions
+ for (const auto& ele : pad_before) {
+ pad_before_int32.push_back(tvm::cast(tvm::DataType::Int(32), ele));
+ }
+ for (const auto& ele : pad_after) {
+ pad_after_int32.push_back(tvm::cast(tvm::DataType::Int(32), ele));
+ }
+
+ // pad the input with paddings provided
+ padded_t = pad(data, pad_before_int32, pad_after_int32,
make_const(DataType::Int(32), 0));
Review comment:
How about adding one `pad_value` parameter whose default value is 0 for
`space_to_batch`? I imagine if we use it in the quantized model, its pad value
will be `zero_point`, not
0.https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/frontend/tflite.py#L2423
##########
File path: python/tvm/topi/testing/space_to_batch_nd.py
##########
@@ -0,0 +1,90 @@
+# 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=invalid-name, line-too-long, unused-variable, too-many-locals
+"""Space to batch ND in python"""
+import numpy as np
+
+
+def space_to_batch_nd_python(data, block_shape, pad_before, pad_after):
Review comment:
Add `pad_value = 0` like previous comment said
##########
File path: include/tvm/topi/nn.h
##########
@@ -459,6 +460,178 @@ inline tvm::te::Tensor group_conv2d_ngchw(const
tvm::te::Tensor& I, const tvm::t
return tvm::te::compute(output_shape, l, name, tag);
}
+/*!
+ * \brief Divide spatial dimensions of the input into a grid of blocks.
+ *
+ * \param data The input tensor.
+ * \param block_shape The size of the spatial block.
+ * \param pad_before The zero-padding size before each spatial dimension.
+ * \param pad_after The zero-padding size after each spatial dimension.
+ * \param name The name of the operation.
+ * \param tag The tag to mark the operation.
+ *
+ * \return A Tensor whose op member is the space_to_batch_nd operation
+ */
+inline tvm::te::Tensor space_to_batch_nd(const tvm::te::Tensor& data,
+ const tvm::Array<Integer>&
block_shape,
+ const tvm::Array<tvm::PrimExpr>&
pad_before,
+ const tvm::Array<tvm::PrimExpr>&
pad_after,
+ std::string name =
"space_to_batch_nd",
+ std::string tag = kInjective) {
+ tvm::te::Tensor padded_t;
+ CHECK_EQ(pad_before.size(), pad_after.size());
+ CHECK_EQ(block_shape.size(), pad_before.size())
+ << "Paddings must be provided for each spatial dimension";
+ tvm::Array<tvm::PrimExpr> pad_before_int32;
+ tvm::Array<tvm::PrimExpr> pad_after_int32;
+
+ // pad size for batch dimension is 0
+ pad_before_int32.push_back(tvm::cast(tvm::DataType::Int(32), 0));
+ pad_after_int32.push_back(tvm::cast(tvm::DataType::Int(32), 0));
+ // insert pad sizes given for spatial dimensions
+ for (const auto& ele : pad_before) {
+ pad_before_int32.push_back(tvm::cast(tvm::DataType::Int(32), ele));
+ }
+ for (const auto& ele : pad_after) {
+ pad_after_int32.push_back(tvm::cast(tvm::DataType::Int(32), ele));
+ }
+
+ // pad the input with paddings provided
+ padded_t = pad(data, pad_before_int32, pad_after_int32,
make_const(DataType::Int(32), 0));
+
+ auto input_shape = data->shape;
+ auto padded_shape = padded_t->shape;
+
+ // infer shapes
+ tvm::Array<PrimExpr> r_shape;
+ tvm::Array<Integer> axis;
+ tvm::Array<PrimExpr> o_shape;
+
+ size_t M = block_shape.size();
Review comment:
I think we shouldn't use capital letter here
##########
File path: include/tvm/topi/nn.h
##########
@@ -459,6 +460,178 @@ inline tvm::te::Tensor group_conv2d_ngchw(const
tvm::te::Tensor& I, const tvm::t
return tvm::te::compute(output_shape, l, name, tag);
}
+/*!
+ * \brief Divide spatial dimensions of the input into a grid of blocks.
+ *
+ * \param data The input tensor.
+ * \param block_shape The size of the spatial block.
+ * \param pad_before The zero-padding size before each spatial dimension.
+ * \param pad_after The zero-padding size after each spatial dimension.
+ * \param name The name of the operation.
+ * \param tag The tag to mark the operation.
+ *
+ * \return A Tensor whose op member is the space_to_batch_nd operation
+ */
+inline tvm::te::Tensor space_to_batch_nd(const tvm::te::Tensor& data,
+ const tvm::Array<Integer>&
block_shape,
+ const tvm::Array<tvm::PrimExpr>&
pad_before,
+ const tvm::Array<tvm::PrimExpr>&
pad_after,
+ std::string name =
"space_to_batch_nd",
+ std::string tag = kInjective) {
+ tvm::te::Tensor padded_t;
+ CHECK_EQ(pad_before.size(), pad_after.size());
+ CHECK_EQ(block_shape.size(), pad_before.size())
+ << "Paddings must be provided for each spatial dimension";
+ tvm::Array<tvm::PrimExpr> pad_before_int32;
+ tvm::Array<tvm::PrimExpr> pad_after_int32;
+
+ // pad size for batch dimension is 0
+ pad_before_int32.push_back(tvm::cast(tvm::DataType::Int(32), 0));
+ pad_after_int32.push_back(tvm::cast(tvm::DataType::Int(32), 0));
+ // insert pad sizes given for spatial dimensions
+ for (const auto& ele : pad_before) {
+ pad_before_int32.push_back(tvm::cast(tvm::DataType::Int(32), ele));
+ }
+ for (const auto& ele : pad_after) {
+ pad_after_int32.push_back(tvm::cast(tvm::DataType::Int(32), ele));
+ }
+
+ // pad the input with paddings provided
+ padded_t = pad(data, pad_before_int32, pad_after_int32,
make_const(DataType::Int(32), 0));
+
+ auto input_shape = data->shape;
+ auto padded_shape = padded_t->shape;
+
+ // infer shapes
+ tvm::Array<PrimExpr> r_shape;
+ tvm::Array<Integer> axis;
+ tvm::Array<PrimExpr> o_shape;
+
+ size_t M = block_shape.size();
+ int batch = static_cast<int>(GetConstInt(input_shape[0]));
+ tvm::PrimExpr block_shape_prod(1);
+ r_shape.push_back(batch);
+
+ for (size_t i = 1; i <= M; i++) {
+ int padded_input = static_cast<int>(GetConstInt(padded_shape[i]));
+ int block_size = static_cast<int>(GetConstInt(block_shape[i - 1]));
+ CHECK_EQ((padded_input % block_size), 0)
+ << "(" << i
+ << ")th "
+ "Input dimension after padding ("
+ << padded_input << ")"
+ << " must be divisible by its block size (" << block_size << ")";
+
+ r_shape.push_back(div(padded_shape[i], block_shape[i - 1]));
+ r_shape.push_back(block_shape[i - 1]);
+ block_shape_prod *= block_shape[i - 1];
+ axis.push_back(Integer(r_shape.size() - 1)); // index of block_shape[i -
1]
+ }
+
+ size_t n = axis.size();
+ axis.push_back(0); // batch is at index 0
+ // index of (padded_shape[i] / block_shape[i - 1]) in r_shape
+ for (size_t i = 0; i < n; i++) {
+ axis.push_back(static_cast<int>(GetConstInt(axis[i] - 1)));
+ }
+ o_shape.push_back(tvm::PrimExpr(batch) * block_shape_prod);
+ for (size_t i = 1; i <= M; i++) {
+ o_shape.push_back(div(padded_shape[i], block_shape[i - 1]));
+ }
+ // append remaining shape
+ for (size_t i = M + 1; i < input_shape.size(); i++) {
+ r_shape.push_back(input_shape[i]);
+ axis.push_back(Integer(r_shape.size() - 1)); // index of remaining shape
in r_shape
+ o_shape.push_back(input_shape[i]);
+ }
+
+ tvm::te::Tensor output = reshape(padded_t, r_shape);
+ output = transpose(output, axis);
+ output = reshape(output, o_shape);
+
+ return output;
+}
+
+/*!
+ * \brief Reshape the batch dimension into spatial dimensions.
+ *
+ * \param data The input tensor.
+ * \param block_shape The size of the spatial block.
+ * \param crop_begin_list The begin crop size for each spatial dimension.
+ * \param crop_end_list The end crop size for each spatial dimension.
+ * \param name The name of the operation.
+ * \param tag The tag to mark the operation.
+ *
+ * \return A Tensor whose op member is the batch_to_space_nd operation
+ */
+inline tvm::te::Tensor batch_to_space_nd(const tvm::te::Tensor& data,
+ const tvm::Array<Integer>&
block_shape,
+ const tvm::Array<tvm::PrimExpr>&
crop_begin_list,
+ const tvm::Array<tvm::PrimExpr>&
crop_end_list,
+ std::string name =
"batch_to_space_nd",
+ std::string tag = kInjective) {
+ // Construct shapes for reshape and transpose operation
+ Array<PrimExpr> in_shape = data->shape;
+ Array<PrimExpr> r_shape;
+ Array<Integer> axis;
+ size_t M = block_shape.size();
+ size_t N = in_shape.size();
Review comment:
ditto
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