New User facing Pad Kernel + ST + Allows for channel padding

style fix

lint fixes

added check in NN layer for > 4 paddings, plus lint fix

fix python lint

lint fix

lint fix

updating to pytest asserts to improve testing

removed unnecc vars from test file fail checks

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/pad_impl.cu

@@ -18,6 +18,7 @@
 #include <stdint.h>
 #include "backend/kernel_compiler/gpu/cuda_impl/pad_impl.cuh"
 
+// For internal OP use, not user facing
 template <typename T>
 __global__ void Pad(const size_t size, const T* input, const int num, const int channels, const int old_height,
                     const int old_width, const int padded_height, const int padded_width, const int pad_top,
@@ -37,6 +38,7 @@ __global__ void Pad(const size_t size, const T* input, const int num, const int
   return;
 }
 
+// For internal OP use, not user facing
 template <typename T>
 __global__ void PadNHWC(const size_t size, const T* input, const int num, const int old_height, const int old_width,
                         const int channels, const int padded_height, const int padded_width, const int pad_top,
@@ -57,6 +59,37 @@ __global__ void PadNHWC(const size_t size, const T* input, const int num, const
   return;
 }
 
+// Used by user facing 'Pad' API
+template <typename T>
+__global__ void PadGeneral(const size_t size, const T *input, const int num, const int channels_orig,
+                           const int pad_channel_before, const int pad_channel_after, const int old_height,
+                           const int old_width, const int padded_height, const int padded_width, const int pad_top,
+                           const int pad_left, float pad_value, T *output) {
+  T pad_value_template = static_cast<T>(pad_value);
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
+    int block_num = (pos / padded_width) / padded_height;       // total blocks = (batch * channels)
+    const int padded_w = pos % padded_width;                  // x coordinate refered to by cur 'pos'
+    const int padded_h = (pos / padded_width) % padded_height;  // y coordinate refered to by cur 'pos'
+
+    int channels_new = channels_orig + pad_channel_after + pad_channel_before;  // new number of channels from padding
+    int channel_num = block_num % channels_new;                                 // current channel
+    int batch_item = block_num / channels_new;                                  // current item in batch
+    int equiv_block_num = 0;  // init variable to select equivalent block to copy data from from input
+
+    if (padded_h - pad_top < 0 || padded_w - pad_left < 0 || padded_h - pad_top >= old_height ||
+        padded_w - pad_left >= old_width || channel_num <= pad_channel_before - 1 ||
+        channel_num > channels_orig + pad_channel_before - 1) {
+      output[pos] = pad_value_template;
+    } else {
+      // on a block/x,y positon that isn't padding, copy data from the correct block/x,y pos the input
+      // calculate from number of blocks of padding (due to channel padding) inserted prior
+      equiv_block_num = block_num - (batch_item * (pad_channel_before + pad_channel_after)) - pad_channel_before;
+      output[pos] = input[(equiv_block_num * old_height + padded_h - pad_top) * old_width + padded_w - pad_left];
+    }
+  }
+  return;
+}
+
 template <typename T>
 __global__ void PadGradNHWC(const size_t size, const T* dy, const int num, const int old_height, const int old_width,
                         const int channels, const int padded_height, const int padded_width, const int pad_top,
@@ -102,6 +135,17 @@ void CalPadNHWC(const size_t size, const T* input, const int num, const int old_
   return;
 }
 
+template <typename T>
+void CalPadGeneral(const size_t size, const T *input, const int num, const int channels_orig,
+                   const int pad_channel_before, const int pad_channel_after, const int old_height, const int old_width,
+                   const int padded_height, const int padded_width, const int pad_top, const int pad_left,
+                   float pad_value, T *output, cudaStream_t cuda_stream) {
+  PadGeneral<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, num, channels_orig, pad_channel_before,
+                                                                pad_channel_after, old_height, old_width, padded_height,
+                                                                padded_width, pad_top, pad_left, pad_value, output);
+  return;
+}
+
 template <typename T>
 void CalPadGradNHWC(const size_t size, const T* dy, const int num, const int old_height, const int old_width,
                  const int channels, const int padded_height, const int padded_width, const int pad_top,
@@ -152,3 +196,13 @@ template void CalPadGradNHWC<half>(const size_t size, const half* dy, const int
                                    const int old_width, const int channels, const int padded_height,
                                    const int padded_width, const int pad_top, const int pad_left, half* dx,
                                    cudaStream_t cuda_stream);
+template void CalPadGeneral<float>(const size_t size, const float *input, const int num, const int channels_orig,
+                                   const int pad_channel_before, const int pad_channel_after, const int old_height,
+                                   const int old_width, const int padded_height, const int padded_width,
+                                   const int pad_top, const int pad_left, float pad_value, float *output,
+                                   cudaStream_t cuda_stream);
+template void CalPadGeneral<half>(const size_t size, const half *input, const int num, const int channels_orig,
+                                  const int pad_channel_before, const int pad_channel_after, const int old_height,
+                                  const int old_width, const int padded_height, const int padded_width,
+                                  const int pad_top, const int pad_left, float pad_value, half *output,
+                                  cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/pad_impl.cuh

@@ -31,9 +31,13 @@ template <typename T>
 void CalPadNHWC(const size_t size, const T* input, const int num, const int old_height, const int old_width,
              const int channels, const int padded_height, const int padded_width, const int pad_top, const int pad_left,
             float pad_value, T* output, cudaStream_t cuda_stream);
-
 template <typename T>
 void CalPadGradNHWC(const size_t size, const T* input, const int num, const int old_height, const int old_width,
                 const int channels, const int padded_height, const int padded_width, const int pad_top,
                 const int pad_left, T* output, cudaStream_t cuda_stream);
+template <typename T>
+void CalPadGeneral(const size_t size, const T *input, const int num, const int channels_orig,
+                   const int pad_channel_before, const int pad_channel_after, const int old_height, const int old_width,
+                   const int padded_height, const int padded_width, const int pad_top, const int pad_left,
+                   float pad_value, T *output, cudaStream_t cuda_stream);
 #endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_PADIMPL_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pad_gpu_kernel.h

@@ -42,9 +42,12 @@ class PadGpuFwdKernel : public GpuKernel {
     size_t size = output_size_ / sizeof(T);
     int pad_left = paddings[3][0];
     int pad_top = paddings[2][0];
+    int pad_channel_before = paddings[1][0];
+    int pad_channel_after = paddings[1][1];
     T pad_value = 0.0;
-    CalPad(size, input, input_shape_[0], input_shape_[1], input_shape_[2], input_shape_[3], output_shape_[2],
-           output_shape_[3], pad_top, pad_left, pad_value, output, reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalPadGeneral(size, input, input_shape_[0], input_shape_[1], pad_channel_before, pad_channel_after, input_shape_[2],
+                  input_shape_[3], output_shape_[2], output_shape_[3], pad_top, pad_left, pad_value, output,
+                  reinterpret_cast<cudaStream_t>(stream_ptr));
     return true;
   }
 

mindspore/nn/layer/basic.py

@@ -470,6 +470,8 @@ class Pad(Cell):
         for item in paddings:
             if len(item) != 2:
                 raise ValueError('The shape of paddings must be (n, 2).')
+        if len(paddings) > 4:
+            raise ValueError('Only padding up to 4 dims is supported')
         if mode == "CONSTANT":
             self.pad = P.Pad(self.paddings)
         else:

tests/st/ops/gpu/test_pad.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed 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.
+# ============================================================================
+
+import pytest
+import numpy as np
+
+import mindspore
+import mindspore.nn as nn
+import mindspore.context as context
+
+from mindspore import Tensor
+from mindspore.ops.composite import GradOperation
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_pad_basic():
+    # confirm array is being padded with 0's
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    test_arr = np.array([[1, 2], [3, 4]]).astype(np.float32)
+    test_arr_expected = np.array(
+        [[0, 0, 0, 0], [0, 1, 2, 0], [0, 3, 4, 0], [0, 0, 0, 0]]).astype(np.float32)
+    x_test = Tensor(test_arr, dtype=mindspore.float32)
+
+    pad_op = nn.Pad(mode='CONSTANT', paddings=((1, 1), (1, 1)))
+    y_test = pad_op(x_test).asnumpy()
+
+    np.testing.assert_array_equal(y_test, test_arr_expected)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_pad_row():
+    # Confirm correct row padding
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+
+    test_arr_1 = np.random.rand(40, 40).astype(np.float32)
+    test_paddings_1 = ((2, 3), (0, 0))
+
+    test_arr_2 = np.random.randn(3, 10, 30, 30).astype(np.float32)
+    test_paddings_2 = ((0, 0), (0, 0), (3, 0), (0, 0))
+
+    pad_op_row_1 = nn.Pad(mode='CONSTANT', paddings=test_paddings_1)
+    pad_op_row_2 = nn.Pad(mode='CONSTANT', paddings=test_paddings_2)
+
+    x_test_1 = Tensor(np.array(test_arr_1), dtype=mindspore.float32)
+    x_test_2 = Tensor(np.array(test_arr_2), dtype=mindspore.float32)
+
+    y_test_1 = pad_op_row_1(x_test_1).asnumpy()
+    y_test_2 = pad_op_row_2(x_test_2).asnumpy()
+
+    # check size
+    assert y_test_1.shape == (45, 40)
+    assert y_test_2.shape == (3, 10, 33, 30)
+
+    # check values - select correct sections
+    np.testing.assert_equal(y_test_1[2:-3, :], test_arr_1)
+    np.testing.assert_equal(y_test_2[:, :, 3:, :], test_arr_2)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_pad_column():
+    # Confirm correct column padding
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    test_arr_1 = np.random.randn(40, 40).astype(np.float32)
+    test_paddings_1 = ((0, 0), (3, 3))
+
+    test_arr_2 = np.random.randn(3, 10, 30, 30).astype(np.float32)
+    test_paddings_2 = ((0, 0), (0, 0), (0, 0), (6, 1))
+
+    pad_op_col_1 = nn.Pad(mode='CONSTANT', paddings=test_paddings_1)
+    pad_op_col_2 = nn.Pad(mode='CONSTANT', paddings=test_paddings_2)
+
+    x_test_1 = Tensor(np.array(test_arr_1), dtype=mindspore.float32)
+    x_test_2 = Tensor(np.array(test_arr_2), dtype=mindspore.float32)
+
+    y_test_1 = pad_op_col_1(x_test_1).asnumpy()
+    y_test_2 = pad_op_col_2(x_test_2).asnumpy()
+
+    # check size
+    assert y_test_1.shape == (40, 46)
+    assert y_test_2.shape == (3, 10, 30, 37)
+
+    # check values - select correct sections - should match
+    np.testing.assert_equal(y_test_1[:, 3:-3], test_arr_1)
+    np.testing.assert_equal(y_test_2[:, :, :, 6:-1], test_arr_2)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_pad_3d_pad():
+    # Confirm correct 3d padding - row, column, channel
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+
+    test_arr = np.random.randn(5, 3, 30, 30).astype(np.float32)
+    test_paddings = ((0, 0), (2, 1), (0, 1), (0, 2))  # padding 3 dims now
+
+    pad_op_3d = nn.Pad(mode='CONSTANT', paddings=test_paddings)
+    x_test = Tensor(np.array(test_arr), dtype=mindspore.float32)
+
+    y_test = pad_op_3d(x_test).asnumpy()
+    assert y_test.shape == (5, 6, 31, 32)
+    np.testing.assert_equal(test_arr, y_test[:, 2:-1, :-1, :-2])
+
+
+# For testing backprop
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(get_all=True, sens_param=True)
+        self.network = network
+
+    def construct(self, input_, output_grad):
+        return self.grad(self.network)(input_, output_grad)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.pad = nn.Pad(mode="CONSTANT", paddings=(
+            (0, 0), (4, 3), (1, 1), (0, 2)))
+
+    def construct(self, x):
+        return self.pad(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_pad_3d_backprop():
+    # Confirm correct 3d padding backprop
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    test_arr = np.random.randn(5, 3, 30, 30).astype(np.float32)
+    x_test = Tensor(test_arr, dtype=mindspore.float32)
+
+    padded_shape = (5, 10, 32, 32)
+    dy = np.random.randn(*padded_shape).astype(np.float32)
+    expected_dx = dy[:, 4:-3, 1:-1, :-2]
+
+    net = Grad(Net())
+    dx = net(x_test, Tensor(dy))
+    dx = dx[0].asnumpy()
+    np.testing.assert_array_equal(dx, expected_dx)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_pad_error_cases():
+    # Test against common errorneous inputs to catch correctly
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    # TEST 1 - Neg padding values
+    test_op = nn.Pad(paddings=((0, 0), (-1, -1)), mode="CONSTANT")
+    test_arr = np.random.randn(3, 3)
+    test_arr_ms = Tensor(test_arr, dtype=mindspore.float32)
+
+    with pytest.raises(ValueError):
+        test_op(test_arr_ms)
+
+    # TEST 2 - Mismatched input size and paddings - 1D tensor
+    test_op = nn.Pad(paddings=((0, 0), (1, 0)), mode="CONSTANT")
+    test_arr = np.random.randn(3)  # 1D Tensor
+    test_arr_ms = Tensor(test_arr, dtype=mindspore.float32)
+
+    with pytest.raises(ValueError):
+        test_op(test_arr_ms)
+
+    # TEST 3 - Mismatched input size and paddings - 2D tensor, 3D padding
+    test_op = nn.Pad(paddings=((0, 0), (1, 0)), mode="CONSTANT")  # 2D Padding
+    test_arr = np.random.randn(1, 3, 3)  # 3D Tensor
+    test_arr_ms = Tensor(test_arr, dtype=mindspore.float32)
+
+    with pytest.raises(ValueError):
+        test_op(test_arr_ms)
+
+    # TEST 4 - 1D Paddings should not work
+    with pytest.raises(TypeError):
+        test_op = nn.Pad(paddings=((0, 2)), mode="CONSTANT")
+
+    # TEST 5 - Padding beyond 4d - (added check in nn file in PR)
+    with pytest.raises(ValueError):
+        _ = nn.Pad(paddings=((0, 0), (0, 0,), (0, 0), (0, 0),
+                             (1, 0)), mode="CONSTANT")  # 2D Padding