introduce dim order tests to op test (#2637)

Summary:
Pull Request resolved: #2637

This diff introduces dim order sanity check utils, as well as dim-order related test to operator tests, to help our system maintain its correctness when introducing new dim order ([0, 2, 3, 1]) which we never support before. The goal is checking whether or not every operator support its input's memory format, and using related tests for regular tests.

The high levels of sanity check and test will be:
1. the dim order of input and output should be same.
2. the dim order of all input tensors should be same, unless operaotr-specific requirement for some input (e.g. some operator may request some input have to be contiguous, although I haven't found the actual example yet.)
3. make the operator support as much dim order as possible (e,g, if a operator can support both contiguous and channels last, then the sanity check has to make the both input valid.)

I also updated `op_abs` in this diff to demonstrate how the sanity check as well as tests will be inserted.

Differential Revision: https://internalfb.com/D55227304

Author: Songhao Jia

kernels/portable/cpu/op_abs.cpp

@@ -28,6 +28,8 @@ Tensor& abs_out(RuntimeContext& ctx, const Tensor& in, Tensor& out) {
       "Failed to resize output tensor.");
 
   ET_KERNEL_CHECK(ctx, tensors_have_same_dtype(in, out), InvalidArgument, out);
+  ET_KERNEL_CHECK(
+      ctx, tensors_have_same_dim_order(in, out), InvalidArgument, out);
 
   ET_SWITCH_REAL_TYPES(in.scalar_type(), ctx, "abs.out", CTYPE, [&] {
     apply_unary_map_fn(

kernels/test/TestUtil.h

@@ -30,6 +30,24 @@
 #define ET_EXPECT_KERNEL_FAILURE_WITH_MSG(_context, _statement, _matcher) \
   EXPECT_ANY_THROW(_statement)
 
+#define ET_TEST_OP_SUPPORTS_MEMORY_FORMATS(                                  \
+    tf, op, input_contiguous, expected_contiguous, channels_last_support)    \
+  Tensor input_channels_last = tf.channels_last_like(input_contiguous);      \
+  Tensor expected_channel_last = tf.channels_last_like(expected_contiguous); \
+                                                                             \
+  Tensor output_contiguous = tf.zeros_like(expected_contiguous);             \
+  Tensor output_channels_last = tf.channels_last_like(output_contiguous);    \
+                                                                             \
+  Tensor ret = op(input_channels_last, output_channels_last);                \
+  if (channels_last_support) {                                               \
+    EXPECT_TENSOR_EQ(output_channels_last, expected_channel_last);           \
+  } else {                                                                   \
+    EXPECT_TENSOR_NE(output_channels_last, expected_channel_last);           \
+  }                                                                          \
+  EXPECT_TENSOR_EQ(output_channels_last, ret);                               \
+  ET_EXPECT_KERNEL_FAILURE(                                                  \
+      context_, op(input_channels_last, output_contiguous));
+
 #else
 
 #define ET_EXPECT_KERNEL_FAILURE(_context, _statement)              \
@@ -52,6 +70,26 @@
     }                                                                 \
   } while (false)
 
+#define ET_TEST_OP_SUPPORTS_MEMORY_FORMATS(                                  \
+    tf, op, input_contiguous, expected_contiguous, channels_last_support)    \
+  Tensor input_channels_last = tf.channels_last_like(input_contiguous);      \
+  Tensor expected_channel_last = tf.channels_last_like(expected_contiguous); \
+                                                                             \
+  Tensor output_contiguous = tf.zeros_like(expected_contiguous);             \
+  Tensor output_channels_last = tf.channels_last_like(output_contiguous);    \
+                                                                             \
+  Tensor ret = op(input_channels_last, output_channels_last);                \
+  if (channels_last_support) {                                               \
+    EXPECT_TENSOR_EQ(output_channels_last, expected_channel_last);           \
+  } else {                                                                   \
+    EXPECT_TENSOR_NE(output_channels_last, expected_channel_last);           \
+  }                                                                          \
+  EXPECT_TENSOR_EQ(output_channels_last, ret);                               \
+  ET_EXPECT_KERNEL_FAILURE(                                                  \
+      context_, op(input_channels_last, output_contiguous));                 \
+  ET_EXPECT_KERNEL_FAILURE(                                                  \
+      context_, op(input_contiguous, output_channels_last));
+
 #endif // USE_ATEN_LIB
 
 /*

kernels/test/op_abs_test.cpp

@@ -38,3 +38,28 @@ TEST_F(OpAbsTest, SanityCheck) {
   EXPECT_TENSOR_EQ(out, ret);
   EXPECT_TENSOR_EQ(out, expected);
 }
+
+TEST_F(OpAbsTest, MemoryFormatCheck) {
+  TensorFactory<ScalarType::Float> tf;
+
+  std::vector<int32_t> sizes = {2, 3, 1, 5};
+
+  Tensor input_contiguous =
+      tf.make(sizes, {0.8737,  0.5359,  0.3743,  -0.3040, -0.7800, -0.2306,
+                      -0.7684, -0.5364, 0.3478,  -0.3289, 0.0829,  0.2939,
+                      -0.8211, 0.8572,  -0.0802, 0.9252,  -0.2093, 0.9013,
+                      -0.4197, 0.3987,  -0.5291, -0.5567, 0.2691,  0.7819,
+                      -0.8009, -0.4286, -0.9299, 0.2143,  0.2565,  -0.5701});
+  Tensor expected_contiguous = tf.make(
+      sizes, {0.8737, 0.5359, 0.3743, 0.3040, 0.7800, 0.2306, 0.7684, 0.5364,
+              0.3478, 0.3289, 0.0829, 0.2939, 0.8211, 0.8572, 0.0802, 0.9252,
+              0.2093, 0.9013, 0.4197, 0.3987, 0.5291, 0.5567, 0.2691, 0.7819,
+              0.8009, 0.4286, 0.9299, 0.2143, 0.2565, 0.5701});
+
+  ET_TEST_OP_SUPPORTS_MEMORY_FORMATS(
+      tf,
+      op_abs_out,
+      input_contiguous,
+      expected_contiguous,
+      /*channels_last_support=*/true);
+}

runtime/core/exec_aten/testing_util/tensor_factory.h

@@ -3,8 +3,10 @@
 #pragma once
 
 #include <algorithm>
+#include <cstdint>
 
 #include <executorch/runtime/core/exec_aten/exec_aten.h>
+#include <executorch/runtime/core/exec_aten/util/dim_order_util.h>
 #include <executorch/runtime/core/exec_aten/util/scalar_type_util.h>
 #include <executorch/runtime/core/tensor_shape_dynamism.h>
 #include <executorch/runtime/platform/assert.h>
@@ -54,7 +56,7 @@ inline size_t sizes_to_numel(const std::vector<int32_t>& sizes) {
 
 inline bool check_strides(
     const std::vector<int32_t> sizes,
-    const std::vector<int32_t> strides) {
+    const std::vector<exec_aten::StridesType> strides) {
   if (sizes.size() != strides.size()) {
     // The length of stride vector shall equal to size vector.
     return false;
@@ -147,14 +149,14 @@ inline bool check_dim_order(
   return true;
 }
 
-inline std::vector<int32_t> strides_from_dim_order(
+inline std::vector<exec_aten::StridesType> strides_from_dim_order(
     const std::vector<int32_t>& sizes,
     const std::vector<uint8_t>& dim_order) {
   bool legal = check_dim_order(sizes, dim_order);
   ET_CHECK_MSG(legal, "The input dim_order variable is illegal.");
 
   size_t ndim = sizes.size();
-  std::vector<int32_t> strides(ndim);
+  std::vector<exec_aten::StridesType> strides(ndim);
   strides[dim_order[ndim - 1]] = 1;
   for (int i = ndim - 2; i >= 0; --i) {
     uint8_t cur_dim = dim_order[i];
@@ -258,7 +260,7 @@ class TensorFactory {
   at::Tensor make(
       const std::vector<int32_t>& sizes,
       const std::vector<ctype>& data,
-      const std::vector<int32_t> strides = {},
+      const std::vector<exec_aten::StridesType> strides = {},
       __ET_UNUSED TensorShapeDynamism dynamism =
           TensorShapeDynamism::DYNAMIC_UNBOUND) {
     auto expected_numel = internal::sizes_to_numel(sizes);
@@ -344,6 +346,67 @@ class TensorFactory {
         sizes, data, internal::channels_last_dim_order(sizes.size()), dynamism);
   }
 
+  /**
+   * Given data in contiguous memory format, returns a new Tensor with the
+   * specified shape and the same data but in channels last memory format.
+   *
+   * @param[in] sizes The sizes of the dimensions of the Tensor.
+   * @param[in] data The data in contiguous memory format that the Tensor should
+   * be initialized with. The size of this vector must be equal to the product
+   * of the elements of `sizes`.
+   *
+   * @return A new Tensor with the specified shape and data in channls last
+   * memory format.
+   */
+  at::Tensor channels_last_like(
+      const Tensor& input,
+      TensorShapeDynamism dynamism = TensorShapeDynamism::STATIC) {
+    const std::vector<int32_t> sizes(
+        input.sizes().begin(), input.sizes().end());
+
+    std::vector<uint8_t> channels_last_dim_order =
+        internal::channels_last_dim_order(sizes.size());
+    std::vector<exec_aten::StridesType> channels_last_strides =
+        internal::strides_from_dim_order(sizes, channels_last_dim_order);
+
+    for (int32_t i = 0; i < input.dim(); i++) {
+      ET_CHECK_MSG(
+          input.strides()[i] == channels_last_strides[i],
+          "Input tensor is not contiguous");
+    }
+
+    int32_t N = sizes[0];
+    int32_t C = sizes[1];
+    int32_t H = sizes[2];
+    int32_t W = sizes[3];
+
+    std::vector<ctype> contiguous_data(
+        input.data_ptr<ctype>(), input.data_ptr<ctype>() + input.numel());
+    std::vector<ctype> channels_last_data(
+        N * C * H * W); // Create a new blob with the same total size to contain
+                        // channels_last data
+    for (int32_t n = 0; n < N; ++n) {
+      for (int32_t c = 0; c < C; ++c) {
+        for (int32_t h = 0; h < H; ++h) {
+          for (int32_t w = 0; w < W; ++w) {
+            // Calculate the index in the original blob
+            int32_t old_index = ((n * C + c) * H + h) * W + w;
+            // Calculate the index in the new blob
+            int32_t new_index = ((n * H + h) * W + w) * C + c;
+            // Copy the data
+            channels_last_data[new_index] = contiguous_data[old_index];
+          }
+        }
+      }
+    }
+
+    return make_with_dimorder(
+        sizes,
+        channels_last_data,
+        internal::channels_last_dim_order(sizes.size()),
+        dynamism);
+  }
+
   /**
    * Returns a new Tensor with the specified shape, containing contiguous
    * data will all elements set to `value`.
@@ -459,14 +522,13 @@ class TensorFactory {
    */
   at::Tensor empty_strided(
       const std::vector<int32_t>& sizes,
-      const std::vector<int32_t>& strides,
+      const std::vector<exec_aten::StridesType>& strides,
       __ET_UNUSED TensorShapeDynamism dynamism =
           TensorShapeDynamism::DYNAMIC_UNBOUND) {
     auto sizes64 = vec_32_to_64(sizes);
-    auto strides64 = vec_32_to_64(strides);
     return at::empty_strided(
         sizes64,
-        strides64,
+        strides,
         DTYPE,
         /*layout_opt=*/at::Layout::Strided,
         /*device_opt=*/at::Device(at::DeviceType::CPU),
@@ -665,7 +727,7 @@ class TensorFactory {
   torch::executor::Tensor make(
       const std::vector<int32_t>& sizes,
       const std::vector<ctype>& data,
-      const std::vector<int32_t> strides = {},
+      const std::vector<exec_aten::StridesType> strides = {},
       TensorShapeDynamism dynamism = TensorShapeDynamism::STATIC) {
     std::vector<int32_t> default_strides;
     // Generate strides from the tensor dimensions, assuming contiguous data if
@@ -745,7 +807,7 @@ class TensorFactory {
 
   /**
    * Returns a new Tensor with the specified shape and data in channels last
-   * memory layout.
+   * memory format.
    *
    * @param[in] sizes The sizes of the dimensions of the Tensor.
    * @param[in] data The data that the Tensor should be initialized with. The
@@ -763,6 +825,60 @@ class TensorFactory {
         sizes, data, internal::channels_last_dim_order(sizes.size()), dynamism);
   }
 
+  /**
+   * Given data in contiguous memory format, returns a new Tensor with the
+   * specified shape and the same data but in channels last memory format.
+   *
+   * @param[in] sizes The sizes of the dimensions of the Tensor.
+   * @param[in] data The data in contiguous memory format that the Tensor should
+   * be initialized with. The size of this vector must be equal to the product
+   * of the elements of `sizes`.
+   *
+   * @return A new Tensor with the specified shape and data in channls last
+   * memory format.
+   */
+  torch::executor::Tensor channels_last_like(
+      const Tensor& input,
+      TensorShapeDynamism dynamism = TensorShapeDynamism::STATIC) {
+    const std::vector<int32_t> sizes(
+        input.sizes().begin(), input.sizes().end());
+
+    ET_CHECK_MSG(sizes.size() == 4, "Only 4D tensors can be channels last");
+    ET_CHECK_MSG(
+        is_contiguous_dim_order(input.dim_order().data(), input.dim()) == true,
+        "Input tensor is not contiguous");
+    int32_t N = sizes[0];
+    int32_t C = sizes[1];
+    int32_t H = sizes[2];
+    int32_t W = sizes[3];
+
+    std::vector<ctype> contiguous_data(
+        input.data_ptr<ctype>(), input.data_ptr<ctype>() + input.numel());
+    std::vector<ctype> channels_last_data(
+        N * C * H * W); // Create a new blob with the same total size to contain
+                        // channels_last data
+    for (int32_t n = 0; n < N; ++n) {
+      for (int32_t c = 0; c < C; ++c) {
+        for (int32_t h = 0; h < H; ++h) {
+          for (int32_t w = 0; w < W; ++w) {
+            // Calculate the index in the original blob
+            int32_t old_index = ((n * C + c) * H + h) * W + w;
+            // Calculate the index in the new blob
+            int32_t new_index = ((n * H + h) * W + w) * C + c;
+            // Copy the data
+            channels_last_data[new_index] = contiguous_data[old_index];
+          }
+        }
+      }
+    }
+
+    return make_with_dimorder(
+        sizes,
+        channels_last_data,
+        internal::channels_last_dim_order(sizes.size()),
+        dynamism);
+  }
+
   /**
    * Returns a new Tensor with the specified shape, containing contiguous data
    * will all elements set to `value`.
@@ -798,7 +914,20 @@ class TensorFactory {
 
   /**
    * Returns a new Tensor with the specified shape, containing contiguous data
-   * with all `0` elements.
+   * in channels last memory format with all `0` elements.
+   *
+   * @param[in] sizes The sizes of the dimensions of the Tensor.
+   * @return A new Tensor with the specified shape.
+   */
+  torch::executor::Tensor zeros_channels_last(
+      const std::vector<int32_t>& sizes,
+      TensorShapeDynamism dynamism = TensorShapeDynamism::STATIC) {
+    return full_channels_last(sizes, 0, dynamism);
+  }
+
+  /**
+   * Returns a new Tensor with the specified shape, containing contiguous data
+   * in contiguous memory format with all `0` elements.
    *
    * @param[in] sizes The sizes of the dimensions of the Tensor.
    * @return A new Tensor with the specified shape.
@@ -877,7 +1006,7 @@ class TensorFactory {
     std::vector<int32_t> sizes_;
     std::vector<ctype> data_;
     std::vector<uint8_t> dim_order_;
-    std::vector<int32_t> strides_;
+    std::vector<exec_aten::StridesType> strides_;
     TensorImpl impl_;
   };