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[ET][Cadence][Hifi] add cpp test for im2row #17178

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22 changes: 22 additions & 0 deletions backends/cadence/hifi/operators/operators.h
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,28 @@ void quantized_add_asym8uxasym8u_asym8u_per_tensor_out(
int64_t out_zero_point,
::executorch::aten::Tensor& out);

void im2row_out(
::executorch::runtime::KernelRuntimeContext& ctx,
const ::executorch::aten::Tensor& input,
::executorch::aten::IntArrayRef kernel_size,
::executorch::aten::IntArrayRef dilation,
::executorch::aten::IntArrayRef padding,
::executorch::aten::IntArrayRef stride,
const ::executorch::aten::Tensor& in_zero_point,
bool channel_last,
::executorch::aten::Tensor& out);

void im2row_per_tensor_out(
::executorch::runtime::KernelRuntimeContext& ctx,
const ::executorch::aten::Tensor& input,
::executorch::aten::IntArrayRef kernel_size,
::executorch::aten::IntArrayRef dilation,
::executorch::aten::IntArrayRef padding,
::executorch::aten::IntArrayRef stride,
int64_t in_zero_point,
bool channel_last,
::executorch::aten::Tensor& out);

} // namespace native
} // namespace HiFi
} // namespace impl
21 changes: 21 additions & 0 deletions backends/cadence/hifi/operators/tests/BUCK
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Expand Up @@ -199,3 +199,24 @@ jarvis_wrapper.cxx_test(
"fbsource//xplat/executorch/runtime/core/exec_aten/testing_util:tensor_util",
],
)

jarvis_wrapper.cxx_test(
name = "test_op_im2row_out",
srcs = [
"test_op_im2row_out.cpp",
],
compatible_backends = ["hifi"],
labels = [ci.linux(ci.mode("fbsource//arvr/mode/platform010/dev"))],
remote_execution = XTENSA_TEST_REMOTE_EXECUTION_ASIC,
platforms = CXX,
visibility = [
"fbsource//xplat/executorch/backends/cadence/...",
"fbcode//executorch/backends/cadence/...",
],
deps = [
"fbsource//xplat/executorch/kernels/test:gtest_utils",
"fbsource//xplat/executorch/backends/cadence/hifi/operators:op_im2row_out",
"fbsource//xplat/executorch/backends/cadence/runtime:et_pal",
"fbsource//xplat/executorch/runtime/core/exec_aten/testing_util:tensor_util",
],
)
329 changes: 329 additions & 0 deletions backends/cadence/hifi/operators/tests/test_op_im2row_out.cpp
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@@ -0,0 +1,329 @@
/*
* Copyright (c) Meta Platforms, Inc. and affiliates.
* All rights reserved.
*
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/

#include <gtest/gtest.h>
#include <sys/times.h>
#include <xtensa/sim.h>

#include <executorch/kernels/test/TestUtil.h>
#include <executorch/runtime/core/error.h>
#include <executorch/runtime/core/exec_aten/exec_aten.h>
#include <executorch/runtime/core/exec_aten/testing_util/tensor_factory.h>
#include <executorch/runtime/core/exec_aten/testing_util/tensor_util.h>
#include <executorch/runtime/platform/runtime.h>

#include <executorch/backends/cadence/hifi/operators/operators.h>

namespace impl {
namespace HiFi {
namespace native {
namespace {

using ::executorch::aten::ArrayRef;
using ::executorch::aten::IntArrayRef;
using ::executorch::aten::Scalar;
using ::executorch::aten::ScalarType;
using ::executorch::aten::Tensor;
using ::executorch::aten::TensorImpl;
using ::executorch::runtime::Error;
using ::executorch::runtime::KernelRuntimeContext;
using ::executorch::runtime::runtime_init;
using ::executorch::runtime::testing::TensorFactory;

class HiFiIm2rowTest : public OperatorTest {
public:
protected:
void im2row_out(
const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef dilation,
IntArrayRef padding,
IntArrayRef stride,
const Tensor& in_zero_point,
bool channel_last,
Tensor& out) {
::impl::HiFi::native::im2row_out(
context_, input, kernel_size, dilation, padding, stride,
in_zero_point, channel_last, out);
}

void im2row_per_tensor_out(
const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef dilation,
IntArrayRef padding,
IntArrayRef stride,
int64_t in_zero_point,
bool channel_last,
Tensor& out) {
::impl::HiFi::native::im2row_per_tensor_out(
context_, input, kernel_size, dilation, padding, stride,
in_zero_point, channel_last, out);
}

// Helper to count occurrences of a value in output tensor
int countValue(const Tensor& tensor, float value) {
const float* data = tensor.const_data_ptr<float>();
int count = 0;
for (int i = 0; i < tensor.numel(); ++i) {
if (data[i] == value) {
count++;
}
}
return count;
}
};

// Test basic 3x3 kernel with NCHW layout, no padding
// Input is all ones, so output should be all ones
TEST_F(HiFiIm2rowTest, Basic3x3Kernel) {
TensorFactory<ScalarType::Float> tf;
TensorFactory<ScalarType::Int> tf_int;

// Input: (1, 8, 5, 4) - batch=1, channels=8, height=5, width=4
const std::vector<int32_t> input_sizes{1, 8, 5, 4};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {0, 0};
const int64_t stride[] = {1, 1};
const bool channel_last = false;

// out_h = (5 - 3) / 1 + 1 = 3
// out_w = (4 - 3) / 1 + 1 = 2
// output: (1, 3*2, 3*3*8) = (1, 6, 72)
const std::vector<int32_t> output_sizes{1, 6, 72};

Tensor input = tf.ones(input_sizes);
Tensor zero_point = tf_int.zeros({1});
Tensor out = tf.zeros(output_sizes);

im2row_out(input, kernel_size, dilation, padding, stride,
zero_point, channel_last, out);

// Without padding, all output values should be 1.0 (from input)
EXPECT_EQ(countValue(out, 1.0f), out.numel());
EXPECT_EQ(countValue(out, 0.0f), 0);
}

// Test with stride=2, no padding
// Input is all ones, so output should be all ones
TEST_F(HiFiIm2rowTest, WithStride2) {
TensorFactory<ScalarType::Float> tf;
TensorFactory<ScalarType::Int> tf_int;

const std::vector<int32_t> input_sizes{1, 8, 5, 4};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {0, 0};
const int64_t stride[] = {2, 2};
const bool channel_last = false;

// out_h = (5 - 3) / 2 + 1 = 2
// out_w = (4 - 3) / 2 + 1 = 1
// output: (1, 2*1, 3*3*8) = (1, 2, 72)
const std::vector<int32_t> output_sizes{1, 2, 72};

Tensor input = tf.ones(input_sizes);
Tensor zero_point = tf_int.zeros({1});
Tensor out = tf.zeros(output_sizes);

im2row_out(input, kernel_size, dilation, padding, stride,
zero_point, channel_last, out);

// Without padding, all output values should be 1.0 (from input)
EXPECT_EQ(countValue(out, 1.0f), out.numel());
EXPECT_EQ(countValue(out, 0.0f), 0);
}

// Test with padding=1
// With zero_point=0, padded regions produce zeros in output
TEST_F(HiFiIm2rowTest, WithPadding) {
TensorFactory<ScalarType::Float> tf;
TensorFactory<ScalarType::Int> tf_int;

const std::vector<int32_t> input_sizes{1, 8, 5, 4};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {1, 1};
const int64_t stride[] = {1, 1};
const bool channel_last = false;

// out_h = (5 + 2*1 - 3) / 1 + 1 = 5
// out_w = (4 + 2*1 - 3) / 1 + 1 = 4
// output: (1, 5*4, 3*3*8) = (1, 20, 72)
const std::vector<int32_t> output_sizes{1, 20, 72};

Tensor input = tf.ones(input_sizes);
Tensor zero_point = tf_int.zeros({1});
Tensor out = tf.zeros(output_sizes);

im2row_out(input, kernel_size, dilation, padding, stride,
zero_point, channel_last, out);

int one_count = countValue(out, 1.0f);
int zero_count = countValue(out, 0.0f);

// With padding and zero_point=0: expect both ones (from input) and zeros (from padding)
EXPECT_GT(one_count, 0) << "Should have ones from input data";
EXPECT_GT(zero_count, 0) << "Should have zeros from padded regions";
EXPECT_EQ(one_count + zero_count, out.numel()) << "All values should be 0 or 1";
}

// Test channels last (NHWC) layout, no padding
// Input is all ones, so output should be all ones
TEST_F(HiFiIm2rowTest, ChannelsLast) {
TensorFactory<ScalarType::Float> tf;
TensorFactory<ScalarType::Int> tf_int;

// Input for NHWC: (1, 5, 8, 8) - batch=1, height=5, width=8, channels=8
const std::vector<int32_t> input_sizes{1, 5, 8, 8};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {0, 0};
const int64_t stride[] = {1, 1};
const bool channel_last = true;

// out_h = (5 - 3) / 1 + 1 = 3
// out_w = (8 - 3) / 1 + 1 = 6
// output: (1, 3*6, 3*3*8) = (1, 18, 72)
const std::vector<int32_t> output_sizes{1, 18, 72};

Tensor input = tf.ones(input_sizes);
Tensor zero_point = tf_int.zeros({1});
Tensor out = tf.zeros(output_sizes);

im2row_out(input, kernel_size, dilation, padding, stride,
zero_point, channel_last, out);

// Without padding, all output values should be 1.0 (from input)
EXPECT_EQ(countValue(out, 1.0f), out.numel());
EXPECT_EQ(countValue(out, 0.0f), 0);
}

// Test with dilation=2 and padding=2
// With zero_point=0, dilated regions outside input produce zeros
TEST_F(HiFiIm2rowTest, WithDilation) {
TensorFactory<ScalarType::Float> tf;
TensorFactory<ScalarType::Int> tf_int;

const std::vector<int32_t> input_sizes{1, 8, 6, 5};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {2, 2};
const int64_t padding[] = {2, 2};
const int64_t stride[] = {1, 1};
const bool channel_last = false;

// effective_kernel_h = 2*(3-1) + 1 = 5
// effective_kernel_w = 2*(3-1) + 1 = 5
// out_h = (6 + 2*2 - 5) / 1 + 1 = 6
// out_w = (5 + 2*2 - 5) / 1 + 1 = 5
// output: (1, 6*5, 3*3*8) = (1, 30, 72)
const std::vector<int32_t> output_sizes{1, 30, 72};

Tensor input = tf.ones(input_sizes);
Tensor zero_point = tf_int.zeros({1});
Tensor out = tf.zeros(output_sizes);

im2row_out(input, kernel_size, dilation, padding, stride,
zero_point, channel_last, out);

int one_count = countValue(out, 1.0f);
int zero_count = countValue(out, 0.0f);

// With dilation/padding and zero_point=0: expect both ones and zeros
EXPECT_GT(one_count, 0) << "Should have ones from input data";
EXPECT_GT(zero_count, 0) << "Should have zeros from padded/dilated regions";
EXPECT_EQ(one_count + zero_count, out.numel()) << "All values should be 0 or 1";
}

// Test im2row_per_tensor_out with zero_point=0, no padding
// Input is all ones, so output should be all ones
TEST_F(HiFiIm2rowTest, PerTensorZeroPointZero) {
TensorFactory<ScalarType::Float> tf;

const std::vector<int32_t> input_sizes{1, 8, 5, 4};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {0, 0};
const int64_t stride[] = {1, 1};
const int64_t in_zero_point = 0;
const bool channel_last = false;

// output: (1, 6, 72)
const std::vector<int32_t> output_sizes{1, 6, 72};

Tensor input = tf.ones(input_sizes);
Tensor out = tf.zeros(output_sizes);

im2row_per_tensor_out(input, kernel_size, dilation, padding, stride,
in_zero_point, channel_last, out);

// Without padding, all output values should be 1.0 (from input)
EXPECT_EQ(countValue(out, 1.0f), out.numel());
EXPECT_EQ(countValue(out, 0.0f), 0);
}

// Test im2row_per_tensor_out with non-zero zero_point=128, no padding
// Input is all ones, so output should be all ones
TEST_F(HiFiIm2rowTest, PerTensorNonZeroZeroPoint) {
TensorFactory<ScalarType::Float> tf;

const std::vector<int32_t> input_sizes{1, 8, 5, 4};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {0, 0};
const int64_t stride[] = {1, 1};
const int64_t in_zero_point = 128;
const bool channel_last = false;

// output: (1, 6, 72)
const std::vector<int32_t> output_sizes{1, 6, 72};

Tensor input = tf.ones(input_sizes);
Tensor out = tf.zeros(output_sizes);

im2row_per_tensor_out(input, kernel_size, dilation, padding, stride,
in_zero_point, channel_last, out);

// Without padding, all output values should be 1.0 (from input)
// zero_point only affects padded regions, which don't exist here
EXPECT_EQ(countValue(out, 1.0f), out.numel());
EXPECT_EQ(countValue(out, 0.0f), 0);
}

// Test im2row_per_tensor_out with channels last layout and non-zero zero_point
// Input is all ones, so output should be all ones
TEST_F(HiFiIm2rowTest, PerTensorChannelsLastNonZeroZeroPoint) {
TensorFactory<ScalarType::Float> tf;

const std::vector<int32_t> input_sizes{1, 5, 8, 8};
const int64_t kernel_size[] = {3, 3};
const int64_t dilation[] = {1, 1};
const int64_t padding[] = {0, 0};
const int64_t stride[] = {1, 1};
const int64_t in_zero_point = 64;
const bool channel_last = true;

// output: (1, 18, 72)
const std::vector<int32_t> output_sizes{1, 18, 72};

Tensor input = tf.ones(input_sizes);
Tensor out = tf.zeros(output_sizes);

im2row_per_tensor_out(input, kernel_size, dilation, padding, stride,
in_zero_point, channel_last, out);

// Without padding, all output values should be 1.0 (from input)
EXPECT_EQ(countValue(out, 1.0f), out.numel());
EXPECT_EQ(countValue(out, 0.0f), 0);
}

} // namespace
} // namespace native
} // namespace HiFi
} // namespace impl
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