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sglang_v0.5.2/pytorch_2.8.0/third_party/XNNPACK/test/argmax-pooling-2d.cc

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// Copyright 2022 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <algorithm>
#include <array>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <memory>
#include <random>
#include <vector>
#include <gtest/gtest.h>
#include "xnnpack.h"
#include "xnnpack/common.h"
#include "xnnpack/node-type.h"
#include "xnnpack/operator.h"
#include "xnnpack/subgraph.h"
#include "xnnpack/buffer.h"
#include "replicable_random_device.h"
namespace {
inline size_t compute_output_dimension(size_t padded_input_dimension, size_t kernel_dimension)
{
return padded_input_dimension / kernel_dimension;
}
} // namespace
class ArgmaxPoolingTestF32 : public ::testing::Test {
protected:
ArgmaxPoolingTestF32() {
input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
pooling_size_dist = std::uniform_int_distribution<uint32_t>(2, 5);
batch_size = input_size_dist(rng);
input_height = input_size_dist(rng);
input_width = input_size_dist(rng);
channels = input_size_dist(rng);
pooling_height = pooling_size_dist(rng);
pooling_width = pooling_size_dist(rng);
input_padding_top = input_size_dist(rng);
input_padding_right = input_size_dist(rng);
input_padding_bottom = input_size_dist(rng);
input_padding_left = input_size_dist(rng);
output_height = compute_output_dimension(input_height + input_padding_top + input_padding_bottom, pooling_height);
output_width = compute_output_dimension(input_width + input_padding_left + input_padding_right, pooling_width);
input_dims = {batch_size, input_height, input_width, channels};
output_dims = {batch_size, output_height, output_width, channels};
input = xnnpack::Buffer<float>(XNN_EXTRA_BYTES / sizeof(float) + batch_size * input_height * input_width * channels);
operator_output = xnnpack::Buffer<float>(batch_size * output_height * output_width * channels);
operator_output_index = xnnpack::Buffer<uint32_t>(batch_size * output_height * output_width * channels);
subgraph_output = xnnpack::Buffer<float>(batch_size * output_height * output_width * channels);
subgraph_output_index = xnnpack::Buffer<uint32_t>(batch_size * output_height * output_width * channels);
}
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<uint32_t> input_size_dist;
std::uniform_int_distribution<uint32_t> pooling_size_dist;
uint32_t batch_size;
uint32_t input_height;
uint32_t input_width;
uint32_t channels;
uint32_t pooling_height;
uint32_t pooling_width;
uint32_t output_height;
uint32_t output_width;
std::array<size_t, 4> input_dims;
std::array<size_t, 4> output_dims;
uint32_t input_padding_top;
uint32_t input_padding_right;
uint32_t input_padding_bottom;
uint32_t input_padding_left;
uint32_t input_id;
uint32_t output_value_id;
uint32_t output_index_id;
xnnpack::Buffer<float> input;
xnnpack::Buffer<float> operator_output;
xnnpack::Buffer<uint32_t> operator_output_index;
xnnpack::Buffer<float> subgraph_output;
xnnpack::Buffer<uint32_t> subgraph_output_index;
};
TEST_F(ArgmaxPoolingTestF32, define)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/3, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, 0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
output_value_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 1,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_value_id));
ASSERT_NE(output_value_id, XNN_INVALID_NODE_ID);
output_index_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 2,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_index_id));
ASSERT_NE(output_index_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success, xnn_define_argmax_pooling_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left,
pooling_height, pooling_width, input_id, output_value_id, output_index_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
const struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_argmax_pooling_2d);
ASSERT_EQ(node->params.pooling_2d.padding_top, input_padding_top);
ASSERT_EQ(node->params.pooling_2d.padding_right, input_padding_right);
ASSERT_EQ(node->params.pooling_2d.padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.pooling_2d.padding_left, input_padding_left);
ASSERT_EQ(node->params.pooling_2d.pooling_height, pooling_height);
ASSERT_EQ(node->params.pooling_2d.pooling_width, pooling_width);
ASSERT_EQ(node->num_inputs, 1);
ASSERT_EQ(node->inputs[0], input_id);
ASSERT_EQ(node->num_outputs, 2);
ASSERT_EQ(node->outputs[0], output_value_id);
ASSERT_EQ(node->outputs[1], output_index_id);
ASSERT_EQ(node->flags, 0);
}
TEST_F(ArgmaxPoolingTestF32, matches_operator_api)
{
std::uniform_real_distribution<float> f32dist(-255.0f, 255.0f);
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
// Call operator API.
xnn_operator_t op = nullptr;
const xnn_status status = xnn_create_argmax_pooling2d_nhwc_f32(
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width,
/*flags=*/0, &op);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
size_t workspace_size = 0;
size_t workspace_alignment = 0;
ASSERT_EQ(
xnn_status_success,
xnn_reshape_argmax_pooling2d_nhwc_f32(
op, batch_size, input_height, input_width,
/*channels=*/channels,
/*input_pixel_stride=*/channels,
/*output_pixel_stride=*/channels,
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/nullptr));
xnnpack::Buffer<char, XNN_ALLOCATION_ALIGNMENT> workspace(workspace_size);
ASSERT_EQ(
xnn_status_success, xnn_setup_argmax_pooling2d_nhwc_f32(
op, workspace.data(), input.data(), operator_output.data(), operator_output_index.data()));
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
// Call subgraph API.
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/3, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
output_value_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, /*external_id=*/1,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_value_id));
ASSERT_NE(output_value_id, XNN_INVALID_NODE_ID);
output_index_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, /*external_id=*/2,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_index_id));
ASSERT_NE(output_index_id, XNN_INVALID_NODE_ID);
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(
xnn_status_success, xnn_define_argmax_pooling_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left,
pooling_height, pooling_width, input_id, output_value_id, output_index_id,
/*flags=*/0));
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
ASSERT_NE(nullptr, runtime);
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
std::array<xnn_external_value, 3> external = {
xnn_external_value{input_id, input.data()}, xnn_external_value{output_value_id, subgraph_output.data()},
xnn_external_value{output_index_id, subgraph_output_index.data()}};
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
ASSERT_EQ(subgraph_output, operator_output);
}
TEST_F(ArgmaxPoolingTestF32, reshape_output)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/3, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
std::vector<size_t> dims{2, 3, 4, 5};
std::vector<size_t> output_dims{2, 3, 5, 5};
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, dims.size(), dims.data(), nullptr, 0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
output_value_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 1,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_value_id));
ASSERT_NE(output_value_id, XNN_INVALID_NODE_ID);
output_index_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 2,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_index_id));
ASSERT_NE(output_index_id, XNN_INVALID_NODE_ID);
const size_t pooling_height = 2;
const size_t pooling_width = 2;
ASSERT_EQ(xnn_status_success, xnn_define_argmax_pooling_2d(
subgraph, /*input_padding_top=*/3, /*input_padding_right=*/2, /*input_padding_bottom=*/1, /*input_padding_left=*/4,
pooling_height, pooling_width, input_id, output_value_id, output_index_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_argmax_pooling_2d);
ASSERT_EQ(node->num_inputs, 1);
ASSERT_EQ(node->inputs[0], input_id);
ASSERT_EQ(node->num_outputs, 2);
ASSERT_EQ(node->outputs[0], output_value_id);
ASSERT_EQ(node->outputs[1], output_index_id);
ASSERT_EQ(node->flags, 0);
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
ASSERT_NE(nullptr, runtime);
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
ASSERT_EQ(node->reshape(&runtime->opdata[0], subgraph->values, subgraph->num_values, /*threadpool=*/nullptr), xnn_status_success);
dims[0] = 2;
dims[1] = 2;
dims[2] = 8;
dims[3] = 17;
ASSERT_EQ(xnn_status_success, xnn_reshape_external_value(runtime, 0, dims.size(), dims.data()));
ASSERT_EQ(node->reshape(&runtime->opdata[0], runtime->values, runtime->num_values, /*threadpool=*/nullptr), xnn_status_reallocation_required);
const xnn_shape* output_shape = &runtime->values[node->outputs[0]].shape;
ASSERT_EQ(output_shape->dim[0], dims[0]);
ASSERT_EQ(output_shape->dim[1], 3);
ASSERT_EQ(output_shape->dim[2], 7);
ASSERT_EQ(output_shape->dim[3], dims[3]);
}
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