[ET-VK][Ops] dequantize_per_tensor.default test setup

backends/vulkan/test/op_tests/dequantize_test.cpp

@@ -21,6 +21,7 @@
 
 #include <cassert>
 #include <iostream>
+#include <limits>
 
 namespace torch {
 namespace executor {
@@ -180,3 +181,387 @@ void check_dequantize_args(
           ")");
   }
 }
+
+//
+// Reference Implementation
+//
+
+/*
+ * Reference implementation of dequantize_per_tensor
+ */
+at::Tensor dequantize_per_tensor_reference_impl(
+    const at::Tensor& input,
+    double scale,
+    int64_t zero_point,
+    int64_t quant_min,
+    int64_t quant_max,
+    at::ScalarType dtype,
+    at::ScalarType out_dtype) {
+  // Create output tensor with the target dtype
+  at::Tensor out = at::empty_like(input, out_dtype);
+
+  // Dequantize the input tensor
+  at::Tensor flat_input = input.flatten();
+  at::Tensor flat_out = out.flatten();
+
+  // Store casted values to avoid repeated casting
+  const int32_t zero_point_int32 = static_cast<int32_t>(zero_point);
+  const float scale_float = static_cast<float>(scale);
+
+  for (int i = 0; i < flat_input.numel(); i++) {
+    double dequantized_value = 0.0;
+
+    // Extract quantized value and dequantize based on input dtype
+    // Following the CPU implementation pattern: (input - zero_point) * scale
+    if (dtype == at::kByte) {
+      uint8_t qvalue = flat_input[i].item<uint8_t>();
+      dequantized_value = (qvalue - zero_point_int32) * scale_float;
+    } else if (dtype == at::kChar) {
+      int8_t qvalue = flat_input[i].item<int8_t>();
+      dequantized_value = (qvalue - zero_point_int32) * scale_float;
+    } else if (dtype == at::kShort) {
+      int16_t qvalue = flat_input[i].item<int16_t>();
+      dequantized_value = (qvalue - zero_point_int32) * scale_float;
+    } else if (dtype == at::kInt) {
+      int32_t qvalue = flat_input[i].item<int32_t>();
+      dequantized_value = (qvalue - zero_point_int32) * scale_float;
+    } else if (dtype == at::kLong) {
+      int64_t qvalue = flat_input[i].item<int64_t>();
+      dequantized_value = (qvalue - zero_point_int32) * scale_float;
+    }
+
+    // Store result based on output dtype
+    if (out_dtype == at::kFloat) {
+      flat_out[i] = static_cast<float>(dequantized_value);
+    } else if (out_dtype == at::kDouble) {
+      flat_out[i] = dequantized_value;
+    } else if (out_dtype == at::kHalf) {
+      flat_out[i] = static_cast<c10::Half>(dequantized_value);
+    }
+  }
+
+  return out.reshape(input.sizes());
+}
+
+// Forward declaration of implementation functions
+void test_vulkan_dequantize_per_tensor_impl(
+    const std::vector<int>& input_sizes,
+    float scale,
+    int zero_point,
+    int64_t quant_min,
+    int64_t quant_max,
+    at::ScalarType dtype,
+    at::ScalarType out_dtype,
+    const vkcompute::utils::StorageType in_storage,
+    const vkcompute::utils::StorageType out_storage);
+
+// Wrapper function to test both buffer and texture storage types
+void test_vulkan_dequantize_per_tensor(
+    const std::vector<int>& input_sizes,
+    float scale,
+    int zero_point,
+    int64_t quant_min,
+    int64_t quant_max,
+    at::ScalarType dtype,
+    at::ScalarType out_dtype) {
+  // Test with buffer storage
+  test_vulkan_dequantize_per_tensor_impl(
+      input_sizes,
+      scale,
+      zero_point,
+      quant_min,
+      quant_max,
+      dtype,
+      out_dtype,
+      vkcompute::utils::kBuffer,
+      vkcompute::utils::kBuffer);
+
+  // Test with texture storage
+  test_vulkan_dequantize_per_tensor_impl(
+      input_sizes,
+      scale,
+      zero_point,
+      quant_min,
+      quant_max,
+      dtype,
+      out_dtype,
+      vkcompute::utils::kTexture3D,
+      vkcompute::utils::kTexture3D);
+}
+
+void test_reference_dequantize_per_tensor(
+    const std::vector<int>& input_sizes,
+    float scale,
+    int zero_point,
+    int64_t quant_min,
+    int64_t quant_max,
+    at::ScalarType dtype,
+    at::ScalarType out_dtype) {
+  check_dequantize_args(quant_min, quant_max, dtype, out_dtype);
+  std::vector<int64_t> input_sizes_int64(
+      input_sizes.begin(), input_sizes.end());
+
+  // Create a quantized input tensor with values from quant_min to quant_max
+  at::Tensor input;
+  if (dtype == at::kByte) {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kByte));
+  } else if (dtype == at::kChar) {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kChar));
+  } else if (dtype == at::kShort) {
+    input =
+        at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kShort));
+  } else if (dtype == at::kInt) {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kInt));
+  } else {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kLong));
+  }
+
+  // Fill with a simple pattern: values from quant_min to quant_max in steps
+  float step = 1.0f;
+  if (input.numel() > 1) {
+    step = static_cast<float>(quant_max - quant_min) / (input.numel() - 1);
+  }
+
+  auto flat_input = input.flatten();
+  for (int i = 0; i < flat_input.numel(); i++) {
+    int64_t qvalue = quant_min + i * step;
+    if (dtype == at::kByte) {
+      flat_input[i] = static_cast<uint8_t>(qvalue);
+    } else if (dtype == at::kChar) {
+      flat_input[i] = static_cast<int8_t>(qvalue);
+    } else if (dtype == at::kShort) {
+      flat_input[i] = static_cast<int16_t>(qvalue);
+    } else if (dtype == at::kInt) {
+      flat_input[i] = static_cast<int32_t>(qvalue);
+    } else if (dtype == at::kLong) {
+      flat_input[i] = static_cast<int64_t>(qvalue);
+    }
+  }
+
+  // Reshape back to original dimensions
+  input = flat_input.reshape(input_sizes_int64);
+
+  // Get reference output
+  at::Tensor reference_out = dequantize_per_tensor_reference_impl(
+      input, scale, zero_point, quant_min, quant_max, dtype, out_dtype);
+
+  // Get implementation output
+  at::Tensor impl_out = torch::executor::native::dequantize_per_tensor_aten(
+      input, scale, zero_point, quant_min, quant_max, dtype, out_dtype);
+
+  // Compare outputs
+  const bool output_correct = at::allclose(reference_out, impl_out);
+  if (!output_correct) {
+    std::cout << "\n"
+              << "Failed with parameters: " << std::endl;
+    std::cout << "  scale: " << scale << std::endl;
+    std::cout << "  zero_point: " << zero_point << std::endl;
+    std::cout << "  quant_min: " << quant_min << std::endl;
+    std::cout << "  quant_max: " << quant_max << std::endl;
+
+    std::cout << "input:" << std::endl;
+    std::cout << input << std::endl;
+    std::cout << "reference:" << std::endl;
+    std::cout << reference_out << std::endl;
+    std::cout << "implementation:" << std::endl;
+    std::cout << impl_out << std::endl;
+  }
+
+  ASSERT_TRUE(output_correct);
+}
+
+void test_vulkan_dequantize_per_tensor_impl(
+    const std::vector<int>& input_sizes,
+    float scale,
+    int zero_point,
+    int64_t quant_min,
+    int64_t quant_max,
+    at::ScalarType dtype,
+    at::ScalarType out_dtype,
+    const vkcompute::utils::StorageType in_storage,
+    const vkcompute::utils::StorageType out_storage) {
+  check_dequantize_args(quant_min, quant_max, dtype, out_dtype);
+  std::vector<int64_t> input_sizes_int64(
+      input_sizes.begin(), input_sizes.end());
+
+  // Create a quantized input tensor with values from quant_min to quant_max
+  at::Tensor input;
+  if (dtype == at::kByte) {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kByte));
+  } else if (dtype == at::kChar) {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kChar));
+  } else if (dtype == at::kShort) {
+    input =
+        at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kShort));
+  } else if (dtype == at::kInt) {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kInt));
+  } else {
+    input = at::zeros(input_sizes_int64, at::device(at::kCPU).dtype(at::kLong));
+  }
+
+  // Fill with a simple pattern: values from quant_min to quant_max in steps
+  float step = 1.0f;
+  if (input.numel() > 1) {
+    step = static_cast<float>(quant_max - quant_min) / (input.numel() - 1);
+  }
+
+  auto flat_input = input.flatten();
+  for (int i = 0; i < flat_input.numel(); i++) {
+    int64_t qvalue = quant_min + i * step;
+    if (dtype == at::kByte) {
+      flat_input[i] = static_cast<uint8_t>(qvalue);
+    } else if (dtype == at::kChar) {
+      flat_input[i] = static_cast<int8_t>(qvalue);
+    } else if (dtype == at::kShort) {
+      flat_input[i] = static_cast<int16_t>(qvalue);
+    } else if (dtype == at::kInt) {
+      flat_input[i] = static_cast<int32_t>(qvalue);
+    } else if (dtype == at::kLong) {
+      flat_input[i] = static_cast<int64_t>(qvalue);
+    }
+  }
+
+  // Reshape back to original dimensions
+  input = flat_input.reshape(input_sizes_int64);
+
+  // Get reference output
+  at::Tensor reference_out =
+      torch::executor::native::dequantize_per_tensor_aten(
+          input, scale, zero_point, quant_min, quant_max, dtype, out_dtype);
+
+  // Build Vulkan dequantize_per_tensor graph
+  using namespace vkcompute;
+
+  GraphConfig config;
+  config.set_storage_type_override(in_storage);
+  ComputeGraph graph(config);
+
+  IOValueRef r_input = graph.add_input_tensor(
+      input.sizes().vec(), from_at_scalartype(dtype), in_storage);
+
+  const ValueRef r_scale = graph.add_scalar<double>(scale);
+  const ValueRef r_zero_point = graph.add_scalar<int64_t>(zero_point);
+  const ValueRef r_quant_min = graph.add_scalar<int64_t>(quant_min);
+  const ValueRef r_quant_max = graph.add_scalar<int64_t>(quant_max);
+
+  const ValueRef r_out = graph.add_tensor(
+      input.sizes().vec(), from_at_scalartype(out_dtype), out_storage);
+
+  VK_GET_OP_FN("dequantize_per_tensor.default")
+  (graph,
+   {
+       r_input.value,
+       r_scale,
+       r_zero_point,
+       r_quant_min,
+       r_quant_max,
+       r_out,
+   });
+
+  ValueRef staging_out = graph.set_output_tensor(r_out);
+
+  graph.prepare();
+  graph.encode_prepack();
+  graph.prepack();
+  graph.encode_execute();
+
+  // Run Vulkan dequantize_per_tensor
+  graph.copy_into_staging(
+      r_input.staging, input.const_data_ptr(), input.numel());
+
+  graph.execute();
+
+  at::Tensor vk_out = at::empty_like(reference_out).contiguous();
+  graph.copy_from_staging(
+      staging_out, vk_out.mutable_data_ptr(), vk_out.numel());
+
+  // Compare outputs
+  const bool output_correct = at::allclose(reference_out, vk_out);
+  if (!output_correct) {
+    std::cout << "\n"
+              << "Failed with parameters: " << std::endl;
+    std::cout << "  scale: " << scale << std::endl;
+    std::cout << "  zero_point: " << zero_point << std::endl;
+    std::cout << "  quant_min: " << quant_min << std::endl;
+    std::cout << "  quant_max: " << quant_max << std::endl;
+    std::cout << "  storage type: "
+              << (in_storage == vkcompute::utils::kBuffer ? "buffer"
+                                                          : "texture")
+              << std::endl;
+
+    std::cout << "input:" << std::endl;
+    std::cout << input << std::endl;
+    std::cout << "reference:" << std::endl;
+    std::cout << reference_out << std::endl;
+    std::cout << "vulkan:" << std::endl;
+    std::cout << vk_out << std::endl;
+  }
+
+  ASSERT_TRUE(output_correct);
+}
+
+// Test cases for dequantize_per_tensor
+TEST(
+    VulkanDequantizePerTensorTest,
+    test_reference_dequantize_per_tensor_uint8_to_float) {
+  test_reference_dequantize_per_tensor(
+      {2, 3, 4}, // input sizes
+      0.1, // scale
+      5, // zero_point
+      0, // quant_min
+      255, // quant_max
+      at::kByte, // input dtype
+      at::kFloat); // output dtype
+}
+
+TEST(
+    VulkanDequantizePerTensorTest,
+    test_reference_dequantize_per_tensor_int8_to_float) {
+  test_reference_dequantize_per_tensor(
+      {3, 4, 5}, // input sizes
+      0.05, // scale
+      0, // zero_point
+      -128, // quant_min
+      127, // quant_max
+      at::kChar, // input dtype
+      at::kFloat); // output dtype
+}
+
+TEST(
+    VulkanDequantizePerTensorTest,
+    test_reference_dequantize_per_tensor_int32_to_float) {
+  test_reference_dequantize_per_tensor(
+      {4, 6, 2}, // input sizes
+      0.2, // scale
+      2, // zero_point
+      std::numeric_limits<int32_t>::min(), // quant_min
+      std::numeric_limits<int32_t>::max(), // quant_max
+      at::kInt, // input dtype
+      at::kFloat); // output dtype
+}
+
+TEST(
+    VulkanDequantizePerTensorTest,
+    test_reference_dequantize_per_tensor_uint8_to_half) {
+  test_reference_dequantize_per_tensor(
+      {7, 4}, // input sizes
+      0.1, // scale
+      10, // zero_point
+      0, // quant_min
+      255, // quant_max
+      at::kByte, // input dtype (uint8)
+      at::kHalf); // output dtype
+}
+
+TEST(
+    VulkanDequantizePerTensorTest,
+    test_reference_dequantize_per_tensor_int32_to_half) {
+  test_reference_dequantize_per_tensor(
+      {2, 6, 5}, // input sizes
+      0.3, // scale
+      -10, // zero_point
+      std::numeric_limits<int32_t>::min(), // quant_min
+      std::numeric_limits<int32_t>::max(), // quant_max
+      at::kInt, // input dtype
+      at::kHalf); // output dtype
+}