feat(//core/quantization): skeleton of INT8 PTQ calibrator

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

.gitignore

@@ -13,4 +13,6 @@ experiments/
 py/build/
 py/tmp/
 py/.eggs
-.vscode/
+.vscode/
+.DS_Store
+._DS_Store

core/conversion/conversionctx/ConversionCtx.cpp

@@ -19,7 +19,8 @@ std::ostream& operator<<(std::ostream& os, const BuilderSettings& s) {
        << "\n    Avg Timing Iterations: " << s.num_avg_timing_iters                \
        << "\n    Max Workspace Size: " << s.workspace_size                         \
        << "\n    Device Type: " << s.device                                        \
-       << "\n    Engine Capability: " << s.capability;
+       << "\n    Engine Capability: " << s.capability                              \
+       << "\n    Calibrator Created: " << s.calibrator ? true : false;
     return os;
 }
 
@@ -36,13 +37,17 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
 
     switch(settings.op_precision) {
     case nvinfer1::DataType::kHALF:
+        TRTORCH_CHECK(builder->platformHasFastFp16(), "Requested inference in FP16 but platform does support FP16");
         cfg->setFlag(nvinfer1::BuilderFlag::kFP16);
         input_type = nvinfer1::DataType::kHALF;
         break;
-    // case nvinfer1::DataType::kINT8:
-    //     cfg->setFlag(nvinfer1::BuilderFlag::kINT8);
-    //         input_type = nvinfer1::DataType::kFLOAT;
-    //     break;
+    case nvinfer1::DataType::kINT8:
+        TRTORCH_CHECK(builder->platformHasFastInt8(), "Requested inference in INT8 but platform does support INT8");
+        cfg->setFlag(nvinfer1::BuilderFlag::kINT8);
+        input_type = nvinfer1::DataType::kINT8;
+        // If the calibrator is nullptr then TRT will use default quantization
+        cfg->setInt8Calibrator(settings.calibrator);
+        break;
     case nvinfer1::DataType::kFLOAT:
     default:
         input_type = nvinfer1::DataType::kFLOAT;

core/conversion/conversionctx/ConversionCtx.h

@@ -24,6 +24,7 @@ struct BuilderSettings {
     bool allow_gpu_fallback = true;
     nvinfer1::DeviceType device = nvinfer1::DeviceType::kGPU;
     nvinfer1::EngineCapability capability = nvinfer1::EngineCapability::kDEFAULT;
+    nvinfer1::IInt8Calibrator* calibrator = nullptr;
     uint64_t num_min_timing_iters = 2;
     uint64_t num_avg_timing_iters = 1;
     uint64_t workspace_size = 0;

core/quantization/TRTEntropyCalibrator.cpp

@@ -0,0 +1,64 @@
+#include "core/util/prelude.h"
+#include "core/quantization/quantization.h"
+
+namespace trtorch {
+namespace core {
+namespace quantization {
+
+Int8CalibratorImpl::Int8CalibratorImpl(QuantizationSettings&& settings)
+  : dataset_(std::move(settings.calibration_dataset),
+    cache_file_path_(settings.calibration_cache_file),
+    use_cache_(settings.use_cache) {
+  buffers_.reserve(dataset_.size);
+
+}
+
+int Int8CalibratorImpl::GetBatchSize() const {
+
+}
+
+bool Int8CalibratorImpl::GetBatch(void* bindings[], const char* names[], int num_bindings) {
+    if (!is_next_batch) {
+        return false;
+    }
+
+    for (size_t i = 0; i < num_bindings; i++) {
+        auto batch = next_binding_batch(names[i]);
+        batch = batch.to(at::kCUDA).contiguous();
+        bindings[i] = batch.data_ptr();
+    }
+    return true;
+}
+
+const void* Int8CalibratorImpl::ReadCalibrationCache(size_t& length) {
+    cache_.clear();
+    std::ifstream cache_file(cache_file_path_, std::ios::binary);
+    cache_file >> std::noskipws;
+    if (use_cache && cache_file.good()) {
+        std::copy(std::istream_iterator<char>(input),
+                  std::istream_iterator<char>(),
+                  std::back_inserter(cache_));
+    }
+    cache_size_ = cache_.size();
+    return cache_size ? cache_.data() : nullptr;
+}
+
+void Int8CalibratorImpl::WriteCalibrationCache(const void* cache, size_t length) {
+    std::ofstream cache_file(cache_file_path_, std::ios::binary);
+    cache_file.write(reinterpret_cast<const char*>(cache_), cache_size_);
+}
+
+nvinfer1::IInt8Calibrator create_int8_calibrator(QuantizationSettings settings) {
+  auto calibrator_impl = Int8CalibratorImpl(settings);
+  switch(settings.calibrator_type) {
+  case CalibratorKind::kMinMax:
+    return TRTInt8MinMaxCalibrator(std::move(calibrator_impl));
+  case CalibratorKind::kEntropy:
+  default:
+    return TRTInt8EntropyCalibrator(std::move(calibrator_impl));
+  }
+}
+
+} // quantization
+} // core
+} // trtorch

core/quantization/quantization.h

@@ -0,0 +1,69 @@
+#pragma once
+#include "ATen/tensor.h"
+#include "NvInfer.h"
+
+namespace trtorch {
+namespace core {
+namespace quantization {
+
+enum class CalibratorKind {
+  kEntropy,
+  kMinMax,
+}
+
+in conveter or whatever
+in order given std::vector<at::Tensor> -> map<input_name, at::Tensor>
+
+struct QuantizationSettings {
+  CalibratorKind calibrator_type = CalibratorKind::kEntropy;
+  const std::string& calibration_cache_file = "";
+  bool use_cache = false;
+  std::unordered_map<std::string, at::Tensor> calibration_dataset;
+};
+
+class CalibrationBatchStream {
+
+};
+
+class Int8CalibratorImpl {
+public:
+  TRTInt8CalibratorImpl(QuantizationSettings& settings);
+  int GetBatchSize() const;
+  bool GetBatch(void* bindings[], const char* names[], int num_bindings);
+  const void* ReadCalibrationCache(size_t& length);
+  void WriteCalibrationCache(const void* cache, size_t length);
+private:
+  std::unordered_map<std::string, at::Tensor> dataset_;
+  const std::string& cache_file_path_;
+  std::vector<char> cache_;
+  bool use_cache_;
+  size_t cache_size_ = 0;
+};
+
+class TRTInt8EntropyCalibrator : nvinfer1::IInt8EntropyCalibrator2 {
+public:
+  TRTInt8EntropyCalibrator(Int8CalibratorImpl impl) : impl_(impl) {}
+  int getBatchSize() const override {return impl_.GetBatchSize();}
+  bool getBatch(void* bindings[], const char* names[], int nbBindings) override {return impl_.GetBatch(bindings, names, nbBindings)};
+  const void* readCalibrationCache(size_t& length) override {return impl_.ReadCalibrationCache(size_t& length)};
+  void writeCalibrationCache(const void* cache, size_t length) override {impl_.WriteCalibrationCache(const void* cache, size_t length)};
+private:
+  Int8CalibratorImpl impl_;
+};
+
+class TRTInt8MinMaxCalibrator : nvinfer1::IInt8MinMaxCalibrator {
+public:
+  TRTInt8EntropyCalibrator(Int8CalibratorImpl impl) : impl_(impl) {}
+  int getBatchSize() const override {return impl_.GetBatchSize();}
+  bool getBatch(void* bindings[], const char* names[], int nbBindings) override {return impl_.GetBatch(bindings, names, nbBindings)};
+  const void* readCalibrationCache(size_t& length) override {return impl_.ReadCalibrationCache(size_t& length)};
+  void writeCalibrationCache(const void* cache, size_t length) override {impl_.WriteCalibrationCache(const void* cache, size_t length)};
+private:
+  Int8CalibratorImpl impl_;
+};
+
+nvinfer1::IInt8Calibrator create_int8_calibrator(QuantizationSettings settings);
+
+} // quantization
+} // core
+} // trtorch

cpp/ptq/BUILD

@@ -0,0 +1,12 @@
+package(default_visibility = ["//visibility:public"])
+
+cc_binary(
+    name = "ptq",
+    srcs = [
+        "main.cpp"
+    ],
+    deps = [
+        "@libtorch//:libtorch",
+        "//cpp/api:trtorch"
+    ],
+)

cpp/ptq/README.md

@@ -0,0 +1,21 @@
+# ptq
+
+This is a short example application that shows how to use TRTorch to perform post-training quantization for a module.
+
+## Compilation
+
+``` shell
+bazel build //cpp/ptq --cxxopt="-DNDEBUG"
+```
+
+If you want insight into what is going under the hood or need debug symbols
+
+``` shell
+bazel build //cpp/ptq --compilation_mode=dbg
+```
+
+## Usage
+
+``` shell
+ptq
+```

cpp/ptq/main.cpp

@@ -0,0 +1,36 @@
+#include "torch/script.h"
+#include "torch/csrc/api/include/torch/data/datasets/mnist.h"
+#include "trtorch/trtorch.h"
+
+#include <iostream>
+#include <sstream>
+#include <memory>
+
+int main(int argc, const char* argv[]) {
+    if (argc < 3) {
+        std::cerr << "usage: ptq <path-to-module> <path-to-mnist>\n";
+        return -1;
+    }
+
+    torch::jit::script::Module mod;
+    try {
+        // Deserialize the ScriptModule from a file using torch::jit::load().
+        mod = torch::jit::load(argv[1]);
+    }
+    catch (const c10::Error& e) {
+        std::cerr << "error loading the model\n";
+        return -1;
+    }
+
+    const std::string data_dir = std::string(argv[2]);
+    auto calibration_dataset = torch::data::datasets::MNIST(data_dir, torch::data::datasets::MNIST::Mode::kTest)
+                                    .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
+                                    .map(torch::data::transforms::Stack<>());
+    auto calibration_dataloader = torch::data::make_data_loader(std::move(calibration_dataset), torch::data::DataLoaderOptions()
+                                                                                                    .batch_size(32)
+                                                                                                    .workers(1))
+
+    for (auto batch : batched_calibration_dataset) {
+        std::cout << batch.data().sizes() << std::endl;
+    }
+}

cpp/trtorchexec/main.cpp

@@ -12,7 +12,7 @@ bool checkRtol(const at::Tensor& diff, const std::vector<at::Tensor> inputs) {
         maxValue = fmax(tensor.abs().max().item<float>(), maxValue);
     }
     std::cout << "Max Difference: " << diff.abs().max().item<float>() << std::endl;
-    return diff.abs().max().item<float>() <= 2e-6 * maxValue;
+    return diff.abs().max().item<float>() <= 2e-5 * maxValue;
 }
 
 bool almostEqual(const at::Tensor& a, const at::Tensor& b) {
@@ -25,8 +25,8 @@ int main(int argc, const char* argv[]) {
                   << "       trtorchexec <path-to-exported-script-module> <min-input-size> <opt-input-size> <max-input-size>\n";
         return -1;
     }
-    
-    
+
+
     torch::jit::script::Module mod;
     try {
         // Deserialize the ScriptModule from a file using torch::jit::load().
@@ -38,7 +38,7 @@ int main(int argc, const char* argv[]) {
     }
 
     mod.to(at::kCUDA);
-    
+
     std::vector<std::vector<int64_t>> dims;
     for (int i = 2; i < argc; i++) {
         auto arg = std::string(argv[i]);
@@ -74,7 +74,7 @@ int main(int argc, const char* argv[]) {
     torch::jit::IValue jit_results_ivalues = mod.forward(jit_inputs_ivalues);
     std::vector<at::Tensor> jit_results;
     jit_results.push_back(jit_results_ivalues.toTensor());
-    
+
     auto trt_mod = trtorch::CompileGraph(mod, dims);
     torch::jit::IValue trt_results_ivalues = trt_mod.forward(trt_inputs_ivalues);
     std::vector<at::Tensor> trt_results;