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kronos-venv/lib/python3.12/site-packages/torch/include/qnnpack_func.h

@@ -0,0 +1,171 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <cstdlib>
+#include <qnnpack/operator.h>
+
+namespace qnnpack {
+class PrePackConvWeights final {
+ public:
+  PrePackConvWeights(
+      const pytorch_qnnp_operator_t convolution,
+      const uint8_t* kernel_zero_points,
+      const uint8_t* kernel,
+      const int32_t* bias);
+
+  void* getPackedWeights() const
+  {
+    return packed_weights_;
+  }
+
+  int64_t getOutputChannels() const
+  {
+    return output_channels_;
+  }
+
+  ~PrePackConvWeights()
+  {
+    if (packed_weights_ != nullptr) {
+      free(packed_weights_);
+    }
+  }
+
+  PrePackConvWeights() = delete;
+  PrePackConvWeights(const PrePackConvWeights&) = delete;
+  PrePackConvWeights& operator=(const PrePackConvWeights&) = delete;
+
+ private:
+  void* packed_weights_ = nullptr;
+  int64_t output_channels_;
+};
+
+class PackBMatrix final {
+ public:
+  PackBMatrix(
+      size_t input_channels,
+      size_t output_channels,
+      const uint8_t* kernel_zero_points,
+      const float* requantization_scale,
+      const uint8_t* kernel,
+      const int32_t* bias);
+
+  // This constructor is to be used for dynamic mode
+  // quantization. In dynamic mode, we dont yet support
+  // per channel quantization, and paying the cost of
+  // memory allocation for per channel zero point and
+  // requant scale will hurt performance.
+  PackBMatrix(
+      size_t input_channels,
+      size_t output_channels,
+      const uint8_t kernel_zero_point,
+      const float requantization_scale,
+      const uint8_t* kernel,
+      const int32_t* bias);
+
+  void* getPackedWeights() const
+  {
+    return packed_weights_;
+  }
+
+  void unpackWeights(
+      const uint8_t* kernel_zero_points,
+      int8_t* kernel
+    ) const;
+
+  size_t getInputChannels() const
+  {
+    return input_channels_;
+  }
+
+  size_t getOutputChannels() const
+  {
+    return output_channels_;
+  }
+
+  ~PackBMatrix()
+  {
+    if (packed_weights_ != nullptr) {
+      free(packed_weights_);
+    }
+  }
+
+  PackBMatrix() = delete;
+  PackBMatrix(const PackBMatrix&) = delete;
+  PackBMatrix& operator=(const PackBMatrix&) = delete;
+
+ private:
+  void* packed_weights_ = nullptr;
+  size_t input_channels_;
+  size_t output_channels_;
+};
+
+enum pytorch_qnnp_status qnnpackLinear(
+    const size_t batch_size,
+    const size_t input_channels,
+    const size_t output_channels,
+    const uint8_t input_zero_point,
+    const uint8_t* kernel_zero_points,
+    const float* requantization_scales,
+    const uint8_t output_zero_point,
+    const uint8_t output_min,
+    const uint8_t output_max,
+    const uint8_t* input,
+    const size_t input_stride,
+    void* packed_weights,
+    uint8_t* output,
+    const size_t output_stride,
+    pthreadpool_t threadpool);
+
+enum pytorch_qnnp_status qnnpackConv(
+    const pytorch_qnnp_operator_t convolution,
+    void* packed_weights,
+    const size_t batch_size,
+    const size_t input_depth,
+    const size_t input_height,
+    const size_t input_width,
+    const uint8_t input_zero_point,
+    const uint8_t* input,
+    const uint8_t* kernel_zero_points,
+    const float* requantization_scales,
+    const uint8_t output_zero_point,
+    const uint8_t output_min,
+    const uint8_t output_max,
+    uint8_t* output,
+    pthreadpool_t threadpool);
+
+enum pytorch_qnnp_status qnnpackDeConv(
+    const pytorch_qnnp_operator_t deconvolution,
+    void* packed_weights,
+    const size_t batch_size,
+    const size_t input_height,
+    const size_t input_width,
+    const uint8_t input_zero_point,
+    const uint8_t* input,
+    const uint8_t* kernel_zero_points,
+    const float* requantization_scales,
+    const uint8_t output_zero_point,
+    const uint8_t output_min,
+    const uint8_t output_max,
+    uint8_t* output,
+    pthreadpool_t threadpool);
+
+enum pytorch_qnnp_status qnnpackLinearDynamic(
+    const size_t batch_size,
+    const size_t input_channels,
+    const size_t output_channels,
+    const uint8_t input_zero_point,
+    const uint8_t* kernel_zero_points,
+    const float* dequantization_scales,
+    const uint8_t* input,
+    const size_t input_stride,
+    void* packed_weights,
+    const float* bias,
+    float* output,
+    const size_t output_stride,
+    pthreadpool_t threadpool);
+
+} // namespace qnnpack
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)