Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

2026-09-24 13:22:23 +07:00
commit 642cc11a9f
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+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+/*
+ * 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.
+ */
+
+#pragma once
+
+#include <cstdint>
+#include <vector>
+#include "./ConvUtils.h" // @manual
+#include "./FbgemmBuild.h" // @manual
+#include "./Utils.h" // @manual
+
+// #define FBGEMM_MEASURE_TIME_BREAKDOWN
+
+#ifdef FBGEMM_MEASURE_TIME_BREAKDOWN
+#include <chrono>
+#include <iostream>
+extern double spmdm_initial_time;
+extern double spmdm_transpose_uint8_time;
+extern double spmdm_transpose_32xN_time;
+extern double spmdm_compute_time;
+extern double spmdm_transpose_Nx32_time;
+extern double spmdm_run_time;
+extern double sconv_run_time;
+#endif
+
+namespace fbgemm {
+
+/**
+ * @brief A class to represent a matrix in Compressed Sparse Column (CSC)
+ * format.
+ *
+ * The second input matrix of matrix multiplication is usually weight and can
+ * be sparse, and it's usually more efficient to use CSC format to represent
+ * the second input matrix.
+ */
+class FBGEMM_API CompressedSparseColumn {
+ public:
+  CompressedSparseColumn(int num_of_rows, int num_of_cols);
+
+  std::vector<std::int32_t>& ColPtr() {
+    return colptr_;
+  }
+  std::vector<std::int16_t>& RowIdx() {
+    return rowidx_;
+  }
+  std::vector<std::int8_t>& Values() {
+    return values_;
+  }
+  std::vector<std::int16_t>& KHs() {
+    return kh_;
+  }
+  std::vector<std::int16_t>& KWs() {
+    return kw_;
+  }
+  /**
+   * ICs include group: i.e. for ith input channels withint group g, ICs contain
+   * g*(groups_per_input_channels) + i
+   */
+  std::vector<std::int16_t>& ICs() {
+    return ic_;
+  }
+
+  std::size_t NumOfRows() const {
+    return num_rows_;
+  }
+  std::size_t NumOfCols() const {
+    return colptr_.size() - 1;
+  }
+  std::int32_t NumOfNonZeros() const {
+    return colptr_.back();
+  }
+
+  /**
+   * @return Total number of non-zero elements as a fraction of total
+   * elements.
+   */
+  double Density() const;
+
+  /**
+   * @return True if the number of non-zeros per row is smaller than a small
+   * threshold.
+   */
+  bool IsHyperSparse() const;
+
+  /**
+   * @brief Perform dense-matrix * sparse matrix.
+   *
+   * C += A (dense matrix) * B (this CSC matrix) if accumulation = true \n
+   * C  = A (dense matrix) * B (this CSC matrix) if accumulation = false
+   */
+  void SpMDM(
+      const block_type_t& block,
+      const std::uint8_t* A,
+      int lda,
+      bool accumulation,
+      std::int32_t* C,
+      int ldc) const;
+
+  void SparseConv(
+      const conv_param_t<>& conv_p,
+      const block_type_t& block,
+      const std::uint8_t* A,
+      std::int32_t A_zero_point,
+      bool accumulation,
+      std::int32_t* C,
+      int ldc) const;
+
+ private:
+  const std::size_t num_rows_;
+  std::vector<std::int32_t> colptr_; // corresponds to out channels
+  std::vector<std::int8_t> values_;
+
+  // For SpMDM
+  std::vector<std::int16_t> rowidx_; // kh kw ic are flattened with im2col
+
+  // For direct sparse convolution
+  std::vector<std::int16_t> kh_;
+  std::vector<std::int16_t> kw_;
+  std::vector<std::int16_t> ic_; // in channels
+
+  // Cache IsHyperSparse to minimize its overhead.
+  mutable bool hyper_sparse_{false};
+
+  // Whether we can reuse the cached hyper_sparse_ is determined by checking
+  // if NumOfNonZeros() is same as old_nnz_ saved in previous invocation of
+  // IsHyperSparse call.
+  mutable std::int32_t old_nnz_{-1};
+};
+
+} // namespace fbgemm
+
+#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)