Commit message: Validate that matrix dimension sizes in SparseMatMul are positive.

PiperOrigin-RevId: 401149683
Change-Id: Ib33eafc561a39c8741ece80b2edce6d4aae9a57d
CVE: CVE-2021-41219
CWE: CWE-125
Vulnerability description: TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Project: tensorflow
File: sparse_matmul_op.cc

  void Compute(OpKernelContext* ctx) override {
    const Tensor& a = ctx->input(0);
    const Tensor& b = ctx->input(1);
    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a.shape()),
                errors::InvalidArgument("a is not a matrix"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b.shape()),
                errors::InvalidArgument("b is not a matrix"));

    const int m = transpose_a_ ? a.dim_size(1) : a.dim_size(0);
    const int k = transpose_a_ ? a.dim_size(0) : a.dim_size(1);
    const int n = transpose_b_ ? b.dim_size(0) : b.dim_size(1);
    const int k2 = transpose_b_ ? b.dim_size(1) : b.dim_size(0);

    OP_REQUIRES(ctx, k == k2,
                errors::InvalidArgument(
                    "Matrix size incompatible: a: ", a.shape().DebugString(),
                    ", b: ", b.shape().DebugString()));
    Tensor* output = nullptr;
    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({m, n}), &output));

    if (k == 0) {
      // If the inner dimension k in the matrix multiplication is zero, we fill
      // the output with zeros.
      functor::SetZeroFunctor<CPUDevice, float> f;
      f(ctx->eigen_device<CPUDevice>(), output->flat<float>());
      return;
    }

    auto out = output->matrix<float>();

    std::unique_ptr<Tensor> a_float;
    std::unique_ptr<Tensor> b_float;
    if (!a_is_sparse_ && !b_is_sparse_) {
      auto left = &a;
      auto right = &b;
      // TODO(agarwal): multi-thread the conversions from bfloat16 to float.
      if (std::is_same<TL, bfloat16>::value) {
        a_float.reset(new Tensor(DT_FLOAT, a.shape()));
        BFloat16ToFloat(a.flat<bfloat16>().data(),
                        a_float->flat<float>().data(), a.NumElements());
        left = a_float.get();
      }
      if (std::is_same<TR, bfloat16>::value) {
        b_float.reset(new Tensor(DT_FLOAT, b.shape()));
        BFloat16ToFloat(b.flat<bfloat16>().data(),
                        b_float->flat<float>().data(), b.NumElements());
        right = b_float.get();
      }
      Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair;
      dim_pair[0].first = transpose_a_ ? 0 : 1;
      dim_pair[0].second = transpose_b_ ? 1 : 0;

      out.device(ctx->template eigen_device<CPUDevice>()) =
          left->matrix<float>().contract(right->matrix<float>(), dim_pair);
      return;
    }

    auto left = &a;
    auto right = &b;
    bool transpose_output = false;
    bool transpose_a = transpose_a_;
    bool transpose_b = transpose_b_;
    if (!a_is_sparse_) {
      // Swap the order of multiplications using the identity:
      // A * B = (B' *  A')'.
      std::swap(left, right);
      std::swap(transpose_a, transpose_b);
      transpose_a = !transpose_a;
      transpose_b = !transpose_b;
      transpose_output = !transpose_output;
    }

    std::unique_ptr<Tensor> right_tr;
    if (transpose_b) {
      // TODO(agarwal): avoid transposing the matrix here and directly handle
      // transpose in CreateDenseSlices.
      OP_REQUIRES(ctx, right->dim_size(0) != 0,
                  errors::InvalidArgument("b has an entry 0 in it's shape."));
      OP_REQUIRES(ctx, right->dim_size(1) != 0,
                  errors::InvalidArgument("b has an entry 0 in it's shape."));
      right_tr.reset(
          new Tensor(right->dtype(),
                     TensorShape({right->dim_size(1), right->dim_size(0)})));

      const auto perm = dsizes_10();
      if (transpose_output) {
        right_tr->matrix<TL>().device(ctx->template eigen_device<CPUDevice>()) =
            right->matrix<TL>().shuffle(perm);
      } else {
        right_tr->matrix<TR>().device(ctx->template eigen_device<CPUDevice>()) =
            right->matrix<TR>().shuffle(perm);
      }
      right = right_tr.get();
    }

    if (transpose_output) {
      DoMatMul<TR, TL>::Compute(&this->cache_tr_, left->matrix<TR>(),
                                right->matrix<TL>(), transpose_a,
                                ctx->device()->tensorflow_cpu_worker_threads(),
                                transpose_output, &out);
    } else {
      DoMatMul<TL, TR>::Compute(&this->cache_nt_, left->matrix<TL>(),
                                right->matrix<TR>(), transpose_a,
                                ctx->device()->tensorflow_cpu_worker_threads(),
                                transpose_output, &out);
    }
  }

Project: tensorflow
File: sparse_matmul_op.cc

  void Compute(OpKernelContext* ctx) override {
    const Tensor& a = ctx->input(0);
    const Tensor& b = ctx->input(1);
    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a.shape()),
                errors::InvalidArgument("a is not a matrix"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b.shape()),
                errors::InvalidArgument("b is not a matrix"));

    const int m = transpose_a_ ? a.dim_size(1) : a.dim_size(0);
    const int k = transpose_a_ ? a.dim_size(0) : a.dim_size(1);
    const int n = transpose_b_ ? b.dim_size(0) : b.dim_size(1);
    const int k2 = transpose_b_ ? b.dim_size(1) : b.dim_size(0);

    OP_REQUIRES(ctx, k == k2,
                errors::InvalidArgument(
                    "Matrix size incompatible: a: ", a.shape().DebugString(),
                    ", b: ", b.shape().DebugString()));
    OP_REQUIRES(ctx, m >= 0 && n >= 0 && k >= 0,
                errors::InvalidArgument(
                    "Matrix dimensions cannot be negative: a: ",
                    a.shape().DebugString(), ", b: ", b.shape().DebugString()));
    Tensor* output = nullptr;
    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({m, n}), &output));

    // Return early if at least one of the output dimension size is 0.
    if (m == 0 || n == 0) {
      return;
    }

    if (k == 0) {
      // If the inner dimension k in the matrix multiplication is zero, we fill
      // the output with zeros.
      functor::SetZeroFunctor<CPUDevice, float> f;
      f(ctx->eigen_device<CPUDevice>(), output->flat<float>());
      return;
    }

    auto out = output->matrix<float>();

    std::unique_ptr<Tensor> a_float;
    std::unique_ptr<Tensor> b_float;
    if (!a_is_sparse_ && !b_is_sparse_) {
      auto left = &a;
      auto right = &b;
      // TODO(agarwal): multi-thread the conversions from bfloat16 to float.
      if (std::is_same<TL, bfloat16>::value) {
        a_float.reset(new Tensor(DT_FLOAT, a.shape()));
        BFloat16ToFloat(a.flat<bfloat16>().data(),
                        a_float->flat<float>().data(), a.NumElements());
        left = a_float.get();
      }
      if (std::is_same<TR, bfloat16>::value) {
        b_float.reset(new Tensor(DT_FLOAT, b.shape()));
        BFloat16ToFloat(b.flat<bfloat16>().data(),
                        b_float->flat<float>().data(), b.NumElements());
        right = b_float.get();
      }
      Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair;
      dim_pair[0].first = transpose_a_ ? 0 : 1;
      dim_pair[0].second = transpose_b_ ? 1 : 0;

      out.device(ctx->template eigen_device<CPUDevice>()) =
          left->matrix<float>().contract(right->matrix<float>(), dim_pair);
      return;
    }

    auto left = &a;
    auto right = &b;
    bool transpose_output = false;
    bool transpose_a = transpose_a_;
    bool transpose_b = transpose_b_;
    if (!a_is_sparse_) {
      // Swap the order of multiplications using the identity:
      // A * B = (B' *  A')'.
      std::swap(left, right);
      std::swap(transpose_a, transpose_b);
      transpose_a = !transpose_a;
      transpose_b = !transpose_b;
      transpose_output = !transpose_output;
    }

    std::unique_ptr<Tensor> right_tr;
    if (transpose_b) {
      // TODO(agarwal): avoid transposing the matrix here and directly handle
      // transpose in CreateDenseSlices.
      OP_REQUIRES(ctx, right->dim_size(0) != 0,
                  errors::InvalidArgument("b has an entry 0 in it's shape."));
      OP_REQUIRES(ctx, right->dim_size(1) != 0,
                  errors::InvalidArgument("b has an entry 0 in it's shape."));
      right_tr.reset(
          new Tensor(right->dtype(),
                     TensorShape({right->dim_size(1), right->dim_size(0)})));

      const auto perm = dsizes_10();
      if (transpose_output) {
        right_tr->matrix<TL>().device(ctx->template eigen_device<CPUDevice>()) =
            right->matrix<TL>().shuffle(perm);
      } else {
        right_tr->matrix<TR>().device(ctx->template eigen_device<CPUDevice>()) =
            right->matrix<TR>().shuffle(perm);
      }
      right = right_tr.get();
    }

    if (transpose_output) {
      DoMatMul<TR, TL>::Compute(&this->cache_tr_, left->matrix<TR>(),
                                right->matrix<TL>(), transpose_a,
                                ctx->device()->tensorflow_cpu_worker_threads(),
                                transpose_output, &out);
    } else {
      DoMatMul<TL, TR>::Compute(&this->cache_nt_, left->matrix<TL>(),
                                right->matrix<TR>(), transpose_a,
                                ctx->device()->tensorflow_cpu_worker_threads(),
                                transpose_output, &out);
    }
  }