[c++] Distance-agnostic triplet margin loss

test/cpp/api/functional.cpp

@@ -682,6 +682,56 @@ TEST_F(FunctionalTest, TripletMarginLoss) {
   ASSERT_TRUE(output.allclose(expected, 1e-04));
 }
 
+TEST_F(FunctionalTest, TripletMarginWithDistanceLossDefaultParity) {
+  // Check that if we use torch::pairwise_distance with the default
+  // TripletMarginLoss options as our distance function, the outputs
+  // are equal (i.e., equal under defaults).
+
+  std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
+      reductions = {torch::kSum, torch::kMean, torch::kNone};
+  std::vector<float> margins = {0.5, 1.0, 1.5};
+  std::vector<bool> swaps = {true, false};
+
+  for (auto& reduction : reductions) {
+    for (auto& margin : margins) {
+      for (const auto& swap : swaps) {
+        auto anchor = 
+            torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+        auto positive =
+            torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+        auto negative =
+            torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+
+        auto basicOptions = F::TripletMarginLossFuncOptions()
+                                .reduction(reduction)
+                                .margin(margin)
+                                .swap(swap);
+        auto distanceOptions =
+            F::TripletMarginWithDistanceLossFuncOptions()
+                .reduction(reduction)
+                .margin(margin)
+                .swap(swap);
+        TripletMarginLoss basicLoss(basicOptions);
+        TripletMarginWithDistanceLoss distanceLoss(distanceOptions);
+
+        auto basicOutput =
+            F::triplet_margin_loss(anchor, positive, negative, basicOptions);
+        auto distanceOutput = F::triplet_margin_with_distance_loss(
+            anchor, positive, negative, distanceOptions);
+
+        ASSERT_TRUE(distanceOutput.allclose(basicOutput, 1e-6, 1e-6));
+
+        // handle for torch::kNone reduction
+        auto sum = distanceOutput.sum();
+        sum.backward();
+        ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
+        ASSERT_EQ(positive.sizes(), positive.grad().sizes());
+        ASSERT_EQ(negative.sizes(), negative.grad().sizes());
+      }
+    }
+  }
+}
+
 TEST_F(FunctionalTest, NLLLoss) {
   auto input = torch::tensor({{-0.1315, -3.1315, -2.5315},
                               {-3.7038, -0.1038, -2.6038},

test/cpp/api/modules.cpp

@@ -2085,6 +2085,115 @@ TEST_F(ModulesTest, TripletMarginLoss) {
   ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
 }
 
+TEST_F(ModulesTest, TripletMarginWithDistanceLossDefaultParity) {
+  // Check that if we use torch::pairwise_distance with the default
+  // TripletMarginLoss options as our distance function, the outputs
+  // are equal (i.e., equal under defaults).
+
+  std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
+      reductions = {torch::kSum, torch::kMean, torch::kNone};
+  std::vector<float> margins = {0.5, 1.0, 1.5};
+  std::vector<bool> swaps = {true, false};
+
+  for (auto& reduction : reductions) {
+    for (auto& margin : margins) {
+      for (const auto& swap : swaps) {
+        auto anchor = 
+            torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+        auto positive =
+            torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+        auto negative =
+            torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+
+        auto basicOptions = TripletMarginLossOptions()
+                                .reduction(reduction)
+                                .margin(margin)
+                                .swap(swap);
+        auto distanceOptions =
+            TripletMarginWithDistanceLossOptions()
+                .reduction(reduction)
+                .margin(margin)
+                .swap(swap);
+        TripletMarginLoss basicLoss(basicOptions);
+        TripletMarginWithDistanceLoss distanceLoss(distanceOptions);
+
+        auto basicOutput = basicLoss->forward(anchor, positive, negative);
+        auto distanceOutput = distanceLoss->forward(anchor, positive, negative);
+        auto basicOperatorOutput = basicLoss(anchor, positive, negative);
+        auto distanceOperatorOutput = distanceLoss(anchor, positive, negative);
+
+        ASSERT_TRUE(distanceOutput.allclose(basicOutput, 1e-6, 1e-6));
+        ASSERT_TRUE(distanceOperatorOutput.allclose(distanceOutput, 1e-6, 1e-6));
+        ASSERT_TRUE(distanceOperatorOutput.allclose(basicOperatorOutput, 1e-6, 1e-6));
+
+        // handle for torch::kNone reduction
+        auto sum = distanceOutput.sum();
+        sum.backward();
+        ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
+        ASSERT_EQ(positive.sizes(), positive.grad().sizes());
+        ASSERT_EQ(negative.sizes(), negative.grad().sizes());
+      }
+    }
+  }
+}
+
+TEST_F(ModulesTest, TripletMarginWithDistanceLossFunctionalParity) {
+  // Check for parity between F::triplet_margin_with_distance_loss and
+  // TripletMarginWithDistanceLoss.
+  auto pairwise_distance = [&](const torch::Tensor& x, const torch::Tensor& y) {
+    return torch::pairwise_distance(x, y);
+  };
+  auto cosine_distance = [&](const torch::Tensor& x,
+                                 const torch::Tensor& y) {
+    return 1.0 - torch::cosine_similarity(x, y);
+  };
+  std::vector<TripletMarginWithDistanceLossOptions::distance_function_t>
+      distance_functions = {pairwise_distance, cosine_distance};
+
+  std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
+      reductions = {torch::kSum, torch::kMean, torch::kNone};
+  std::vector<float> margins = {0.5, 1.0, 1.5};
+  std::vector<bool> swaps = {true, false};
+
+  for (auto& function : distance_functions) {
+    for (auto& reduction : reductions) {
+      for (auto& margin : margins) {
+        for (const auto& swap : swaps) {
+          auto moduleOptions =
+              TripletMarginWithDistanceLossOptions()
+                  .distance_function(function)
+                  .reduction(reduction)
+                  .margin(margin)
+                  .swap(swap);
+          auto functionOptions =
+              torch::nn::functional::TripletMarginWithDistanceLossFuncOptions()
+                  .distance_function(function)
+                  .reduction(reduction)
+                  .margin(margin)
+                  .swap(swap);
+
+          auto anchor = torch::randn(
+              {100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+          auto positive = torch::randn(
+              {100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+          auto negative = torch::randn(
+              {100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
+
+          TripletMarginWithDistanceLoss distanceLoss(moduleOptions);
+
+          auto moduleOutput = distanceLoss->forward(anchor, positive, negative);
+          auto moduleOperatorOutput = distanceLoss(anchor, positive, negative);
+          auto functionOutput = torch::nn::functional::triplet_margin_with_distance_loss(
+            anchor, positive, negative, functionOptions);
+
+          ASSERT_TRUE(moduleOutput.allclose(functionOutput, 1e-6, 1e-6));
+          ASSERT_TRUE(moduleOperatorOutput.allclose(functionOutput, 1e-6, 1e-6));
+        }
+      }
+    }
+  }
+}
+
 TEST_F(ModulesTest, NLLLoss) {
   NLLLoss loss;
   auto input = torch::tensor({{-0.1315, -3.1315, -2.5315},
@@ -3529,9 +3638,9 @@ TEST_F(ModulesTest, PrettyPrintIdentity) {
 }
 
 TEST_F(ModulesTest, PrettyPrintFlatten) {
-  ASSERT_EQ(c10::str(Flatten()), 
+  ASSERT_EQ(c10::str(Flatten()),
     "torch::nn::Flatten(start_dim=1, end_dim=-1)");
-  ASSERT_EQ(c10::str(Flatten(FlattenOptions().start_dim(2).end_dim(4))), 
+  ASSERT_EQ(c10::str(Flatten(FlattenOptions().start_dim(2).end_dim(4))),
     "torch::nn::Flatten(start_dim=2, end_dim=4)");
 }
 
@@ -4394,6 +4503,20 @@ TEST_F(ModulesTest, PrettyPrintTripletMarginLoss) {
       "torch::nn::TripletMarginLoss(margin=3, p=2, eps=1e-06, swap=false)");
 }
 
+TEST_F(ModulesTest, PrettyPrintTripletMarginWithDistanceLoss) {
+  auto distanceOptions = TripletMarginWithDistanceLossOptions()
+                             .distance_function([&](const torch::Tensor& x,
+                                                    const torch::Tensor& y) {
+                               return torch::pairwise_distance(x, y, 2.0, 1e-6);
+                             })
+                             .margin(1.5)
+                             .swap(true)
+                             .reduction(torch::kMean);
+  ASSERT_EQ(
+      c10::str(TripletMarginWithDistanceLoss(distanceOptions)),
+      "torch::nn::TripletMarginWithDistanceLoss(margin=1.5, swap=true)");
+}
+
 TEST_F(ModulesTest, PrettyPrintNLLLoss) {
   ASSERT_EQ(
       c10::str(NLLLoss()), "torch::nn::NLLLoss()");

torch/csrc/api/include/torch/nn/functional/loss.h

@@ -527,6 +527,85 @@ inline Tensor triplet_margin_loss(
 
 // ============================================================================
 
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+namespace detail {
+inline Tensor triplet_margin_with_distance_loss(
+    const Tensor& anchor,
+    const Tensor& positive,
+    const Tensor& negative,
+    c10::optional<TripletMarginWithDistanceLossFuncOptions::distance_function_t> distance_function,
+    double margin,
+    bool swap,
+    TripletMarginWithDistanceLossFuncOptions::reduction_t reduction) {
+  Tensor dist_pos, dist_neg;
+  if (distance_function.has_value()) {
+    auto distance_function_impl = distance_function.value();
+    dist_pos = distance_function_impl(anchor, positive);
+    dist_neg = distance_function_impl(anchor, negative);
+  } else {
+    dist_pos = pairwise_distance(anchor, positive);
+    dist_neg = pairwise_distance(anchor, negative);
+  }
+
+  if (swap) {
+    Tensor dist_swap;
+    if (distance_function.has_value()) {
+      dist_swap = distance_function.value()(positive, negative);
+    } else {
+      dist_swap = pairwise_distance(positive, negative);
+    }
+    dist_neg = torch::min(dist_neg, dist_swap);
+  }
+
+  auto loss = torch::clamp_min(dist_pos - dist_neg + margin, 0);
+
+  Tensor ret;
+  if (c10::get_if<enumtype::kNone>(&reduction)) {
+    ret = loss;
+  } else if (c10::get_if<enumtype::kMean>(&reduction)) {
+    ret = loss.mean();
+  } else if (c10::get_if<enumtype::kSum>(&reduction)) {
+    ret = loss.sum();
+  } else {
+    ret = anchor;
+    TORCH_INTERNAL_ASSERT(
+      false,
+      enumtype::get_enum_name(reduction),
+      " is not valid");
+  }
+  return ret;
+}
+} // namespace detail
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+/// See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.triplet_margin_with_distance_loss
+/// about the exact behavior of this functional.
+///
+/// See the documentation for `torch::nn::functional::TripletMarginWithDistanceLossFuncOptions` class to learn what
+/// optional arguments are supported for this functional.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::triplet_margin_with_distance_loss(anchor, positive, negative, F::TripletMarginWithDistanceLossFuncOptions().margin(1.0));
+/// ```
+inline Tensor triplet_margin_with_distance_loss(
+    const Tensor& anchor,
+    const Tensor& positive,
+    const Tensor& negative,
+    const TripletMarginWithDistanceLossFuncOptions& options = {}) {
+  return detail::triplet_margin_with_distance_loss(
+    anchor,
+    positive,
+    negative,
+    options.distance_function(),
+    options.margin(),
+    options.swap(),
+    options.reduction());
+}
+
+// ============================================================================
+
 #ifndef DOXYGEN_SHOULD_SKIP_THIS
 namespace detail {
 inline Tensor ctc_loss(const Tensor& log_probs,

torch/csrc/api/include/torch/nn/modules/loss.h

@@ -309,7 +309,7 @@ struct TORCH_API SmoothL1LossImpl : public Cloneable<SmoothL1LossImpl> {
 TORCH_MODULE(SmoothL1Loss);
 
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MultiLabelMarginLoss ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-  
+
 /// Creates a criterion that optimizes a multi-class multi-classification
 /// hinge loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`)
 /// and output :math:`y` (which is a 2D `Tensor` of target class indices).
@@ -421,9 +421,9 @@ TORCH_MODULE(MultiLabelSoftMarginLoss);
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TripletMarginLoss ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 /// Creates a criterion that measures the triplet loss given an input
-/// tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater 
+/// tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater
 /// than :math:`0`. This is used for measuring a relative similarity between
-/// samples. A triplet is composed by `a`, `p` and `n` (i.e., `anchor`, 
+/// samples. A triplet is composed by `a`, `p` and `n` (i.e., `anchor`,
 /// `positive examples` and `negative examples` respectively). The
 /// shapes of all input tensors should be :math:`(N, D)`.
 /// See https://pytorch.org/docs/master/nn.html#torch.nn.TripletMarginLoss to learn
@@ -461,6 +461,50 @@ struct TORCH_API TripletMarginLossImpl : public Cloneable<TripletMarginLossImpl>
 /// module storage semantics.
 TORCH_MODULE(TripletMarginLoss);
 
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TripletMarginWithDistanceLoss ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Creates a criterion that measures the triplet loss given input
+/// tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
+/// positive, and negative examples, respectively); and a nonnegative, real-valued function
+/// ("distance function") used to compute the relationships between the anchor
+/// and positive example ("positive distance") and the anchor and negative
+/// example ("negative distance").
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.TripletMarginWithDistanceLoss to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::TripletMarginWithDistanceLossOptions` class to learn what
+/// constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// TripletMarginWithDistanceLoss model(TripletMarginWithDistanceLossOptions().margin(3).swap(false));
+/// ```
+struct TORCH_API TripletMarginWithDistanceLossImpl : public Cloneable<TripletMarginWithDistanceLossImpl> {
+  explicit TripletMarginWithDistanceLossImpl(
+      TripletMarginWithDistanceLossOptions options_ = {});
+
+  void reset() override;
+
+  /// Pretty prints the `TripletMarginWithDistanceLoss` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+
+  Tensor forward(
+      const Tensor& anchor,
+      const Tensor& positive,
+      const Tensor& negative);
+
+  /// The options with which this `Module` was constructed.
+  TripletMarginWithDistanceLossOptions options;
+};
+
+/// A `ModuleHolder` subclass for `TripletMarginWithDistanceLossImpl`.
+/// See the documentation for `TripletMarginWithDistanceLossImpl` class to learn what methods it
+/// provides, and examples of how to use `TripletMarginWithDistanceLoss` with
+/// `torch::nn::TripletMarginWithDistanceLossOptions`.
+/// See the documentation for `ModuleHolder` to learn about PyTorch's
+/// module storage semantics.
+TORCH_MODULE(TripletMarginWithDistanceLoss);
+
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CTCLoss ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 /// The Connectionist Temporal Classification loss.
@@ -626,9 +670,9 @@ TORCH_MODULE(NLLLoss);
 struct TORCH_API CrossEntropyLossImpl : public Cloneable<CrossEntropyLossImpl> {
   explicit CrossEntropyLossImpl(
       const CrossEntropyLossOptions& options_ = {});
-    
+
   void reset() override;
-    
+
   /// Pretty prints the `CrossEntropyLoss` module into the given `stream`.
   void pretty_print(std::ostream& stream) const override;