Add top-p

Author: hyeygit

test/topp/test_topp_mask.py

@@ -0,0 +1,81 @@
+import torch
+import torch_xla
+from torch_xla import xm
+import unittest
+from torch_xla.experimental.topp_mask import topp_mask
+import math
+
+
+class TestTopPMask(unittest.TestCase):
+
+  def setUp(self):
+    self.device = torch_xla.device()
+
+  def test_invalid_p(self):
+    # Test that an invalid p throws an assertion error
+    logits = torch.tensor([[0.2, 0.5, 0.3], [0.1, 0.7, 0.2]],
+                          dtype=torch.float32,
+                          device=self.device)
+
+    with self.assertRaises(AssertionError):
+      topp_mask(logits, -0.1)  # p < 0
+
+    with self.assertRaises(AssertionError):
+      topp_mask(logits, 1.1)  # p > 1
+
+  def test_basic(self):
+    logits = torch.tensor(
+        [[math.log(0.2), math.log(0.3),
+          math.log(0.5)], [math.log(0.5),
+                           math.log(0.2),
+                           math.log(0.3)]],
+        dtype=torch.float32,
+        device=self.device)
+    mask = topp_mask(logits, 0.79)
+
+    expected_mask = torch.tensor([[0, 1, 1], [1, 0, 1]],
+                                 dtype=torch.float32,
+                                 device=self.device)
+    self.assertTrue(torch.allclose(expected_mask, mask, atol=1e-6))
+
+  def test_dim(self):
+    logits = torch.tensor([[math.log(0.2), math.log(0.3)],
+                           [math.log(0.3), math.log(0.2)],
+                           [math.log(0.5), math.log(0.5)]],
+                          dtype=torch.float32,
+                          device=self.device)
+    mask = topp_mask(logits, 0.79, dim=0)
+
+    print(mask)
+
+    expected_mask = torch.tensor([[0, 1], [1, 0], [1, 1]],
+                                 dtype=torch.float32,
+                                 device=self.device)
+    self.assertTrue(torch.allclose(expected_mask, mask, atol=1e-6))
+
+  def test_p_is_zero(self):
+    logits = torch.tensor([[0.2, 0.5, 5], [0.1, 2, 0.2]],
+                          dtype=torch.float32,
+                          device=self.device)
+    mask = topp_mask(logits, 0.0)
+
+    expected_mask = torch.tensor([[0, 0, 1], [0, 1, 0]],
+                                 dtype=torch.float32,
+                                 device=self.device)
+    self.assertTrue(torch.allclose(expected_mask, mask, atol=1e-6))
+
+  def test_p_is_one(self):
+    logits = torch.tensor([[0.2, 0.5, 5], [0.1, 2, 0.2]],
+                          dtype=torch.float32,
+                          device=self.device)
+    mask = topp_mask(logits, 1.0)
+
+    # All elements should be selected.
+    expected_mask = torch.tensor([[1, 1, 1], [1, 1, 1]],
+                                 dtype=torch.float32,
+                                 device=self.device)
+    self.assertTrue(torch.allclose(expected_mask, mask, atol=1e-6))
+
+
+if __name__ == '__main__':
+  unittest.main()

torch_xla/csrc/init_python_bindings.cpp

@@ -818,6 +818,12 @@ xla::Shape GetTensorShape(const at::Tensor& tensor,
   return CreateComputationShapeFromTensor(tensor, &device);
 }
 
+at::Tensor TopPMask(const at::Tensor& input, float p, int64_t dim) {
+  auto result = tensor_methods::topp_mask(bridge::GetXlaTensor(input), p, dim,
+                                          /*stable=*/false);
+  return bridge::AtenFromXlaTensor(std::move(result));
+}
+
 py::dict GetMemoryInfo(const std::string& device_str) {
   runtime::ComputationClient::MemoryInfo mem_info;
   {
@@ -3008,6 +3014,9 @@ void InitXlaModuleBindings(py::module m) {
       [](std::string name, std::shared_ptr<const runtime::PjRtPlugin> plugin) {
         runtime::RegisterPjRtPlugin(name, plugin);
       });
+  m.def("_xla_topp_mask", [](const at::Tensor& input, float p, int64_t dim) {
+    return TopPMask(input, p, dim);
+  });
   py::class_<runtime::PjRtPlugin, PyPjRtPlugin,
              std::shared_ptr<runtime::PjRtPlugin>>(m, "PjRtPlugin")
       .def(py::init<>())

torch_xla/csrc/ops/topp_mask.cpp

@@ -0,0 +1,34 @@
+#include "torch_xla/csrc/ops/topp_mask.h"
+
+#include "torch_xla/csrc/lowering_context.h"
+#include "torch_xla/csrc/ops/infer_output_shape.h"
+#include "torch_xla/csrc/xla_lower_util.h"
+
+namespace torch_xla {
+
+TopPMask::TopPMask(const torch::lazy::Value& input, float p, int64_t dim,
+                   bool stable)
+    : XlaNode(torch::lazy::OpKind(at::aten::topk), {input}, GetXlaShape(input),
+              /*num_outputs=*/1, torch::lazy::MHash(p, dim, stable)),
+      p_(p),
+      dim_(dim),
+      stable_(stable) {}
+
+torch::lazy::NodePtr TopPMask::Clone(torch::lazy::OpList operands) const {
+  return torch_xla::MakeNode<TopPMask>(operands.at(0), p_, dim_, stable_);
+}
+
+XlaOpVector TopPMask::Lower(LoweringContext* loctx) const {
+  xla::XlaOp input = loctx->GetOutputOp(operand(0));
+  return ReturnOp(CreateTopPMask(loctx->device(), input, p_, dim_, stable_),
+                  loctx);
+}
+
+std::string TopPMask::ToString() const {
+  std::stringstream ss;
+  ss << XlaNode::ToString() << ", p=" << p_ << ", dim=" << dim_
+     << ", stable=" << stable_;
+  return ss.str();
+}
+
+}  // namespace torch_xla

torch_xla/csrc/ops/topp_mask.h

@@ -0,0 +1,32 @@
+#ifndef XLA_TORCH_XLA_CSRC_OPS_TOPP_MASK_H_
+#define XLA_TORCH_XLA_CSRC_OPS_TOPP_MASK_H_
+
+#include "torch_xla/csrc/ir.h"
+
+namespace torch_xla {
+
+class TopPMask : public XlaNode {
+ public:
+  TopPMask(const torch::lazy::Value& input, float p, int64_t dim, bool stable);
+
+  std::string ToString() const override;
+
+  torch::lazy::NodePtr Clone(torch::lazy::OpList operands) const override;
+
+  XlaOpVector Lower(LoweringContext* loctx) const override;
+
+  float p() const { return p_; };
+
+  int64_t dim() const { return dim_; };
+
+  bool stable() const { return stable_; }
+
+ private:
+  float p_;
+  int64_t dim_;
+  bool stable_;
+};
+
+}  // namespace torch_xla
+
+#endif  // XLA_TORCH_XLA_CSRC_OPS_TOPP_MASK_H_

torch_xla/csrc/ops/xla_ops.cpp

@@ -40,5 +40,6 @@ const OpKindWrapper xla_update_slice("xla::update_slice");
 const OpKindWrapper xla_custom_sharding("xla::custom_sharding");
 const OpKindWrapper xla_tpu_custom_call("xla::tpu_custom_call");
 const OpKindWrapper xla_gpu_custom_call("xla::gpu_custom_call");
+const OpKindWrapper xla_topp_mask("xla::topp_mask");
 
 }  // namespace torch_xla

torch_xla/csrc/ops/xla_ops.h

@@ -65,7 +65,8 @@ extern const OpKindWrapper xla_update_slice;
 extern const OpKindWrapper xla_custom_sharding;
 extern const OpKindWrapper xla_tpu_custom_call;
 extern const OpKindWrapper xla_gpu_custom_call;
+extern const OpKindWrapper xla_topp_mask;
 
 }  // namespace torch_xla
 
-#endif  // XLA_TORCH_XLA_CSRC_OPS_XLA_OPS_H_
+#endif  // XLA_TORCH_XLA_CSRC_OPS_XLA_OPS_H_

torch_xla/csrc/tensor_methods.cpp

@@ -132,6 +132,7 @@
 #include "torch_xla/csrc/ops/threshold.h"
 #include "torch_xla/csrc/ops/threshold_backward.h"
 #include "torch_xla/csrc/ops/topk.h"
+#include "torch_xla/csrc/ops/topp_mask.h"
 #include "torch_xla/csrc/ops/tpu_custom_call.h"
 #include "torch_xla/csrc/ops/triangular_solve.h"
 #include "torch_xla/csrc/ops/uniform.h"
@@ -3438,6 +3439,14 @@ std::tuple<XLATensorPtr, XLATensorPtr> topk(const XLATensorPtr& input,
   return std::make_tuple(t1, t2);
 }
 
+XLATensorPtr topp_mask(const XLATensorPtr& input, float p, int64_t dim,
+                       bool stable) {
+  return input->CreateFrom(torch_xla::MakeNode<TopPMask>(
+      input->GetIrValue(), p,
+      torch::lazy::GetCanonicalDimensionIndex(dim, input->shape().get().rank()),
+      stable));
+}
+
 XLATensorPtr trace(const XLATensorPtr& input) {
   auto input_shape_ref = input->shape();
   XLA_CHECK_EQ((*input_shape_ref).rank(), 2)

torch_xla/csrc/tensor_methods.h

@@ -973,6 +973,9 @@ std::tuple<XLATensorPtr, XLATensorPtr> topk(const XLATensorPtr& input,
                                             bool largest, bool sorted,
                                             bool stable);
 
+XLATensorPtr topp_mask(const XLATensorPtr& input, float p, int64_t dim,
+                       bool stable);
+
 // Returns the sum of the elements of the diagonal of the input 2-D matrix.
 XLATensorPtr trace(const XLATensorPtr& input);
 

torch_xla/csrc/xla_lower_util.cpp

@@ -17,6 +17,7 @@
 #include "torch_xla/csrc/runtime/debug_macros.h"
 #include "torch_xla/csrc/runtime/util.h"
 #include "torch_xla/csrc/shape_helper.h"
+#include "torch_xla/csrc/softmax_builder.h"
 #include "torch_xla/csrc/tensor_util.h"
 #include "xla/client/lib/arithmetic.h"
 #include "xla/client/lib/comparators.h"
@@ -390,6 +391,52 @@ std::vector<xla::XlaOp> CreateTopK(xla::XlaOp input, int64_t k, int64_t dim,
                                                xla::PrimitiveType::S64))};
 }
 
+xla::XlaOp CreateTopPMask(const torch::lazy::BackendDevice& device,
+                          xla::XlaOp logits, float p, int64_t dim,
+                          bool stable) {
+  // Convert logits to probabilities.
+  xla::XlaOp probs = BuildSoftmax(logits, dim);
+  const xla::Shape& shape = ShapeHelper::ShapeOfXlaOp(probs);
+
+  // Sort the probabilities in ascending order and keep track of the sorted
+  // indices.
+  xla::Shape iota_shape =
+      xla::ShapeUtil::MakeShape(xla::PrimitiveType::S32, shape.dimensions());
+  xla::XlaOp iota = xla::Iota(probs.builder(), iota_shape, dim);
+  xla::XlaComputation comparator = xla::CreateScalarLtComputation(
+      {shape.element_type(), xla::PrimitiveType::S32}, probs.builder());
+  xla::XlaOp sort_result = xla::Sort({probs, iota}, comparator, dim, stable);
+
+  // Compute cumulative probabilities.
+  xla::XlaOp zero = xla::Zero(probs.builder(), shape.element_type());
+  xla::XlaComputation reducer =
+      XlaHelpers::CreateAddComputation(shape.element_type());
+  xla::XlaOp cumprobs = BuildCumulativeComputation(
+      xla::GetTupleElement(sort_result, 0), dim, reducer, zero);
+
+  // Create a mask for the "p-set" elements.
+  xla::XlaOp one = xla::One(probs.builder(), shape.element_type());
+  xla::XlaOp p_mask = BuildThreshold(cumprobs, one, 1 - p, 0);
+
+  // The largest element should always be included.
+  std::vector<int64_t> sizes = XlaHelpers::SizesOfXlaOp(p_mask);
+  sizes[dim] = 1;
+  xla::XlaOp ones = xla::Broadcast(one, sizes);
+  std::vector<int64_t> index_to_update = XlaHelpers::SizesOfXlaOp(p_mask);
+  for (int i = 0; i < index_to_update.size(); ++i) {
+    if (i != dim) index_to_update[i] = 0;
+  }
+  xla::XlaOp p_mask_updated = BuildUpdateSlice(p_mask, ones, index_to_update);
+
+  // Re-order the mask back to pre-sorted order.
+  xla::XlaOp sorted_indices = xla::GetTupleElement(sort_result, 1);
+  ScatterOptions options(/*combiner=*/nullptr);
+  xla::XlaOp p_mask_reordered = CreateScatter(
+      device, p_mask_updated, sorted_indices, p_mask_updated, dim, options);
+
+  return p_mask_reordered;
+}
+
 xla::XlaOp CreateMatMul(xla::XlaOp lhs, xla::XlaOp rhs) {
   // Expand cases in https://pytorch.org/docs/stable/torch.html#torch.matmul
   xla::Shape lhs_shape = ShapeHelper::ShapeOfXlaOp(lhs);

torch_xla/csrc/xla_lower_util.h

@@ -20,6 +20,9 @@ std::vector<xla::XlaOp> CreateKthValue(xla::XlaOp input, int64_t k, int64_t dim,
 std::vector<xla::XlaOp> CreateTopK(xla::XlaOp input, int64_t k, int64_t dim,
                                    bool largest, bool stable);
 
+xla::XlaOp CreateTopPMask(const torch::lazy::BackendDevice& device,
+                          xla::XlaOp input, float p, int64_t dim, bool stable);
+
 xla::XlaOp CreateMatMul(xla::XlaOp lhs, xla::XlaOp rhs);
 
 xla::XlaOp BuildMatMul(xla::XlaOp lhs, xla::XlaOp rhs, xla::XlaOp bias);