Add Slice Tensor for int64 index in ElemenetwiseKernel (#57313)

AnnaTrainingG · web-flow · commit f147f4b729ee · 2023-10-10T15:37:42.000+08:00
diff --git a/paddle/phi/kernels/funcs/broadcast_function.h b/paddle/phi/kernels/funcs/broadcast_function.h
@@ -518,7 +518,7 @@ HOSTDEVICE static int64_t ConvertSrcIdxToDstIdx(
     const phi::Array<int64_t, phi::DDim::kMaxRank + 1> &dst_strides,
     int rank) {
   int64_t dst_idx = 0;
-  int64_t old_src_idx = src_idx;
+  int64_t origin_src_idx = src_idx;
   for (int k = 0; k < rank; ++k) {
     auto local_idx = src_idx / src_strides[k + 1];
     src_idx -= local_idx * src_strides[k + 1];
@@ -866,59 +866,6 @@ BroadcastKernelForDifferentVecSize(const KPDevice &ctx,
   int vec_size = GetVectorizedSizeForTensors(ins, *outs);
 #endif
 
-#ifndef PADDLE_WITH_XPU_KP
-  constexpr bool kEnabledInt64IndexKernel = (NumOuts == 1 && Arity <= 3);
-  bool use_int64_index_kernel =
-      kEnabledInt64IndexKernel &&
-      (*outs)[0]->numel() >= std::numeric_limits<int32_t>::max();
-  if (use_int64_index_kernel) {
-    switch (vec_size) {
-      case VecSizeL: {
-        LaunchBroadcastKernelWithInt64IndexHelper<OutT,
-                                                  Functor,
-                                                  Arity,
-                                                  NumOuts,
-                                                  VecSizeL>::Run(ctx,
-                                                                 ins,
-                                                                 outs,
-                                                                 axis,
-                                                                 func);
-        break;
-      }
-      case VecSizeM: {
-        LaunchBroadcastKernelWithInt64IndexHelper<OutT,
-                                                  Functor,
-                                                  Arity,
-                                                  NumOuts,
-                                                  VecSizeM>::Run(ctx,
-                                                                 ins,
-                                                                 outs,
-                                                                 axis,
-                                                                 func);
-        break;
-      }
-      case VecSizeS: {
-        LaunchBroadcastKernelWithInt64IndexHelper<OutT,
-                                                  Functor,
-                                                  Arity,
-                                                  NumOuts,
-                                                  VecSizeS>::Run(ctx,
-                                                                 ins,
-                                                                 outs,
-                                                                 axis,
-                                                                 func);
-        break;
-      }
-      default: {
-        PADDLE_THROW(phi::errors::Unimplemented(
-            "Unsupported vectorized size: %d!", vec_size));
-        break;
-      }
-    }
-    return;
-  }
-#endif
-
   auto classifier =
       BroadcastTypeClassifier<OutT, Functor, Arity, NumOuts>(ins, outs, axis);
   switch (vec_size) {
@@ -945,6 +892,195 @@ BroadcastKernelForDifferentVecSize(const KPDevice &ctx,
   }
 }
 
+static void updateStridesDims(std::vector<int64_t> *strides,
+                              std::vector<int64_t> *dims) {
+  for (int i = 1; i < strides->size(); i++) {
+    (*strides)[i] = (*strides)[i - 1] * (*dims)[i - 1];
+  }
+  // reverse origin_in_dim and origin_in_stride if in's dim_size > 0
+  std::reverse(strides->begin(), strides->end());
+  std::reverse(dims->begin(), dims->end());
+}
+
+static void SliceTensor(DenseTensor *x,
+                        const DenseTensor *share,
+                        const std::vector<int64_t> &out_compute_dims,
+                        int64_t offset) {
+  auto new_dim = make_ddim(out_compute_dims);
+  DenseTensorMeta meta(share->dtype(),
+                       new_dim,
+                       share->layout(),
+                       offset * SizeOf(share->dtype()));
+  x->set_meta(meta);
+  x->ShareBufferWith(*(share), true);
+  x->Resize(new_dim);
+}
+
+template <typename OutT, typename Functor, int kArity, int NumOuts = 1>
+void BroadcastKernelSplit(const KPDevice &ctx,
+                          const std::vector<const DenseTensor *> &ins,
+                          std::vector<DenseTensor *> *outs,
+                          int axis,
+                          Functor func,
+                          const int64_t compute_size) {
+  const auto dims_simplifier =
+      BroadcastDimsSimplifier(ins, (*outs)[0]->dims(), axis);
+  if (VLOG_IS_ON(6)) {
+    DimsSimplifiedLogger<int64_t>::Log(
+        ins, outs, dims_simplifier, "GPU Broadcast");
+  }
+
+  int all_rank = dims_simplifier.rank;
+  std::vector<int64_t> origin_out_strides(all_rank, 1);
+  auto origin_in_dims = dims_simplifier.in_dims;
+  auto origin_out_dims = dims_simplifier.out_dims;
+  auto origin_in_strides = dims_simplifier.in_dims;
+
+  // for split
+  std::vector<int64_t> loop_num_out(all_rank, 1);
+  std::vector<int64_t> loop_num_out_stride(all_rank, 1);
+
+  // for input's offset
+  std::vector<int64_t> ins_offset(kArity, 0);
+  std::vector<int64_t> ins_scale_for_dim(kArity, 0);
+
+  // init offset and check in's dim
+  for (int k = 0; k < kArity; k++) {
+    ins_scale_for_dim[k] = ins[k]->dims().size() == 0 ? 0 : 1;
+    if (ins_scale_for_dim[k]) {
+      origin_in_strides[k][0] = 1;
+    }
+  }
+
+  updateStridesDims(&origin_out_strides, &origin_out_dims);
+  for (int k = 0; k < kArity; k++) {
+    if (ins_scale_for_dim[k]) {
+      updateStridesDims(&origin_in_strides[k], &origin_in_dims[k]);
+    }
+  }
+
+  // init out_split_dim and in_split_dims
+  auto out_split_dim = origin_out_dims;
+  auto in_split_dims = origin_in_dims;
+
+  // init
+  int64_t loop_num = 1;
+  int64_t split_idx = 0;
+
+  for (int r = 0; r < all_rank; r++) {
+    // if the compute_size was too small the split_size must be 0, but the
+    // dim_num must ge 1
+    int64_t split_size = compute_size / origin_out_strides[r];
+    out_split_dim[r] = std::max(split_size, static_cast<int64_t>(1));
+    loop_num_out[r] =
+        (origin_out_dims[r] + out_split_dim[r] - 1) / out_split_dim[r];
+    loop_num *= loop_num_out[r];
+
+    for (int k = 0; k < kArity; k++) {
+      if (ins_scale_for_dim[k]) {
+        in_split_dims[k][r] = std::min(origin_in_dims[k][r], out_split_dim[r]);
+      }
+    }
+
+    // split_idx is the index for lash split dim
+    if (split_size != 0) {
+      split_idx = r;
+      break;
+    }
+  }
+
+  loop_num_out_stride[all_rank - 1] = 1;
+  for (int r = all_rank - 2; r >= 0; r--) {
+    loop_num_out_stride[r] = loop_num_out_stride[r + 1] * loop_num_out[r + 1];
+  }
+
+  // compute
+
+  for (int iter = 0; iter < loop_num; iter++) {
+    std::vector<const DenseTensor *> new_ins = {};
+    std::vector<DenseTensor *> new_outs = {};
+    phi::DenseTensor tmp_in[kArity];
+    DenseTensor tmp_out[NumOuts];
+
+    int64_t tmp_size = iter;
+    int64_t out_offset = 0;
+    // compute the offset before  last split dim
+    for (int i = 0; i < split_idx; i++) {
+      auto repeat_times = tmp_size / loop_num_out_stride[i];
+      out_offset += repeat_times * origin_out_strides[i];
+      for (int k = 0; k < kArity; k++) {
+        if (ins_scale_for_dim[k]) {
+          ins_offset[k] +=
+              (repeat_times % origin_in_dims[k][i]) * origin_in_strides[k][i];
+        }
+      }
+      tmp_size = tmp_size % loop_num_out_stride[i];
+    }
+    // tmp_size is the last split_dims's repeat idx
+    auto pre_deal_size = tmp_size * out_split_dim[split_idx];
+    out_offset += pre_deal_size * origin_out_strides[split_idx];
+    // compute_size
+    auto remainder_size = origin_out_dims[split_idx] - pre_deal_size;
+
+    // get current compute size
+    auto out_compute_dims = out_split_dim;
+    out_compute_dims[split_idx] =
+        std::min(out_split_dim[split_idx], remainder_size);
+
+    // in + compute_size
+    auto in_compute_dims = in_split_dims;
+    for (int k = 0; k < kArity; k++) {
+      if (ins_scale_for_dim[k]) {
+        auto split_repeat =
+            origin_in_dims[k][split_idx] == origin_out_dims[split_idx]
+                ? tmp_size
+                : 0;
+        ins_offset[k] += split_repeat * in_split_dims[k][split_idx] *
+                         origin_in_strides[k][split_idx];
+        in_compute_dims[k][split_idx] =
+            std::min(in_split_dims[k][split_idx], out_compute_dims[split_idx]);
+      }
+      SliceTensor(&tmp_in[k],
+                  ins[k],
+                  in_compute_dims[k],
+                  ins_scale_for_dim[k] * ins_offset[k]);
+      new_ins.emplace_back(&tmp_in[k]);
+      ins_offset[k] = 0;
+    }
+
+    for (int n = 0; n < NumOuts; n++) {
+      SliceTensor(&tmp_out[n], (*outs)[n], out_compute_dims, out_offset);
+      new_outs.emplace_back(&tmp_out[n]);
+    }
+
+    BroadcastKernelForDifferentVecSize<OutT, Functor, kArity, NumOuts>(
+        ctx, new_ins, &new_outs, axis, func);
+  }
+}
+
+template <typename OutT, typename Functor, int kArity, int NumOuts = 1>
+void BroadcastKernelApply(const KPDevice &ctx,
+                          const std::vector<const DenseTensor *> &ins,
+                          std::vector<DenseTensor *> *outs,
+                          int axis,
+                          Functor func) {
+#ifndef PADDLE_WITH_XPU_KP
+  constexpr bool kEnabledInt64IndexKernel = (NumOuts == 1 && kArity <= 3);
+  // check whether need broadcast
+  auto compute_size = std::numeric_limits<int32_t>::max();
+  bool use_int64_index_kernel =
+      kEnabledInt64IndexKernel && (*outs)[0]->numel() >= compute_size;
+
+  if (use_int64_index_kernel) {  // use_int64_index_kernel
+    BroadcastKernelSplit<OutT, Functor, kArity, NumOuts>(
+        ctx, ins, outs, axis, func, compute_size);
+    return;
+  }
+#endif
+  BroadcastKernelForDifferentVecSize<OutT, Functor, kArity, NumOuts>(
+      ctx, ins, outs, axis, func);
+}
+
 template <typename OutT, typename Functor, int NumOuts = 1>
 void BroadcastKernel(const KPDevice &ctx,
                      const std::vector<const DenseTensor *> &ins,
@@ -1009,7 +1145,7 @@ void BroadcastKernel(const KPDevice &ctx,
     max_rank = std::max(max_rank, (*outs)[0]->dims().size());
   }
   axis = axis == -1 ? max_rank - min_rank : axis;
-  BroadcastKernelForDifferentVecSize<OutT, Functor, kArity, NumOuts>(
+  BroadcastKernelApply<OutT, Functor, kArity, NumOuts>(
       ctx, ins, outs, axis, func);
 }
 
diff --git a/test/legacy_test/test_elementwise_tensor_split.py b/test/legacy_test/test_elementwise_tensor_split.py
@@ -0,0 +1,67 @@
+#  Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.base import core
+
+
+class TestElementwiseOp(unittest.TestCase):
+    def setUp(self):
+        self.op_type = "elementwise_sub"
+        self.python_api = paddle.subtract
+        self.public_python_api = paddle.subtract
+        self.prim_op_type = "prim"
+
+    def test_float16_sub(self):
+        if not core.is_compiled_with_cuda():
+            return
+
+        gpu_info = paddle.device.cuda.get_device_properties()
+
+        gpu_name = gpu_info.name
+        try:
+            re_result = re.split(r'[ , -]', gpu_name)
+            memory = int(re_result[-1][:-2])
+        except:
+            memory = int(gpu_info.total_memory) // (1000**3)
+        if memory < 37:  # 37GB
+            return
+
+        paddle.disable_static()
+        tensor_a = paddle.rand(shape=[5120, 4, 384, 384], dtype="float16")
+        tensor_b = paddle.rand(shape=[5120, 1, 384, 384], dtype="float16")
+        tensor_z = paddle.subtract(tensor_a, tensor_b)
+
+        in0, in1 = paddle.split(tensor_a, num_or_sections=2, axis=1)
+        (
+            out0,
+            out1,
+        ) = paddle.split(tensor_z, num_or_sections=2, axis=1)
+
+        split_add0 = paddle.subtract(tensor_b, in0)
+        split_add1 = paddle.subtract(tensor_b, in1)
+
+        result1 = paddle.any(paddle.equal(out0, split_add0), [0, 1, 2, 3])
+        result2 = paddle.any(paddle.equal(out1, split_add1), [0, 1, 2, 3])
+        np.testing.assert_equal(result1.numpy(), True)
+        np.testing.assert_equal(result2.numpy(), True)
+
+
+if __name__ == '__main__':
+    unittest.main()
