Revert D23858329: [PT Model Split] Support 2 operators in PT by C2 conversion

Test Plan: revert-hammer

Differential Revision:
D23858329 (pytorch@721cfbf)

Original commit changeset: ed37118ca7f0

fbshipit-source-id: 30c700f80665be11afc608b00a77766064e60b35

Author: Mike Ruberry

caffe2/operators/gather_ranges_to_dense_op.cc

@@ -104,11 +104,3 @@ NO_GRADIENT(GatherRangesToDense);
 
 } // namespace
 } // namespace caffe2
-
-using GatherRangesToDenseCPUOp =
-    caffe2::GatherRangesToDenseOp<caffe2::CPUContext>;
-
-C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
-    GatherRangesToDense,
-    "_caffe2::GatherRangesToDense(Tensor data, Tensor ranges, Tensor? key, int[] lengths, int min_observation, float max_mismatched_ratio, float max_empty_ratio) -> Tensor[] outputs",
-    GatherRangesToDenseCPUOp);

caffe2/operators/gather_ranges_to_dense_op.h

@@ -5,7 +5,6 @@
 
 #include "caffe2/core/common_omp.h"
 #include "caffe2/core/context.h"
-#include "caffe2/core/export_caffe2_op_to_c10.h"
 #include "caffe2/core/logging.h"
 #include "caffe2/core/operator.h"
 #include "caffe2/core/types.h"
@@ -16,8 +15,6 @@
 #include <map>
 #include <utility>
 
-C10_DECLARE_EXPORT_CAFFE2_OP_TO_C10(GatherRangesToDense);
-
 namespace caffe2 {
 template <class Context>
 class GatherRangesToDenseOp final : public Operator<Context> {

caffe2/python/operator_test/torch_integration_test.py

@@ -7,12 +7,10 @@
 import torch
 import unittest
 
-from caffe2.python import core, dyndep, workspace
+from caffe2.python import core, workspace
 from hypothesis import given, settings
 from scipy.stats import norm
 
-dyndep.InitOpsLibrary('@/caffe2/caffe2/fb/operators:calibration_op')
-
 
 def generate_rois(roi_counts, im_dims):
     assert len(roi_counts) == len(im_dims)
@@ -877,71 +875,6 @@ def _batch_bucket_one_hot_ref(data, lengths, boundaries):
         )
         torch.testing.assert_allclose(expected_output, actual_output.cpu())
 
-    def test_gather_ranges_to_dense_op(self):
-        data = np.array([1, 2, 3, 4, 5, 6, 7, 8])
-        ranges = np.array([[[2, 4]], [[0, 0]]])
-        key = np.array([0, 1, 3, 2, 1, 0, 1, 0])
-        lengths = np.array([4])
-        min_observation = 2
-        max_mismatched_ratio = 0.5
-        max_empty_ratio = 1.0
-
-        outputs_name = ["X_{}".format(i) for i in range(len(lengths))]
-        ref_op = core.CreateOperator(
-            "GatherRangesToDense",
-            ["data", "ranges", "key"],
-            outputs_name,
-            lengths=lengths,
-            min_observation=min_observation,
-            max_mismatched_ratio=max_mismatched_ratio,
-            max_empty_ratio=max_empty_ratio,
-        )
-        workspace.FeedBlob("data", data)
-        workspace.FeedBlob("ranges", ranges)
-        workspace.FeedBlob("key", key)
-        workspace.RunOperatorOnce(ref_op)
-        ref_outputs = []
-        for output_name in outputs_name:
-            ref_outputs.append(workspace.FetchBlob(output_name))
-
-        outputs = torch.ops._caffe2.GatherRangesToDense(
-            torch.from_numpy(data),
-            torch.from_numpy(ranges),
-            torch.from_numpy(key),
-            lengths=lengths,
-            min_observation=min_observation,
-            max_mismatched_ratio=max_mismatched_ratio,
-            max_empty_ratio=max_empty_ratio,
-        )
-
-        self.assertEqual(len(ref_outputs), len(outputs))
-        for i in range(0, len(ref_outputs)):
-            np.testing.assert_array_almost_equal(ref_outputs[i], outputs[i].numpy())
-
-    def test_prior_correct_calibration_prediction_op(self):
-        beta = np.array([1.0, 2.0], dtype=np.float32)
-        gamma = np.array([3.0, 4.0], dtype=np.float32)
-        pred = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32)
-
-        ref_op = core.CreateOperator(
-            "PriorCorrectionCalibrationPrediction",
-            ["beta", "gamma", "pred"],
-            ["new_pred"],
-        )
-        workspace.FeedBlob("beta", beta)
-        workspace.FeedBlob("gamma", gamma)
-        workspace.FeedBlob("pred", pred)
-        workspace.RunOperatorOnce(ref_op)
-        ref_output = workspace.FetchBlob("new_pred")
-
-        output = torch.ops._caffe2.PriorCorrectionCalibrationPrediction(
-            torch.from_numpy(beta),
-            torch.from_numpy(gamma),
-            torch.from_numpy(pred),
-        )
-        torch.testing.assert_allclose(ref_output, output)
-
-
     @given(lengths_0=st.integers(1, 10), lengths_1=st.integers(1, 10))
     @settings(deadline=1000)
     def test_merge_id_lists(self, lengths_0, lengths_1):