[OPENCL][TEXTURE] Test case enhancements and fixes for RPC

python/tvm/relay/frontend/tflite.py

@@ -1549,7 +1549,7 @@ def convert_gather(self, op):
         assert axis < data_dim, "Axis out of bounds"
 
         if self.has_expr(indices.tensor_idx):
-            indices_expr = self.get_expr(indices.tensor_idx)
+            indices_expr = _op.cast(self.get_expr(indices.tensor_idx), "int32")
         else:
             indices_val = self.get_tensor_value(indices)
             indices_expr = self.exp_tab.new_const(

tests/python/relay/opencl_texture/conftest.py

@@ -0,0 +1,39 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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
+#
+#   http://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 os
+import tvm
+from tvm import rpc
+import pytest
+
+
+@pytest.fixture(scope="session")
+def remote():
+    if (
+        "TVM_TRACKER_HOST" in os.environ
+        and "TVM_TRACKER_PORT" in os.environ
+        and "RPC_DEVICE_KEY" in os.environ
+    ):
+
+        rpc_tracker_host = os.environ["TVM_TRACKER_HOST"]
+        rpc_tracker_port = int(os.environ["TVM_TRACKER_PORT"])
+        rpc_device_key = os.environ["RPC_DEVICE_KEY"]
+        tracker = rpc.connect_tracker(rpc_tracker_host, rpc_tracker_port)
+        remote = tracker.request(rpc_device_key, priority=0, session_timeout=600)
+        return remote
+    else:
+        return None

tests/python/relay/opencl_texture/test_conv2d_nchw_texture.py

@@ -30,7 +30,7 @@
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_inceptionv3_64x35x35_96x64x3x3_nopad(target, dtype):
+def test_conv2d_inceptionv3_64x35x35_96x64x3x3_nopad(remote, target, dtype):
     input_shape = (1, 32, 42, 42)
     filter_shape = (96, 32, 3, 3)
     bias_shape = (1, 96, 1, 1)
@@ -65,12 +65,14 @@ def test_conv2d_inceptionv3_64x35x35_96x64x3x3_nopad(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, [], gpu_preprocess)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, [], gpu_preprocess
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_inceptionv3_64x35x35_96x64x3x3_nopad_pass(target, dtype):
+def test_conv2d_inceptionv3_64x35x35_96x64x3x3_nopad_pass(remote, target, dtype):
     input_shape = (1, 32, 40, 40)
     filter_shape = (96, 32, 2, 2)
     bias_shape = (1, 96, 1, 1)
@@ -105,12 +107,14 @@ def test_conv2d_inceptionv3_64x35x35_96x64x3x3_nopad_pass(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, [], gpu_preprocess)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, [], gpu_preprocess
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_inceptionv3_35_35_strides(target, dtype):
+def test_conv2d_inceptionv3_35_35_strides(remote, target, dtype):
     input_shape = (1, 48, 35, 35)
     filter_shape = (64, 48, 5, 5)
     bias_shape = (1, 64, 1, 1)
@@ -145,12 +149,14 @@ def test_conv2d_inceptionv3_35_35_strides(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, [], gpu_preprocess)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, [], gpu_preprocess
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_resnet50_v2_nchw_3c(target, dtype):
+def test_conv2d_resnet50_v2_nchw_3c(remote, target, dtype):
     input_shape = (1, 3, 224, 224)
     filter_shape = (64, 3, 7, 7)
     bias_shape = (1, 64, 1, 1)
@@ -186,12 +192,12 @@ def test_conv2d_resnet50_v2_nchw_3c(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
+    build_run_compare(remote, mod, params1, {"data": input_shape}, {"data": dtype}, target)
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_inceptionv3_nchw_3c(target, dtype):
+def test_conv2d_inceptionv3_nchw_3c(remote, target, dtype):
     input_shape = (1, 3, 299, 299)
     filter_shape = (64, 3, 3, 3)
     bias_shape = (1, 64, 1, 1)
@@ -226,12 +232,12 @@ def test_conv2d_inceptionv3_nchw_3c(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
+    build_run_compare(remote, mod, params1, {"data": input_shape}, {"data": dtype}, target)
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_1x1_16c16spatial(target, dtype):
+def test_conv2d_1x1_16c16spatial(remote, target, dtype):
     input_shape = (1, 16, 256, 256)
     filter_shape = (32, 16, 4, 4)
     bias_shape = (1, 32, 1, 1)
@@ -266,12 +272,12 @@ def test_conv2d_1x1_16c16spatial(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
+    build_run_compare(remote, mod, params1, {"data": input_shape}, {"data": dtype}, target)
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_4x4_16c16pad(target, dtype):
+def test_conv2d_4x4_16c16pad(remote, target, dtype):
     input_shape = (1, 32, 256, 256)
     filter_shape = (32, 32, 4, 4)
     bias_shape = (1, 32, 1, 1)
@@ -306,12 +312,12 @@ def test_conv2d_4x4_16c16pad(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
+    build_run_compare(remote, mod, params1, {"data": input_shape}, {"data": dtype}, target)
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_4x4x4_16c16pad(target, dtype):
+def test_conv2d_4x4x4_16c16pad(remote, target, dtype):
     input_shape = (1, 32, 256, 256)
     filter_shape = (4, 32, 4, 4)
     bias_shape = (1, 4, 1, 1)
@@ -346,12 +352,12 @@ def test_conv2d_4x4x4_16c16pad(target, dtype):
         "bias": tvm.nd.array(bias_data),
     }
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
+    build_run_compare(remote, mod, params1, {"data": input_shape}, {"data": dtype}, target)
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_yolov3_v2_nchw_3c(target, dtype):
+def test_conv2d_yolov3_v2_nchw_3c(remote, target, dtype):
     input_shape = (1, 1024, 13, 13)
     filter_shape = (255, 1024, 1, 1)
     A = relay.var("data", shape=input_shape, dtype=dtype)
@@ -379,12 +385,12 @@ def test_conv2d_yolov3_v2_nchw_3c(target, dtype):
         "weight": tvm.nd.array(filter_data),
     }
 
-    build_run_compare(mod, params, {"data": input_shape}, dtype, target)
+    build_run_compare(remote, mod, params, {"data": input_shape}, {"data": dtype}, target)
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_vgg16_winograd_4d(target, dtype):
+def test_conv2d_vgg16_winograd_4d(remote, target, dtype):
     input_shape = (1, 512, 28, 28)
     filter_shape = (512, 512, 3, 3)
     bias_shape = (1, 512, 1, 1)
@@ -424,15 +430,15 @@ def test_conv2d_vgg16_winograd_4d(target, dtype):
             f'{{"input": ["opencl -keys=adreno,opencl,gpu -device=adreno -max_num_threads=256", "conv2d_nchw_winograd.image2d", [["TENSOR", [1, 512, 28, 28], "{dtype}"], ["TENSOR", [512, 512, 3, 3], "{dtype}"], [1, 1], [1, 1, 1, 1], [1, 1], "{dtype}"], {{}}], "config": {{"index": 1591, "code_hash": null, "entity": [["auto_unroll_max_step", "ot", 4], ["tile_y", "sp", [-1, 1, 32]], ["tile_x", "sp", [-1, 4, 2]], ["tile_rc", "sp", [-1, 8]]]}}, "result": [[0.0037244], 0, 7.06374192237854, 1653898629.7427933], "version": 0.2, "tvm_version": "0.8.dev0"}}\n'
         )
     graph = build_run_compare(
-        mod, params1, {"data": input_shape}, dtype, target, stat_file=stat_file
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, stat_file=stat_file
     )
     matches = re.findall("winograd", graph)
     assert len(matches) > 0
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_winograd_conv(target, dtype):
+def test_conv2d_winograd_conv(remote, target, dtype):
     input_shape = (1, 4, 3, 3)
     A = relay.var("data", shape=input_shape, dtype=dtype)
     filter_shape3 = (8, 4, 3, 3)
@@ -471,15 +477,15 @@ def test_conv2d_winograd_conv(target, dtype):
             f'{{"input": ["opencl -keys=adreno,opencl,gpu -device=adreno -max_num_threads=256", "conv2d_nchw_winograd.image2d", [["TENSOR", [1, 4, 3, 3], "{dtype}"], ["TENSOR", [8, 4, 3, 3], "{dtype}"], [1, 1], [1, 1, 1, 1], [1, 1], "{dtype}"], {{}}], "config": {{"index": 1591, "code_hash": null, "entity": [["auto_unroll_max_step", "ot", 4], ["tile_y", "sp", [-1, 1, 32]], ["tile_x", "sp", [-1, 4, 2]], ["tile_rc", "sp", [-1, 8]]]}}, "result": [[0.0037244], 0, 7.06374192237854, 1653898629.7427933], "version": 0.2, "tvm_version": "0.8.dev0"}}\n'
         )
     graph = build_run_compare(
-        mod, params1, {"data": input_shape}, dtype, target, stat_file=stat_file
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, stat_file=stat_file
     )
     matches = re.findall("winograd", graph)
     assert len(matches) > 0
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_residual_block(target, dtype):
+def test_residual_block(remote, target, dtype):
     """
     - some kind of residual block followed by convolution to have texture after residual block
     - scalar data type verification which should be mapped to global memory scope
@@ -596,12 +602,14 @@ def test_residual_block(target, dtype):
             "",
         ]
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_concat(target, dtype):
+def test_concat(remote, target, dtype):
     """
         layout_transform (NCHW->NCHW4c)
                   |                      <- buffer
@@ -708,12 +716,14 @@ def test_concat(target, dtype):
 
     static_memory_scope = []
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_pooling_branching_texture_params(target, dtype):
+def test_pooling_branching_texture_params(remote, target, dtype):
     """
     Verification of the pooling and many branches having textures
                 layout_transform (NCHW->NCHW4c)
@@ -834,12 +844,14 @@ def test_pooling_branching_texture_params(target, dtype):
         "",
     ]
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_branching_texture_params(target, dtype):
+def test_branching_texture_params(remote, target, dtype):
     """
     Verification of passing texture to several consumers markup of relay variables in
     primary functions + on_device
@@ -958,13 +970,15 @@ def test_branching_texture_params(target, dtype):
         "",
     ]
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
 
 
 # function repeat, params scope are different in reused functions
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_different_lowering_same_op(target, dtype):
+def test_conv2d_different_lowering_same_op(remote, target, dtype):
     """
     Use case for verification of caching compiled functions
     Three convolutions following by each other in this case should be
@@ -1040,12 +1054,14 @@ def test_conv2d_different_lowering_same_op(target, dtype):
         "",
     ]
 
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
 
 
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_conv2d_winograd_non_rect(target, dtype):
+def test_conv2d_winograd_non_rect(remote, target, dtype):
     input_shape = (1, 771, 36, 64)
     A = relay.var("data", shape=input_shape, dtype=dtype)
     filter_shape = (128, 771, 3, 3)
@@ -1070,7 +1086,7 @@ def test_conv2d_winograd_non_rect(target, dtype):
             f'{{"input": ["opencl -keys=adreno,opencl,gpu -device=adreno -max_num_threads=256 -texture_spatial_limit=16384 -thread_warp_size=1", "conv2d_nchw_winograd.image2d", [["TENSOR", [1, 771, 36, 64], "{dtype}"], ["TENSOR", [128, 771, 3, 3], "{dtype}"], [1, 1], [1, 1, 1, 1], [1, 1], "{dtype}"], {{}}], "config": {{"index": 5399, "code_hash": null, "entity": [["auto_unroll_max_step", "ot", 16], ["tile_y", "sp", [-1, 1, 32]], ["tile_x", "sp", [-1, 4, 8]], ["tile_rc", "sp", [-1, 193]]]}}, "result": [[0.0037244], 0, 7.06374192237854, 1653898629.7427933], "version": 0.2, "tvm_version": "0.8.dev0"}}\n'
         )
     graph = build_run_compare(
-        mod, params1, {"data": input_shape}, dtype, target, stat_file=stat_file
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, stat_file=stat_file
     )
     matches = re.findall("winograd", graph)
     assert len(matches) > 0
@@ -1079,7 +1095,7 @@ def test_conv2d_winograd_non_rect(target, dtype):
 # function repeat, params scope are different in reused functions
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_injective_nwo_inputs1(target, dtype):
+def test_injective_nwo_inputs1(remote, target, dtype):
     """
     Use case for verification of stability of annotation primary functions
     having several ops accepting data outside of Primary function
@@ -1170,13 +1186,15 @@ def test_injective_nwo_inputs1(target, dtype):
         "global",
         "global",
     ]
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
 
 
 # function repeat, params scope are different in reused functions
 @tvm.testing.requires_opencl
 @tvm.testing.parametrize_targets("opencl -device=adreno")
-def test_injective_nwo_inputs2(target, dtype):
+def test_injective_nwo_inputs2(remote, target, dtype):
     """
     Use case for verification of stability of annotation primary functions
     having several ops accepting data outside of Primary function
@@ -1266,4 +1284,10 @@ def test_injective_nwo_inputs2(target, dtype):
         "global.texture",
         "global",
     ]
-    build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+    build_run_compare(
+        remote, mod, params1, {"data": input_shape}, {"data": dtype}, target, static_memory_scope
+    )
+
+
+if __name__ == "__main__":
+    tvm.testing.main()