From 81e4c7f50ba3118ce341220d076a575c1af2bbb1 Mon Sep 17 00:00:00 2001 From: Ritwik Das Date: Wed, 2 Dec 2020 12:27:12 -0800 Subject: [PATCH 1/5] Fix trt Test --- tests/python/contrib/test_tensorrt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py index de9822289528..47270c18e773 100644 --- a/tests/python/contrib/test_tensorrt.py +++ b/tests/python/contrib/test_tensorrt.py @@ -1058,7 +1058,7 @@ def test_tensorrt_dynamic_batch(): mod = tvm.IRModule() mod["main"] = f if use_trt: - mod = relay.tensorrt.EnableTrt(mod) + mod = tensorrt.partition_for_tensorrt(mod, params) if not skip_runtime_test(): with relay.build_config(opt_level=3): From 8e2ce9aff1cb0232f8cc67d3451c9d37908b7883 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Wed, 2 Dec 2020 21:36:43 +0000 Subject: [PATCH 2/5] Fixed stuff --- tests/python/contrib/test_tensorrt.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py index de9822289528..4713130d7112 100644 --- a/tests/python/contrib/test_tensorrt.py +++ b/tests/python/contrib/test_tensorrt.py @@ -1050,26 +1050,26 @@ def test_tensorrt_dynamic_batch(): batches_to_test = [1, 1, 0, 2, 3, 0, 1, 3, 2] x_shape = (relay.Any(), 1, 8, 8) x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32") - result_dict = {} for use_trt in [True, False]: + result_dict = {} x = relay.var("x", shape=x_shape, dtype="float32") out = relay.nn.relu(x) f = relay.Function([x], out) mod = tvm.IRModule() mod["main"] = f if use_trt: - mod = relay.tensorrt.EnableTrt(mod) + mod, _ = tensorrt.partition_for_tensorrt(mod) if not skip_runtime_test(): with relay.build_config(opt_level=3): relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm") for i, batch_size in enumerate(batches_to_test): - result_dict[(i, use_trt)] = relay_exec.evaluate()(x_data[:batch_size, ...]) + result_dict[use_trt] = relay_exec.evaluate()(x_data[:batch_size, ...]) - if not skip_runtime_test(): - for i in range(len(batches_to_test)): - assert_result_matches(result_dict[(i, True)], result_dict[(i, False)]) + if not skip_runtime_test(): + for i in range(len(batches_to_test)): + assert_result_dict_holds(result_dict) def test_tensorrt_dynamic_batch_conv(): @@ -1080,8 +1080,8 @@ def test_tensorrt_dynamic_batch_conv(): x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32") k_shape = (16, 32, 3, 3) params = {"kernel": np.random.uniform(-1, 1, k_shape).astype("float32")} - result_dict = {} for use_trt in [True, False]: + result_dict = {} x = relay.var("x", shape=x_shape, dtype="float32") kernel = relay.var("kernel", shape=k_shape, dtype="float32") out = relay.nn.conv2d(x, kernel, channels=16, kernel_size=(3, 3), groups=1) @@ -1089,20 +1089,18 @@ def test_tensorrt_dynamic_batch_conv(): mod = tvm.IRModule() mod["main"] = f if use_trt: - mod = tensorrt.partition_for_tensorrt(mod, params) + mod, _ = tensorrt.partition_for_tensorrt(mod, params) if not skip_runtime_test(): with relay.build_config(opt_level=3): relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm") for i, batch_size in enumerate(batches_to_test): - result_dict[(i, use_trt)] = relay_exec.evaluate()( - x=x_data[:batch_size, ...], **params - ) + result_dict[use_trt] = relay_exec.evaluate()(x=x_data[:batch_size, ...], **params) - if not skip_runtime_test(): - for i in range(len(batches_to_test)): - assert_result_matches(result_dict[(i, True)], result_dict[(i, False)]) + if not skip_runtime_test(): + for i in range(len(batches_to_test)): + assert_result_dict_holds(result_dict) def test_maskrcnn_resnet50() -> None: From 4e160e986422d28ea35b390b0ec02961604815c7 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Wed, 2 Dec 2020 22:16:25 +0000 Subject: [PATCH 3/5] Done --- tests/python/contrib/test_tensorrt.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py index 4713130d7112..8c63c7bdb15b 100644 --- a/tests/python/contrib/test_tensorrt.py +++ b/tests/python/contrib/test_tensorrt.py @@ -1050,8 +1050,8 @@ def test_tensorrt_dynamic_batch(): batches_to_test = [1, 1, 0, 2, 3, 0, 1, 3, 2] x_shape = (relay.Any(), 1, 8, 8) x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32") + result_arr = [{} for _ in range(len(batches_to_test))] for use_trt in [True, False]: - result_dict = {} x = relay.var("x", shape=x_shape, dtype="float32") out = relay.nn.relu(x) f = relay.Function([x], out) @@ -1065,23 +1065,23 @@ def test_tensorrt_dynamic_batch(): relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm") for i, batch_size in enumerate(batches_to_test): - result_dict[use_trt] = relay_exec.evaluate()(x_data[:batch_size, ...]) + result_arr[i][use_trt] = relay_exec.evaluate()(x_data[:batch_size, ...]) - if not skip_runtime_test(): - for i in range(len(batches_to_test)): - assert_result_dict_holds(result_dict) + if not skip_runtime_test(): + for i in range(len(batches_to_test)): + assert_result_dict_holds(result_arr[i]) def test_tensorrt_dynamic_batch_conv(): if skip_codegen_test(): return - batches_to_test = [1, 1, 0, 2, 3, 0, 1, 3, 2] + batches_to_test = [1, 1, 2, 3, 1, 3, 2] x_shape = (relay.Any(), 32, 8, 8) x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32") k_shape = (16, 32, 3, 3) params = {"kernel": np.random.uniform(-1, 1, k_shape).astype("float32")} + result_arr = [{} for _ in range(len(batches_to_test))] for use_trt in [True, False]: - result_dict = {} x = relay.var("x", shape=x_shape, dtype="float32") kernel = relay.var("kernel", shape=k_shape, dtype="float32") out = relay.nn.conv2d(x, kernel, channels=16, kernel_size=(3, 3), groups=1) @@ -1096,11 +1096,11 @@ def test_tensorrt_dynamic_batch_conv(): relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm") for i, batch_size in enumerate(batches_to_test): - result_dict[use_trt] = relay_exec.evaluate()(x=x_data[:batch_size, ...], **params) + result_arr[i][use_trt] = relay_exec.evaluate()(x_data[:batch_size, ...], **params) - if not skip_runtime_test(): - for i in range(len(batches_to_test)): - assert_result_dict_holds(result_dict) + if not skip_runtime_test(): + for i in range(len(batches_to_test)): + assert_result_dict_holds(result_arr[i]) def test_maskrcnn_resnet50() -> None: From 3104113046dd982b3bc20c3df91fb6b24442c902 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Wed, 2 Dec 2020 22:20:11 +0000 Subject: [PATCH 4/5] fix 0 --- tests/python/contrib/test_tensorrt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py index 8c63c7bdb15b..aadfa1303655 100644 --- a/tests/python/contrib/test_tensorrt.py +++ b/tests/python/contrib/test_tensorrt.py @@ -1075,7 +1075,7 @@ def test_tensorrt_dynamic_batch(): def test_tensorrt_dynamic_batch_conv(): if skip_codegen_test(): return - batches_to_test = [1, 1, 2, 3, 1, 3, 2] + batches_to_test = [1, 1, 0, 2, 3, 0, 1, 3, 2] x_shape = (relay.Any(), 32, 8, 8) x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32") k_shape = (16, 32, 3, 3) From 24116fd698938dad751b2d6b8590da5b13ac5b35 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Thu, 3 Dec 2020 00:00:12 +0000 Subject: [PATCH 5/5] Trigger Build