[incubator-tvm-site] branch asf-site updated: Docs build at Thu Nov 19 16:28:41 UTC 2020

[co...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

comaniac pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-tvm-site.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new f373bd1  Docs build at Thu Nov 19 16:28:41 UTC 2020
f373bd1 is described below

commit f373bd1f09af6f50cb073f8864684d2fe11517a1
Author: Cody Yu <co...@gmail.com>
AuthorDate: Thu Nov 19 16:28:42 2020 +0000

    Docs build at Thu Nov 19 16:28:41 UTC 2020
---
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 .../tutorials/frontend/deploy_classification.html  |   18 +-
 .../vta/tutorials/frontend/sg_execution_times.html |    4 +-
 docs/vta/tutorials/matrix_multiply.html            |    4 +-
 .../vta/tutorials/optimize/sg_execution_times.html |    6 +-
 docs/vta/tutorials/sg_execution_times.html         |    6 +-
 docs/vta/tutorials/vta_get_started.html            |    4 +-
 327 files changed, 18977 insertions(+), 16091 deletions(-)

diff --git a/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py b/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
index 4756ea3..723b8d1 100644
--- a/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
+++ b/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
@@ -102,10 +102,10 @@ def get_network(name, batch_size, layout="NHWC", dtype="float32"):
             batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape
         )
     elif name == "squeezenet_v1.1":
+        assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"
         mod, params = relay.testing.squeezenet.get_workload(
             version="1.1",
             batch_size=batch_size,
-            layout=layout,
             dtype=dtype,
             image_shape=image_shape,
         )
@@ -148,9 +148,6 @@ log_file = "%s-%s-B%d.json" % (network, layout, batch_size)
 # latency of a task and :code:`weight[t]` is the weight of the task.
 # The task scheduler will just optimize this objective.
 
-# Enable auto-scheduler in relay
-auto_scheduler.enable_relay_integration()
-
 # Extract tasks from the network
 print("Extract tasks...")
 mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)
@@ -219,29 +216,32 @@ def run_tuning():
 #     ----------------------------------------------------------------------
 #     |  ID  | Latency (ms) | Speed (GFLOPS) | Trials |
 #     -------------------------------------------------
-#     |    0 |        0.014 |          72.07 |     64 |
-#     |    1 |        0.185 |        1250.68 |    128 |
-#     |    2 |        0.142 |        1626.36 |    192 |
-#     |    3 |        0.137 |        1689.42 |    128 |
-#     |    4 |        0.097 |        1189.75 |    128 |
-#     |    5 |        0.092 |        2505.25 |    128 |
-#     |    6 |        0.080 |        2893.08 |    128 |
-#     |    7 |        0.119 |        1947.84 |    128 |
-#     |    8 |        0.090 |        1292.62 |     64 |
-#     |    9 |        0.107 |        2172.30 |     64 |
-#     |   10 |        0.095 |        2439.36 |     64 |
-#     |   11 |        0.077 |        3003.22 |     64 |
-#     |   12 |        0.068 |        1695.13 |     64 |
-#     |   13 |        0.058 |        3979.29 |     64 |
-#     |   14 |        0.048 |        4859.95 |    128 |
-#     |   15 |        0.073 |        3151.76 |     64 |
-#     |   16 |        0.056 |        4265.94 |     64 |
-#     |   17 |        0.009 |        2754.90 |     64 |
-#     |   18 |        0.011 |        1156.08 |     64 |
-#     |   19 |        0.013 |         955.80 |     64 |
-#     |   20 |        0.029 |         437.71 |     64 |
+#     |    0 |        0.005 |           0.88 |     64 |
+#     |    1 |        0.010 |          99.10 |     64 |
+#     |    2 |        0.006 |           0.00 |     64 |
+#     |    3 |        0.145 |         979.78 |    384 |
+#     |    4 |        0.130 |        1097.02 |    384 |
+#     |    5 |        0.143 |         992.69 |    384 |
+#     |    6 |        0.076 |        1526.86 |    192 |
+#     |    7 |        0.115 |         999.44 |    320 |
+#     |    8 |        0.079 |        1449.39 |    320 |
+#     |    9 |        0.122 |         938.73 |    384 |
+#     |   10 |        0.063 |        1832.98 |    192 |
+#     |   11 |        0.072 |        1763.62 |    256 |
+#     |   12 |        0.062 |        2036.40 |    192 |
+#     |   13 |        0.068 |        1874.44 |    192 |
+#     |   14 |        0.049 |        2346.50 |    128 |
+#     |   15 |        0.076 |        1694.31 |    256 |
+#     |   16 |        0.067 |        1933.30 |    448 |
+#     |   17 |        0.076 |        1680.90 |    256 |
+#     |   18 |        0.022 |          98.43 |     64 |
+#     |   19 |        0.076 |        3112.55 |    192 |
+#     |   20 |        0.013 |        2026.44 |     64 |
+#     |   21 |        0.011 |        1136.69 |     64 |
+#     |   22 |        0.013 |         992.47 |     64 |
+#     |   23 |        0.020 |         627.56 |     64 |
 #     -------------------------------------------------
-#     Estimated total latency: 1.649 ms  Trials: 1920  Used time : 3598 s  Next ID: 9
+#     Estimated total latency: 1.587 ms  Trials: 4992  Used time : 13296 s  Next ID: 3
 #
 #   This table lists the latency and (estimated) speed of all tasks.
 #   It also lists the allocation of measurement trials for all tasks.
@@ -276,7 +276,7 @@ def run_tuning():
 # Compile with the history best
 print("Compile...")
 with auto_scheduler.ApplyHistoryBest(log_file):
-    with tvm.transform.PassContext(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3, config={"relay.backend.use_auto_scheduler": True}):
         lib = relay.build(mod, target=target, params=params)
 
 # Create graph runtime
diff --git a/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb b/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb
index 7814f32..b3df1b7 100644
--- a/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb
+++ b/docs/_downloads/dad91669fd0ea707f1374fe331b0dffe/tune_network_cuda.ipynb
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "def get_network(name, batch_size, layout=\"NHWC\", dtype=\"float32\"):\n    \"\"\"Get the symbol definition and random weight of a network\"\"\"\n\n    # auto-scheduler prefers NHWC layout\n    if layout == \"NHWC\":\n        image_shape = (224, 224, 3)\n    elif layout == \"NCHW\":\n        image_shape = (3, 224, 224)\n    else:\n        raise ValueError(\"Invalid layout: \" + layout)\n\n    input_shape = (batch_size,) + image_shape\n    output_shape = (batch_size, 1000)\n\n    [...]
+        "def get_network(name, batch_size, layout=\"NHWC\", dtype=\"float32\"):\n    \"\"\"Get the symbol definition and random weight of a network\"\"\"\n\n    # auto-scheduler prefers NHWC layout\n    if layout == \"NHWC\":\n        image_shape = (224, 224, 3)\n    elif layout == \"NCHW\":\n        image_shape = (3, 224, 224)\n    else:\n        raise ValueError(\"Invalid layout: \" + layout)\n\n    input_shape = (batch_size,) + image_shape\n    output_shape = (batch_size, 1000)\n\n    [...]
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "# Enable auto-scheduler in relay\nauto_scheduler.enable_relay_integration()\n\n# Extract tasks from the network\nprint(\"Extract tasks...\")\nmod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)\ntasks, task_weights = auto_scheduler.extract_tasks(mod[\"main\"], params, target)\n\nfor idx, task in enumerate(tasks):\n    print(\"========== Task %d  (workload key: %s) ==========\" % (idx, task.workload_key))\n    print(task.compute_dag)"
+        "# Extract tasks from the network\nprint(\"Extract tasks...\")\nmod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)\ntasks, task_weights = auto_scheduler.extract_tasks(mod[\"main\"], params, target)\n\nfor idx, task in enumerate(tasks):\n    print(\"========== Task %d  (workload key: %s) ==========\" % (idx, task.workload_key))\n    print(task.compute_dag)"
       ]
     },
     {
@@ -87,7 +87,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "<div class=\"alert alert-info\"><h4>Note</h4><p>Explain the printed information during tuning\n\n  During the tuning, a lot of information will be printed on the console.\n  They are used for debugging purposes. The most important info is the output\n  of the task scheduler. The following table is a sample output.\n\n  .. code-block:: c\n\n    ----------------------------------------------------------------------\n    ------------------------------  [ Task Scheduler ]\n    ----- [...]
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>Explain the printed information during tuning\n\n  During the tuning, a lot of information will be printed on the console.\n  They are used for debugging purposes. The most important info is the output\n  of the task scheduler. The following table is a sample output.\n\n  .. code-block:: c\n\n    ----------------------------------------------------------------------\n    ------------------------------  [ Task Scheduler ]\n    ----- [...]
       ]
     },
     {
@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "# Compile with the history best\nprint(\"Compile...\")\nwith auto_scheduler.ApplyHistoryBest(log_file):\n    with tvm.transform.PassContext(opt_level=3):\n        lib = relay.build(mod, target=target, params=params)\n\n# Create graph runtime\nctx = tvm.context(str(target), 0)\nmodule = graph_runtime.GraphModule(lib[\"default\"](ctx))\ndata_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))\nmodule.set_input(\"data\", data_tvm)\n\n# Evaluate\nprint(\"Evaluate [...]
+        "# Compile with the history best\nprint(\"Compile...\")\nwith auto_scheduler.ApplyHistoryBest(log_file):\n    with tvm.transform.PassContext(opt_level=3, config={\"relay.backend.use_auto_scheduler\": True}):\n        lib = relay.build(mod, target=target, params=params)\n\n# Create graph runtime\nctx = tvm.context(str(target), 0)\nmodule = graph_runtime.GraphModule(lib[\"default\"](ctx))\ndata_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))\nmodule.set_inpu [...]
       ]
     },
     {
diff --git a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
index 03c79e9..e5b4a2a 100644
--- a/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
 
 Computation times
 =================
-**05:00.161** total execution time for **tutorials_auto_scheduler** files:
+**05:06.605** total execution time for **tutorials_auto_scheduler** files:
 
-- **02:42.357**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:53.558**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_matmul_x86.py` (``tune_matmul_x86.py``)
-- **00:24.246**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **02:40.179**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:55.197**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_matmul_x86.py` (``tune_matmul_x86.py``)
+- **00:31.229**: :ref:`sphx_glr_tutorials_auto_scheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
index 03aafc6..08a0904 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_conv2d_layer_cuda.rst.txt
@@ -205,63 +205,572 @@ cooperative fetching, unrolling and operator fusion.
 
     primfn(data_1: handle, kernel_1: handle, bias_1: handle, compute_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {bias: Buffer(bias_2: Pointer(float32), float32, [1, 512, 1, 1], []),
-                 compute: Buffer(compute_2: Pointer(float32), float32, [1, 512, 7, 7], []),
+      buffers = {compute: Buffer(compute_2: Pointer(float32), float32, [1, 512, 7, 7], []),
+                 bias: Buffer(bias_2: Pointer(float32), float32, [1, 512, 1, 1], []),
                  kernel: Buffer(kernel_2: Pointer(float32), float32, [512, 512, 3, 3], []),
                  data: Buffer(data_2: Pointer(float32), float32, [1, 512, 7, 7], [])}
       buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
-      attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 16;
+      attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 32;
       attr [compute_3: Pointer(float32)] "storage_scope" = "local";
-      allocate(compute_3, float32, [14]);
+      allocate(compute_3, float32, [7]);
       attr [pad_temp.shared: Pointer(float32)] "storage_scope" = "shared";
-      allocate(pad_temp.shared, float32, [81]);
+      allocate(pad_temp.shared, float32, [648]);
       attr [kernel.shared: Pointer(float32)] "storage_scope" = "shared";
-      allocate(kernel.shared, float32, [288]);
+      allocate(kernel.shared, float32, [1152]);
       attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112 {
-        for (ff.inner.init: int32, 0, 2) {
-          compute_3[ff.inner.init] = 0f32
-          compute_3[(ff.inner.init + 2)] = 0f32
-          compute_3[(ff.inner.init + 4)] = 0f32
-          compute_3[(ff.inner.init + 6)] = 0f32
-          compute_3[(ff.inner.init + 8)] = 0f32
-          compute_3[(ff.inner.init + 10)] = 0f32
-          compute_3[(ff.inner.init + 12)] = 0f32
-        }
-        for (rc.outer.outer: int32, 0, 512) {
+        compute_3[0] = 0f32
+        compute_3[1] = 0f32
+        compute_3[2] = 0f32
+        compute_3[3] = 0f32
+        compute_3[4] = 0f32
+        compute_3[5] = 0f32
+        compute_3[6] = 0f32
+        for (rc.outer.outer: int32, 0, 64) {
           attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
-          if @tir.likely((threadIdx.x_1 < 9), dtype=bool) {
-            for (ax0.ax1.fused.ax2.fused.ax3.fused.inner.s: int32, 0, 9) {
-              pad_temp.shared[((threadIdx.x_1*9) + ax0.ax1.fused.ax2.fused.ax3.fused.inner.s)] = @tir.if_then_else(((((1 <= threadIdx.x_1) && (threadIdx.x_1 < 8)) && (1 <= ax0.ax1.fused.ax2.fused.ax3.fused.inner.s)) && (ax0.ax1.fused.ax2.fused.ax3.fused.inner.s < 8)), (float32*)data_2[((((rc.outer.outer*49) + (threadIdx.x_1*7)) + ax0.ax1.fused.ax2.fused.ax3.fused.inner.s) - 8)], 0f32, dtype=float32)
-            }
-          }
-          for (ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer: int32, 0, 3) {
-            attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
-            if @tir.likely((((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*112) + threadIdx.x_2) < 288), dtype=bool) {
-              kernel.shared[((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*112) + threadIdx.x_2)] = (float32*)kernel_2[((((blockIdx.x*147456) + (floordiv(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*112) + threadIdx.x_2), 9)*4608)) + (rc.outer.outer*9)) + floormod(((ax0.ax1.fused.ax2.fused.ax3.fused.outer.outer*112) + threadIdx.x_2), 9))]
-            }
+          pad_temp.shared[threadIdx.x_1] = @tir.if_then_else(((((9 <= floormod(threadIdx.x_1, 81)) && (floormod(threadIdx.x_1, 81) < 72)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv(threadIdx.x_1, 81)*49)) + (floordiv(floormod(threadIdx.x_1, 81), 9)*7)) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          pad_temp.shared[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 31), 81)) && (floormod((threadIdx.x_1 + 31), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 112), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 31), 81), 9)*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          pad_temp.shared[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 62), 81)) && (floormod((threadIdx.x_1 + 62), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 224), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 62), 81), 9)*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          pad_temp.shared[(threadIdx.x_1 + 336)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 12), 81)) && (floormod((threadIdx.x_1 + 12), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 336), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 12), 81), 9)*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          pad_temp.shared[(threadIdx.x_1 + 448)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 43), 81)) && (floormod((threadIdx.x_1 + 43), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 448), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 43), 81), 9)*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          if @tir.likely((threadIdx.x_1 < 88), dtype=bool) {
+            pad_temp.shared[(threadIdx.x_1 + 560)] = @tir.if_then_else(((((9 <= floormod((threadIdx.x_1 + 74), 81)) && (floormod((threadIdx.x_1 + 74), 81) < 72)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), (float32*)data_2[(((((rc.outer.outer*392) + (floordiv((threadIdx.x_1 + 560), 81)*49)) + (floordiv(floormod((threadIdx.x_1 + 74), 81), 9)*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
           }
-          for (ry.outer.inner: int32, 0, 3) {
-            for (rx.inner: int32, 0, 3) {
-              for (ff.inner: int32, 0, 2) {
-                compute_3[ff.inner] = ((float32*)compute_3[ff.inner] + ((float32*)pad_temp.shared[(((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-                compute_3[(ff.inner + 2)] = ((float32*)compute_3[(ff.inner + 2)] + ((float32*)pad_temp.shared[((((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner) + 1)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-                compute_3[(ff.inner + 4)] = ((float32*)compute_3[(ff.inner + 4)] + ((float32*)pad_temp.shared[((((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner) + 2)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-                compute_3[(ff.inner + 6)] = ((float32*)compute_3[(ff.inner + 6)] + ((float32*)pad_temp.shared[((((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner) + 3)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-                compute_3[(ff.inner + 8)] = ((float32*)compute_3[(ff.inner + 8)] + ((float32*)pad_temp.shared[((((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner) + 4)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-                compute_3[(ff.inner + 10)] = ((float32*)compute_3[(ff.inner + 10)] + ((float32*)pad_temp.shared[((((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner) + 5)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-                compute_3[(ff.inner + 12)] = ((float32*)compute_3[(ff.inner + 12)] + ((float32*)pad_temp.shared[((((ry.outer.inner*9) + (floormod(threadIdx.x, 7)*9)) + rx.inner) + 6)]*(float32*)kernel.shared[((((floordiv(threadIdx.x, 7)*18) + (ff.inner*9)) + (ry.outer.inner*3)) + rx.inner)]))
-              }
-            }
+          attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[threadIdx.x_2] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv(threadIdx.x_2, 72)*4608)) + (rc.outer.outer*72)) + floormod(threadIdx.x_2, 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 112)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 112), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 40), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 224)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 224), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 8), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 336)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 336), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 48), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 448)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 448), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 16), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 560)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 560), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 56), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 672)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 672), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 24), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 784)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 784), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 64), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 896)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 896), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 32), 72))]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          kernel.shared[(threadIdx.x_2 + 1008)] = (float32*)kernel_2[(((((blockIdx.x*73728) + (floordiv(threadIdx.x_2, 72)*4608)) + (rc.outer.outer*72)) + floormod(threadIdx.x_2, 72)) + 64512)]
+          attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 112;
+          if @tir.likely((threadIdx.x_2 < 32), dtype=bool) {
+            kernel.shared[(threadIdx.x_2 + 1120)] = (float32*)kernel_2[((((blockIdx.x*73728) + (floordiv((threadIdx.x_2 + 1120), 72)*4608)) + (rc.outer.outer*72)) + floormod((threadIdx.x_2 + 40), 72))]
           }
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[floormod(threadIdx.x, 7)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 9)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 18)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 27)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 36)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 45)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 54)]*(float32*)kernel.shared[(floordiv(threadIdx.x, 7)*72)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 1)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 10)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 19)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 28)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 37)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 46)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 55)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 1)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 2)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 11)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 20)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 29)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 38)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 47)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 56)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 2)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 9)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 18)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 27)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 36)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 45)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 54)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 63)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 3)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 10)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 19)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 28)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 37)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 46)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 55)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 64)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 4)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 11)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 20)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 29)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 38)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 47)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 56)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 65)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 5)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 18)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 27)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 36)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 45)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 54)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 63)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 72)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 6)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 19)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 28)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 37)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 46)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 55)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 64)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 73)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 7)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 20)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 29)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 38)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 47)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 56)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 65)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 74)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 8)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 81)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 90)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 99)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 108)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 117)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 126)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 135)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 9)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 82)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 91)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 100)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 109)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 118)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 127)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 136)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 10)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 83)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 92)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 101)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 110)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 119)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 128)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 137)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 11)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 90)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 99)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 108)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 117)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 126)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 135)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 144)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 12)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 91)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 100)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 109)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 118)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 127)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 136)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 145)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 13)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 92)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 101)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 110)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 119)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 128)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 137)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 146)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 14)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 99)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 108)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 117)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 126)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 135)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 144)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 153)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 15)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 100)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 109)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 118)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 127)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 136)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 145)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 154)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 16)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 101)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 110)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 119)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 128)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 137)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 146)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 155)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 17)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 162)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 171)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 180)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 189)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 198)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 207)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 216)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 18)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 163)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 172)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 181)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 190)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 199)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 208)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 217)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 19)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 164)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 173)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 182)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 191)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 200)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 209)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 218)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 20)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 171)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 180)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 189)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 198)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 207)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 216)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 225)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 21)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 172)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 181)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 190)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 199)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 208)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 217)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 226)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 22)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 173)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 182)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 191)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 200)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 209)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 218)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 227)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 23)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 180)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 189)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 198)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 207)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 216)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 225)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 234)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 24)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 181)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 190)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 199)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 208)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 217)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 226)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 235)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 25)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 182)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 191)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 200)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 209)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 218)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 227)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 236)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 26)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 243)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 252)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 261)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 270)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 279)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 288)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 297)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 27)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 244)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 253)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 262)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 271)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 280)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 289)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 298)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 28)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 245)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 254)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 263)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 272)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 281)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 290)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 299)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 29)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 252)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 261)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 270)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 279)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 288)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 297)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 306)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 30)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 253)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 262)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 271)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 280)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 289)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 298)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 307)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 31)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 254)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 263)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 272)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 281)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 290)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 299)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 308)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 32)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 261)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 270)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 279)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 288)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 297)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 306)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 315)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 33)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 262)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 271)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 280)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 289)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 298)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 307)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 316)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 34)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 263)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 272)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 281)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 290)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 299)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 308)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 317)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 35)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 324)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 333)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 342)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 351)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 360)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 369)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 378)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 36)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 325)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 334)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 343)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 352)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 361)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 370)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 379)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 37)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 326)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 335)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 344)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 353)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 362)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 371)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 380)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 38)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 333)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 342)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 351)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 360)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 369)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 378)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 387)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 39)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 334)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 343)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 352)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 361)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 370)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 379)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 388)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 40)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 335)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 344)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 353)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 362)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 371)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 380)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 389)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 41)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 342)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 351)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 360)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 369)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 378)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 387)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 396)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 42)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 343)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 352)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 361)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 370)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 379)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 388)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 397)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 43)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 344)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 353)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 362)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 371)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 380)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 389)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 398)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 44)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 405)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 414)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 423)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 432)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 441)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 450)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 459)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 45)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 406)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 415)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 424)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 433)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 442)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 451)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 460)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 46)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 407)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 416)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 425)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 434)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 443)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 452)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 461)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 47)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 414)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 423)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 432)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 441)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 450)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 459)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 468)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 48)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 415)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 424)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 433)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 442)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 451)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 460)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 469)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 49)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 416)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 425)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 434)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 443)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 452)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 461)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 470)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 50)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 423)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 432)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 441)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 450)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 459)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 468)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 477)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 51)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 424)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 433)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 442)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 451)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 460)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 469)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 478)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 52)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 425)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 434)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 443)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 452)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 461)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 470)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 479)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 53)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 486)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 495)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 504)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 513)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 522)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 531)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 540)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 54)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 487)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 496)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 505)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 514)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 523)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 532)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 541)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 55)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 488)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 497)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 506)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 515)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 524)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 533)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 542)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 56)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 495)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 504)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 513)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 522)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 531)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 540)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 549)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 57)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 496)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 505)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 514)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 523)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 532)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 541)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 550)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 58)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 497)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 506)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 515)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 524)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 533)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 542)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 551)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 59)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 504)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 513)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 522)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 531)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 540)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 549)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 558)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 60)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 505)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 514)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 523)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 532)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 541)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 550)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 559)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 61)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 506)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 515)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 524)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 533)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 542)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 551)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 560)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 62)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 567)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 576)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 585)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 594)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 603)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 612)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 621)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 63)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 568)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 577)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 586)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 595)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 604)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 613)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 622)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 64)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 569)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 578)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 587)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 596)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 605)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 614)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 623)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 65)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 576)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 585)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 594)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 603)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 612)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 621)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 630)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 66)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 577)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 586)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 595)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 604)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 613)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 622)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 631)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 67)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 578)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 587)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 596)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 605)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 614)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 623)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 632)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 68)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 585)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 594)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 603)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 612)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 621)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 630)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 639)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 69)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 586)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 595)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 604)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 613)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 622)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 631)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 640)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 70)]))
+          compute_3[0] = ((float32*)compute_3[0] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 587)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
+          compute_3[1] = ((float32*)compute_3[1] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 596)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
+          compute_3[2] = ((float32*)compute_3[2] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 605)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
+          compute_3[3] = ((float32*)compute_3[3] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 614)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
+          compute_3[4] = ((float32*)compute_3[4] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 623)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
+          compute_3[5] = ((float32*)compute_3[5] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 632)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
+          compute_3[6] = ((float32*)compute_3[6] + ((float32*)pad_temp.shared[(floormod(threadIdx.x, 7) + 641)]*(float32*)kernel.shared[((floordiv(threadIdx.x, 7)*72) + 71)]))
         }
-        for (i1.inner: int32, 0, 2) {
-          compute_2[((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7))] = max(((float32*)compute_3[i1.inner] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
-          compute_2[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 1)] = max(((float32*)compute_3[(i1.inner + 2)] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
-          compute_2[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 2)] = max(((float32*)compute_3[(i1.inner + 4)] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
-          compute_2[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 3)] = max(((float32*)compute_3[(i1.inner + 6)] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
-          compute_2[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 4)] = max(((float32*)compute_3[(i1.inner + 8)] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
-          compute_2[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 5)] = max(((float32*)compute_3[(i1.inner + 10)] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
-          compute_2[(((((blockIdx.x*1568) + (floordiv(threadIdx.x, 7)*98)) + (i1.inner*49)) + (floormod(threadIdx.x, 7)*7)) + 6)] = max(((float32*)compute_3[(i1.inner + 12)] + (float32*)bias_2[(((blockIdx.x*32) + (floordiv(threadIdx.x, 7)*2)) + i1.inner)]), 0f32)
+        for (i2.inner: int32, 0, 7) {
+          compute_2[((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*49)) + (i2.inner*7)) + floormod(threadIdx.x, 7))] = max(((float32*)compute_3[i2.inner] + (float32*)bias_2[((blockIdx.x*16) + floordiv(threadIdx.x, 7))]), 0f32)
         }
       }
     }
@@ -314,7 +823,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 0.394 ms
+    Execution time of this operator: 0.203 ms
 
 
 
@@ -364,20 +873,20 @@ print the equivalent python schedule API, and build the binary again.
     compute_nn_o_o_i, compute_nn_o_i = s[compute].split(compute_nn_o_i, factor=1)
     compute_nn_o_o_o_i, compute_nn_o_o_i = s[compute].split(compute_nn_o_o_i, factor=1)
     compute_nn_o_o_o_o, compute_nn_o_o_o_i = s[compute].split(compute_nn_o_o_o_i, factor=1)
-    compute_ff_o_i, compute_ff_i = s[compute].split(compute_ff, factor=2)
+    compute_ff_o_i, compute_ff_i = s[compute].split(compute_ff, factor=1)
     compute_ff_o_o_i, compute_ff_o_i = s[compute].split(compute_ff_o_i, factor=1)
     compute_ff_o_o_o_i, compute_ff_o_o_i = s[compute].split(compute_ff_o_o_i, factor=16)
     compute_ff_o_o_o_o, compute_ff_o_o_o_i = s[compute].split(compute_ff_o_o_o_i, factor=1)
-    compute_yy_o_i, compute_yy_i = s[compute].split(compute_yy, factor=1)
+    compute_yy_o_i, compute_yy_i = s[compute].split(compute_yy, factor=7)
     compute_yy_o_o_i, compute_yy_o_i = s[compute].split(compute_yy_o_i, factor=1)
-    compute_yy_o_o_o_i, compute_yy_o_o_i = s[compute].split(compute_yy_o_o_i, factor=7)
+    compute_yy_o_o_o_i, compute_yy_o_o_i = s[compute].split(compute_yy_o_o_i, factor=1)
     compute_yy_o_o_o_o, compute_yy_o_o_o_i = s[compute].split(compute_yy_o_o_o_i, factor=1)
     compute_xx_o_i, compute_xx_i = s[compute].split(compute_xx, factor=1)
     compute_xx_o_o_i, compute_xx_o_i = s[compute].split(compute_xx_o_i, factor=1)
-    compute_xx_o_o_o_i, compute_xx_o_o_i = s[compute].split(compute_xx_o_o_i, factor=1)
-    compute_xx_o_o_o_o, compute_xx_o_o_o_i = s[compute].split(compute_xx_o_o_o_i, factor=7)
+    compute_xx_o_o_o_i, compute_xx_o_o_i = s[compute].split(compute_xx_o_o_i, factor=7)
+    compute_xx_o_o_o_o, compute_xx_o_o_o_i = s[compute].split(compute_xx_o_o_o_i, factor=1)
     compute_rc_o_i, compute_rc_i = s[compute].split(compute_rc, factor=1)
-    compute_rc_o_o, compute_rc_o_i = s[compute].split(compute_rc_o_i, factor=1)
+    compute_rc_o_o, compute_rc_o_i = s[compute].split(compute_rc_o_i, factor=8)
     compute_ry_o_i, compute_ry_i = s[compute].split(compute_ry, factor=1)
     compute_ry_o_o, compute_ry_o_i = s[compute].split(compute_ry_o_i, factor=3)
     compute_rx_o_i, compute_rx_i = s[compute].split(compute_rx, factor=3)
@@ -386,15 +895,15 @@ print the equivalent python schedule API, and build the binary again.
     compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
     compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
     compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
-    compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=2)
+    compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
     compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=16)
     compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
-    compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
-    compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=7)
+    compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=7)
+    compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
     compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
     compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
-    compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
-    compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
+    compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
+    compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
     s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
     s[compute].compute_at(s[compute], compute_i3_o_i)
     kernel_shared = s.cache_read(kernel, "shared", [compute])
@@ -416,11 +925,11 @@ print the equivalent python schedule API, and build the binary again.
     kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
     s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
     pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
-    pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=9)
+    pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
     s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
     pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=112)
     s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
-    s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 0)
+    s[compute].pragma(compute_nn_o_o_o_o, "auto_unroll_max_step", 512)
     s[compute].pragma(compute_nn_o_o_o_o, "unroll_explicit", True)
 
 
@@ -468,7 +977,7 @@ In the example below we resume the status and do more 5 trials.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  42.357 seconds)
+   **Total running time of the script:** ( 2 minutes  40.179 seconds)
 
 
 .. _sphx_glr_download_tutorials_auto_scheduler_tune_conv2d_layer_cuda.py:
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
index 8a41c85..3f2bf42 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_matmul_x86.rst.txt
@@ -252,7 +252,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 2.243 ms
+    Execution time of this operator: 2.215 ms
 
 
 
@@ -368,7 +368,7 @@ In the example below we resume the status and do more 5 trials.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  53.558 seconds)
+   **Total running time of the script:** ( 1 minutes  55.197 seconds)
 
 
 .. _sphx_glr_download_tutorials_auto_scheduler_tune_matmul_x86.py:
diff --git a/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt b/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
index e01863a..8bd0088 100644
--- a/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/tutorials/auto_scheduler/tune_network_cuda.rst.txt
@@ -106,10 +106,10 @@ for convolutional neural networks, so we use NHWC layout in this tutorial.
                 batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape
             )
         elif name == "squeezenet_v1.1":
+            assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"
             mod, params = relay.testing.squeezenet.get_workload(
                 version="1.1",
                 batch_size=batch_size,
-                layout=layout,
                 dtype=dtype,
                 image_shape=image_shape,
             )
@@ -161,9 +161,6 @@ The task scheduler will just optimize this objective.
 .. code-block:: default
 
 
-    # Enable auto-scheduler in relay
-    auto_scheduler.enable_relay_integration()
-
     # Extract tasks from the network
     print("Extract tasks...")
     mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)
@@ -184,14 +181,26 @@ The task scheduler will just optimize this objective.
  .. code-block:: none
 
     Extract tasks...
-    ========== Task 0  (workload key: ["d09dc1a6bb90d59c91b68989ad3492ff"]) ==========
+    ========== Task 0  (workload key: ["b32ed43fb351136894c322ee49097a1a"]) ==========
+    placeholder = PLACEHOLDER [1, 1000]
+    T_softmax_maxelem(i0) max= placeholder[i0, k]
+    T_softmax_exp(i0, i1) = tir.exp((placeholder[i0, i1] - T_softmax_maxelem[i0]))
+    T_softmax_expsum(i0) += T_softmax_exp[i0, k]
+    T_softmax_norm(i0, i1) = (T_softmax_exp[i0, i1]/T_softmax_expsum[i0])
+
+    ========== Task 1  (workload key: ["d09dc1a6bb90d59c91b68989ad3492ff"]) ==========
     placeholder = PLACEHOLDER [1, 512]
     placeholder = PLACEHOLDER [1000, 512]
     T_dense(i, j) += (placeholder[i, k]*placeholder[j, k])
     placeholder = PLACEHOLDER [1000]
     T_add(ax0, ax1) = (T_dense[ax0, ax1] + placeholder[ax1])
 
-    ========== Task 1  (workload key: ["8d5a93959138dc7b2ee1f1b3219dfa14"]) ==========
+    ========== Task 2  (workload key: ["7de313da0ca29a8c63f647791692430d"]) ==========
+    placeholder = PLACEHOLDER [1, 7, 7, 512]
+    tensor(ax0, ax1, ax2, ax3) += placeholder[ax0, ((ax1*7) + rv0), ((ax2*7) + rv1), ax3]
+    tensor(ax0, ax1, ax2, ax3) = (tensor[ax0, ax1, ax2, ax3]/(float32((select((bool)1, ((ax1 + 1)*7), (((ax1 + 1)*7) + 1)) - (ax1*7)))*float32((select((bool)1, ((ax2 + 1)*7), (((ax2 + 1)*7) + 1)) - (ax2*7)))))
+
+    ========== Task 3  (workload key: ["8d5a93959138dc7b2ee1f1b3219dfa14"]) ==========
     placeholder = PLACEHOLDER [1, 7, 7, 512]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*2) + eps), ((floormod(p, 4)*2) + nu), ci]
@@ -210,7 +219,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (T_multiply[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 2  (workload key: ["ac6920940de3797cc3f9f9c260675e5d"]) ==========
+    ========== Task 4  (workload key: ["ac6920940de3797cc3f9f9c260675e5d"]) ==========
     placeholder = PLACEHOLDER [1, 7, 7, 512]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*2) + eps), ((floormod(p, 4)*2) + nu), ci]
@@ -225,7 +234,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 3  (workload key: ["7e83a2ee5cd5d50282ed19310700046a"]) ==========
+    ========== Task 5  (workload key: ["7e83a2ee5cd5d50282ed19310700046a"]) ==========
     placeholder = PLACEHOLDER [1, 7, 7, 512]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*2) + eps), ((floormod(p, 4)*2) + nu), ci]
@@ -239,7 +248,7 @@ The task scheduler will just optimize this objective.
     placeholder = PLACEHOLDER [1, 7, 7, 512]
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
 
-    ========== Task 4  (workload key: ["1f6cd3637ec856bf5cf5010a623eed05"]) ==========
+    ========== Task 6  (workload key: ["1f6cd3637ec856bf5cf5010a623eed05"]) ==========
     placeholder = PLACEHOLDER [1, 14, 14, 256]
     PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     placeholder = PLACEHOLDER [3, 3, 256, 512]
@@ -248,7 +257,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 5  (workload key: ["424ba83160af31badc0b098136e1a3b0"]) ==========
+    ========== Task 7  (workload key: ["424ba83160af31badc0b098136e1a3b0"]) ==========
     placeholder = PLACEHOLDER [1, 14, 14, 256]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*2) + eps), ((floormod(p, 7)*2) + nu), ci]
@@ -265,7 +274,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 6  (workload key: ["a169cd0053d3a7ca82998fcb62e42c58"]) ==========
+    ========== Task 8  (workload key: ["a169cd0053d3a7ca82998fcb62e42c58"]) ==========
     placeholder = PLACEHOLDER [1, 14, 14, 256]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*2) + eps), ((floormod(p, 7)*2) + nu), ci]
@@ -280,7 +289,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 7  (workload key: ["0141ffc4fbabc10cc5a94c954419055b"]) ==========
+    ========== Task 9  (workload key: ["0141ffc4fbabc10cc5a94c954419055b"]) ==========
     placeholder = PLACEHOLDER [1, 14, 14, 256]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*2) + eps), ((floormod(p, 7)*2) + nu), ci]
@@ -294,7 +303,7 @@ The task scheduler will just optimize this objective.
     placeholder = PLACEHOLDER [1, 14, 14, 256]
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
 
-    ========== Task 8  (workload key: ["81aae4b8e2c076a4014d403e8a2c70a1"]) ==========
+    ========== Task 10  (workload key: ["81aae4b8e2c076a4014d403e8a2c70a1"]) ==========
     placeholder = PLACEHOLDER [1, 28, 28, 128]
     PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     placeholder = PLACEHOLDER [3, 3, 128, 256]
@@ -303,7 +312,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 9  (workload key: ["c7a6b56bdc04b94c829fb2ef9874019e"]) ==========
+    ========== Task 11  (workload key: ["c7a6b56bdc04b94c829fb2ef9874019e"]) ==========
     placeholder = PLACEHOLDER [1, 28, 28, 128]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*2) + eps), ((floormod(p, 14)*2) + nu), ci]
@@ -320,7 +329,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 10  (workload key: ["c035cc8b0568a8e054d06bd7f4950550"]) ==========
+    ========== Task 12  (workload key: ["c035cc8b0568a8e054d06bd7f4950550"]) ==========
     placeholder = PLACEHOLDER [1, 28, 28, 128]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*2) + eps), ((floormod(p, 14)*2) + nu), ci]
@@ -335,7 +344,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 11  (workload key: ["c5ee3e05edd9754492d0763aa41fd025"]) ==========
+    ========== Task 13  (workload key: ["c5ee3e05edd9754492d0763aa41fd025"]) ==========
     placeholder = PLACEHOLDER [1, 28, 28, 128]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*2) + eps), ((floormod(p, 14)*2) + nu), ci]
@@ -349,7 +358,7 @@ The task scheduler will just optimize this objective.
     placeholder = PLACEHOLDER [1, 28, 28, 128]
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
 
-    ========== Task 12  (workload key: ["022ebb6b7c55c5ed030421380ec83a04"]) ==========
+    ========== Task 14  (workload key: ["022ebb6b7c55c5ed030421380ec83a04"]) ==========
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     placeholder = PLACEHOLDER [3, 3, 64, 128]
@@ -358,7 +367,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 13  (workload key: ["de0df0893e01892cfe69f7bc2c24111f"]) ==========
+    ========== Task 15  (workload key: ["de0df0893e01892cfe69f7bc2c24111f"]) ==========
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*4) + eps), ((floormod(p, 14)*4) + nu), ci]
@@ -375,7 +384,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 14  (workload key: ["f2e3c09a00e7d0a9897f70497e089f1e"]) ==========
+    ========== Task 16  (workload key: ["f2e3c09a00e7d0a9897f70497e089f1e"]) ==========
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*4) + eps), ((floormod(p, 14)*4) + nu), ci]
@@ -390,7 +399,7 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 15  (workload key: ["fa26946d7ac51126bfa859cb183f9ca1"]) ==========
+    ========== Task 17  (workload key: ["fa26946d7ac51126bfa859cb183f9ca1"]) ==========
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
     input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*4) + eps), ((floormod(p, 14)*4) + nu), ci]
@@ -404,7 +413,15 @@ The task scheduler will just optimize this objective.
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
 
-    ========== Task 16  (workload key: ["a0eb8d6048282a4a0986cc2ccf14eaa2"]) ==========
+    ========== Task 18  (workload key: ["944921d3fd999ba7aa9ffe5a592a9241"]) ==========
+    placeholder = PLACEHOLDER [1, 112, 112, 64]
+    pad_temp(ax0, ax1, ax2, ax3) = tir.if_then_else(((((ax1 >= 1) && (ax1 < 113)) && (ax2 >= 1)) && (ax2 < 113)), placeholder[ax0, (ax1 - 1), (ax2 - 1), ax3], -3.40282e+38f)
+    tensor(ax0, ax1, ax2, ax3) max= pad_temp[ax0, ((ax1*2) + rv), ((ax2*2) + rv), ax3]
+    placeholder = PLACEHOLDER [1, 1, 1, 64]
+    T_add(ax0, ax1, ax2, ax3) = (tensor[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
+    T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
+
+    ========== Task 19  (workload key: ["a0eb8d6048282a4a0986cc2ccf14eaa2"]) ==========
     placeholder = PLACEHOLDER [1, 224, 224, 3]
     PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 3) && (i1 < 227)) && (i2 >= 3)) && (i2 < 227)), placeholder[i0, (i1 - 3), (i2 - 3), i3], 0f)
     placeholder = PLACEHOLDER [7, 7, 3, 64]
@@ -413,25 +430,25 @@ The task scheduler will just optimize this objective.
     T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
     T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
 
-    ========== Task 17  (workload key: ["bf78a7bf0209980f72953637dfd14a6f"]) ==========
+    ========== Task 20  (workload key: ["bf78a7bf0209980f72953637dfd14a6f"]) ==========
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
     placeholder = PLACEHOLDER [1, 1, 64, 64]
     Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])
 
-    ========== Task 18  (workload key: ["6630936c26852f2b89dbfa2ff37fbb9c"]) ==========
+    ========== Task 21  (workload key: ["6630936c26852f2b89dbfa2ff37fbb9c"]) ==========
     placeholder = PLACEHOLDER [1, 56, 56, 64]
     PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
     placeholder = PLACEHOLDER [1, 1, 64, 128]
     Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
 
-    ========== Task 19  (workload key: ["ba5f918733ccbbd4a1d7fd3724665a2f"]) ==========
+    ========== Task 22  (workload key: ["ba5f918733ccbbd4a1d7fd3724665a2f"]) ==========
     placeholder = PLACEHOLDER [1, 28, 28, 128]
     PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
     placeholder = PLACEHOLDER [1, 1, 128, 256]
     Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
 
-    ========== Task 20  (workload key: ["21ad409d72953de188314010134e3acd"]) ==========
+    ========== Task 23  (workload key: ["21ad409d72953de188314010134e3acd"]) ==========
     placeholder = PLACEHOLDER [1, 14, 14, 256]
     PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
     placeholder = PLACEHOLDER [1, 1, 256, 512]
@@ -507,29 +524,32 @@ Now, we set some options for tuning and launch the search tasks
     ----------------------------------------------------------------------
     |  ID  | Latency (ms) | Speed (GFLOPS) | Trials |
     -------------------------------------------------
-    |    0 |        0.014 |          72.07 |     64 |
-    |    1 |        0.185 |        1250.68 |    128 |
-    |    2 |        0.142 |        1626.36 |    192 |
-    |    3 |        0.137 |        1689.42 |    128 |
-    |    4 |        0.097 |        1189.75 |    128 |
-    |    5 |        0.092 |        2505.25 |    128 |
-    |    6 |        0.080 |        2893.08 |    128 |
-    |    7 |        0.119 |        1947.84 |    128 |
-    |    8 |        0.090 |        1292.62 |     64 |
-    |    9 |        0.107 |        2172.30 |     64 |
-    |   10 |        0.095 |        2439.36 |     64 |
-    |   11 |        0.077 |        3003.22 |     64 |
-    |   12 |        0.068 |        1695.13 |     64 |
-    |   13 |        0.058 |        3979.29 |     64 |
-    |   14 |        0.048 |        4859.95 |    128 |
-    |   15 |        0.073 |        3151.76 |     64 |
-    |   16 |        0.056 |        4265.94 |     64 |
-    |   17 |        0.009 |        2754.90 |     64 |
-    |   18 |        0.011 |        1156.08 |     64 |
-    |   19 |        0.013 |         955.80 |     64 |
-    |   20 |        0.029 |         437.71 |     64 |
+    |    0 |        0.005 |           0.88 |     64 |
+    |    1 |        0.010 |          99.10 |     64 |
+    |    2 |        0.006 |           0.00 |     64 |
+    |    3 |        0.145 |         979.78 |    384 |
+    |    4 |        0.130 |        1097.02 |    384 |
+    |    5 |        0.143 |         992.69 |    384 |
+    |    6 |        0.076 |        1526.86 |    192 |
+    |    7 |        0.115 |         999.44 |    320 |
+    |    8 |        0.079 |        1449.39 |    320 |
+    |    9 |        0.122 |         938.73 |    384 |
+    |   10 |        0.063 |        1832.98 |    192 |
+    |   11 |        0.072 |        1763.62 |    256 |
+    |   12 |        0.062 |        2036.40 |    192 |
+    |   13 |        0.068 |        1874.44 |    192 |
+    |   14 |        0.049 |        2346.50 |    128 |
+    |   15 |        0.076 |        1694.31 |    256 |
+    |   16 |        0.067 |        1933.30 |    448 |
+    |   17 |        0.076 |        1680.90 |    256 |
+    |   18 |        0.022 |          98.43 |     64 |
+    |   19 |        0.076 |        3112.55 |    192 |
+    |   20 |        0.013 |        2026.44 |     64 |
+    |   21 |        0.011 |        1136.69 |     64 |
+    |   22 |        0.013 |         992.47 |     64 |
+    |   23 |        0.020 |         627.56 |     64 |
     -------------------------------------------------
-    Estimated total latency: 1.649 ms  Trials: 1920  Used time : 3598 s  Next ID: 9
+    Estimated total latency: 1.587 ms  Trials: 4992  Used time : 13296 s  Next ID: 3
 
   This table lists the latency and (estimated) speed of all tasks.
   It also lists the allocation of measurement trials for all tasks.
@@ -565,7 +585,7 @@ so we can read the log file and load the best schedules.
     # Compile with the history best
     print("Compile...")
     with auto_scheduler.ApplyHistoryBest(log_file):
-        with tvm.transform.PassContext(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3, config={"relay.backend.use_auto_scheduler": True}):
             lib = relay.build(mod, target=target, params=params)
 
     # Create graph runtime
@@ -593,7 +613,7 @@ so we can read the log file and load the best schedules.
 
     Compile...
     Evaluate inference time cost...
-    Mean inference time (std dev): 3.14 ms (0.01 ms)
+    Mean inference time (std dev): 1.60 ms (0.01 ms)
 
 
 
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index 3afcd64..123dfef 100644
--- a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
 
 Computation times
 =================
-**01:07.514** total execution time for **tutorials_autotvm** files:
-
-- **00:35.925**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:30.897**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:00.204**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.169**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.160**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.159**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+**00:59.267** total execution time for **tutorials_autotvm** files:
+
+- **00:30.860**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:27.725**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:00.200**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.168**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.158**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.156**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index 81e1a70..cdef2cf 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -241,26 +241,26 @@ for this template
        7 unroll_explicit: OtherOption([0, 1]) len=2
     )
     Get devices for measurement successfully!
-    No: 1   GFLOPS: 309.56/309.56   result: MeasureResult(costs=(0.0007478322784810127,), error_no=0, all_cost=1.6631748676300049, timestamp=1605451988.403362)     [('tile_f', [-1, 2, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4881186
-    No: 2   GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 3   GFLOPS: 180.09/309.56   result: MeasureResult(costs=(0.0012854856451612903,), error_no=0, all_cost=1.655426025390625, timestamp=1605451990.2436233)     [('tile_f', [-1, 4, 32, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3605182
-    No: 4   GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 5   GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 6   GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 7   GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 8   GFLOPS: 1.75/309.56     result: MeasureResult(costs=(0.1320260235,), error_no=0, all_cost=3.5821640491485596, timestamp=1605451994.7318807)     [('tile_f', [-1, 2, 4, 64]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2716108
-    No: 9   GFLOPS: 12.11/309.56    result: MeasureResult(costs=(0.019121657333333333,), error_no=0, all_cost=1.8620920181274414, timestamp=1605451998.4005475)     [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1263092
-    No: 10  GFLOPS: 228.36/309.56   result: MeasureResult(costs=(0.0010137748686868686,), error_no=0, all_cost=1.599214792251587, timestamp=1605451999.5098352)     [('tile_f', [-1, 1, 32, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8921130
-    No: 11  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 12  GFLOPS: 120.00/309.56   result: MeasureResult(costs=(0.0019292001346153846,), error_no=0, all_cost=1.4698126316070557, timestamp=1605452001.1850023)    [('tile_f', [-1, 2, 32, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5036371
-    No: 13  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 14  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 15  GFLOPS: 70.72/309.56    result: MeasureResult(costs=(0.003273344657894737,), error_no=0, all_cost=1.608949899673462, timestamp=1605452003.2869494)      [('tile_f', [-1, 1, 1, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3582580
-    No: 16  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 17  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 18  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 19  GFLOPS: 17.38/309.56    result: MeasureResult(costs=(0.013322496,), error_no=0, all_cost=1.7255771160125732, timestamp=1605452007.9485376)      [('tile_f', [-1, 8, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4107668
-    No: 20  GFLOPS: 0.00/309.56     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fed2105a3c1]\n  [bt] (3) /workspace/build/libtvm.so(+0x69fb87) [0x7fed2045ab87]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7fed20459f9e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 1   GFLOPS: 143.10/143.10   result: MeasureResult(costs=(0.0016177774193548385,), error_no=0, all_cost=1.508270263671875, timestamp=1605734850.0650384)     [('tile_f', [-1, 2, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4881186
+    No: 2   GFLOPS: 0.00/143.10     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 3   GFLOPS: 180.75/180.75   result: MeasureResult(costs=(0.0012808059365079366,), error_no=0, all_cost=1.5927340984344482, timestamp=1605734851.4354348)    [('tile_f', [-1, 4, 32, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 1, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3605182
+    No: 4   GFLOPS: 0.00/180.75     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 5   GFLOPS: 0.00/180.75     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 6   GFLOPS: 0.00/180.75     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 7   GFLOPS: 0.00/180.75     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 8   GFLOPS: 1.78/180.75     result: MeasureResult(costs=(0.1300837715,), error_no=0, all_cost=3.4681036472320557, timestamp=1605734854.8293872)     [('tile_f', [-1, 2, 4, 64]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2716108
+    No: 9   GFLOPS: 17.60/180.75    result: MeasureResult(costs=(0.013151463,), error_no=0, all_cost=1.7769639492034912, timestamp=1605734858.0323057)      [('tile_f', [-1, 1, 4, 2]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1263092
+    No: 10  GFLOPS: 222.77/222.77   result: MeasureResult(costs=(0.0010391899548387097,), error_no=0, all_cost=1.6705076694488525, timestamp=1605734859.1131384)    [('tile_f', [-1, 1, 32, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8921130
+    No: 11  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 12  GFLOPS: 120.61/222.77   result: MeasureResult(costs=(0.0019194455094339621,), error_no=0, all_cost=1.4224746227264404, timestamp=1605734860.257979)     [('tile_f', [-1, 2, 32, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,5036371
+    No: 13  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 14  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 15  GFLOPS: 86.04/222.77    result: MeasureResult(costs=(0.002690609307692308,), error_no=0, all_cost=1.6256229877471924, timestamp=1605734861.701972)      [('tile_f', [-1, 1, 1, 4]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 1, 8]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3582580
+    No: 16  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 17  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 18  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 19  GFLOPS: 11.00/222.77    result: MeasureResult(costs=(0.021047285333333332,), error_no=0, all_cost=1.6438183784484863, timestamp=1605734865.1065369)     [('tile_f', [-1, 8, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4107668
+    No: 20  GFLOPS: 0.00/222.77     result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f585d45a7a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x6dab27) [0x7f585c880b27]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x40e) [0x7f585c87ff3e]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
 
 
 
@@ -312,8 +312,8 @@ and measure running time.
 
 
     Best config:
-    [('tile_f', [-1, 2, 64, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 2, 2]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4881186
-    Time cost of this operator: 0.001034
+    [('tile_f', [-1, 1, 32, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,8921130
+    Time cost of this operator: 0.001471
 
 
 
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index 7725a66..6a57f9f 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -369,16 +369,16 @@ used to get the best config later.
  .. code-block:: none
 
     Get devices for measurement successfully!
-    No: 1   GFLOPS: 0.52/0.52       result: MeasureResult(costs=(0.5180510666,), error_no=0, all_cost=8.781435012817383, timestamp=1605451963.079987)       [('tile_y', [-1, 64]), ('tile_x', [-1, 1])],None,6
-    No: 2   GFLOPS: 2.14/2.14       result: MeasureResult(costs=(0.125252425,), error_no=0, all_cost=2.5115718841552734, timestamp=1605451965.740256)       [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
-    No: 3   GFLOPS: 2.71/2.71       result: MeasureResult(costs=(0.099166239,), error_no=0, all_cost=2.10246205329895, timestamp=1605451967.9825048)        [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
-    No: 4   GFLOPS: 7.80/7.80       result: MeasureResult(costs=(0.0344016246,), error_no=0, all_cost=1.0503571033477783, timestamp=1605451969.1972082)     [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
-    No: 5   GFLOPS: 13.09/13.09     result: MeasureResult(costs=(0.020505473599999997,), error_no=0, all_cost=0.8432226181030273, timestamp=1605451970.1748426)     [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
-    No: 6   GFLOPS: 12.21/13.09     result: MeasureResult(costs=(0.0219806834,), error_no=0, all_cost=0.8422861099243164, timestamp=1605451971.1708436)     [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
-    No: 7   GFLOPS: 0.92/13.09      result: MeasureResult(costs=(0.29195622499999996,), error_no=0, all_cost=5.095428705215454, timestamp=1605451976.5535822)       [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
-    No: 8   GFLOPS: 2.40/13.09      result: MeasureResult(costs=(0.11178283959999999,), error_no=0, all_cost=2.2193515300750732, timestamp=1605451978.9914048)      [('tile_y', [-1, 8]), ('tile_x', [-1, 4])],None,23
-    No: 9   GFLOPS: 11.22/13.09     result: MeasureResult(costs=(0.0239272356,), error_no=0, all_cost=0.7559356689453125, timestamp=1605451980.961141)      [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
-    No: 10  GFLOPS: 14.58/14.58     result: MeasureResult(costs=(0.0184163582,), error_no=0, all_cost=0.790762186050415, timestamp=1605451981.9231868)      [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
+    No: 1   GFLOPS: 0.52/0.52       result: MeasureResult(costs=(0.5182494204,), error_no=0, all_cost=8.724752187728882, timestamp=1605734828.082824)       [('tile_y', [-1, 64]), ('tile_x', [-1, 1])],None,6
+    No: 2   GFLOPS: 2.26/2.26       result: MeasureResult(costs=(0.1185889194,), error_no=0, all_cost=2.3457014560699463, timestamp=1605734830.4097345)     [('tile_y', [-1, 512]), ('tile_x', [-1, 8])],None,39
+    No: 3   GFLOPS: 2.85/2.85       result: MeasureResult(costs=(0.0942345782,), error_no=0, all_cost=1.9207239151000977, timestamp=1605734832.3446274)     [('tile_y', [-1, 2]), ('tile_x', [-1, 8])],None,31
+    No: 4   GFLOPS: 7.81/7.81       result: MeasureResult(costs=(0.034355367799999995,), error_no=0, all_cost=0.9937822818756104, timestamp=1605734833.31885)       [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
+    No: 5   GFLOPS: 13.49/13.49     result: MeasureResult(costs=(0.0198918758,), error_no=0, all_cost=0.798100471496582, timestamp=1605734834.0646734)      [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
+    No: 6   GFLOPS: 12.27/13.49     result: MeasureResult(costs=(0.021870170799999998,), error_no=0, all_cost=0.8130497932434082, timestamp=1605734834.8450441)     [('tile_y', [-1, 256]), ('tile_x', [-1, 512])],None,98
+    No: 7   GFLOPS: 0.92/13.49      result: MeasureResult(costs=(0.2910668738,), error_no=0, all_cost=5.050994634628296, timestamp=1605734839.9385755)      [('tile_y', [-1, 128]), ('tile_x', [-1, 2])],None,17
+    No: 8   GFLOPS: 2.57/13.49      result: MeasureResult(costs=(0.1042869344,), error_no=0, all_cost=2.07497501373291, timestamp=1605734842.038044)        [('tile_y', [-1, 8]), ('tile_x', [-1, 4])],None,23
+    No: 9   GFLOPS: 11.56/13.49     result: MeasureResult(costs=(0.0232207364,), error_no=0, all_cost=0.7023093700408936, timestamp=1605734843.6069791)     [('tile_y', [-1, 256]), ('tile_x', [-1, 32])],None,58
+    No: 10  GFLOPS: 14.81/14.81     result: MeasureResult(costs=(0.018119586400000002,), error_no=0, all_cost=0.7337870597839355, timestamp=1605734844.3252716)     [('tile_y', [-1, 64]), ('tile_x', [-1, 128])],None,76
 
 
 
diff --git a/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt b/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
index c5f9343..f437fd3 100644
--- a/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/tutorials/dev/bring_your_own_datatypes.rst.txt
@@ -521,7 +521,7 @@ Now, to actually convert the entire network, we have written `a pass in Relay <h
 
  .. code-block:: none
 
-      Check failed: lower == false: Intrinsic lowering function for target llvm, intrinsic name tir.sqrt, type 150 not found
+      Check failed: lower == false: FloatImm lowering function for target llvm type 150 not found
 
 
 
diff --git a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
index d0dcd93..956cdb1 100644
--- a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
+++ b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
@@ -221,8 +221,8 @@ Thus, a good place to put this transformation pass is just after Phase 1.
 
     primfn(a_1: handle, b_1: handle, c_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {c: Buffer(c_2: Pointer(float32), float32, [128], []),
-                 b: Buffer(b_2: Pointer(float32), float32, [128], []),
+      buffers = {b: Buffer(b_2: Pointer(float32), float32, [128], []),
+                 c: Buffer(c_2: Pointer(float32), float32, [128], []),
                  a: Buffer(a_2: Pointer(float32), float32, [128], [])}
       buffer_map = {a_1: a, b_1: b, c_1: c} {
       for (i.outer: int32, 0, 16) {
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index c0aacff..29641d5 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
 
 Computation times
 =================
-**00:32.590** total execution time for **tutorials_dev** files:
+**00:31.843** total execution time for **tutorials_dev** files:
 
-- **00:32.007**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:00.400**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.184**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:31.281**: :ref:`sphx_glr_tutorials_dev_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:00.386**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.175**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
index df9198f..cb01c0b 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -421,7 +421,7 @@ Execute on TVM
 
     TVM prediction top-1: tiger cat
     Evaluate inference time cost...
-    Mean inference time (std dev): 5.88 ms (0.13 ms)
+    Mean inference time (std dev): 5.89 ms (0.11 ms)
 
 
 
diff --git a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
index 6059e2e..3ced8ee 100644
--- a/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_object_detection_pytorch.rst.txt
@@ -247,7 +247,7 @@ Get boxes with score larger than 0.9
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  5.731 seconds)
+   **Total running time of the script:** ( 2 minutes  7.043 seconds)
 
 
 .. _sphx_glr_download_tutorials_frontend_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
index 4eb5415..4c47234 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -350,7 +350,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
  .. code-block:: none
 
-    Elapsed average ms: 20.06191538
+    Elapsed average ms: 20.023543319999998
 
 
 
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index 0fe5b13..a218eef 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
@@ -368,7 +368,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
  .. code-block:: none
 
-    Elapsed average ms: 36.5043426
+    Elapsed average ms: 36.87257932
 
 
 
@@ -401,7 +401,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  38.431 seconds)
+   **Total running time of the script:** ( 2 minutes  36.682 seconds)
 
 
 .. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index 989f729..666463f 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -195,7 +195,7 @@ Display result
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  55.744 seconds)
+   **Total running time of the script:** ( 1 minutes  49.261 seconds)
 
 
 .. _sphx_glr_download_tutorials_frontend_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
index 60dd9b6..3bdd905 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -155,7 +155,7 @@ Compile the graph to llvm target with given input specification.
 
  .. code-block:: none
 
-
    ...47%, 0.01 MB, 40 KB/s, 0 seconds passed
    ...94%, 0.02 MB, 80 KB/s, 0 seconds passed
    ...100%, 0.02 MB, 120 KB/s, 0 seconds passed
+
    ...47%, 0.01 MB, 41 KB/s, 0 seconds passed
    ...94%, 0.02 MB, 82 KB/s, 0 seconds passed
    ...100%, 0.02 MB, 122 KB/s, 0 seconds passed
     Cannot find config for target=llvm -keys=cpu, workload=('dense_nopack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
 
 
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index e71be30..a1c70f9 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -195,80 +195,80 @@ Results:
 
  .. code-block:: none
 
-    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2949: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
+    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2894: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
       "will be used for operator %s." % node.name
-    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:735: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
+    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:745: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
       warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.expand_dims
     WARNING:root:Attribute Tdim is ignored in relay.sym.expand_dims
     WARNING:root:Attribute T is ignored in relay.sym.expand_dims
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.expand_dims
     WARNING:root:Attribute _node_name is ignored in relay.sym.expand_dims
     WARNING:root:Attribute _target_layout is ignored in relay.sym.expand_dims
-    WARNING:root:Attribute T is ignored in relay.sym.resize
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.resize
+    WARNING:root:Attribute T is ignored in relay.sym.resize
     WARNING:root:Attribute half_pixel_centers is ignored in relay.sym.resize
     WARNING:root:Attribute _node_name is ignored in relay.sym.resize
     WARNING:root:Attribute _target_layout is ignored in relay.sym.resize
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
@@ -276,10 +276,10 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
@@ -291,13 +291,13 @@ Results:
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
@@ -306,16 +306,16 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -325,89 +325,89 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
@@ -419,42 +419,42 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -462,19 +462,19 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
@@ -482,41 +482,41 @@ Results:
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -524,95 +524,95 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.concatenate
-    WARNING:root:Attribute N is ignored in relay.sym.concatenate
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
+    WARNING:root:Attribute N is ignored in relay.sym.concatenate
+    WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
@@ -620,19 +620,19 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
@@ -649,13 +649,13 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
@@ -663,41 +663,41 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -705,8 +705,8 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
@@ -720,13 +720,13 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
@@ -739,24 +739,24 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
@@ -764,22 +764,22 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
@@ -787,22 +787,22 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -810,42 +810,42 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
+    WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
-    WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
+    WARNING:root:Attribute T is ignored in relay.sym.concatenate
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
@@ -854,32 +854,32 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
@@ -888,50 +888,50 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -939,9 +939,9 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
@@ -949,27 +949,27 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -977,33 +977,33 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
@@ -1016,27 +1016,27 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
@@ -1044,19 +1044,19 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
@@ -1074,27 +1074,27 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
@@ -1102,9 +1102,9 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
@@ -1116,33 +1116,33 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
@@ -1158,10 +1158,10 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
@@ -1169,70 +1169,70 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
@@ -1240,18 +1240,18 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
-    WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
+    WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
@@ -1263,9 +1263,9 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
@@ -1274,41 +1274,41 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
@@ -1321,56 +1321,56 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
@@ -1378,41 +1378,41 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
@@ -1420,9 +1420,9 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
@@ -1435,8 +1435,8 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
@@ -1444,56 +1444,56 @@ Results:
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -1501,14 +1501,14 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
@@ -1520,10 +1520,10 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
@@ -1541,12 +1541,12 @@ Results:
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
@@ -1554,33 +1554,33 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
@@ -1592,47 +1592,47 @@ Results:
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
@@ -1640,14 +1640,14 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
-    WARNING:root:Attribute N is ignored in relay.sym.concatenate
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
+    WARNING:root:Attribute N is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
@@ -1664,36 +1664,36 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -1701,14 +1701,14 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
@@ -1716,8 +1716,8 @@ Results:
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
@@ -1726,32 +1726,32 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
@@ -1759,22 +1759,22 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
+    WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
-    WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
@@ -1793,22 +1793,22 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
@@ -1830,8 +1830,8 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
@@ -1840,57 +1840,57 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
@@ -1902,38 +1902,38 @@ Results:
     WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
@@ -1941,36 +1941,36 @@ Results:
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute N is ignored in relay.sym.concatenate
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
+    WARNING:root:Attribute N is ignored in relay.sym.concatenate
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
@@ -1979,8 +1979,8 @@ Results:
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
@@ -1992,18 +1992,18 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
@@ -2011,19 +2011,19 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
@@ -2032,12 +2032,12 @@ Results:
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.copy
@@ -2045,42 +2045,42 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
     WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute T is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
     WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.relu
@@ -2097,33 +2097,33 @@ Results:
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
-    WARNING:root:Attribute T is ignored in relay.sym.copy
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
-    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.copy
+    WARNING:root:Attribute message is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
+    WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute ksize is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.max_pool2d
-    WARNING:root:Attribute T is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.max_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.max_pool2d
-    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
     WARNING:root:Attribute use_cudnn_on_gpu is ignored in relay.sym.conv2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.conv2d
+    WARNING:root:Attribute explicit_paddings is ignored in relay.sym.conv2d
     WARNING:root:Attribute T is ignored in relay.sym.conv2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.conv2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.conv2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute T is ignored in relay.sym.batch_norm
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _node_name is ignored in relay.sym.batch_norm
     WARNING:root:Attribute _target_layout is ignored in relay.sym.batch_norm
     WARNING:root:Attribute message is ignored in relay.sym.copy
@@ -2131,8 +2131,8 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.copy
     WARNING:root:Attribute _node_name is ignored in relay.sym.copy
     WARNING:root:Attribute _target_layout is ignored in relay.sym.copy
-    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.relu
+    WARNING:root:Attribute T is ignored in relay.sym.relu
     WARNING:root:Attribute _node_name is ignored in relay.sym.relu
     WARNING:root:Attribute _target_layout is ignored in relay.sym.relu
     WARNING:root:Attribute N is ignored in relay.sym.concatenate
@@ -2140,20 +2140,20 @@ Results:
     WARNING:root:Attribute T is ignored in relay.sym.concatenate
     WARNING:root:Attribute _node_name is ignored in relay.sym.concatenate
     WARNING:root:Attribute _target_layout is ignored in relay.sym.concatenate
-    WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
-    WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute T is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute _output_shapes is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute ksize is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _node_name is ignored in relay.sym.avg_pool2d
     WARNING:root:Attribute _target_layout is ignored in relay.sym.avg_pool2d
+    WARNING:root:Attribute T is ignored in relay.sym.reshape
     WARNING:root:Attribute Tshape is ignored in relay.sym.reshape
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.reshape
-    WARNING:root:Attribute T is ignored in relay.sym.reshape
     WARNING:root:Attribute _node_name is ignored in relay.sym.reshape
     WARNING:root:Attribute _target_layout is ignored in relay.sym.reshape
+    WARNING:root:Attribute transpose_a is ignored in relay.sym.dense
+    WARNING:root:Attribute T is ignored in relay.sym.dense
     WARNING:root:Attribute _output_shapes is ignored in relay.sym.dense
     WARNING:root:Attribute transpose_b is ignored in relay.sym.dense
-    WARNING:root:Attribute T is ignored in relay.sym.dense
-    WARNING:root:Attribute transpose_a is ignored in relay.sym.dense
     WARNING:root:Attribute _node_name is ignored in relay.sym.dense
     WARNING:root:Attribute _target_layout is ignored in relay.sym.dense
     WARNING:root:Attribute T is ignored in relay.sym.softmax
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index f081e91..99c1e46 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
 
 Computation times
 =================
-**10:42.749** total execution time for **tutorials_frontend** files:
+**10:30.164** total execution time for **tutorials_frontend** files:
 
-- **02:38.431**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **02:05.731**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **01:55.744**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:38.307**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:32.694**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:28.983**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:25.999**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:23.300**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:16.374**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:14.854**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:13.189**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:12.212**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:11.363**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:09.914**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:09.060**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:03.443**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.793**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
-- **00:01.165**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:00.194**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
+- **02:36.682**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **02:07.043**: :ref:`sphx_glr_tutorials_frontend_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **01:49.261**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:38.917**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:29.697**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:29.111**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:25.844**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:23.244**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:16.524**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:14.853**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:12.729**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:12.323**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:09.888**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:09.090**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:08.850**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:03.077**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.699**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:01.141**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:00.193**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
index 2700fa6..6c46f5f 100644
--- a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
 
  .. code-block:: none
 
-    1.17e-07 secs/op
+    1.223e-07 secs/op
 
 
 
diff --git a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
index 86b0b54..fcaa766 100644
--- a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
@@ -224,7 +224,7 @@ in this example. Then the machine code will be generated as the module library.
 
  .. code-block:: none
 
-
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+
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 ds passed
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     Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=('dense_small_batch.cuda', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
 
 
diff --git a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
index e3c0b7d..76930d2 100644
--- a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
 
 Computation times
 =================
-**00:16.955** total execution time for **tutorials_get_started** files:
+**00:17.471** total execution time for **tutorials_get_started** files:
 
-- **00:16.370**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.363**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.131**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.091**: :ref:`sphx_glr_tutorials_get_started_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:16.889**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.367**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.128**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.087**: :ref:`sphx_glr_tutorials_get_started_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
diff --git a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
index f86234d..2371c0d 100644
--- a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
@@ -325,7 +325,7 @@ The following code first performs the following steps:
 
  .. code-block:: none
 
-    ['myadd.tvm_meta.json', 'myadd.ptx', 'myadd.so', 'myadd.o']
+    ['myadd.tvm_meta.json', 'myadd.o', 'myadd.ptx', 'myadd.so']
 
 
 
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index 7520112..bedd793 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -533,15 +533,15 @@ compute_root
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
-                 B: Buffer(B_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+      buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+                 C: Buffer(C_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
                  A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
       for (i: int32, 0, m) {
-        B_2[(i*stride_1)] = ((float32*)A_2[(i*stride_2)] + 1f32)
+        B_2[(i*stride)] = ((float32*)A_2[(i*stride_2)] + 1f32)
       }
       for (i_1: int32, 0, m) {
-        C_2[(i_1*stride)] = ((float32*)B_2[(i_1*stride_1)]*2f32)
+        C_2[(i_1*stride_1)] = ((float32*)B_2[(i_1*stride)]*2f32)
       }
     }
 
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index 38d2a30..36273eb 100644
--- a/docs/_sources/tutorials/language/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
 
 Computation times
 =================
-**00:04.585** total execution time for **tutorials_language** files:
+**00:04.633** total execution time for **tutorials_language** files:
 
-- **00:01.632**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.818**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.613**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.565**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.317**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.225**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.215**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
-- **00:00.200**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:01.666**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.861**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.609**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.566**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.302**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.227**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.207**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:00.195**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
diff --git a/docs/_sources/tutorials/language/tensorize.rst.txt b/docs/_sources/tutorials/language/tensorize.rst.txt
index 34a0fad..7bf1d77 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -236,8 +236,8 @@ such placeholder can be put to let TVM automatically bind the inferred value for
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
-                 C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+      buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+                 B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
                  A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
       for (i: int32, 0, 1024) {
@@ -312,8 +312,8 @@ The importing needs to happen before the tensorized GEMV being executed.
                  B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
                  A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
-      attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpt9fq1r9q/input0.cc'
-    source_filename = "/tmp/tmpt9fq1r9q/input0.cc"
+      attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpjpf_5ucy/input0.cc'
+    source_filename = "/tmp/tmpjpf_5ucy/input0.cc"
     target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
     target triple = "x86_64-pc-linux-gnu"
 
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 2e75b9d..9ddc391 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -136,16 +136,16 @@ with :py:func:`te.comm_reducer` as below:
     primfn(idx_1: handle, val_1: handle, T.v0_1: handle, T.v1_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
       buffers = {T.v1: Buffer(T.v1_2: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
-                 T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m], [stride_1: int32], type="auto"),
-                 val: Buffer(val_2: Pointer(int32), int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
+                 val: Buffer(val_2: Pointer(int32), int32, [m, n: int32], [stride_1: int32, stride_2: int32], type="auto"),
+                 T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m], [stride_3: int32], type="auto"),
                  idx: Buffer(idx_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
       buffer_map = {idx_1: idx, val_1: val, T.v0_1: T.v0, T.v1_1: T.v1} {
       for (i: int32, 0, m) {
-        T.v0_2[(i*stride_1)] = -1
+        T.v0_2[(i*stride_3)] = -1
         T.v1_2[(i*stride)] = -2147483648
         for (k: int32, 0, n) {
-          T.v0_2[(i*stride_1)] = @tir.if_then_else(((int32*)val_2[((i*stride_2) + (k*stride_3))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v0_2[(i*stride_1)], (int32*)idx_2[((i*stride_4) + (k*stride_5))], dtype=int32)
-          T.v1_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_2) + (k*stride_3))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v1_2[(i*stride)], (int32*)val_2[((i*stride_2) + (k*stride_3))], dtype=int32)
+          T.v0_2[(i*stride_3)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v0_2[(i*stride_3)], (int32*)idx_2[((i*stride_4) + (k*stride_5))], dtype=int32)
+          T.v1_2[(i*stride)] = @tir.if_then_else(((int32*)val_2[((i*stride_1) + (k*stride_2))] <= (int32*)T.v1_2[(i*stride)]), (int32*)T.v1_2[(i*stride)], (int32*)val_2[((i*stride_1) + (k*stride_2))], dtype=int32)
         }
       }
     }
diff --git a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
index d5b2ed8..5d9e79a 100644
--- a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:06.360** total execution time for **tutorials_micro** files:
+**00:06.486** total execution time for **tutorials_micro** files:
 
-- **00:06.156**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.204**: :ref:`sphx_glr_tutorials_micro_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:06.286**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.200**: :ref:`sphx_glr_tutorials_micro_micro_reference_vm.py` (``micro_reference_vm.py``)
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index dfc1f6b..fec22ae 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -296,7 +296,7 @@ latency of convolution.
 
  .. code-block:: none
 
-    Convolution: 53.219982 ms
+    Convolution: 36.625235 ms
 
 
 
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index 0682e83..bdbeddf 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -624,7 +624,7 @@ be able to run on our build server
 
  .. code-block:: none
 
-    conv2d with tensor core: 13.385741 ms
+    conv2d with tensor core: 9.648312 ms
 
 
 
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index 3db363f..c387b63 100644
--- a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
 
  .. code-block:: none
 
-    Numpy running time: 0.007639
-    Baseline: 3.516577
+    Numpy running time: 0.007677
+    Baseline: 3.217869
 
 
 
@@ -206,7 +206,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
 
  .. code-block:: none
 
-    Opt1: 0.275058
+    Opt1: 0.285702
 
 
 
@@ -300,7 +300,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
 
  .. code-block:: none
 
-    Opt2: 0.318300
+    Opt2: 0.319613
 
 
 
@@ -389,7 +389,7 @@ the access pattern for A matrix is more cache friendly.
 
  .. code-block:: none
 
-    Opt3: 0.112766
+    Opt3: 0.109707
 
 
 
@@ -499,7 +499,7 @@ the corresponding value from the packed array.
 
  .. code-block:: none
 
-    Opt4: 0.105734
+    Opt4: 0.105111
 
 
 
@@ -609,7 +609,7 @@ write to C when all the block results are ready.
 
  .. code-block:: none
 
-    Opt5: 0.107047
+    Opt5: 0.106331
 
 
 
@@ -633,8 +633,8 @@ Here is the generated IR after blocking.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
-                 B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+      buffers = {B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+                 C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
                  A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
       attr [packedB: Pointer(float32)] "storage_scope" = "global";
@@ -725,7 +725,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
 
  .. code-block:: none
 
-    Opt6: 0.035177
+    Opt6: 0.034921
 
 
 
@@ -749,8 +749,8 @@ Here is the generated IR after parallelization.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
-                 B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+      buffers = {B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+                 C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
                  A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
       attr [packedB: Pointer(float32)] "storage_scope" = "global";
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index e4b849f..8f87f27 100644
--- a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
 
 Computation times
 =================
-**00:33.670** total execution time for **tutorials_optimize** files:
+**00:26.784** total execution time for **tutorials_optimize** files:
 
-- **00:25.214**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:05.499**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:02.747**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.211**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:24.363**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.236**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.975**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.210**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index 7a0970f..ae87cb1 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -231,7 +231,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
 
  .. code-block:: none
 
-    [stage(a, placeholder(a, 0x18ef4c780)), stage(b, placeholder(b, 0x18def4980)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range( [...]
+    [stage(a, placeholder(a, 0x17a9b9f50)), stage(b, placeholder(b, 0x17bde01a0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range( [...]
 
 
 
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index 20d3ecf..9756fe1 100644
--- a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
 
 Computation times
 =================
-**00:00.647** total execution time for **tutorials_topi** files:
+**00:00.648** total execution time for **tutorials_topi** files:
 
-- **00:00.647**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.648**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 84ca233..9716dee 100644
--- a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
 
 Computation times
 =================
-**00:07.912** total execution time for **vta_tutorials_autotvm** files:
+**00:07.808** total execution time for **vta_tutorials_autotvm** files:
 
-- **00:07.912**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:07.808**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
index 04a198c..8221ac4 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -497,7 +497,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
  .. code-block:: none
 
     Extract tasks...
-
    ...1%, 0.01 MB, 41 KB/s, 0 seconds passed
    ...2%, 0.02 MB, 82 KB/s, 0 seconds passed
    ...3%, 0.02 MB, 122 KB/s, 0 seconds passed
    ...4%, 0.03 MB, 163 KB/s, 0 seconds passed
    ...5%, 0.04 MB, 198 KB/s, 0 seconds passed
    ...6%, 0.05 MB, 237 KB/s, 0 seconds passed
    ...7%, 0.05 MB, 276 KB/s, 0 seconds passed
    ...8%, 0.06 MB, 316 KB/s, 0 seconds passed
    ...9%, 0.07 MB, 354 KB/s, 0 seconds passed
    ...10%, 0.08 MB, 393 KB/s, 0 seconds passed
    ...11%, 0.09 MB, 432 KB/s, 0 seconds passed
    ...13%, 0.09 MB, 469 KB/s, 0 seconds passed
    ...14%, 0.10 MB, 508 KB/s, 0 seconds passed
    ...15%, 0.11 MB, 545 KB/s, 0 seconds passed
    ...16%, 0.12 MB, 574 KB/s, 0 seconds passed
    ...17%, 0.12 MB, 611 KB/s, 0 seconds passed
    ...18%, 0.13 MB, 649 KB/s, 0 seconds passed
    ...19%, 0.14 MB, 685 KB/s, 0 seconds passed
    ...20%, 0.15 MB, 722 KB/s, 0 seconds passed
    ...21%, 0.16 MB, 760 KB/s, 0 seconds passed
    ...22%, 0.16 MB, 798 KB/s, 0 seconds passed
 
    ...23%, 0.17 MB, 833 KB/s, 0 seconds passed
    ...24%, 0.18 MB, 869 KB/s, 0 seconds passed
    ...26%, 0.19 MB, 907 KB/s, 0 seconds passed
    ...27%, 0.20 MB, 944 KB/s, 0 seconds passed
    ...28%, 0.20 MB, 981 KB/s, 0 seconds passed
    ...29%, 0.21 MB, 1016 KB/s, 0 seconds passed
    ...30%, 0.22 MB, 1053 KB/s, 0 seconds passed
    ...31%, 0.23 MB, 1090 KB/s, 0 seconds passed
    ...32%, 0.23 MB, 1127 KB/s, 0 seconds passed
    ...33%, 0.24 MB, 1162 KB/s, 0 seconds passed
    ...34%, 0.25 MB, 1199 KB/s, 0 seconds passed
    ...35%, 0.26 MB, 1236 KB/s, 0 seconds passed
    ...36%, 0.27 MB, 1255 KB/s, 0 seconds passed
    ...38%, 0.27 MB, 1292 KB/s, 0 seconds passed
    ...39%, 0.28 MB, 1328 KB/s, 0 seconds passed
    ...40%, 0.29 MB, 1360 KB/s, 0 seconds passed
    ...41%, 0.30 MB, 1396 KB/s, 0 seconds passed
    ...42%, 0.30 MB, 1427 KB/s, 0 seconds passed
    ...43%, 0.31 MB, 1463 KB/s, 0 seconds passed
    ...44%, 0.32 MB, 1499 KB/s, 0 seconds passed
    ...45%, 0.33 MB, 
 1533 KB/s, 0 seconds passed
    ...46%, 0.34 MB, 1568 KB/s, 0 seconds passed
    ...47%, 0.34 MB, 1604 KB/s, 0 seconds passed
    ...48%, 0.35 MB, 1635 KB/s, 0 seconds passed
    ...49%, 0.36 MB, 1670 KB/s, 0 seconds passed
    ...51%, 0.37 MB, 1701 KB/s, 0 seconds passed
    ...52%, 0.38 MB, 1736 KB/s, 0 seconds passed
    ...53%, 0.38 MB, 1772 KB/s, 0 seconds passed
    ...54%, 0.39 MB, 1801 KB/s, 0 seconds passed
    ...55%, 0.40 MB, 1836 KB/s, 0 seconds passed
    ...56%, 0.41 MB, 1859 KB/s, 0 seconds passed
    ...57%, 0.41 MB, 1893 KB/s, 0 seconds passed
    ...58%, 0.42 MB, 1928 KB/s, 0 seconds passed
    ...59%, 0.43 MB, 1955 KB/s, 0 seconds passed
    ...60%, 0.44 MB, 1990 KB/s, 0 seconds passed
    ...61%, 0.45 MB, 2017 KB/s, 0 seconds passed
    ...63%, 0.45 MB, 2052 KB/s, 0 seconds passed
    ...64%, 0.46 MB, 2086 KB/s, 0 seconds passed
    ...65%, 0.47 MB, 2120 KB/s, 0 seconds passed
    ...66%, 0.48 MB, 2154 KB/s, 0 seconds passed
    ...67%, 0.48 MB, 2180 KB/s, 0 seco
 nds passed
    ...68%, 0.49 MB, 2214 KB/s, 0 seconds passed
    ...69%, 0.50 MB, 2248 KB/s, 0 seconds passed
    ...70%, 0.51 MB, 2277 KB/s, 0 seconds passed
    ...71%, 0.52 MB, 2311 KB/s, 0 seconds passed
    ...72%, 0.52 MB, 2345 KB/s, 0 seconds passed
    ...73%, 0.53 MB, 2375 KB/s, 0 seconds passed
    ...74%, 0.54 MB, 2409 KB/s, 0 seconds passed
    ...76%, 0.55 MB, 2437 KB/s, 0 seconds passed
    ...77%, 0.55 MB, 2471 KB/s, 0 seconds passed
    ...78%, 0.56 MB, 2505 KB/s, 0 seconds passed
    ...79%, 0.57 MB, 2513 KB/s, 0 seconds passed
    ...80%, 0.58 MB, 2546 KB/s, 0 seconds passed
    ...81%, 0.59 MB, 2580 KB/s, 0 seconds passed
    ...82%, 0.59 MB, 2611 KB/s, 0 seconds passed
    ...83%, 0.60 MB, 2644 KB/s, 0 seconds passed
    ...84%, 0.61 MB, 2672 KB/s, 0 seconds passed
    ...85%, 0.62 MB, 2705 KB/s, 0 seconds passed
    ...86%, 0.62 MB, 2738 KB/s, 0 seconds passed
    ...87%, 0.63 MB, 2758 KB/s, 0 seconds passed
    ...89%, 0.64 MB, 2791 KB/s, 0 seconds passed
    ..
 .90%, 0.65 MB, 2824 KB/s, 0 seconds passed
    ...91%, 0.66 MB, 2855 KB/s, 0 seconds passed
    ...92%, 0.66 MB, 2888 KB/s, 0 seconds passed
    ...93%, 0.67 MB, 2916 KB/s, 0 seconds passed
    ...94%, 0.68 MB, 2949 KB/s, 0 seconds passed
    ...95%, 0.69 MB, 2981 KB/s, 0 seconds passed
    ...96%, 0.70 MB, 3003 KB/s, 0 seconds passed
    ...97%, 0.70 MB, 3035 KB/s, 0 seconds passed
    ...98%, 0.71 MB, 3068 KB/s, 0 seconds passed
    ...99%, 0.72 MB, 3094 KB/s, 0 seconds passed
    ...100%, 0.73 MB, 3125 KB/s, 0 seconds passed
+
    ...1%, 0.01 MB, 25 KB/s, 0 seconds passed
    ...2%, 0.02 MB, 51 KB/s, 0 seconds passed
    ...3%, 0.02 MB, 76 KB/s, 0 seconds passed
    ...4%, 0.03 MB, 102 KB/s, 0 seconds passed
    ...5%, 0.04 MB, 127 KB/s, 0 seconds passed
    ...6%, 0.05 MB, 150 KB/s, 0 seconds passed
    ...7%, 0.05 MB, 175 KB/s, 0 seconds passed
    ...8%, 0.06 MB, 200 KB/s, 0 seconds passed
    ...9%, 0.07 MB, 224 KB/s, 0 seconds passed
    ...10%, 0.08 MB, 249 KB/s, 0 seconds passed
    ...11%, 0.09 MB, 274 KB/s, 0 seconds passed
    ...13%, 0.09 MB, 298 KB/s, 0 seconds passed
    ...14%, 0.10 MB, 318 KB/s, 0 seconds passed
    ...15%, 0.11 MB, 342 KB/s, 0 seconds passed
    ...16%, 0.12 MB, 366 KB/s, 0 seconds passed
    ...17%, 0.12 MB, 391 KB/s, 0 seconds passed
    ...18%, 0.13 MB, 415 KB/s, 0 seconds passed
    ...19%, 0.14 MB, 438 KB/s, 0 seconds passed
    ...20%, 0.15 MB, 462 KB/s, 0 seconds passed
    ...21%, 0.16 MB, 487 KB/s, 0 seconds passed
    ...22%, 0.16 MB, 511 KB/s, 0 seconds passed
     ...23%, 0.17 MB, 534 KB/s, 0 seconds passed
    ...24%, 0.18 MB, 558 KB/s, 0 seconds passed
    ...26%, 0.19 MB, 582 KB/s, 0 seconds passed
    ...27%, 0.20 MB, 606 KB/s, 0 seconds passed
    ...28%, 0.20 MB, 630 KB/s, 0 seconds passed
    ...29%, 0.21 MB, 653 KB/s, 0 seconds passed
    ...30%, 0.22 MB, 677 KB/s, 0 seconds passed
    ...31%, 0.23 MB, 692 KB/s, 0 seconds passed
    ...32%, 0.23 MB, 716 KB/s, 0 seconds passed
    ...33%, 0.24 MB, 740 KB/s, 0 seconds passed
    ...34%, 0.25 MB, 763 KB/s, 0 seconds passed
    ...35%, 0.26 MB, 787 KB/s, 0 seconds passed
    ...36%, 0.27 MB, 811 KB/s, 0 seconds passed
    ...38%, 0.27 MB, 834 KB/s, 0 seconds passed
    ...39%, 0.28 MB, 858 KB/s, 0 seconds passed
    ...40%, 0.29 MB, 880 KB/s, 0 seconds passed
    ...41%, 0.30 MB, 904 KB/s, 0 seconds passed
    ...42%, 0.30 MB, 927 KB/s, 0 seconds passed
    ...43%, 0.31 MB, 951 KB/s, 0 seconds passed
    ...44%, 0.32 MB, 974 KB/s, 0 seconds passed
    ...45%, 0.33 MB, 998 KB/s, 0 seco
 nds passed
    ...46%, 0.34 MB, 1021 KB/s, 0 seconds passed
    ...47%, 0.34 MB, 1045 KB/s, 0 seconds passed
    ...48%, 0.35 MB, 1068 KB/s, 0 seconds passed
    ...49%, 0.36 MB, 1092 KB/s, 0 seconds passed
    ...51%, 0.37 MB, 1115 KB/s, 0 seconds passed
    ...52%, 0.38 MB, 1139 KB/s, 0 seconds passed
    ...53%, 0.38 MB, 1162 KB/s, 0 seconds passed
    ...54%, 0.39 MB, 1185 KB/s, 0 seconds passed
    ...55%, 0.40 MB, 1208 KB/s, 0 seconds passed
    ...56%, 0.41 MB, 1231 KB/s, 0 seconds passed
    ...57%, 0.41 MB, 1255 KB/s, 0 seconds passed
    ...58%, 0.42 MB, 1278 KB/s, 0 seconds passed
    ...59%, 0.43 MB, 1301 KB/s, 0 seconds passed
    ...60%, 0.44 MB, 1325 KB/s, 0 seconds passed
    ...61%, 0.45 MB, 1347 KB/s, 0 seconds passed
    ...63%, 0.45 MB, 1370 KB/s, 0 seconds passed
    ...64%, 0.46 MB, 1393 KB/s, 0 seconds passed
    ...65%, 0.47 MB, 1416 KB/s, 0 seconds passed
    ...66%, 0.48 MB, 1440 KB/s, 0 seconds passed
    ...67%, 0.48 MB, 1463 KB/s, 0 seconds passed
    ..
 .68%, 0.49 MB, 1469 KB/s, 0 seconds passed
    ...69%, 0.50 MB, 1492 KB/s, 0 seconds passed
    ...70%, 0.51 MB, 1511 KB/s, 0 seconds passed
    ...71%, 0.52 MB, 1534 KB/s, 0 seconds passed
    ...72%, 0.52 MB, 1557 KB/s, 0 seconds passed
    ...73%, 0.53 MB, 1580 KB/s, 0 seconds passed
    ...74%, 0.54 MB, 1603 KB/s, 0 seconds passed
    ...76%, 0.55 MB, 1626 KB/s, 0 seconds passed
    ...77%, 0.55 MB, 1648 KB/s, 0 seconds passed
    ...78%, 0.56 MB, 1671 KB/s, 0 seconds passed
    ...79%, 0.57 MB, 1692 KB/s, 0 seconds passed
    ...80%, 0.58 MB, 1714 KB/s, 0 seconds passed
    ...81%, 0.59 MB, 1737 KB/s, 0 seconds passed
    ...82%, 0.59 MB, 1760 KB/s, 0 seconds passed
    ...83%, 0.60 MB, 1783 KB/s, 0 seconds passed
    ...84%, 0.61 MB, 1806 KB/s, 0 seconds passed
    ...85%, 0.62 MB, 1825 KB/s, 0 seconds passed
    ...86%, 0.62 MB, 1848 KB/s, 0 seconds passed
    ...87%, 0.63 MB, 1868 KB/s, 0 seconds passed
    ...89%, 0.64 MB, 1891 KB/s, 0 seconds passed
    ...90%, 0.65 MB, 19
 13 KB/s, 0 seconds passed
    ...91%, 0.66 MB, 1936 KB/s, 0 seconds passed
    ...92%, 0.66 MB, 1958 KB/s, 0 seconds passed
    ...93%, 0.67 MB, 1981 KB/s, 0 seconds passed
    ...94%, 0.68 MB, 2001 KB/s, 0 seconds passed
    ...95%, 0.69 MB, 2023 KB/s, 0 seconds passed
    ...96%, 0.70 MB, 2043 KB/s, 0 seconds passed
    ...97%, 0.70 MB, 2066 KB/s, 0 seconds passed
    ...98%, 0.71 MB, 2088 KB/s, 0 seconds passed
    ...99%, 0.72 MB, 2111 KB/s, 0 seconds passed
    ...100%, 0.73 MB, 2133 KB/s, 0 seconds passed
     Extracted 10 conv2d tasks:
     (1, 14, 14, 256, 512, 1, 1, 0, 0, 2, 2)
     (1, 28, 28, 128, 256, 1, 1, 0, 0, 2, 2)
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index e7c602c..190e18e 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -243,8 +243,8 @@ The compilation steps are:
 
  .. code-block:: none
 
-
    ...12%, 0.01 MB, 42 KB/s, 0 seconds passed
    ...25%, 0.02 MB, 85 KB/s, 0 seconds passed
    ...38%, 0.02 MB, 127 KB/s, 0 seconds passed
    ...51%, 0.03 MB, 170 KB/s, 0 seconds passed
    ...64%, 0.04 MB, 205 KB/s, 0 seconds passed
    ...77%, 0.05 MB, 246 KB/s, 0 seconds passed
    ...89%, 0.05 MB, 286 KB/s, 0 seconds passed
    ...100%, 0.06 MB, 326 KB/s, 0 seconds passed
-    resnet18_v1 inference graph built in 8.22s!
+
    ...12%, 0.01 MB, 33 KB/s, 0 seconds passed
    ...25%, 0.02 MB, 66 KB/s, 0 seconds passed
    ...38%, 0.02 MB, 98 KB/s, 0 seconds passed
    ...51%, 0.03 MB, 131 KB/s, 0 seconds passed
    ...64%, 0.04 MB, 163 KB/s, 0 seconds passed
    ...77%, 0.05 MB, 192 KB/s, 0 seconds passed
    ...89%, 0.05 MB, 224 KB/s, 0 seconds passed
    ...100%, 0.06 MB, 254 KB/s, 0 seconds passed
+    resnet18_v1 inference graph built in 8.20s!
 
 
 
diff --git a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
index 3497ff3..05c7ab6 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
 
 Computation times
 =================
-**00:29.299** total execution time for **vta_tutorials_frontend** files:
+**00:29.363** total execution time for **vta_tutorials_frontend** files:
 
-- **00:29.299**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:29.363**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
index aa5bdcc..8607cb1 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -304,8 +304,8 @@ After we construct the schedule, by default the schedule computes
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
-                 B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+      buffers = {B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+                 C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
                  A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
       attr [A_buf: Pointer(int8)] "storage_scope" = "global";
diff --git a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
index 3a4ed1c..9ffac7e 100644
--- a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:03.782** total execution time for **vta_tutorials_optimize** files:
+**00:03.766** total execution time for **vta_tutorials_optimize** files:
 
-- **00:03.232**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.550**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:03.225**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.541**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
index 9ddf4a8..80aa34e 100644
--- a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:00.989** total execution time for **vta_tutorials** files:
+**00:00.993** total execution time for **vta_tutorials** files:
 
-- **00:00.504**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.485**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.505**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.488**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/vta/tutorials/vta_get_started.rst.txt b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
index b90d68a..ec845bc 100644
--- a/docs/_sources/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
@@ -423,8 +423,8 @@ with an :code:`env.alu` pragma.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"global_symbol": "main", "tir.noalias": True}
-      buffers = {B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
-                 C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+      buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+                 B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
                  A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
       attr [A_buf: Pointer(int32)] "storage_scope" = "local.acc_buffer" {
diff --git a/docs/api/doxygen/analyzer_8h.html b/docs/api/doxygen/analyzer_8h.html
index ba81da4..7966be2 100644
--- a/docs/api/doxygen/analyzer_8h.html
+++ b/docs/api/doxygen/analyzer_8h.html
@@ -112,7 +112,7 @@ Include dependency graph for analyzer.h:</div>
 </div><div class="textblock"><div class="dynheader">
 This graph shows which files directly or indirectly include this file:</div>
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-<div class="center"><iframe scrolling="no" frameborder="0" src="analyzer_8h__dep__incl.svg" width="4584" height="663"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="analyzer_8h__dep__incl.svg" width="4394" height="663"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
 </div>
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diff --git a/docs/api/doxygen/analyzer_8h__dep__incl.svg b/docs/api/doxygen/analyzer_8h__dep__incl.svg
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+++ b/docs/api/doxygen/analyzer_8h__dep__incl.svg
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-<tr id="row_1_4_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html" target="_self">ClipAttrs</a></td><td class="desc">Attributes for Clip operator </td></tr>
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-<tr id="row_1_4_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html" target="_self">Conv1DTransposeAttrs</a></td><td class="desc">Attributes used in 1D transposed convolution operator </td></tr>
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-<tr id="row_1_4_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html" target="_self">Conv2DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
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-<tr id="row_1_4_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html" target="_self">ConvGemmWeightTransformAttrs</a></td><td class="desc">Attributes used in gemm weight transformation operators </td></tr>
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-<tr id="row_1_4_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html" target="_self">CropAndResizeAttrs</a></td><td class="desc">Attributes used in image crop_and_resize operator </td></tr>
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-<tr id="row_1_4_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html" target="_self">DenseAttrs</a></td><td class="desc">Attributes for dense operator </td></tr>
-<tr id="row_1_4_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html" target="_self">DeviceCopyAttrs</a></td><td class="desc">Options for the device copy operators </td></tr>
-<tr id="row_1_4_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
-<tr id="row_1_4_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
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-<tr id="row_1_4_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" target="_self">DFPatternFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first <a class="el" href="classtvm_1_1relay_1_1DFPattern.html" title="Managed reference to dataflow patterns. ">DFPattern</a> argument </ [...]
-<tr id="row_1_4_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">DFPatternFunctor&lt; R(const DFPattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html" target="_self">DFPatternNode</a></td><td class="desc">Base type of all dataflow patterns </td></tr>
-<tr id="row_1_4_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html" target="_self">DFPatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" title="A dynamical functor that dispatches on in the first DFPattern argument. ">DFPatt [...]
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-<tr id="row_1_4_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html" target="_self">Dilation2DAttrs</a></td><td class="desc">Attributes used in dilation operators </td></tr>
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-<tr id="row_1_4_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html" target="_self">ExpandDimsAttrs</a></td><td class="desc">Attributes used in expand_dims operators </td></tr>
-<tr id="row_1_4_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit <a class="el" href="cl [...]
-<tr id="row_1_4_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor&lt; R(const Expr &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
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-<tr id="row_1_4_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
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-<tr id="row_1_4_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more p [...]
-<tr id="row_1_4_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html" target="_self">FeatureSet</a></td><td class="desc">A finite set of Feature </td></tr>
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-<tr id="row_1_4_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" target="_self">FunctionNode</a></td><td class="desc">Relay <a class="el" href="classtvm_1_1relay_1_1Function.html" title="Managed reference to FunctionNode. ">Function</a> container </td></tr>
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-<tr id="row_1_4_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html" target="_self">GetValidCountsAttrs</a></td><td class="desc">Attributes used in get_valid_counts operator </td></tr>
-<tr id="row_1_4_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html" target="_self">GlobalPool2DAttrs</a></td><td class="desc">Attributes for global pool operator </td></tr>
-<tr id="row_1_4_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html" target="_self">GridSampleAttrs</a></td><td class="desc">Attributes used in image grid_sample operator </td></tr>
-<tr id="row_1_4_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html" target="_self">GroupNormAttrs</a></td><td class="desc">Attributes used in group_norm operator </td></tr>
-<tr id="row_1_4_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">Id</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IdNode.html" target="_self">IdNode</a></td><td class="desc">The unique identifier of variables </td></tr>
-<tr id="row_1_4_88_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">If</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_89_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfNode.html" target="_self">IfNode</a></td><td class="desc">Container of <a class="el" href="classtvm_1_1relay_1_1If.html">If</a> </td></tr>
-<tr id="row_1_4_90_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html" target="_self">InitOpAttrs</a></td><td class="desc">Attributes that specify a tensor </td></tr>
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-<tr id="row_1_4_92_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">InterpreterClosure</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_93_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html" target="_self">InterpreterClosureObj</a></td><td class="desc">The container type of Closures used by the interpreter </td></tr>
-<tr id="row_1_4_94_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html" target="_self">L2NormalizeAttrs</a></td><td class="desc">Attributes for L2Normalize operator </td></tr>
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-<tr id="row_1_4_101_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">Match</a></td><td class="desc"></td></tr>
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-<tr id="row_1_4_103_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html" target="_self">MatrixSetDiagAttrs</a></td><td class="desc">Attributes used in matrix_set_diag operator </td></tr>
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-<tr id="row_1_4_116_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html" target="_self">OneHotAttrs</a></td><td class="desc">Attributes used in one-hot operator </td></tr>
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-<tr id="row_1_4_118_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html" target="_self">OpImplementationNode</a></td><td class="desc">Operator implementation that includes compute and schedule function </td></tr>
-<tr id="row_1_4_119_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
-<tr id="row_1_4_120_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html" target="_self">OpSpecializationNode</a></td><td class="desc">Specialized implementations for operators under certain conditions </td></tr>
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-<tr id="row_1_4_125_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">PatternConstructor</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_126_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html" target="_self">PatternConstructorNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_1_4_127_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" target="_self">PatternFunctor</a></td><td class="desc">A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a more powerful visitor, since it allows you to define the types of further arguments to Vi [...]
-<tr id="row_1_4_128_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">PatternFunctor&lt; R(const Pattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_129_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html" target="_self">PatternMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu.. [...]
-<tr id="row_1_4_130_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternNode.html" target="_self">PatternNode</a></td><td class="desc">Base type for declaring relay pattern </td></tr>
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-<tr id="row_1_4_132_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html" target="_self">PatternTupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_1_4_133_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">PatternVar</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_134_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html" target="_self">PatternVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_1_4_135_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can us [...]
-<tr id="row_1_4_136_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">PatternWildcard</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_137_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html" target="_self">PatternWildcardNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> container node </td></tr>
-<tr id="row_1_4_138_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html" target="_self">PReluAttrs</a></td><td class="desc">Attributes for prelu operator </td></tr>
-<tr id="row_1_4_139_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ProposalAttrs.html" target="_self">ProposalAttrs</a></td><td class="desc">Attributes used in proposal operators </td></tr>
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-<tr id="row_1_4_144_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreateNode.html" target="_self">RefCreateNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_145_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">RefRead</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_146_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html" target="_self">RefReadNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_147_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">RefValue</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_148_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RefValueObj.html" target="_self">RefValueObj</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_149_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">RefWrite</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_150_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html" target="_self">RefWriteNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_151_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RelayNode.html" target="_self">RelayNode</a></td><td class="desc">This is the base node container of all relay structures </td></tr>
-<tr id="row_1_4_152_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RepeatAttrs.html" target="_self">RepeatAttrs</a></td><td class="desc">Attributes used in repeat operators </td></tr>
-<tr id="row_1_4_153_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeAttrs.html" target="_self">ReshapeAttrs</a></td><td class="desc">Attributes used in reshape operators </td></tr>
-<tr id="row_1_4_154_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeLikeAttrs.html" target="_self">ReshapeLikeAttrs</a></td><td class="desc">Attributes used in MXNet-style reshape_like operators </td></tr>
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-<tr id="row_1_4_157_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html" target="_self">ResizeAttrs</a></td><td class="desc">Attributes used in image resize operator </td></tr>
-<tr id="row_1_4_158_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html" target="_self">ReverseAttrs</a></td><td class="desc">Attributes used in reverse operators </td></tr>
-<tr id="row_1_4_159_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseSequenceAttrs.html" target="_self">ReverseSequenceAttrs</a></td><td class="desc">Attributes used in reverse_sequence operators </td></tr>
-<tr id="row_1_4_160_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html" target="_self">ROIAlignAttrs</a></td><td class="desc">Attributes used in roi_align operators </td></tr>
-<tr id="row_1_4_161_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIPoolAttrs.html" target="_self">ROIPoolAttrs</a></td><td class="desc">Attributes used in roi_pool operators </td></tr>
-<tr id="row_1_4_162_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAddAttrs.html" target="_self">ScatterAddAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_163_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html" target="_self">ScatterAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_164_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SequenceMaskAttrs.html" target="_self">SequenceMaskAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_165_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html" target="_self">ShapeFuncAttrs</a></td><td class="desc">Options for the shape function operator </td></tr>
-<tr id="row_1_4_166_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html" target="_self">ShapeOfAttrs</a></td><td class="desc">Attributes for ShapeOf operator </td></tr>
-<tr id="row_1_4_167_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_1_4_168_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html" target="_self">ShapePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Shapes </td></tr>
-<tr id="row_1_4_169_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html" target="_self">SliceLikeAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_170_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html" target="_self">SoftmaxAttrs</a></td><td class="desc">Attributes used in softmax operators </td></tr>
-<tr id="row_1_4_171_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseDenseAttrs.html" target="_self">SparseDenseAttrs</a></td><td class="desc">Attributes for sparse_dense operator </td></tr>
-<tr id="row_1_4_172_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseToDenseAttrs.html" target="_self">SparseToDenseAttrs</a></td><td class="desc">Attributes used in sparse_to_dense operator </td></tr>
-<tr id="row_1_4_173_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseTransposeAttrs.html" target="_self">SparseTransposeAttrs</a></td><td class="desc">Attributes for sparse_transpose operator </td></tr>
-<tr id="row_1_4_174_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SplitAttrs.html" target="_self">SplitAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_175_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SqueezeAttrs.html" target="_self">SqueezeAttrs</a></td><td class="desc">Attributes used in squeeze operators </td></tr>
-<tr id="row_1_4_176_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StackAttrs.html" target="_self">StackAttrs</a></td><td class="desc">Attributes used in stack operators </td></tr>
-<tr id="row_1_4_177_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html" target="_self">StridedSliceAttrs</a></td><td class="desc">Attributes for StridedSlice operator </td></tr>
-<tr id="row_1_4_178_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html" target="_self">SubPixelAttrs</a></td><td class="desc">Attributes used in subpixel operators </td></tr>
-<tr id="row_1_4_179_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html" target="_self">TakeAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_180_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">TempExpr</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_181_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExprNode.html" target="_self">TempExprNode</a></td><td class="desc">Base class of the temporary expression </td></tr>
-<tr id="row_1_4_182_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TileAttrs.html" target="_self">TileAttrs</a></td><td class="desc">Attributes used in tile operators </td></tr>
-<tr id="row_1_4_183_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TopKAttrs.html" target="_self">TopKAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_184_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html" target="_self">TransposeAttrs</a></td><td class="desc">Attributes used in transpose operators </td></tr>
-<tr id="row_1_4_185_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">Tuple</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_186_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">TupleGetItem</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_187_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">TupleGetItemNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_188_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">TupleGetItemPattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_189_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html" target="_self">TupleGetItemPatternNode</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_190_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
-<tr id="row_1_4_191_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">TuplePattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_192_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePatternNode.html" target="_self">TuplePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
-<tr id="row_1_4_193_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_1_4_194_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePatternNode.html" target="_self">TypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
-<tr id="row_1_4_195_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSampling3DAttrs.html" target="_self">UpSampling3DAttrs</a></td><td class="desc">Attributes for upsampling3d operator </td></tr>
-<tr id="row_1_4_196_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html" target="_self">UpSamplingAttrs</a></td><td class="desc">Attributes for upsampling operator </td></tr>
-<tr id="row_1_4_197_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">Var</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_198_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1VarianceAttrs.html" target="_self">VarianceAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_199_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_1_4_200_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">VarPattern</a></td><td class="desc"></td></tr>
-<tr id="row_1_4_201_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPatternNode.html" target="_self">VarPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_1_4_202_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
-<tr id="row_1_4_203_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPatternNode.html" target="_self">WildcardPatternNode</a></td><td class="desc">Wildcard <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> </td></tr>
-<tr id="row_1_4_204_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1YoloReorgAttrs.html" target="_self">YoloReorgAttrs</a></td><td class="desc">Attributes used in yolo reorg operators </td></tr>
+<tr id="row_1_4_16_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BatchToSpaceNDAttrs.html" target="_self">BatchToSpaceNDAttrs</a></td><td class="desc">Attributes used in BatchToSpaceND operator </td></tr>
+<tr id="row_1_4_17_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BiasAddAttrs.html" target="_self">BiasAddAttrs</a></td><td class="desc">Add a 1D Tensor to an axis of a data </td></tr>
+<tr id="row_1_4_18_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BinaryConv2DAttrs.html" target="_self">BinaryConv2DAttrs</a></td><td class="desc">Attribues used in bitserial convolution operators </td></tr>
+<tr id="row_1_4_19_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BinaryDenseAttrs.html" target="_self">BinaryDenseAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_20_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1BitPackAttrs.html" target="_self">BitPackAttrs</a></td><td class="desc">Attributes used in bitpack operators </td></tr>
+<tr id="row_1_4_21_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Call.html" target="_self">Call</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_22_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> container </td></tr>
+<tr id="row_1_4_23_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPattern.html" target="_self">CallPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_24_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html" target="_self">CallPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1CallPattern.html">CallPattern</a> container </td></tr>
+<tr id="row_1_4_25_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html" target="_self">CastAttrs</a></td><td class="desc">Data type cast </td></tr>
+<tr id="row_1_4_26_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html" target="_self">CastHintAttrs</a></td><td class="desc">Annotate an expression to be cast into specific data type </td></tr>
+<tr id="row_1_4_27_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Clause.html" target="_self">Clause</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_28_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ClauseNode.html" target="_self">ClauseNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> container node </td></tr>
+<tr id="row_1_4_29_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ClipAttrs.html" target="_self">ClipAttrs</a></td><td class="desc">Attributes for Clip operator </td></tr>
+<tr id="row_1_4_30_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CompilerAttrs.html" target="_self">CompilerAttrs</a></td><td class="desc">Options for the operators used to annotate a compiler </td></tr>
+<tr id="row_1_4_31_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html" target="_self">ConcatenateAttrs</a></td><td class="desc">Attributes used in concatenate operators </td></tr>
+<tr id="row_1_4_32_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Constant.html" target="_self">Constant</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_33_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html" target="_self">ConstantNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> tensor type </td></tr>
+<tr id="row_1_4_34_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">ConstantPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_35_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html" target="_self">ConstantPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> </td></tr>
+<tr id="row_1_4_36_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">ConstructorValue</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_37_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html" target="_self">ConstructorValueObj</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_38_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html" target="_self">Conv1DAttrs</a></td><td class="desc">Attributes used in 1D convolution operators </td></tr>
+<tr id="row_1_4_39_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html" target="_self">Conv1DTransposeAttrs</a></td><td class="desc">Attributes used in 1D transposed convolution operator </td></tr>
+<tr id="row_1_4_40_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html" target="_self">Conv2DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
+<tr id="row_1_4_41_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html" target="_self">Conv2DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
+<tr id="row_1_4_42_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html" target="_self">Conv2DWinogradAttrs</a></td><td class="desc">Attributes used in convolution operators with winograd algorithm </td></tr>
+<tr id="row_1_4_43_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html" target="_self">Conv2DWinogradNNPACKWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
+<tr id="row_1_4_44_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html" target="_self">Conv3DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
+<tr id="row_1_4_45_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html" target="_self">Conv3DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
+<tr id="row_1_4_46_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html" target="_self">Conv3DWinogradAttrs</a></td><td class="desc">Attributes used in 3d winograd convolution operators </td></tr>
+<tr id="row_1_4_47_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvGemmWeightTransformAttrs.html" target="_self">ConvGemmWeightTransformAttrs</a></td><td class="desc">Attributes used in gemm weight transformation operators </td></tr>
+<tr id="row_1_4_48_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html" target="_self">ConvWinogradWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
+<tr id="row_1_4_49_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html" target="_self">CorrelationAttrs</a></td><td class="desc">Attributes used in correlation operators </td></tr>
+<tr id="row_1_4_50_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html" target="_self">CropAndResizeAttrs</a></td><td class="desc">Attributes used in image crop_and_resize operator </td></tr>
+<tr id="row_1_4_51_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_1_4_52_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html" target="_self">DataTypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
+<tr id="row_1_4_53_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html" target="_self">DebugAttrs</a></td><td class="desc">Options for the debug operators </td></tr>
+<tr id="row_1_4_54_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html" target="_self">DeformableConv2DAttrs</a></td><td class="desc">Attributes for DeformableConv2D operator </td></tr>
+<tr id="row_1_4_55_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html" target="_self">DenseAttrs</a></td><td class="desc">Attributes for dense operator </td></tr>
+<tr id="row_1_4_56_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html" target="_self">DeviceCopyAttrs</a></td><td class="desc">Options for the device copy operators </td></tr>
+<tr id="row_1_4_57_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
+<tr id="row_1_4_58_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
+<tr id="row_1_4_59_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html" target="_self">DFPatternCallbackNode</a></td><td class="desc">Base type of all dataflow pattern callbacks </td></tr>
+<tr id="row_1_4_60_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" target="_self">DFPatternFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first <a class="el" href="classtvm_1_1relay_1_1DFPattern.html" title="Managed reference to dataflow patterns. ">DFPattern</a> argument </ [...]
+<tr id="row_1_4_61_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">DFPatternFunctor&lt; R(const DFPattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_62_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html" target="_self">DFPatternNode</a></td><td class="desc">Base type of all dataflow patterns </td></tr>
+<tr id="row_1_4_63_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html" target="_self">DFPatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" title="A dynamical functor that dispatches on in the first DFPattern argument. ">DFPatt [...]
+<tr id="row_1_4_64_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html" target="_self">DilateAttrs</a></td><td class="desc">Attributes used in dilate operator </td></tr>
+<tr id="row_1_4_65_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html" target="_self">Dilation2DAttrs</a></td><td class="desc">Attributes used in dilation operators </td></tr>
+<tr id="row_1_4_66_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
+<tr id="row_1_4_67_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html" target="_self">DominatorPatternNode</a></td><td class="desc">Dominated Graph <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> <a class="el" href="cla [...]
+<tr id="row_1_4_68_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html" target="_self">DropoutAttrs</a></td><td class="desc">Attributes used in dropout operator </td></tr>
+<tr id="row_1_4_69_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html" target="_self">ExpandDimsAttrs</a></td><td class="desc">Attributes used in expand_dims operators </td></tr>
+<tr id="row_1_4_70_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit <a class="el" href="cl [...]
+<tr id="row_1_4_71_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor&lt; R(const Expr &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_72_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">Expr [...]
+<tr id="row_1_4_73_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
+<tr id="row_1_4_74_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html" target="_self">ExprPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Relay Expression </td></tr>
+<tr id="row_1_4_75_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html" target="_self">ExprRewriter</a></td><td class="desc">A non-iterating Expression Rewriter </td></tr>
+<tr id="row_1_4_76_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more p [...]
+<tr id="row_1_4_77_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html" target="_self">FeatureSet</a></td><td class="desc">A finite set of Feature </td></tr>
+<tr id="row_1_4_78_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html" target="_self">FIFOBufferAttrs</a></td><td class="desc">Attributes for FIFO buffer operator </td></tr>
+<tr id="row_1_4_79_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FixedPointMultiplyAttrs.html" target="_self">FixedPointMultiplyAttrs</a></td><td class="desc">Attributes for FixedPointMultiply operator </td></tr>
+<tr id="row_1_4_80_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_1_4_81_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" target="_self">FunctionNode</a></td><td class="desc">Relay <a class="el" href="classtvm_1_1relay_1_1Function.html" title="Managed reference to FunctionNode. ">Function</a> container </td></tr>
+<tr id="row_1_4_82_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html" target="_self">GatherAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_83_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html" target="_self">GetValidCountsAttrs</a></td><td class="desc">Attributes used in get_valid_counts operator </td></tr>
+<tr id="row_1_4_84_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html" target="_self">GlobalPool2DAttrs</a></td><td class="desc">Attributes for global pool operator </td></tr>
+<tr id="row_1_4_85_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html" target="_self">GridSampleAttrs</a></td><td class="desc">Attributes used in image grid_sample operator </td></tr>
+<tr id="row_1_4_86_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html" target="_self">GroupNormAttrs</a></td><td class="desc">Attributes used in group_norm operator </td></tr>
+<tr id="row_1_4_87_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">Id</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_88_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IdNode.html" target="_self">IdNode</a></td><td class="desc">The unique identifier of variables </td></tr>
+<tr id="row_1_4_89_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">If</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_90_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfNode.html" target="_self">IfNode</a></td><td class="desc">Container of <a class="el" href="classtvm_1_1relay_1_1If.html">If</a> </td></tr>
+<tr id="row_1_4_91_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html" target="_self">InitOpAttrs</a></td><td class="desc">Attributes that specify a tensor </td></tr>
+<tr id="row_1_4_92_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html" target="_self">InstanceNormAttrs</a></td><td class="desc">Attributes used in instance_norm operator </td></tr>
+<tr id="row_1_4_93_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">InterpreterClosure</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_94_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html" target="_self">InterpreterClosureObj</a></td><td class="desc">The container type of Closures used by the interpreter </td></tr>
+<tr id="row_1_4_95_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html" target="_self">L2NormalizeAttrs</a></td><td class="desc">Attributes for L2Normalize operator </td></tr>
+<tr id="row_1_4_96_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html" target="_self">LayerNormAttrs</a></td><td class="desc">Attributes used in layer_norm operator </td></tr>
+<tr id="row_1_4_97_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html" target="_self">LayoutTransformAttrs</a></td><td class="desc">Attributes for LayoutTransform operator </td></tr>
+<tr id="row_1_4_98_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html" target="_self">LeakyReluAttrs</a></td><td class="desc">Attributes for leaky relu operator </td></tr>
+<tr id="row_1_4_99_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">Let</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_100_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc">A binding of a sub-network </td></tr>
+<tr id="row_1_4_101_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html" target="_self">LRNAttrs</a></td><td class="desc">Attributes for LRN operator </td></tr>
+<tr id="row_1_4_102_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">Match</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_103_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MatchNode.html" target="_self">MatchNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> container node </td></tr>
+<tr id="row_1_4_104_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MatrixSetDiagAttrs.html" target="_self">MatrixSetDiagAttrs</a></td><td class="desc">Attributes used in matrix_set_diag operator </td></tr>
+<tr id="row_1_4_105_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html" target="_self">MaxPool1DAttrs</a></td><td class="desc">Attributes for 1D max pool operator </td></tr>
+<tr id="row_1_4_106_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html" target="_self">MaxPool2DAttrs</a></td><td class="desc">Attributes for max pool operator </td></tr>
+<tr id="row_1_4_107_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html" target="_self">MaxPool3DAttrs</a></td><td class="desc">Attributes for 3D max pool operator </td></tr>
+<tr id="row_1_4_108_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MeshgridAttrs.html" target="_self">MeshgridAttrs</a></td><td class="desc">Attributes used in meshgrid operators </td></tr>
+<tr id="row_1_4_109_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html" target="_self">MirrorPadAttrs</a></td><td class="desc">Attributes used for the MirrorPadding operator </td></tr>
+<tr id="row_1_4_110_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html" target="_self">MixedModeMutator</a></td><td class="desc">Non-recursive DFS Graph Traversal for Custom Rewriting Passes </td></tr>
+<tr id="row_1_4_111_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html" target="_self">MixedModeVisitor</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" title="A simple visitor wrapper around ExprFunctor. Recursively visit the content. ">ExprVisitor</a> which [...]
+<tr id="row_1_4_112_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html" target="_self">MultiBoxPriorAttrs</a></td><td class="desc">Attributes used in multibox_prior operators </td></tr>
+<tr id="row_1_4_113_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html" target="_self">MultiBoxTransformLocAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_114_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html" target="_self">NdarraySizeAttrs</a></td><td class="desc">Attributes for ndarray_size operator </td></tr>
+<tr id="row_1_4_115_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html" target="_self">NonMaximumSuppressionAttrs</a></td><td class="desc">Attributes used in non_maximum_suppression operator </td></tr>
+<tr id="row_1_4_116_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html" target="_self">OnDeviceAttrs</a></td><td class="desc">Options for the device annotation operators </td></tr>
+<tr id="row_1_4_117_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html" target="_self">OneHotAttrs</a></td><td class="desc">Attributes used in one-hot operator </td></tr>
+<tr id="row_1_4_118_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
+<tr id="row_1_4_119_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html" target="_self">OpImplementationNode</a></td><td class="desc">Operator implementation that includes compute and schedule function </td></tr>
+<tr id="row_1_4_120_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
+<tr id="row_1_4_121_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html" target="_self">OpSpecializationNode</a></td><td class="desc">Specialized implementations for operators under certain conditions </td></tr>
+<tr id="row_1_4_122_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
+<tr id="row_1_4_123_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html" target="_self">OpStrategyNode</a></td><td class="desc">Operator strategy to choose implementation </td></tr>
+<tr id="row_1_4_124_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html" target="_self">PadAttrs</a></td><td class="desc">Attributes used for the padding operator </td></tr>
+<tr id="row_1_4_125_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> is the base type for an ADT match pattern in Relay </td></tr>
+<tr id="row_1_4_126_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">PatternConstructor</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_127_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html" target="_self">PatternConstructorNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_1_4_128_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" target="_self">PatternFunctor</a></td><td class="desc">A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a more powerful visitor, since it allows you to define the types of further arguments to Vi [...]
+<tr id="row_1_4_129_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">PatternFunctor&lt; R(const Pattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_130_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html" target="_self">PatternMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu.. [...]
+<tr id="row_1_4_131_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternNode.html" target="_self">PatternNode</a></td><td class="desc">Base type for declaring relay pattern </td></tr>
+<tr id="row_1_4_132_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">PatternTuple</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_133_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html" target="_self">PatternTupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_1_4_134_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">PatternVar</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_135_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html" target="_self">PatternVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_1_4_136_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can us [...]
+<tr id="row_1_4_137_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">PatternWildcard</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_138_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html" target="_self">PatternWildcardNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> container node </td></tr>
+<tr id="row_1_4_139_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html" target="_self">PReluAttrs</a></td><td class="desc">Attributes for prelu operator </td></tr>
+<tr id="row_1_4_140_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ProposalAttrs.html" target="_self">ProposalAttrs</a></td><td class="desc">Attributes used in proposal operators </td></tr>
+<tr id="row_1_4_141_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">RecClosure</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_142_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html" target="_self">RecClosureObj</a></td><td class="desc">The container type of <a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> </td></tr>
+<tr id="row_1_4_143_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReduceAttrs.html" target="_self">ReduceAttrs</a></td><td class="desc">Attributes for Reduce operators </td></tr>
+<tr id="row_1_4_144_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">RefCreate</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_145_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreateNode.html" target="_self">RefCreateNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_146_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">RefRead</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_147_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html" target="_self">RefReadNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_148_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">RefValue</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_149_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RefValueObj.html" target="_self">RefValueObj</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_150_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">RefWrite</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_151_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html" target="_self">RefWriteNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_152_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RelayNode.html" target="_self">RelayNode</a></td><td class="desc">This is the base node container of all relay structures </td></tr>
+<tr id="row_1_4_153_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RepeatAttrs.html" target="_self">RepeatAttrs</a></td><td class="desc">Attributes used in repeat operators </td></tr>
+<tr id="row_1_4_154_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeAttrs.html" target="_self">ReshapeAttrs</a></td><td class="desc">Attributes used in reshape operators </td></tr>
+<tr id="row_1_4_155_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeLikeAttrs.html" target="_self">ReshapeLikeAttrs</a></td><td class="desc">Attributes used in MXNet-style reshape_like operators </td></tr>
+<tr id="row_1_4_156_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeTensorAttrs.html" target="_self">ReshapeTensorAttrs</a></td><td class="desc">Attributes for VM reshape_tensor operator </td></tr>
+<tr id="row_1_4_157_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Resize3dAttrs.html" target="_self">Resize3dAttrs</a></td><td class="desc">Attributes used in image resize3d operator </td></tr>
+<tr id="row_1_4_158_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html" target="_self">ResizeAttrs</a></td><td class="desc">Attributes used in image resize operator </td></tr>
+<tr id="row_1_4_159_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html" target="_self">ReverseAttrs</a></td><td class="desc">Attributes used in reverse operators </td></tr>
+<tr id="row_1_4_160_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseSequenceAttrs.html" target="_self">ReverseSequenceAttrs</a></td><td class="desc">Attributes used in reverse_sequence operators </td></tr>
+<tr id="row_1_4_161_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html" target="_self">ROIAlignAttrs</a></td><td class="desc">Attributes used in roi_align operators </td></tr>
+<tr id="row_1_4_162_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIPoolAttrs.html" target="_self">ROIPoolAttrs</a></td><td class="desc">Attributes used in roi_pool operators </td></tr>
+<tr id="row_1_4_163_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAddAttrs.html" target="_self">ScatterAddAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_164_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html" target="_self">ScatterAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_165_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SequenceMaskAttrs.html" target="_self">SequenceMaskAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_166_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html" target="_self">ShapeFuncAttrs</a></td><td class="desc">Options for the shape function operator </td></tr>
+<tr id="row_1_4_167_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html" target="_self">ShapeOfAttrs</a></td><td class="desc">Attributes for ShapeOf operator </td></tr>
+<tr id="row_1_4_168_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_1_4_169_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html" target="_self">ShapePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Shapes </td></tr>
+<tr id="row_1_4_170_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html" target="_self">SliceLikeAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_171_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html" target="_self">SoftmaxAttrs</a></td><td class="desc">Attributes used in softmax operators </td></tr>
+<tr id="row_1_4_172_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SpaceToBatchNDAttrs.html" target="_self">SpaceToBatchNDAttrs</a></td><td class="desc">Attributes used in SpaceToBatchND operator </td></tr>
+<tr id="row_1_4_173_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseDenseAttrs.html" target="_self">SparseDenseAttrs</a></td><td class="desc">Attributes for sparse_dense operator </td></tr>
+<tr id="row_1_4_174_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseToDenseAttrs.html" target="_self">SparseToDenseAttrs</a></td><td class="desc">Attributes used in sparse_to_dense operator </td></tr>
+<tr id="row_1_4_175_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseTransposeAttrs.html" target="_self">SparseTransposeAttrs</a></td><td class="desc">Attributes for sparse_transpose operator </td></tr>
+<tr id="row_1_4_176_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SplitAttrs.html" target="_self">SplitAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_177_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SqueezeAttrs.html" target="_self">SqueezeAttrs</a></td><td class="desc">Attributes used in squeeze operators </td></tr>
+<tr id="row_1_4_178_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StackAttrs.html" target="_self">StackAttrs</a></td><td class="desc">Attributes used in stack operators </td></tr>
+<tr id="row_1_4_179_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html" target="_self">StridedSliceAttrs</a></td><td class="desc">Attributes for StridedSlice operator </td></tr>
+<tr id="row_1_4_180_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html" target="_self">SubPixelAttrs</a></td><td class="desc">Attributes used in subpixel operators </td></tr>
+<tr id="row_1_4_181_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html" target="_self">TakeAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_182_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">TempExpr</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_183_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExprNode.html" target="_self">TempExprNode</a></td><td class="desc">Base class of the temporary expression </td></tr>
+<tr id="row_1_4_184_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TileAttrs.html" target="_self">TileAttrs</a></td><td class="desc">Attributes used in tile operators </td></tr>
+<tr id="row_1_4_185_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TopKAttrs.html" target="_self">TopKAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_186_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html" target="_self">TransposeAttrs</a></td><td class="desc">Attributes used in transpose operators </td></tr>
+<tr id="row_1_4_187_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">Tuple</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_188_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">TupleGetItem</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_189_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">TupleGetItemNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_190_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">TupleGetItemPattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_191_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html" target="_self">TupleGetItemPatternNode</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_192_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
+<tr id="row_1_4_193_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">TuplePattern</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_194_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePatternNode.html" target="_self">TuplePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
+<tr id="row_1_4_195_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_1_4_196_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePatternNode.html" target="_self">TypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
+<tr id="row_1_4_197_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSampling3DAttrs.html" target="_self">UpSampling3DAttrs</a></td><td class="desc">Attributes for upsampling3d operator </td></tr>
+<tr id="row_1_4_198_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html" target="_self">UpSamplingAttrs</a></td><td class="desc">Attributes for upsampling operator </td></tr>
+<tr id="row_1_4_199_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">Var</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_200_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1VarianceAttrs.html" target="_self">VarianceAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_1_4_201_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
+<tr id="row_1_4_202_" class="even" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">VarPattern</a></td><td class="desc"></td></tr>
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diff --git a/docs/api/doxygen/broadcast_8h.html b/docs/api/doxygen/broadcast_8h.html
index cc7d9fc..a95c634 100644
--- a/docs/api/doxygen/broadcast_8h.html
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-<polygon fill="midnightblue" stroke="midnightblue" points="527.387,-191.396 530.887,-201.396 534.387,-191.396 527.387,-191.396"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="291.946,-259.639 285.982,-268.396 296.105,-265.27 291.946,-259.639"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="3046.39,-204.351 3056.84,-202.632 3047.6,-197.456 3046.39,-204.351"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2678.74,-136.662 2688.88,-139.734 2682.87,-131.009 2678.74,-136.662"/>
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-<g id="edge31" class="edge"><title>Node23&#45;&gt;Node24</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="3223.74,-262.537 3214.96,-268.476 3225.52,-269.305 3223.74,-262.537"/>
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+<polygon fill="midnightblue" stroke="midnightblue" points="2816.92,-135.295 2807.41,-139.969 2817.76,-142.244 2816.92,-135.295"/>
+</g>
+<!-- Node18&#45;&gt;Node21 -->
+<g id="edge26" class="edge"><title>Node18&#45;&gt;Node21</title>
+<path fill="none" stroke="midnightblue" d="M2722.2,-132.135C2710.86,-119.423 2695.58,-102.298 2686.5,-92.1273"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2719.71,-134.603 2728.98,-139.734 2724.93,-129.942 2719.71,-134.603"/>
+</g>
+<!-- Node18&#45;&gt;Node23 -->
+<g id="edge29" class="edge"><title>Node18&#45;&gt;Node23</title>
+<path fill="none" stroke="midnightblue" d="M2767.75,-136.122C2829.28,-111.51 2966.65,-56.5624 3031.55,-30.6016"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2766.18,-132.977 2758.2,-139.941 2768.78,-139.477 2766.18,-132.977"/>
+</g>
+<!-- Node21&#45;&gt;Node22 -->
+<g id="edge27" class="edge"><title>Node21&#45;&gt;Node22</title>
+<path fill="none" stroke="midnightblue" d="M2632.97,-70.5467C2585.51,-59.2052 2511.39,-41.4926 2461.04,-29.4613"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2632.42,-74.0154 2642.96,-72.9355 2634.05,-67.2071 2632.42,-74.0154"/>
+</g>
+<!-- Node21&#45;&gt;Node23 -->
+<g id="edge28" class="edge"><title>Node21&#45;&gt;Node23</title>
+<path fill="none" stroke="midnightblue" d="M2739.81,-71.2788C2812.21,-59.1504 2932.24,-39.0419 3004.99,-26.8551"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2739.21,-67.8313 2729.93,-72.9355 2740.37,-74.7351 2739.21,-67.8313"/>
+</g>
+<!-- Node24&#45;&gt;Node17 -->
+<g id="edge31" class="edge"><title>Node24&#45;&gt;Node17</title>
+<path fill="none" stroke="midnightblue" d="M3042.1,-263.714C3022.07,-253.463 2997.68,-240.983 2979.31,-231.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3040.75,-266.956 3051.24,-268.396 3043.94,-260.724 3040.75,-266.956"/>
+</g>
+<!-- Node24&#45;&gt;Node18 -->
+<g id="edge36" class="edge"><title>Node24&#45;&gt;Node18</title>
+<path fill="none" stroke="midnightblue" d="M3068.78,-259.271C3059.43,-240.475 3043.62,-214.919 3021.8,-201 2987.97,-179.411 2885.38,-165.101 2813.02,-157.392"/>
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+</g>
+<!-- Node24&#45;&gt;Node20 -->
+<g id="edge34" class="edge"><title>Node24&#45;&gt;Node20</title>
+<path fill="none" stroke="midnightblue" d="M3099.98,-260.996C3142.56,-217.763 3236.96,-121.926 3266.08,-92.3601"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3097.42,-258.608 3092.9,-268.188 3102.41,-263.52 3097.42,-258.608"/>
+</g>
+<!-- Node24&#45;&gt;Node21 -->
+<g id="edge35" class="edge"><title>Node24&#45;&gt;Node21</title>
+<path fill="none" stroke="midnightblue" d="M3076.65,-258.292C3073.79,-240.226 3067.17,-216.146 3051.8,-201 2971.28,-121.657 2837.72,-95.8187 2754.17,-87.4496"/>
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+</g>
+<!-- Node24&#45;&gt;Node23 -->
+<g id="edge37" class="edge"><title>Node24&#45;&gt;Node23</title>
+<path fill="none" stroke="midnightblue" d="M3077.98,-258.135C3077.42,-241.937 3076.62,-220.194 3075.8,-201 3073.09,-137.561 3069.19,-61.8852 3067.56,-30.851"/>
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+</g>
+<!-- Node24&#45;&gt;Node25 -->
+<g id="edge32" class="edge"><title>Node24&#45;&gt;Node25</title>
+<path fill="none" stroke="midnightblue" d="M3012.84,-266.009C2969.57,-255.192 2913.69,-241.222 2873.02,-231.054"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="3012.16,-269.446 3022.71,-268.476 3013.85,-262.655 3012.16,-269.446"/>
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+<!-- Node24&#45;&gt;Node26 -->
+<g id="edge33" class="edge"><title>Node24&#45;&gt;Node26</title>
+<path fill="none" stroke="midnightblue" d="M3122.45,-264.544C3147.73,-254.143 3179.1,-241.238 3202.59,-231.577"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1892.5,-264.293 1902.26,-268.396 1897.19,-259.097 1892.5,-264.293"/>
+<g id="edge39" class="edge"><title>Node27&#45;&gt;Node3</title>
+<path fill="none" stroke="midnightblue" d="M1885.3,-262.734C1870.29,-252.676 1852.4,-240.685 1838.81,-231.577"/>
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+<path fill="none" stroke="midnightblue" d="M1942.25,-262.451C1955.95,-252.452 1972.17,-240.6 1984.53,-231.577"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="810.504,-280.056 820.76,-277.4 811.086,-273.08 810.504,-280.056"/>
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+<path fill="none" stroke="midnightblue" d="M842.075,-277.334C966.175,-268.314 1230,-249.028 1452.8,-232 1494.34,-228.826 1541.14,-225.136 1577.14,-222.273"/>
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+<path fill="none" stroke="midnightblue" d="M780.252,-258.904C789.335,-239.652 805.055,-213.645 827.802,-201 892.284,-165.154 1417.24,-170.622 1490.8,-165 1519.18,-162.831 1550.59,-159.816 1577.06,-157.098"/>
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+<path fill="none" stroke="midnightblue" d="M841.914,-272.286C906.676,-262.736 1004.87,-247.636 1089.8,-232 1092.81,-231.446 1095.89,-230.862 1099,-230.259"/>
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+<g id="edge53" class="edge"><title>Node28&#45;&gt;Node11</title>
+<path fill="none" stroke="midnightblue" d="M758.058,-259.178C750.31,-241.506 744.113,-217.525 756.802,-201 772.471,-180.595 835.787,-166.655 884.867,-158.674"/>
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-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html">ObjectTypeChecker</a> (<a class="el" href="namespa [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html">ObjectTypeChecker&lt; Array&lt;  [...]
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-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namesp [...]
-<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href="name [...]
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-<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Clause.html">Clause</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GTNode.html">GTNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1OpAttrMap.html">OpAttrMap</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td va [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1auto__scheduler_1_1CacheWriteStep.html">CacheWriteStep</a> (<a class="el" href="namespacetvm_1_1auto__scheduler.html">tvm::auto_scheduler</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GE.html">GE</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1Object.html">Object</a> (<a class="el" href="namespacetvm_1 [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1GenericFuncNode.html">GenericFuncNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html">ObjectHash</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)&#160; [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1Call.html">Call</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1tir_1_1GENode.html">GENode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html">ObjectPtr</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)&# [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CallNode.html">CallNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html">GetValidCountsAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html">ObjectPtrEqual</a> (<a class="el" href="nam [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1relay_1_1CallPatternNode.html">CallPatternNode</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1GlobalTypeVarNode.html">GlobalTypeVarNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html">ObjectTypeChecker</a> (<a class="el" href="namespa [...]
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+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastAttrs.html">CastAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html">GridSampleAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html">OnDeviceAttrs</a> (<a class="el" href="namesp [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1CastHintAttrs.html">CastHintAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html">GroupNormAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html">OneHotAttrs</a> (<a class="el" href="name [...]
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-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html">CmpOpNode</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1SHashReducer_1_1Handler.html">SHashReducer::Handler</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1OpNode.html">OpNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td [...]
-<tr><td valign="top"><a class="el" href="classtvm_1_1tir_1_1CommReducer.html">CommReducer</a> (<a class="el" href="namespacetvm_1_1tir.html">tvm::tir</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="structdmlc_1_1serializer_1_1Handler_3_01DLContext_01_4.html">Handler&lt; DLContext &gt;</a> (<a class="el" href="namespacedmlc_1_1serializer.html">dmlc::serializer</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1OpRegEntry.html">OpRegEntry</a> (<a cl [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstIntBoundNode.html">ConstIntBoundNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1IncompleteType.html">IncompleteType</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContext.html">PassContext</a> (<a class="el" href="namespacetvm_1_1tran [...]
+<tr><td valign="top"><a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1IncompleteTypeNode.html">IncompleteTypeNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassContextNode.html">PassContextNode</a> (<a class="el" href="name [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1ConstructorNode.html">ConstructorNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html">InplaceArrayBase</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1transform_1_1PassInfoNode.html">PassInfoNode</a> (<a class="el" href="namespacetvm [...]
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+<tr><td valign="top"><a class="el" href="classtvm_1_1runtime_1_1NDArray_1_1Container.html">NDArray::Container</a> (<a class="el" href="namespacetvm_1_1runtime.html">tvm::runtime</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraints.html">IntConstraints</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html">PatternConstructor< [...]
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+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html">Conv1DAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransform.html">IntConstraintsTransform</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html">PatternFunctor</a> (<a cla [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html">Conv1DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1arith_1_1IntConstraintsTransformNode.html">IntConstraintsTransformNode</a> (<a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_ [...]
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+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html">Conv2DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html">InterpreterClosure</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternNode.html">PatternNode</a> (<a c [...]
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+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html">Conv3DTransposeAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1IntImm.html">IntImm</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html">PatternVarNode</a> (<a class="el" href="namespacetvm_1_1relay.html" [...]
+<tr><td valign="top"><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html">Conv3DWinogradAttrs</a> (<a class="el" href="namespacetvm_1_1relay.html">tvm::relay</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1IntImmNode.html">IntImmNode</a> (<a class="el" href="namespacetvm.html">tvm</a>)&#160;&#160;&#160;</td><td valign="top"><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html">PatternVisitor</a> (<a class="el" href="namespacetvm_1_1relay [...]
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diff --git a/docs/api/doxygen/classtvm_1_1BaseAttrsNode.html b/docs/api/doxygen/classtvm_1_1BaseAttrsNode.html
index b17adaf..8732de8 100644
--- a/docs/api/doxygen/classtvm_1_1BaseAttrsNode.html
+++ b/docs/api/doxygen/classtvm_1_1BaseAttrsNode.html
@@ -104,7 +104,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
 
 <p>Inherits Object.</p>
 
-<p>Inherited by <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AdaptivePool2DAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AdaptivePool3DAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AffineGridAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AllocStorageAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AllocTensorAttrs &gt;</a>,  [...]
+<p>Inherited by <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AdaptivePool2DAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AdaptivePool3DAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AffineGridAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AllocStorageAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html">tvm::AttrsNode&lt; AllocTensorAttrs &gt;</a>,  [...]
 <div class="dynheader">
 Collaboration diagram for tvm::BaseAttrsNode:</div>
 <div class="dyncontent">
@@ -252,7 +252,7 @@ Static Public Attributes</h2></td></tr>
 </dl>
 <dl class="section note"><dt>Note</dt><dd>This function throws when the required field is not present. </dd></dl>
 
-<p>Implemented in <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532559a">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532559a">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532559a">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532 [...]
+<p>Implemented in <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532559a">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532559a">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532559a">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acfba199ef906818f35432d2e5532 [...]
 
 </div>
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@@ -314,7 +314,7 @@ template&lt;typename... Args&gt; </div>
 <p>Get the field information. </p>
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-<p>Implemented in <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6a981">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6a981">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6a981">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6 [...]
+<p>Implemented in <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6a981">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6a981">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6a981">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acefe615381b5d881870af9db7ce6 [...]
 
 </div>
 </div>
@@ -398,7 +398,7 @@ template&lt;typename... Args&gt; </div>
 </table>
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-<p>Reimplemented in <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a34a9c72">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a34a9c72">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a34a9c72">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a3 [...]
+<p>Reimplemented in <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a34a9c72">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a34a9c72">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a34a9c72">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#a5da687ced06b4f2dfa04b142a3 [...]
 
 </div>
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@@ -433,7 +433,7 @@ template&lt;typename... Args&gt; </div>
   </dd>
 </dl>
 
-<p>Implemented in <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da88 [...]
+<p>Implemented in <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode&lt; DerivedType &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode&lt; DilateAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da885205">tvm::AttrsNode&lt; Conv2DWinogradNNPACKWeightTransformAttrs &gt;</a>, <a class="el" href="classtvm_1_1AttrsNode.html#acd05137ba529ac7cd07053e3da88 [...]
 
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