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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/04/01 20:45:31 UTC
[incubator-tvm-site] branch asf-site updated: Docs build at Wed Apr
1 13:45:20 PDT 2020
This is an automated email from the ASF dual-hosted git repository.
tqchen 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 c96f37d Docs build at Wed Apr 1 13:45:20 PDT 2020
c96f37d is described below
commit c96f37d66746f71526d21feb2f27aa360e332032
Author: tqchen <tq...@octoml.ai>
AuthorDate: Wed Apr 1 13:45:20 2020 -0700
Docs build at Wed Apr 1 13:45:20 PDT 2020
---
docs/.buildinfo | 4 +
.../opt_matmul_auto_tensorcore.py | 2 +-
.../matrix_multiply.py | 2 +-
.../tutorials_jupyter.zip | Bin 473912 -> 473920 bytes
.../matrix_multiply_opt.ipynb | 2 +-
.../tune_relay_vta.ipynb | 2 +-
.../tune_relay_vta.py | 2 +-
.../deploy_detection.ipynb | 2 +-
.../matrix_multiply_opt.py | 2 +-
.../tutorials_python.zip | Bin 116302 -> 116332 bytes
.../opt_matmul_auto_tensorcore.ipynb | 2 +-
.../deploy_classification.ipynb | 2 +-
.../convolution_opt.ipynb | 2 +-
.../tutorials_python.zip | Bin 409257 -> 409265 bytes
.../convolution_opt.py | 2 +-
.../matrix_multiply.ipynb | 2 +-
.../tutorials_jupyter.zip | Bin 131891 -> 131921 bytes
.../deploy_detection.py | 2 +-
.../deploy_classification.py | 2 +-
docs/_sources/install/nnpack.md.txt | 2 +
.../tutorials/autotvm/sg_execution_times.rst.txt | 12 +-
.../tutorials/autotvm/tune_conv2d_cuda.rst.txt | 44 +-
.../tutorials/autotvm/tune_relay_arm.rst.txt | 2 +-
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.../tutorials/autotvm/tune_simple_template.rst.txt | 22 +-
.../tutorials/cross_compilation_and_rpc.rst.txt | 4 +-
.../tutorials/dev/low_level_custom_pass.rst.txt | 2 +-
.../tutorials/dev/relay_pass_infra.rst.txt | 2 +-
.../tutorials/dev/sg_execution_times.rst.txt | 6 +-
docs/_sources/tutorials/frontend/build_gcn.rst.txt | 2 +-
.../frontend/deploy_model_on_android.rst.txt | 4 +-
.../frontend/deploy_model_on_rasp.rst.txt | 2 +-
.../tutorials/frontend/deploy_quantized.rst.txt | 2 +-
.../tutorials/frontend/deploy_ssd_gluoncv.rst.txt | 80 +-
.../tutorials/frontend/from_caffe2.rst.txt | 2 +-
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.../tutorials/frontend/sg_execution_times.rst.txt | 32 +-
.../tutorials/frontend/using_external_lib.rst.txt | 2 +-
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.../tutorials/language/intrin_math.rst.txt | 2 +-
docs/_sources/tutorials/language/reduction.rst.txt | 2 +-
docs/_sources/tutorials/language/scan.rst.txt | 2 +-
.../tutorials/language/schedule_primitives.rst.txt | 2 +-
.../tutorials/language/sg_execution_times.rst.txt | 18 +-
docs/_sources/tutorials/language/tedd.rst.txt | 2 +-
docs/_sources/tutorials/language/tensorize.rst.txt | 4 +-
.../tutorials/language/tuple_inputs.rst.txt | 2 +-
.../tutorials/optimize/opt_conv_cuda.rst.txt | 4 +-
.../tutorials/optimize/opt_conv_tensorcore.rst.txt | 4 +-
docs/_sources/tutorials/optimize/opt_gemm.rst.txt | 18 +-
.../optimize/opt_matmul_auto_tensorcore.rst.txt | 4 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 10 +-
docs/_sources/tutorials/relay_quick_start.rst.txt | 6 +-
docs/_sources/tutorials/sg_execution_times.rst.txt | 8 +-
.../tutorials/tensor_expr_get_started.rst.txt | 4 +-
docs/_sources/tutorials/topi/intro_topi.rst.txt | 4 +-
.../tutorials/topi/sg_execution_times.rst.txt | 4 +-
docs/_sources/vta/dev/config.rst.txt | 2 +-
docs/_sources/vta/dev/hardware.rst.txt | 10 +-
docs/_sources/vta/install.md.txt | 38 +-
.../tutorials/autotvm/sg_execution_times.rst.txt | 4 +-
.../vta/tutorials/autotvm/tune_relay_vta.rst.txt | 6 +-
.../frontend/deploy_classification.rst.txt | 8 +-
.../tutorials/frontend/deploy_detection.rst.txt | 6 +-
.../tutorials/frontend/sg_execution_times.rst.txt | 6 +-
.../_sources/vta/tutorials/matrix_multiply.rst.txt | 4 +-
.../vta/tutorials/optimize/convolution_opt.rst.txt | 4 +-
.../tutorials/optimize/matrix_multiply_opt.rst.txt | 4 +-
.../tutorials/optimize/sg_execution_times.rst.txt | 6 +-
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docs/objects.inv | Bin 13806 -> 13845 bytes
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docs/tutorials/frontend/build_gcn.html | 2 +-
.../frontend/deploy_model_on_android.html | 4 +-
docs/tutorials/frontend/deploy_model_on_rasp.html | 2 +-
docs/tutorials/frontend/deploy_quantized.html | 2 +-
docs/tutorials/frontend/deploy_ssd_gluoncv.html | 82 +-
docs/tutorials/frontend/from_caffe2.html | 2 +-
docs/tutorials/frontend/from_coreml.html | 2 +-
docs/tutorials/frontend/from_darknet.html | 2 +-
docs/tutorials/frontend/from_keras.html | 2 +-
docs/tutorials/frontend/from_mxnet.html | 2 +-
docs/tutorials/frontend/from_onnx.html | 8 +-
docs/tutorials/frontend/from_pytorch.html | 2 +-
docs/tutorials/frontend/from_tensorflow.html | 2 +-
docs/tutorials/frontend/from_tflite.html | 2 +-
docs/tutorials/frontend/sg_execution_times.html | 32 +-
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docs/tutorials/language/extern_op.html | 2 +-
docs/tutorials/language/intrin_math.html | 2 +-
docs/tutorials/language/reduction.html | 2 +-
docs/tutorials/language/scan.html | 2 +-
docs/tutorials/language/schedule_primitives.html | 2 +-
docs/tutorials/language/sg_execution_times.html | 18 +-
docs/tutorials/language/tedd.html | 2 +-
docs/tutorials/language/tensorize.html | 4 +-
docs/tutorials/language/tuple_inputs.html | 2 +-
docs/tutorials/optimize/opt_conv_cuda.html | 4 +-
docs/tutorials/optimize/opt_conv_tensorcore.html | 4 +-
docs/tutorials/optimize/opt_gemm.html | 18 +-
.../optimize/opt_matmul_auto_tensorcore.html | 4 +-
docs/tutorials/optimize/sg_execution_times.html | 10 +-
docs/tutorials/relay_quick_start.html | 106 +-
docs/tutorials/sg_execution_times.html | 8 +-
docs/tutorials/tensor_expr_get_started.html | 4 +-
docs/tutorials/topi/intro_topi.html | 4 +-
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docs/vta/dev/hardware.html | 10 +-
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docs/vta/tutorials/autotvm/sg_execution_times.html | 4 +-
docs/vta/tutorials/autotvm/tune_relay_vta.html | 188 +--
.../tutorials/frontend/deploy_classification.html | 22 +-
docs/vta/tutorials/frontend/deploy_detection.html | 6 +-
.../vta/tutorials/frontend/sg_execution_times.html | 6 +-
docs/vta/tutorials/matrix_multiply.html | 4 +-
docs/vta/tutorials/optimize/convolution_opt.html | 4 +-
.../tutorials/optimize/matrix_multiply_opt.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 | 2 +-
265 files changed, 11624 insertions(+), 9931 deletions(-)
diff --git a/docs/.buildinfo b/docs/.buildinfo
new file mode 100644
index 0000000..65a29f9
--- /dev/null
+++ b/docs/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 1ba81d7f59a4af53a0ec8b3a796842eb
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py b/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py
index aae1333..50cc1eb 100644
--- a/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py
+++ b/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py
@@ -86,7 +86,7 @@ def matmul_nn(A, B, L, dtype='float16', layout='NN'):
# (2) The warp tile size is not 16x16x16 on CUDA9, or not one of {16x16x16, 32x8x16, 8x32x16} on CUDA version >= 10.0.
#
# In this schedule, storage_align is used to reduce bank conflicts of shared memory. Please refer to this
-# `doc <https://docs.tvm.ai/api/python/schedule.html#tvm.te.schedule.Stage.storage_align>`_
+# `doc <https://tvm.apache.org/docs/api/python/schedule.html#tvm.te.schedule.Stage.storage_align>`_
# for the usage of storage_align primitive. In short, we need to add an offset to some shared memory buffer
# to reduce bank conflicts.
# According to the `wmma doc <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#wmma-description>`_,
diff --git a/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py b/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
index 227144e..024e179 100644
--- a/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
+++ b/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
@@ -43,7 +43,7 @@ from tvm import rpc
from tvm.contrib import util
from vta.testing import simulator
-# Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# We read the Pynq RPC host IP address and port number from the OS environment
diff --git a/docs/_downloads/1d6c63023d70a80db938b62570b7281c/tutorials_jupyter.zip b/docs/_downloads/1d6c63023d70a80db938b62570b7281c/tutorials_jupyter.zip
index ff3caa6..08a34b2 100644
Binary files a/docs/_downloads/1d6c63023d70a80db938b62570b7281c/tutorials_jupyter.zip and b/docs/_downloads/1d6c63023d70a80db938b62570b7281c/tutorials_jupyter.zip differ
diff --git a/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb b/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb
index 96f65be..9fc93b2 100644
--- a/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb
+++ b/docs/_downloads/2daaacf3c023a8ad30b14e52b9aaa635/matrix_multiply_opt.ipynb
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the vta/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.get(\"V [...]
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.ge [...]
]
},
{
diff --git a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
index 4d9ef9d..17b3109 100644
--- a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
+++ b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
@@ -83,7 +83,7 @@
},
"outputs": [],
"source": [
- "# Tracker host and port can be set by your environment\ntracker_host = os.environ.get(\"TVM_TRACKER_HOST\", '0.0.0.0')\ntracker_port = int(os.environ.get(\"TVM_TRACKER_PORT\", 9190))\n\n# Load VTA parameters from the vta/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\nd [...]
+ "# Tracker host and port can be set by your environment\ntracker_host = os.environ.get(\"TVM_TRACKER_HOST\", '0.0.0.0')\ntracker_port = int(os.environ.get(\"TVM_TRACKER_PORT\", 9190))\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPG [...]
]
},
{
diff --git a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
index b0870b1..571dde6 100644
--- a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
+++ b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
@@ -181,7 +181,7 @@ def compile_network(env, target, model, start_pack, stop_pack):
tracker_host = os.environ.get("TVM_TRACKER_HOST", '0.0.0.0')
tracker_port = int(os.environ.get("TVM_TRACKER_PORT", 9190))
-# Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
diff --git a/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb b/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
index f4718e2..372bb0a 100644
--- a/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
+++ b/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "# Load VTA parameters from the vta/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\ndevice = \"vta\"\ntarget = env.target if device == \"vta\" else env.target_vta_cpu\n\npack_dict = {\n \"yolov3-tiny\": [\"nn.max_pool2d\", \"cast\", 4, 185],\n}\n\n# Name of Darknet model to compile\n# The ``start_pack`` and ``stop_pack`` labels indicate where\n# to start and end th [...]
+ "# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\ndevice = \"vta\"\ntarget = env.target if device == \"vta\" else env.target_vta_cpu\n\npack_dict = {\n \"yolov3-tiny\": [\"nn.max_pool2d\", \"cast\", 4, 185],\n}\n\n# Name of Darknet model to compile\n# The ``start_pack`` and ``stop_pack`` labels indicate where\n# to start and e [...]
]
},
{
diff --git a/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py b/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py
index da3b9bb..77b0381 100644
--- a/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py
+++ b/docs/_downloads/67c18c78b0f12c3be5dc41b22637d719/matrix_multiply_opt.py
@@ -46,7 +46,7 @@ from tvm import rpc
from tvm.contrib import util
from vta.testing import simulator
-# Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# We read the Pynq RPC host IP address and port number from the OS environment
diff --git a/docs/_downloads/6a30ed3c7bfd73f6d626ef79df7e54b3/tutorials_python.zip b/docs/_downloads/6a30ed3c7bfd73f6d626ef79df7e54b3/tutorials_python.zip
index 3b0f4d0..39fe8a3 100644
Binary files a/docs/_downloads/6a30ed3c7bfd73f6d626ef79df7e54b3/tutorials_python.zip and b/docs/_downloads/6a30ed3c7bfd73f6d626ef79df7e54b3/tutorials_python.zip differ
diff --git a/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb b/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb
index 8386fb1..6287d6e 100644
--- a/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb
+++ b/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb
@@ -40,7 +40,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Scheduling the Computation\n--------------------------\nThis schedule is no different than a non-tensorcore matmul schedule on GPU.\nPlease refer to `opt-gemm` tutorial for basics of optimizing matmul schedule.\nWhen the \"tensor_core\" pragma is set, the \"rewrite for tensorcore\" ir pass\nwill automatically transform the schedule for tensorcore codegen,\notherwise normal CUDA code, with lower performance but equal functionality, will be generated.\n\n<div class=\"alert alert-i [...]
+ "Scheduling the Computation\n--------------------------\nThis schedule is no different than a non-tensorcore matmul schedule on GPU.\nPlease refer to `opt-gemm` tutorial for basics of optimizing matmul schedule.\nWhen the \"tensor_core\" pragma is set, the \"rewrite for tensorcore\" ir pass\nwill automatically transform the schedule for tensorcore codegen,\notherwise normal CUDA code, with lower performance but equal functionality, will be generated.\n\n<div class=\"alert alert-i [...]
]
},
{
diff --git a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
index e8aa55c..cf6b413 100644
--- a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
+++ b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
@@ -51,7 +51,7 @@
},
"outputs": [],
"source": [
- "# Load VTA parameters from the vta/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\ndevice = \"vta\"\ntarget = env.target if device == \"vta\" else env.target_vta_cpu\n\n# Dictionary lookup for when to start/end bit packing\npack_dict = {\n \"resnet18_v1\": [\"nn.max_pool2d\", \"nn.global_avg_pool2d\"],\n \"resnet34_v1\": [\"nn.max_pool2d\", \"nn.global_avg_po [...]
+ "# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\ndevice = \"vta\"\ntarget = env.target if device == \"vta\" else env.target_vta_cpu\n\n# Dictionary lookup for when to start/end bit packing\npack_dict = {\n \"resnet18_v1\": [\"nn.max_pool2d\", \"nn.global_avg_pool2d\"],\n \"resnet34_v1\": [\"nn.max_pool2d\", \"nn.global_a [...]
]
},
{
diff --git a/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb b/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb
index cff9e0d..6f532d4 100644
--- a/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb
+++ b/docs/_downloads/c634482de86bbc712f15077b3ec3a92f/convolution_opt.ipynb
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\n\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the vta/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.get(\ [...]
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\n\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ. [...]
]
},
{
diff --git a/docs/_downloads/cb3cbb4ea88433c5b624f34496695502/tutorials_python.zip b/docs/_downloads/cb3cbb4ea88433c5b624f34496695502/tutorials_python.zip
index 01a823b..2b99228 100644
Binary files a/docs/_downloads/cb3cbb4ea88433c5b624f34496695502/tutorials_python.zip and b/docs/_downloads/cb3cbb4ea88433c5b624f34496695502/tutorials_python.zip differ
diff --git a/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py b/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py
index f609a72..0564a6a 100644
--- a/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py
+++ b/docs/_downloads/cbbaae24e5c894dda1ebeded6fded2c3/convolution_opt.py
@@ -47,7 +47,7 @@ from tvm import rpc
from tvm.contrib import util
from vta.testing import simulator
-# Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# We read the Pynq RPC host IP address and port number from the OS environment
diff --git a/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb b/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb
index 1ea954e..b4daeaf 100644
--- a/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb
+++ b/docs/_downloads/d37aecc420f8b90ce29faee4df5d0bcd/matrix_multiply.ipynb
@@ -33,7 +33,7 @@
},
"outputs": [],
"source": [
- "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the vta/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.get(\"V [...]
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import util\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.ge [...]
]
},
{
diff --git a/docs/_downloads/d70fd54d93ff81c79032a082537fdf92/tutorials_jupyter.zip b/docs/_downloads/d70fd54d93ff81c79032a082537fdf92/tutorials_jupyter.zip
index 449f2fa..6bacd0f 100644
Binary files a/docs/_downloads/d70fd54d93ff81c79032a082537fdf92/tutorials_jupyter.zip and b/docs/_downloads/d70fd54d93ff81c79032a082537fdf92/tutorials_jupyter.zip differ
diff --git a/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py b/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
index 83fa8fb..efcd2c4 100644
--- a/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
+++ b/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
@@ -111,7 +111,7 @@ names = [x.strip() for x in content]
# --------------------------------------
# Execute on CPU vs. VTA, and define the model.
-# Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# Set ``device=arm_cpu`` to run inference on the CPU
# or ``device=vta`` to run inference on the FPGA.
diff --git a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
index 7ce3853..62fb321 100644
--- a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
+++ b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
@@ -68,7 +68,7 @@ assert tvm.runtime.enabled("rpc")
# -------------------------------------
# Execute on CPU vs. VTA, and define the model.
-# Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# Set ``device=arm_cpu`` to run inference on the CPU
diff --git a/docs/_sources/install/nnpack.md.txt b/docs/_sources/install/nnpack.md.txt
index 6e46a83..e1bcb70 100644
--- a/docs/_sources/install/nnpack.md.txt
+++ b/docs/_sources/install/nnpack.md.txt
@@ -64,6 +64,8 @@ export PATH="${PATH}:~/ninja"
The new CMAKE version of NNPACK download [Peach](https://github.com/Maratyszcza/PeachPy) and other dependencies alone
+Note: at least on OS X, running `ninja install` below will overwrite googletest libraries installed in `/usr/local/lib`. If you build googletest again to replace the nnpack copy, be sure to pass `-DBUILD_SHARED_LIBS=ON` to `cmake`.
+
```bash
git clone --recursive https://github.com/Maratyszcza/NNPACK.git
cd NNPACK
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index 5839598..0820dea 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
=================
-**00:50.140** total execution time for **tutorials_autotvm** files:
+**00:50.584** total execution time for **tutorials_autotvm** files:
-- **00:26.717**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:22.807**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.165**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.151**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:28.351**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:21.581**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.200**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.153**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
- **00:00.150**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.150**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.149**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index e5e8abd..a8b439a 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -234,26 +234,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 7 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 8 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 9 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 10 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 11 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 12 GFLOPS: 58.20/58.20 result: MeasureResult(costs=(0.003977931769230769,), error_no=0, all_cost=1.9641742706298828, timestamp=1585556429.2650185) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
- No: 13 GFLOPS: 0.00/58.20 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 14 GFLOPS: 0.00/58.20 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 15 GFLOPS: 0.00/58.20 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 16 GFLOPS: 0.00/58.20 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 17 GFLOPS: 0.00/58.20 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 18 GFLOPS: 0.00/58.20 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (0) /workspace/build/libtvm.so(+0xc8bf2b) [0x7fd8bb1b4f2b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
- No: 19 GFLOPS: 789.69/789.69 result: MeasureResult(costs=(0.00029315388418708243,), error_no=0, all_cost=2.4680051803588867, timestamp=1585556433.4341273) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
- No: 20 GFLOPS: 6.97/789.69 result: MeasureResult(costs=(0.03319892275,), error_no=0, all_cost=2.184508800506592, timestamp=1585556434.8201776) [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
+ No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 7 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 8 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 9 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 10 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 11 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 12 GFLOPS: 22.82/22.82 result: MeasureResult(costs=(0.0101451466,), error_no=0, all_cost=1.6779661178588867, timestamp=1585762379.5627096) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
+ No: 13 GFLOPS: 0.00/22.82 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 14 GFLOPS: 0.00/22.82 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 15 GFLOPS: 0.00/22.82 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 16 GFLOPS: 0.00/22.82 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 17 GFLOPS: 0.00/22.82 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 18 GFLOPS: 0.00/22.82 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (1) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f65e575be31]\n [bt] (0) /workspace/build/libtvm.so(+0xcad61b) [0x7f65e575761b]\n File "/workspace/docs/../python/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun\n rv = local_pyfunc(*pyargs)\n File "/workspace/docs/../python/tvm/autotvm/measure/measure_methods.py", line 621, in verify_pass\n raise I [...]
+ No: 19 GFLOPS: 512.42/512.42 result: MeasureResult(costs=(0.00045177718081180807,), error_no=0, all_cost=2.259528875350952, timestamp=1585762383.449056) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
+ No: 20 GFLOPS: 4.98/512.42 result: MeasureResult(costs=(0.04648650475,), error_no=0, all_cost=1.9373598098754883, timestamp=1585762384.5231574) [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
@@ -307,14 +307,14 @@ and measure running time.
Best config:
[('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
- Time cost of this operator: 0.000355
+ Time cost of this operator: 0.000776
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 22.807 seconds)
+ **Total running time of the script:** ( 0 minutes 21.581 seconds)
.. _sphx_glr_download_tutorials_autotvm_tune_conv2d_cuda.py:
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
index 3834265..2367ce2 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
@@ -431,7 +431,7 @@ It takes about 2 hours on a 32T AMD Ryzen Threadripper.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.151 seconds)
+ **Total running time of the script:** ( 0 minutes 0.149 seconds)
.. _sphx_glr_download_tutorials_autotvm_tune_relay_arm.py:
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
index c09c244..91c2176 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
@@ -418,7 +418,7 @@ to replace the corresponding part above.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.150 seconds)
+ **Total running time of the script:** ( 0 minutes 0.153 seconds)
.. _sphx_glr_download_tutorials_autotvm_tune_relay_cuda.py:
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
index 7ea1b08..006f927 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
@@ -269,7 +269,7 @@ One sample output is listed below.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.165 seconds)
+ **Total running time of the script:** ( 0 minutes 0.200 seconds)
.. _sphx_glr_download_tutorials_autotvm_tune_relay_x86.py:
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index c795477..0ef4049 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -361,16 +361,16 @@ used to get the best config later.
.. code-block:: none
Get devices for measurement successfully!
- No: 1 GFLOPS: 6.96/6.96 result: MeasureResult(costs=(0.0385728382,), error_no=0, all_cost=0.823296308517456, timestamp=1585556397.3558123) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
- No: 2 GFLOPS: 2.25/6.96 result: MeasureResult(costs=(0.1192342126,), error_no=0, all_cost=2.1185624599456787, timestamp=1585556399.5840642) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
- No: 3 GFLOPS: 7.27/7.27 result: MeasureResult(costs=(0.0369096948,), error_no=0, all_cost=1.1788554191589355, timestamp=1585556400.4997094) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 4 GFLOPS: 4.35/7.27 result: MeasureResult(costs=(0.061714442800000005,), error_no=0, all_cost=1.3026938438415527, timestamp=1585556401.8039923) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
- No: 5 GFLOPS: 6.01/7.27 result: MeasureResult(costs=(0.0446915612,), error_no=0, all_cost=0.986182689666748, timestamp=1585556402.8620286) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
- No: 6 GFLOPS: 6.50/7.27 result: MeasureResult(costs=(0.0412838544,), error_no=0, all_cost=1.0038743019104004, timestamp=1585556403.8686693) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
- No: 7 GFLOPS: 22.93/22.93 result: MeasureResult(costs=(0.0117043134,), error_no=0, all_cost=0.5391783714294434, timestamp=1585556404.3909621) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
- No: 8 GFLOPS: 20.32/22.93 result: MeasureResult(costs=(0.0132121842,), error_no=0, all_cost=0.4541635513305664, timestamp=1585556404.9171762) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
- No: 9 GFLOPS: 0.80/22.93 result: MeasureResult(costs=(0.3345709118,), error_no=0, all_cost=5.640729188919067, timestamp=1585556410.6114697) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
- No: 10 GFLOPS: 0.00/22.93 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (3) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fd8bb1b9741]\n [bt] (2) /workspace/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::RPCModuleNode::WrapRemote(void*)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime:: [...]
+ No: 1 GFLOPS: 3.54/3.54 result: MeasureResult(costs=(0.07590410239999999,), error_no=0, all_cost=1.6031978130340576, timestamp=1585762348.2834651) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+ No: 2 GFLOPS: 2.11/3.54 result: MeasureResult(costs=(0.1269865824,), error_no=0, all_cost=2.3244104385375977, timestamp=1585762350.6949053) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+ No: 3 GFLOPS: 10.60/10.60 result: MeasureResult(costs=(0.0253257884,), error_no=0, all_cost=1.2683050632476807, timestamp=1585762351.495554) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 4 GFLOPS: 9.09/10.60 result: MeasureResult(costs=(0.0295168472,), error_no=0, all_cost=0.8757941722869873, timestamp=1585762352.3540964) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+ No: 5 GFLOPS: 3.21/10.60 result: MeasureResult(costs=(0.0836431542,), error_no=0, all_cost=1.7907695770263672, timestamp=1585762354.0755901) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+ No: 6 GFLOPS: 3.37/10.60 result: MeasureResult(costs=(0.0797073572,), error_no=0, all_cost=1.7097434997558594, timestamp=1585762355.7363749) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
+ No: 7 GFLOPS: 8.96/10.60 result: MeasureResult(costs=(0.0299547868,), error_no=0, all_cost=0.8524417877197266, timestamp=1585762356.6053643) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
+ No: 8 GFLOPS: 14.31/14.31 result: MeasureResult(costs=(0.0187645802,), error_no=0, all_cost=0.6022071838378906, timestamp=1585762357.2941837) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
+ No: 9 GFLOPS: 0.88/14.31 result: MeasureResult(costs=(0.3058133368,), error_no=0, all_cost=5.160778522491455, timestamp=1585762362.9668663) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+ No: 10 GFLOPS: 0.45/14.31 result: MeasureResult(costs=(0.5970449322,), error_no=0, all_cost=9.82101845741272, timestamp=1585762372.906013) [('tile_y', [-1, 512]), ('tile_x', [-1, 1])],None,9
@@ -408,7 +408,7 @@ with the same argument.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 26.717 seconds)
+ **Total running time of the script:** ( 0 minutes 28.351 seconds)
.. _sphx_glr_download_tutorials_autotvm_tune_simple_template.py:
diff --git a/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
index a197629..13423e2 100644
--- a/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.723e-07 secs/op
+ 1.229e-07 secs/op
@@ -314,7 +314,7 @@ features in TVM.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.133 seconds)
+ **Total running time of the script:** ( 0 minutes 0.127 seconds)
.. _sphx_glr_download_tutorials_cross_compilation_and_rpc.py:
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 3d45274..100c412 100644
--- a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
+++ b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
@@ -260,7 +260,7 @@ This tutorial gives a quick view of writing a customized IR transformation pass:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.160 seconds)
+ **Total running time of the script:** ( 0 minutes 0.153 seconds)
.. _sphx_glr_download_tutorials_dev_low_level_custom_pass.py:
diff --git a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt b/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
index 08befc6..feb3ebf 100644
--- a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
+++ b/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
@@ -521,7 +521,7 @@ them.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.395 seconds)
+ **Total running time of the script:** ( 0 minutes 0.361 seconds)
.. _sphx_glr_download_tutorials_dev_relay_pass_infra.py:
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index af0ce11..845e3c7 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.554** total execution time for **tutorials_dev** files:
+**00:00.514** total execution time for **tutorials_dev** files:
-- **00:00.395**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
-- **00:00.160**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:00.361**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
+- **00:00.153**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/frontend/build_gcn.rst.txt b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
index 6afacfe..348badb 100644
--- a/docs/_sources/tutorials/frontend/build_gcn.rst.txt
+++ b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
@@ -482,7 +482,7 @@ Run the TVM model, test for accuracy and verify with DGL
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 4.613 seconds)
+ **Total running time of the script:** ( 0 minutes 5.505 seconds)
.. _sphx_glr_download_tutorials_frontend_build_gcn.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 55b6be5..a5fc236 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -415,7 +415,7 @@ Execute on TVM
TVM prediction top-1: tiger cat
Evaluate inference time cost...
- Mean inference time (std dev): 3.66 ms (0.15 ms)
+ Mean inference time (std dev): 3.91 ms (0.09 ms)
@@ -446,7 +446,7 @@ To speed up, we need to write and optimize the schedule according to the GPU arc
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 16.656 seconds)
+ **Total running time of the script:** ( 0 minutes 19.512 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_model_on_android.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
index 3b61921..7be0be8 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
@@ -311,7 +311,7 @@ to the remote device.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 4.752 seconds)
+ **Total running time of the script:** ( 0 minutes 5.777 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_model_on_rasp.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
index 807f1d5..30c69d0 100644
--- a/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
@@ -223,7 +223,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 19.529 seconds)
+ **Total running time of the script:** ( 0 minutes 26.171 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_quantized.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index ac9cb07..14a3959 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -169,6 +169,45 @@ Create TVM runtime and do inference
.. code-block:: none
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -224,45 +263,6 @@ Create TVM runtime and do inference
Cannot find config for target=cuda, workload=('conv2d_nchw_winograd.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda, workload=('conv2d_nchw_winograd.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -287,7 +287,7 @@ Display result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 30.139 seconds)
+ **Total running time of the script:** ( 0 minutes 35.197 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/tutorials/frontend/from_caffe2.rst.txt b/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
index 21e303d..c7193ff 100644
--- a/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
@@ -191,7 +191,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 11.917 seconds)
+ **Total running time of the script:** ( 0 minutes 13.186 seconds)
.. _sphx_glr_download_tutorials_frontend_from_caffe2.py:
diff --git a/docs/_sources/tutorials/frontend/from_coreml.rst.txt b/docs/_sources/tutorials/frontend/from_coreml.rst.txt
index 0f173c5..e1cb3f4 100644
--- a/docs/_sources/tutorials/frontend/from_coreml.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_coreml.rst.txt
@@ -201,7 +201,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 7.164 seconds)
+ **Total running time of the script:** ( 0 minutes 7.437 seconds)
.. _sphx_glr_download_tutorials_frontend_from_coreml.py:
diff --git a/docs/_sources/tutorials/frontend/from_darknet.rst.txt b/docs/_sources/tutorials/frontend/from_darknet.rst.txt
index 741af7b..7f2f76f 100644
--- a/docs/_sources/tutorials/frontend/from_darknet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_darknet.rst.txt
@@ -294,7 +294,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 17.350 seconds)
+ **Total running time of the script:** ( 0 minutes 21.056 seconds)
.. _sphx_glr_download_tutorials_frontend_from_darknet.py:
diff --git a/docs/_sources/tutorials/frontend/from_keras.rst.txt b/docs/_sources/tutorials/frontend/from_keras.rst.txt
index 23ea8e2..bc5497f 100644
--- a/docs/_sources/tutorials/frontend/from_keras.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_keras.rst.txt
@@ -185,7 +185,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 16.721 seconds)
+ **Total running time of the script:** ( 0 minutes 18.447 seconds)
.. _sphx_glr_download_tutorials_frontend_from_keras.py:
diff --git a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
index ecc27fc..69b61c9 100644
--- a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
@@ -214,7 +214,7 @@ for a normal mxnet model, we start from here
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 5.138 seconds)
+ **Total running time of the script:** ( 0 minutes 6.591 seconds)
.. _sphx_glr_download_tutorials_frontend_from_mxnet.py:
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index 765c247..7e5867a 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -151,7 +151,7 @@ Execute on TVM
.. code-block:: none
-
...47%, 0.01 MB, 45 KB/s, 0 seconds passed
...94%, 0.02 MB, 91 KB/s, 0 seconds passed
...100%, 0.02 MB, 137 KB/s, 0 seconds passed
+
...47%, 0.01 MB, 42 KB/s, 0 seconds passed
...94%, 0.02 MB, 81 KB/s, 0 seconds passed
...100%, 0.02 MB, 121 KB/s, 0 seconds passed
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 224, 224), 'float32'), ('TENSOR', (9, 32, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 224, 224), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1, 224, 224), 'float32'), ('TENSOR', (64, 1, 5, 5), 'float32'), (1, 1), (2, 2, 2, 2), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -187,7 +187,7 @@ We put input and output image neck to neck
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 1.659 seconds)
+ **Total running time of the script:** ( 0 minutes 1.903 seconds)
.. _sphx_glr_download_tutorials_frontend_from_onnx.py:
diff --git a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
index f745f99..2db7f58 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -241,7 +241,7 @@ Look up prediction top 1 index in 1000 class synset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 5.601 seconds)
+ **Total running time of the script:** ( 0 minutes 5.746 seconds)
.. _sphx_glr_download_tutorials_frontend_from_pytorch.py:
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index cf404fb..57e3c61 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -373,7 +373,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 20.524 seconds)
+ **Total running time of the script:** ( 0 minutes 26.943 seconds)
.. _sphx_glr_download_tutorials_frontend_from_tensorflow.py:
diff --git a/docs/_sources/tutorials/frontend/from_tflite.rst.txt b/docs/_sources/tutorials/frontend/from_tflite.rst.txt
index 323e76f..49196bd 100644
--- a/docs/_sources/tutorials/frontend/from_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tflite.rst.txt
@@ -276,7 +276,7 @@ Display results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 20.383 seconds)
+ **Total running time of the script:** ( 0 minutes 25.351 seconds)
.. _sphx_glr_download_tutorials_frontend_from_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index 6533dfe..2f208ba 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
Computation times
=================
-**03:04.468** total execution time for **tutorials_frontend** files:
+**03:41.816** total execution time for **tutorials_frontend** files:
-- **00:30.139**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:20.524**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:20.383**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:19.529**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:17.350**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:16.721**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:16.656**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:11.917**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:07.164**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:05.601**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:05.138**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:04.752**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:04.613**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:02.320**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.659**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:35.197**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:26.943**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:26.171**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:25.351**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:21.056**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:19.512**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:18.447**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:13.186**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:07.437**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:06.591**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:05.777**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:05.746**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:05.505**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:02.995**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.903**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
diff --git a/docs/_sources/tutorials/frontend/using_external_lib.rst.txt b/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
index d4eb40c..952904f 100644
--- a/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
+++ b/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
@@ -616,7 +616,7 @@ To achieve this goal, we should continue developing better optimizations for TVM
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 2.320 seconds)
+ **Total running time of the script:** ( 0 minutes 2.995 seconds)
.. _sphx_glr_download_tutorials_frontend_using_external_lib.py:
diff --git a/docs/_sources/tutorials/language/extern_op.rst.txt b/docs/_sources/tutorials/language/extern_op.rst.txt
index d0f40e5..cd309e1 100644
--- a/docs/_sources/tutorials/language/extern_op.rst.txt
+++ b/docs/_sources/tutorials/language/extern_op.rst.txt
@@ -172,7 +172,7 @@ Summary
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.303 seconds)
+ **Total running time of the script:** ( 0 minutes 0.282 seconds)
.. _sphx_glr_download_tutorials_language_extern_op.py:
diff --git a/docs/_sources/tutorials/language/intrin_math.rst.txt b/docs/_sources/tutorials/language/intrin_math.rst.txt
index 8cadabe..402ab35 100644
--- a/docs/_sources/tutorials/language/intrin_math.rst.txt
+++ b/docs/_sources/tutorials/language/intrin_math.rst.txt
@@ -314,7 +314,7 @@ Summary
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 1.212 seconds)
+ **Total running time of the script:** ( 0 minutes 1.336 seconds)
.. _sphx_glr_download_tutorials_language_intrin_math.py:
diff --git a/docs/_sources/tutorials/language/reduction.rst.txt b/docs/_sources/tutorials/language/reduction.rst.txt
index f4198cc..edcf768 100644
--- a/docs/_sources/tutorials/language/reduction.rst.txt
+++ b/docs/_sources/tutorials/language/reduction.rst.txt
@@ -452,7 +452,7 @@ This tutorial provides a walk through of reduction schedule.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.461 seconds)
+ **Total running time of the script:** ( 0 minutes 0.473 seconds)
.. _sphx_glr_download_tutorials_language_reduction.py:
diff --git a/docs/_sources/tutorials/language/scan.rst.txt b/docs/_sources/tutorials/language/scan.rst.txt
index deec675..7607b78 100644
--- a/docs/_sources/tutorials/language/scan.rst.txt
+++ b/docs/_sources/tutorials/language/scan.rst.txt
@@ -286,7 +286,7 @@ This tutorial provides a walk through of scan primitive.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.440 seconds)
+ **Total running time of the script:** ( 0 minutes 0.510 seconds)
.. _sphx_glr_download_tutorials_language_scan.py:
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index 29f6f4f..53b0743 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -512,7 +512,7 @@ general workflow often is:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.181 seconds)
+ **Total running time of the script:** ( 0 minutes 0.222 seconds)
.. _sphx_glr_download_tutorials_language_schedule_primitives.py:
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index de84888..6e10b74 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
=================
-**00:03.552** total execution time for **tutorials_language** files:
+**00:03.859** total execution time for **tutorials_language** files:
-- **00:01.212**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.598**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.461**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.440**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.303**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.199**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
-- **00:00.181**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.157**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:01.336**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.639**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.510**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.473**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.282**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.226**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:00.222**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.170**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/tutorials/language/tedd.rst.txt b/docs/_sources/tutorials/language/tedd.rst.txt
index 78df9e6..a2f95f0 100644
--- a/docs/_sources/tutorials/language/tedd.rst.txt
+++ b/docs/_sources/tutorials/language/tedd.rst.txt
@@ -204,7 +204,7 @@ it before and after any schedule primitive to inspect its effect.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.199 seconds)
+ **Total running time of the script:** ( 0 minutes 0.226 seconds)
.. _sphx_glr_download_tutorials_language_tedd.py:
diff --git a/docs/_sources/tutorials/language/tensorize.rst.txt b/docs/_sources/tutorials/language/tensorize.rst.txt
index 1b462ed..f36b8f9 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -288,7 +288,7 @@ The importing needs to happen before the tensorized GEMV being executed.
.. code-block:: none
produce C {
- // attr [iter_var(i, )] pragma_import_llvm = "; ModuleID = '/tmp/tmpjqfizk17/input0.cc'\nsource_filename = \"/tmp/tmpjqfizk17/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca float*, align 8\n %10 = alloca i [...]
+ // attr [iter_var(i, )] pragma_import_llvm = "; ModuleID = '/tmp/tmp_tqfzpjl/input0.cc'\nsource_filename = \"/tmp/tmp_tqfzpjl/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n %7 = alloca float*, align 8\n %8 = alloca float*, align 8\n %9 = alloca float*, align 8\n %10 = alloca i [...]
for (i, 0, 1024) {
for (j.outer, 0, 32) {
gemv_update(tvm_access_ptr(type_annotation(), C, ((i*512) + (j.outer*16)), 16, 2), tvm_access_ptr(type_annotation(), A, (i*64), 64, 1), tvm_access_ptr(type_annotation(), B, (j.outer*1024), 1024, 1), 16, 64, 64)
@@ -471,7 +471,7 @@ which helps users inject asm easily into the schedule.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.598 seconds)
+ **Total running time of the script:** ( 0 minutes 0.639 seconds)
.. _sphx_glr_download_tutorials_language_tensorize.py:
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 65be998..7a6b90e 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -207,7 +207,7 @@ This tutorial introduces the usage of tuple inputs operation.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.157 seconds)
+ **Total running time of the script:** ( 0 minutes 0.170 seconds)
.. _sphx_glr_download_tutorials_language_tuple_inputs.py:
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index c12a79e..a08732a 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -293,14 +293,14 @@ latency of convolution.
.. code-block:: none
- Convolution: 19.552622 ms
+ Convolution: 36.016536 ms
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.664 seconds)
+ **Total running time of the script:** ( 0 minutes 0.886 seconds)
.. _sphx_glr_download_tutorials_optimize_opt_conv_cuda.py:
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index b96446d..84c1897 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -567,7 +567,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 6.304654 ms
+ conv2d with tensor core: 13.299916 ms
@@ -579,7 +579,7 @@ call TensorCores on specific GPUs.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.892 seconds)
+ **Total running time of the script:** ( 0 minutes 1.112 seconds)
.. _sphx_glr_download_tutorials_optimize_opt_conv_tensorcore.py:
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index 763b7dd..605f8a9 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: 0.007367
- Baseline: 6.090805
+ Numpy running time: 0.011041
+ Baseline: 3.404642
@@ -200,7 +200,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.112883
+ Opt1: 0.288281
@@ -288,7 +288,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.119189
+ Opt2: 0.321037
@@ -371,7 +371,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.060986
+ Opt3: 0.111675
@@ -473,7 +473,7 @@ the corresponding value from the packed array.
.. code-block:: none
- Opt4: 0.068338
+ Opt4: 0.104570
@@ -578,7 +578,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.080476
+ Opt5: 0.099314
@@ -691,7 +691,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.015324
+ Opt6: 0.032255
@@ -763,7 +763,7 @@ tutorial by yourself to observe the performance gain acheived by TVM.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 25.764 seconds)
+ **Total running time of the script:** ( 0 minutes 25.108 seconds)
.. _sphx_glr_download_tutorials_optimize_opt_gemm.py:
diff --git a/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt
index d405cc8..507361d 100644
--- a/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt
@@ -82,7 +82,7 @@ otherwise normal CUDA code, with lower performance but equal functionality, will
(2) The warp tile size is not 16x16x16 on CUDA9, or not one of {16x16x16, 32x8x16, 8x32x16} on CUDA version >= 10.0.
In this schedule, storage_align is used to reduce bank conflicts of shared memory. Please refer to this
-`doc <https://docs.tvm.ai/api/python/schedule.html#tvm.te.schedule.Stage.storage_align>`_
+`doc <https://tvm.apache.org/docs/api/python/schedule.html#tvm.te.schedule.Stage.storage_align>`_
for the usage of storage_align primitive. In short, we need to add an offset to some shared memory buffer
to reduce bank conflicts.
According to the `wmma doc <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#wmma-description>`_,
@@ -546,7 +546,7 @@ to generate tensorcore kernels.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.162 seconds)
+ **Total running time of the script:** ( 0 minutes 0.200 seconds)
.. _sphx_glr_download_tutorials_optimize_opt_matmul_auto_tensorcore.py:
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index 0a96e51..4b5a6df 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
=================
-**00:27.482** total execution time for **tutorials_optimize** files:
+**00:27.306** total execution time for **tutorials_optimize** files:
-- **00:25.764**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:00.892**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:00.664**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.162**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:25.108**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.112**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.886**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.200**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
diff --git a/docs/_sources/tutorials/relay_quick_start.rst.txt b/docs/_sources/tutorials/relay_quick_start.rst.txt
index 4ce219f..09a79f7 100644
--- a/docs/_sources/tutorials/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/relay_quick_start.rst.txt
@@ -223,7 +223,7 @@ in this example. Then the machine code will be generated as the module library.
.. code-block:: none
-
...1%, 0.01 MB, 34 KB/s, 0 seconds passed
...3%, 0.02 MB, 69 KB/s, 0 seconds passed
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ssed
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1385 KB/s, 0 seconds passed
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+
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...73%, 0.29 MB, 1286 KB/s, 0 seconds passed
...75%, 0.30 MB, 1318 KB/s, 0 seconds passed
...77%, 0.30 MB, 1353 KB/s, 0 seconds passed
...79%, 0.31 MB, 1387 KB/s, 0 seconds passed
...81%, 0.32 MB, 1421 KB/s, 0 seconds passed
...83%, 0.33 MB
, 1455 KB/s, 0 seconds passed
...85%, 0.34 MB, 1490 KB/s, 0 seconds passed
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...99%, 0.39 MB, 1722 KB/s, 0 seconds passed
...100%, 0.40 MB, 1756 KB/s, 0 seconds passed
Cannot find config for target=cuda -model=unknown, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 56, 56), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda -model=unknown, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 28, 28), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=cuda -model=unknown, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 14, 14), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -302,7 +302,7 @@ back in deploy environment.
.. code-block:: none
- ['deploy_lib.tar', 'deploy_param.params', 'deploy_graph.json']
+ ['deploy_lib.tar', 'deploy_graph.json', 'deploy_param.params']
@@ -344,7 +344,7 @@ back in deploy environment.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 10.784 seconds)
+ **Total running time of the script:** ( 0 minutes 11.949 seconds)
.. _sphx_glr_download_tutorials_relay_quick_start.py:
diff --git a/docs/_sources/tutorials/sg_execution_times.rst.txt b/docs/_sources/tutorials/sg_execution_times.rst.txt
index 422d865..dba2bd7 100644
--- a/docs/_sources/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:11.240** total execution time for **tutorials** files:
+**00:12.412** total execution time for **tutorials** files:
-- **00:10.784**: :ref:`sphx_glr_tutorials_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.323**: :ref:`sphx_glr_tutorials_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.133**: :ref:`sphx_glr_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:11.949**: :ref:`sphx_glr_tutorials_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.335**: :ref:`sphx_glr_tutorials_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.127**: :ref:`sphx_glr_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
diff --git a/docs/_sources/tutorials/tensor_expr_get_started.rst.txt b/docs/_sources/tutorials/tensor_expr_get_started.rst.txt
index c547b0e..3bd2b40 100644
--- a/docs/_sources/tutorials/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorials/tensor_expr_get_started.rst.txt
@@ -324,7 +324,7 @@ The following code first performs the following steps:
.. code-block:: none
- ['myadd.tvm_meta.json', 'myadd.so', 'myadd.ptx', 'myadd.o']
+ ['myadd.tvm_meta.json', 'myadd.ptx', 'myadd.so', 'myadd.o']
@@ -450,7 +450,7 @@ and other features in TVM.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.323 seconds)
+ **Total running time of the script:** ( 0 minutes 0.335 seconds)
.. _sphx_glr_download_tutorials_tensor_expr_get_started.py:
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index 94fe497..996173a 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -218,7 +218,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, 0x103c1c890), stage(b, 0x105cf1aa0), stage(T_add, 0xf30add10), stage(T_multiply, 0xf6cfb5a0), stage(T_elemwise_sum, 0xf30aa600), stage(T_divide, 0x106619d90), stage(T_divide_red.rf, 0x105cf6550), stage(T_divide_red, 0x126faa7a0)]
+ [stage(a, 0xa3d45d30), stage(b, 0x1158f53e0), stage(T_add, 0xf80cd810), stage(T_multiply, 0x116438130), stage(T_elemwise_sum, 0x1024c97d0), stage(T_divide, 0x10fc1a680), stage(T_divide_red.rf, 0xf39351d0), stage(T_divide_red, 0xf4e50f00)]
@@ -643,7 +643,7 @@ In this tutorial, we have seen
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.492 seconds)
+ **Total running time of the script:** ( 0 minutes 0.528 seconds)
.. _sphx_glr_download_tutorials_topi_intro_topi.py:
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index 6829646..7c7c583 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
=================
-**00:00.492** total execution time for **tutorials_topi** files:
+**00:00.528** total execution time for **tutorials_topi** files:
-- **00:00.492**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.528**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/dev/config.rst.txt b/docs/_sources/vta/dev/config.rst.txt
index e9690cb..2f98d77 100644
--- a/docs/_sources/vta/dev/config.rst.txt
+++ b/docs/_sources/vta/dev/config.rst.txt
@@ -21,7 +21,7 @@ VTA Configuration
The VTA stack incorporates both a hardware accelerator stack and
a TVM based software stack.
VTA incorporates flexibility out of the box: by modifying the
-``vta/vta-hw/config/vta_config.json`` high-level configuration file,
+``3rdparty/vta-hw/config/vta_config.json`` high-level configuration file,
the user can change the shape of the tensor intrinsic,
clock frequency, pipelining, data type width, and on-chip buffer sizes.
diff --git a/docs/_sources/vta/dev/hardware.rst.txt b/docs/_sources/vta/dev/hardware.rst.txt
index 84cfc45..6eb3040 100644
--- a/docs/_sources/vta/dev/hardware.rst.txt
+++ b/docs/_sources/vta/dev/hardware.rst.txt
@@ -53,17 +53,17 @@ HLS Hardware Source Organization
The VTA design is currently specified in Vivado HLS C++, which is only supported
by Xilinx toolchains.
-The VTA hardware sources are contained under ``vta/vta-hw/hardware/xilinx/sources``:
+The VTA hardware sources are contained under ``3rdparty/vta-hw/hardware/xilinx/sources``:
- ``vta.cc`` contains the definitions for each VTA module, as well as a top
level behavioral model for the top-level VTA design.
- ``vta.h`` contains type definitions using Xilinx ``ap_int`` types, and
function prototypes declarations.
-In addition preprocessor macros are defined under ``vta/vta-hw/include/vta/hw_spec.h``.
+In addition preprocessor macros are defined under ``3rdparty/vta-hw/include/vta/hw_spec.h``.
Much of these macro definitions are derived from the parameters listed in the
-``vta/vta-hw/config/vta_config.json`` file.
-The json file is processed by ``vta/vta-hw/config/vta_config.py`` to produce a string of
+``3rdparty/vta-hw/config/vta_config.json`` file.
+The json file is processed by ``3rdparty/vta-hw/config/vta_config.py`` to produce a string of
compile flags that define the preprocessor macros.
That string is used by the makefile in order to set those high-level
parameters in both the HLS hardware synthesis compiler, and the C++
@@ -220,7 +220,7 @@ Microarchitectural Overview
---------------------------
We describe the modules that compose the VTA design.
-The module definitions are contained in ``vta/vta-hw/hardware/xilinx/sources/vta.cc``.
+The module definitions are contained in ``3rdparty/vta-hw/hardware/xilinx/sources/vta.cc``.
Fetch Module
~~~~~~~~~~~~
diff --git a/docs/_sources/vta/install.md.txt b/docs/_sources/vta/install.md.txt
index e428514..a938a67 100644
--- a/docs/_sources/vta/install.md.txt
+++ b/docs/_sources/vta/install.md.txt
@@ -26,13 +26,13 @@ We present three installation guides, each extending on the previous one:
## VTA Simulator Installation
-You need [TVM installed](https://docs.tvm.ai/install/index.html) on your machine.
-For a quick and easy start, use the pre-built [TVM Docker image](https://docs.tvm.ai/install/docker.html).
+You need [TVM installed](https://tvm.apache.org/docs/install/index.html) on your machine.
+For a quick and easy start, checkout the [Docker Guide](https://tvm.apache.org/docs/install/docker.html).
You'll need to set the following paths to use VTA:
```bash
export TVM_PATH=<path to TVM root>
-export VTA_HW_PATH=$TVM_PATH/vta/vta-hw
+export VTA_HW_PATH=$TVM_PATH/3rdparty/vta-hw
```
The VTA functional simulation library needs to be enabled when building TVM.
@@ -60,13 +60,13 @@ python <tvm root>/vta/tests/python/integration/test_benchmark_topi_conv2d.py
> Note: You'll notice that for every convolution layer, the throughput gets reported in GOPS. These numbers are actually the computational throughput that the simulator achieves, by evaluating the convolutions in software.
-You are invited to try out our [VTA programming tutorials](https://docs.tvm.ai/vta/tutorials/index.html).
+You are invited to try out our [VTA programming tutorials](https://tvm.apache.org/docs/vta/tutorials/index.html).
### Advanced Configuration (optional)
VTA is a generic configurable deep learning accelerator.
-The configuration is specified by `vta_config.json` under `vta/vta-hw/config`.
+The configuration is specified by `vta_config.json` under `3rdparty/vta-hw/config`.
This file provides an architectural specification of the VTA accelerator to parameterize the TVM compiler stack and the VTA hardware stack.
The VTA configuration file also specifies the TVM compiler target.
@@ -76,7 +76,7 @@ To do so,
```bash
cd <tvm root>
-vim vta/vta-hw/config/vta_config.json
+vim 3rdparty/vta-hw/config/vta_config.json
# edit vta_config.json
make
```
@@ -134,7 +134,7 @@ mkdir build
cp cmake/config.cmake build/.
echo 'set(USE_VTA_FPGA ON)' >> build/config.cmake
# Copy pynq specific configuration
-cp vta/vta-hw/config/pynq_sample.json vta/vta-hw/config/vta_config.json
+cp 3rdparty/vta-hw/config/pynq_sample.json 3rdparty/vta-hw/config/vta_config.json
cd build
cmake ..
make runtime vta -j2
@@ -168,7 +168,7 @@ In addition, you'll need to edit the `vta_config.json` file on the host to indic
```bash
# On the Host-side
cd <tvm root>
-cp vta/vta-hw/config/pynq_sample.json vta/vta-hw/config/vta_config.json
+cp 3rdparty/vta-hw/config/pynq_sample.json 3rdparty/vta-hw/config/vta_config.json
```
This time again, we will run the 2D convolution testbench.
@@ -193,7 +193,7 @@ python <tvm root>/vta/tests/python/integration/test_benchmark_topi_conv2d.py
The performance metrics measured on the Pynq board will be reported for each convolutional layer.
-You can also try out our [VTA programming tutorials](https://docs.tvm.ai/vta/tutorials/index.html).
+You can also try out our [VTA programming tutorials](https://tvm.apache.org/docs/vta/tutorials/index.html).
## VTA Custom Test Setup for Intel FPGA
@@ -359,11 +359,11 @@ For this custom VTA bitstream compilation exercise, we'll change the frequency o
* Set the `HW_FREQ` field to `142`. The Pynq board supports 100, 142, 167 and 200MHz clocks. Note that the higher the frequency, the harder it will be to close timing. Increasing the frequency can lead to timing violation and thus faulty hardware execution.
* Set the `HW_CLK_TARGET` to `6`. This parameters refers to the target clock period in nano seconds for HLS - a lower clock period leads to more aggressive pipelining to achieve timing closure at higher frequencies. Technically a 142MHz clock would require a 7ns target, but we intentionally lower the clock target to 6ns to more aggressively pipeline our design.
-Bitstream generation is driven by a top-level `Makefile` under `<tvm root>/vta/vta-hw/hardware/xilinx/`.
+Bitstream generation is driven by a top-level `Makefile` under `<tvm root>/3rdparty/vta-hw/hardware/xilinx/`.
If you just want to simulate the VTA design in software emulation to make sure that it is functional, enter:
```bash
-cd <tvm root>/vta/vta-hw/hardware/xilinx
+cd <tvm root>/3rdparty/vta-hw/hardware/xilinx
make ip MODE=sim
```
@@ -371,7 +371,7 @@ If you just want to generate the HLS-based VTA IP cores without launching the en
```bash
make ip
```
-You'll be able to view the HLS synthesis reports under `<tvm root>/vta/vta-hw/build/hardware/xilinx/hls/` `<configuration>/<block>/solution0/syn/report/<block>_csynth.rpt`
+You'll be able to view the HLS synthesis reports under `<tvm root>/3rdparty/vta-hw/build/hardware/xilinx/hls/` `<configuration>/<block>/solution0/syn/report/<block>_csynth.rpt`
> Note: The `<configuration>` name is a string that summarizes the VTA configuration parameters listed in the `vta_config.json`. The `<block>` name refers to the specific module (or HLS function) that compose the high-level VTA pipeline.
Finally to run the full hardware compilation and generate the VTA bitstream, run:
@@ -383,20 +383,20 @@ make
This process is lengthy, and can take around up to an hour to complete depending on your machine's specs.
We recommend setting the `VTA_HW_COMP_THREADS` variable in the Makefile to take full advantage of all the cores on your development machine.
-Once the compilation completes, the generated bitstream can be found under `<tvm root>/vta/vta-hw/build/hardware/xilinx/vivado/<configuration>/export/vta.bit`.
+Once the compilation completes, the generated bitstream can be found under `<tvm root>/3rdparty/vta-hw/build/hardware/xilinx/vivado/<configuration>/export/vta.bit`.
### Chisel-based Custom VTA Bitstream Compilation for DE10-Nano
-Similar to the HLS-based design, high-level hardware parameters in Chisel-based design are listed in the VTA configuration file [Configs.scala](https://github.com/apache/incubator-tvm/blob/master/vta/vta-hw/hardware/chisel/src/main/scala/core/Configs.scala), and they can be customized by the user.
+Similar to the HLS-based design, high-level hardware parameters in Chisel-based design are listed in the VTA configuration file [Configs.scala](https://github.com/apache/incubator-tvm/blob/master/3rdparty/vta-hw/hardware/chisel/src/main/scala/core/Configs.scala), and they can be customized by the user.
-For Intel FPGA, bitstream generation is driven by a top-level `Makefile` under `<tvmroot>/vta/vta-hw/hardware/intel`.
+For Intel FPGA, bitstream generation is driven by a top-level `Makefile` under `<tvm root>/3rdparty/vta-hw/hardware/intel`.
If you just want to generate the Chisel-based VTA IP core for the DE10-Nano board without compiling the design for the FPGA hardware, enter:
```bash
-cd <tvmroot>/vta/vta-hw/hardware/intel
+cd <tvm root>/3rdparty/vta-hw/hardware/intel
make ip
```
-Then you'll be able to locate the generated verilog file at `<tvmroot>/vta/vta-hw/build/hardware/intel/chisel/<configuration>/VTA.DefaultDe10Config.v`.
+Then you'll be able to locate the generated verilog file at `<tvm root>/3rdparty/vta-hw/build/hardware/intel/chisel/<configuration>/VTA.DefaultDe10Config.v`.
If you would like to run the full hardware compilation for the `de10nano` board:
```bash
@@ -405,14 +405,14 @@ make
This process might be a bit lengthy, and might take up to half an hour to complete depending on the performance of your PC. The Quartus Prime software would automatically detect the number of cores available on your PC and try to utilize all of them to perform such process.
-Once the compilation completes, the generated bistream can be found under `<tvmroot>vtay/vta-hw/build/hardware/intel/quartus/<configuration>/export/vta.rbf`. You can also open the Quartus project file (.qpf) available at `<tvmroot>/vta/vta-hw/build/hardware/intel/quartus/<configuration>/de10_nano_top.qpf` to look around the generated reports.
+Once the compilation completes, the generated bistream can be found under `<tvm root>/3rdparty/vta-hw/build/hardware/intel/quartus/<configuration>/export/vta.rbf`. You can also open the Quartus project file (.qpf) available at `<tvm root>/3rdparty/vta-hw/build/hardware/intel/quartus/<configuration>/de10_nano_top.qpf` to look around the generated reports.
### Use the Custom Bitstream
We can program the new VTA FPGA bitstream by setting the bitstream path of the `vta.program_fpga()` function in the tutorial examples, or in the `test_program_rpc.py` script.
```python
-vta.program_fpga(remote, bitstream="<tvm root>/vta/vta-hw/build/hardware/xilinx/vivado/<configuration>/export/vta.bit")
+vta.program_fpga(remote, bitstream="<tvm root>/3rdparty/vta-hw/build/hardware/xilinx/vivado/<configuration>/export/vta.bit")
```
Instead of downloading a pre-built bitstream from the VTA bitstream repository, TVM will instead use the new bitstream you just generated, which is a VTA design clocked at a higher frequency.
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 bf97614..a768bbe 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
=================
-**00:04.362** total execution time for **vta_tutorials_autotvm** files:
+**00:05.575** total execution time for **vta_tutorials_autotvm** files:
-- **00:04.362**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:05.575**: :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 43363e8..76f6114 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -191,7 +191,7 @@ Here we use an Pynq-Z1 board as an example.
tracker_host = os.environ.get("TVM_TRACKER_HOST", '0.0.0.0')
tracker_port = int(os.environ.get("TVM_TRACKER_PORT", 9190))
- # Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+ # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
@@ -481,7 +481,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
.. code-block:: none
Extract tasks...
-
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...78%, 0.56 MB, 3135 KB/s, 0 seconds passed
...79%, 0.57 MB, 3177 KB/s, 0 seconds passed
...80%, 0.58 MB, 3218 KB/s, 0 seconds passed
...81%, 0.59 MB, 3260 KB/s, 0 seconds passed
...82%, 0.59 MB, 3301 KB/s, 0 seconds passed
...83%, 0.60 MB, 3343 KB/s, 0 seconds passed
...84%, 0.61 MB, 3379 KB/s, 0 seconds passed
...85%, 0.62 MB, 3420 KB/s, 0 seconds passed
...87%, 0.62 MB, 3461 KB/s, 0 seconds passed
...88%, 0.63 MB, 3502 KB/s, 0 seconds passed
...89%, 0.64 MB, 3537 KB/s, 0 seconds passed
...90%, 0.65 MB, 3578 KB/s, 0 seconds passed
...91%, 0.66 MB, 3619 KB/s, 0 seconds passed
...92%, 0.66 MB, 3660 KB/s, 0 seconds passed
...93%, 0.67 MB, 3700 KB/s, 0 seconds passed
...94%, 0.68 MB, 3742 KB/s, 0 seconds passed
...95%, 0.69 MB, 3782 KB/s, 0 seconds passed
...96%, 0.70 MB, 3823 KB/s, 0 seconds passed
...97%, 0.70 MB, 3858 KB/s, 0 seconds passed
...99%, 0.71 MB, 3899 KB/s, 0 seconds passed
...100%, 0.72 MB, 3937 KB/s, 0 seconds passed
+
...1%, 0.01 MB, 35 KB/s, 0 seconds passed
...2%, 0.02 MB, 70 KB/s, 0 seconds passed
...3%, 0.02 MB, 104 KB/s, 0 seconds passed
...4%, 0.03 MB, 136 KB/s, 0 seconds passed
...5%, 0.04 MB, 170 KB/s, 0 seconds passed
...6%, 0.05 MB, 203 KB/s, 0 seconds passed
...7%, 0.05 MB, 237 KB/s, 0 seconds passed
...8%, 0.06 MB, 270 KB/s, 0 seconds passed
...9%, 0.07 MB, 304 KB/s, 0 seconds passed
...10%, 0.08 MB, 336 KB/s, 0 seconds passed
...11%, 0.09 MB, 369 KB/s, 0 seconds passed
...13%, 0.09 MB, 398 KB/s, 0 seconds passed
...14%, 0.10 MB, 431 KB/s, 0 seconds passed
...15%, 0.11 MB, 463 KB/s, 0 seconds passed
...16%, 0.12 MB, 496 KB/s, 0 seconds passed
...17%, 0.12 MB, 527 KB/s, 0 seconds passed
...18%, 0.13 MB, 560 KB/s, 0 seconds passed
...19%, 0.14 MB, 593 KB/s, 0 seconds passed
...20%, 0.15 MB, 626 KB/s, 0 seconds passed
...21%, 0.16 MB, 657 KB/s, 0 seconds passed
...22%, 0.16 MB, 689 KB/s, 0 seconds passed
...23%, 0.17 MB, 720 KB/s, 0 seconds passed
...25%, 0.18 MB, 752 KB/s, 0 seconds passed
...26%, 0.19 MB, 785 KB/s, 0 seconds passed
...27%, 0.20 MB, 817 KB/s, 0 seconds passed
...28%, 0.20 MB, 840 KB/s, 0 seconds passed
...29%, 0.21 MB, 872 KB/s, 0 seconds passed
...30%, 0.22 MB, 905 KB/s, 0 seconds passed
...31%, 0.23 MB, 937 KB/s, 0 seconds passed
...32%, 0.23 MB, 968 KB/s, 0 seconds passed
...33%, 0.24 MB, 1000 KB/s, 0 seconds passed
...34%, 0.25 MB, 1031 KB/s, 0 seconds passed
...35%, 0.26 MB, 1063 KB/s, 0 seconds passed
...36%, 0.27 MB, 1093 KB/s, 0 seconds passed
...38%, 0.27 MB, 1124 KB/s, 0 seconds passed
...39%, 0.28 MB, 1156 KB/s, 0 seconds passed
...40%, 0.29 MB, 1188 KB/s, 0 seconds passed
...41%, 0.30 MB, 1217 KB/s, 0 seconds passed
...42%, 0.30 MB, 1249 KB/s, 0 seconds passed
...43%, 0.31 MB, 1280 KB/s, 0 seconds passed
...44%, 0.32 MB, 1312 KB/s, 0 seconds passed
...45%, 0.33 MB, 1343
KB/s, 0 seconds passed
...46%, 0.34 MB, 1375 KB/s, 0 seconds passed
...47%, 0.34 MB, 1407 KB/s, 0 seconds passed
...48%, 0.35 MB, 1438 KB/s, 0 seconds passed
...50%, 0.36 MB, 1467 KB/s, 0 seconds passed
...51%, 0.37 MB, 1498 KB/s, 0 seconds passed
...52%, 0.38 MB, 1530 KB/s, 0 seconds passed
...53%, 0.38 MB, 1561 KB/s, 0 seconds passed
...54%, 0.39 MB, 1578 KB/s, 0 seconds passed
...55%, 0.40 MB, 1609 KB/s, 0 seconds passed
...56%, 0.41 MB, 1640 KB/s, 0 seconds passed
...57%, 0.41 MB, 1671 KB/s, 0 seconds passed
...58%, 0.42 MB, 1699 KB/s, 0 seconds passed
...59%, 0.43 MB, 1730 KB/s, 0 seconds passed
...60%, 0.44 MB, 1761 KB/s, 0 seconds passed
...62%, 0.45 MB, 1792 KB/s, 0 seconds passed
...63%, 0.45 MB, 1820 KB/s, 0 seconds passed
...64%, 0.46 MB, 1851 KB/s, 0 seconds passed
...65%, 0.47 MB, 1882 KB/s, 0 seconds passed
...66%, 0.48 MB, 1913 KB/s, 0 seconds passed
...67%, 0.48 MB, 1937 KB/s, 0 seconds
passed
...68%, 0.49 MB, 1968 KB/s, 0 seconds passed
...69%, 0.50 MB, 1998 KB/s, 0 seconds passed
...70%, 0.51 MB, 2029 KB/s, 0 seconds passed
...71%, 0.52 MB, 2058 KB/s, 0 seconds passed
...72%, 0.52 MB, 2088 KB/s, 0 seconds passed
...73%, 0.53 MB, 2119 KB/s, 0 seconds passed
...75%, 0.54 MB, 2150 KB/s, 0 seconds passed
...76%, 0.55 MB, 2177 KB/s, 0 seconds passed
...77%, 0.55 MB, 2207 KB/s, 0 seconds passed
...78%, 0.56 MB, 2238 KB/s, 0 seconds passed
...79%, 0.57 MB, 2268 KB/s, 0 seconds passed
...80%, 0.58 MB, 2292 KB/s, 0 seconds passed
...81%, 0.59 MB, 2323 KB/s, 0 seconds passed
...82%, 0.59 MB, 2342 KB/s, 0 seconds passed
...83%, 0.60 MB, 2372 KB/s, 0 seconds passed
...84%, 0.61 MB, 2395 KB/s, 0 seconds passed
...85%, 0.62 MB, 2425 KB/s, 0 seconds passed
...87%, 0.62 MB, 2455 KB/s, 0 seconds passed
...88%, 0.63 MB, 2485 KB/s, 0 seconds passed
...89%, 0.64 MB, 2515 KB/s, 0 seconds passed
...90%
, 0.65 MB, 2545 KB/s, 0 seconds passed
...91%, 0.66 MB, 2572 KB/s, 0 seconds passed
...92%, 0.66 MB, 2602 KB/s, 0 seconds passed
...93%, 0.67 MB, 2631 KB/s, 0 seconds passed
...94%, 0.68 MB, 2661 KB/s, 0 seconds passed
...95%, 0.69 MB, 2684 KB/s, 0 seconds passed
...96%, 0.70 MB, 2713 KB/s, 0 seconds passed
...97%, 0.70 MB, 2743 KB/s, 0 seconds passed
...99%, 0.71 MB, 2773 KB/s, 0 seconds passed
...100%, 0.72 MB, 2802 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)
@@ -554,7 +554,7 @@ It takes about 2 hours on a 16T CPU, and 6 Pynq boards.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 4.362 seconds)
+ **Total running time of the script:** ( 0 minutes 5.575 seconds)
.. _sphx_glr_download_vta_tutorials_autotvm_tune_relay_vta.py:
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index 1da6583..58accc5 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -71,7 +71,7 @@ Execute on CPU vs. VTA, and define the model.
.. code-block:: default
- # Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+ # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# Set ``device=arm_cpu`` to run inference on the CPU
@@ -243,8 +243,8 @@ The compilation steps are:
.. code-block:: none
-
...12%, 0.01 MB, 19 KB/s, 0 seconds passed
...25%, 0.02 MB, 39 KB/s, 0 seconds passed
...38%, 0.02 MB, 59 KB/s, 0 seconds passed
...51%, 0.03 MB, 78 KB/s, 0 seconds passed
...64%, 0.04 MB, 98 KB/s, 0 seconds passed
...77%, 0.05 MB, 117 KB/s, 0 seconds passed
...90%, 0.05 MB, 137 KB/s, 0 seconds passed
...100%, 0.06 MB, 156 KB/s, 0 seconds passed
- resnet18_v1 inference graph built in 5.76s!
+
...12%, 0.01 MB, 33 KB/s, 0 seconds passed
...25%, 0.02 MB, 65 KB/s, 0 seconds passed
...38%, 0.02 MB, 98 KB/s, 0 seconds passed
...51%, 0.03 MB, 128 KB/s, 0 seconds passed
...64%, 0.04 MB, 160 KB/s, 0 seconds passed
...77%, 0.05 MB, 191 KB/s, 0 seconds passed
...90%, 0.05 MB, 222 KB/s, 0 seconds passed
...100%, 0.06 MB, 254 KB/s, 0 seconds passed
+ resnet18_v1 inference graph built in 5.68s!
@@ -364,7 +364,7 @@ and an input test image.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 27.929 seconds)
+ **Total running time of the script:** ( 0 minutes 25.108 seconds)
.. _sphx_glr_download_vta_tutorials_frontend_deploy_classification.py:
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
index dfc7f2e..e3a5ad6 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -151,7 +151,7 @@ Execute on CPU vs. VTA, and define the model.
.. code-block:: default
- # Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+ # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# Set ``device=arm_cpu`` to run inference on the CPU
# or ``device=vta`` to run inference on the FPGA.
@@ -315,7 +315,7 @@ The compilation steps are:
.. code-block:: none
- yolov3-tiny inference graph built in 6.96s!
+ yolov3-tiny inference graph built in 6.79s!
@@ -432,7 +432,7 @@ Download test image
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 34.708 seconds)
+ **Total running time of the script:** ( 0 minutes 37.287 seconds)
.. _sphx_glr_download_vta_tutorials_frontend_deploy_detection.py:
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 9b5cc34..1790223 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**01:02.636** total execution time for **vta_tutorials_frontend** files:
+**01:02.396** total execution time for **vta_tutorials_frontend** files:
-- **00:34.708**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:27.929**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:37.287**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:25.108**: :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 20e0d2f..d658a4f 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -37,7 +37,7 @@ as we did in the VTA introductory tutorial.
from tvm.contrib import util
from vta.testing import simulator
- # Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+ # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# We read the Pynq RPC host IP address and port number from the OS environment
@@ -722,7 +722,7 @@ The general workflow includes:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.349 seconds)
+ **Total running time of the script:** ( 0 minutes 0.403 seconds)
.. _sphx_glr_download_vta_tutorials_matrix_multiply.py:
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index eca7fdb..f22d768 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -41,7 +41,7 @@ We start by programming the Pynq's FPGA and building its RPC runtime.
from tvm.contrib import util
from vta.testing import simulator
- # Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+ # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# We read the Pynq RPC host IP address and port number from the OS environment
@@ -869,7 +869,7 @@ virtual threading.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 2.598 seconds)
+ **Total running time of the script:** ( 0 minutes 3.110 seconds)
.. _sphx_glr_download_vta_tutorials_optimize_convolution_opt.py:
diff --git a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index 6ff494e..bd2dd3a 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -40,7 +40,7 @@ We start by programming the Pynq's FPGA and building its RPC runtime.
from tvm.contrib import util
from vta.testing import simulator
- # Load VTA parameters from the vta/vta-hw/config/vta_config.json file
+ # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()
# We read the Pynq RPC host IP address and port number from the OS environment
@@ -689,7 +689,7 @@ hardware accelerator resources.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.392 seconds)
+ **Total running time of the script:** ( 0 minutes 0.448 seconds)
.. _sphx_glr_download_vta_tutorials_optimize_matrix_multiply_opt.py:
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 5f01dab..a9edca1 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
=================
-**00:02.990** total execution time for **vta_tutorials_optimize** files:
+**00:03.558** total execution time for **vta_tutorials_optimize** files:
-- **00:02.598**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.392**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:03.110**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.448**: :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 2115e4b..d7eed32 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
=================
-**00:00.690** total execution time for **vta_tutorials** files:
+**00:00.789** total execution time for **vta_tutorials** files:
-- **00:00.349**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.341**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.403**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.386**: :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 ae36821..e6f4da3 100644
--- a/docs/_sources/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
@@ -612,7 +612,7 @@ and other features supported by TVM to program VTA.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 0.341 seconds)
+ **Total running time of the script:** ( 0 minutes 0.386 seconds)
.. _sphx_glr_download_vta_tutorials_vta_get_started.py:
diff --git a/docs/api/python/relay/transform.html b/docs/api/python/relay/transform.html
index ae12d9f..97b4ecb 100644
--- a/docs/api/python/relay/transform.html
+++ b/docs/api/python/relay/transform.html
@@ -284,52 +284,55 @@
<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.Legalize" title="tvm.relay.transform.Legalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Legalize</span></code></a>([legalize_map_attr_name])</p></td>
<td><p>Legalizes an expression with another expression.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.MergeComposite" title="tvm.relay.transform.MergeComposite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MergeComposite</span></code></a>(pattern_table)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.MergeCompilerRegions" title="tvm.relay.transform.MergeCompilerRegions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MergeCompilerRegions</span></code></a>()</p></td>
+<td><p>Merge together compiler regions.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.MergeComposite" title="tvm.relay.transform.MergeComposite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MergeComposite</span></code></a>(pattern_table)</p></td>
<td><p>Merge multiple operators into a single composite relay function.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.PartialEvaluate" title="tvm.relay.transform.PartialEvaluate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PartialEvaluate</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.PartialEvaluate" title="tvm.relay.transform.PartialEvaluate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PartialEvaluate</span></code></a>()</p></td>
<td><p>Evaluate the static fragment of the code.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.PartitionGraph" title="tvm.relay.transform.PartitionGraph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PartitionGraph</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.PartitionGraph" title="tvm.relay.transform.PartitionGraph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PartitionGraph</span></code></a>()</p></td>
<td><p>Partition a Relay program into regions that can be executed on different backends.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.PrintIR" title="tvm.relay.transform.PrintIR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrintIR</span></code></a>([show_meta_data])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.PrintIR" title="tvm.relay.transform.PrintIR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrintIR</span></code></a>([show_meta_data])</p></td>
<td><p>Print the IR for a module to help debugging.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.RemoveUnusedFunctions" title="tvm.relay.transform.RemoveUnusedFunctions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RemoveUnusedFunctions</span></code></a>([entry_functions])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.RemoveUnusedFunctions" title="tvm.relay.transform.RemoveUnusedFunctions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RemoveUnusedFunctions</span></code></a>([entry_functions])</p></td>
<td><p>Remove unused global relay functions in a relay module.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.RewriteAnnotatedOps" title="tvm.relay.transform.RewriteAnnotatedOps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RewriteAnnotatedOps</span></code></a>(fallback_device)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.RewriteAnnotatedOps" title="tvm.relay.transform.RewriteAnnotatedOps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RewriteAnnotatedOps</span></code></a>(fallback_device)</p></td>
<td><p>Rewrite the annotated program where annotation operators, e.g.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.SimplifyInference" title="tvm.relay.transform.SimplifyInference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SimplifyInference</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.SimplifyInference" title="tvm.relay.transform.SimplifyInference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SimplifyInference</span></code></a>()</p></td>
<td><p>Simplify the data-flow graph for inference phase.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.ToANormalForm" title="tvm.relay.transform.ToANormalForm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ToANormalForm</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.ToANormalForm" title="tvm.relay.transform.ToANormalForm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ToANormalForm</span></code></a>()</p></td>
<td><p>Turn Graph Normal Form expression into A Normal Form Expression.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.ToCPS" title="tvm.relay.transform.ToCPS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ToCPS</span></code></a>(expr[, mod])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.ToCPS" title="tvm.relay.transform.ToCPS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ToCPS</span></code></a>(expr[, mod])</p></td>
<td><p>Turn expression into continuation passing style(CPS).</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.ToGraphNormalForm" title="tvm.relay.transform.ToGraphNormalForm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ToGraphNormalForm</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.ToGraphNormalForm" title="tvm.relay.transform.ToGraphNormalForm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ToGraphNormalForm</span></code></a>()</p></td>
<td><p>Turn a Relay program in A Normal Form into Graph Normal Form</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.build_config" title="tvm.relay.transform.build_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_config</span></code></a>([opt_level, fallback_device, …])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.build_config" title="tvm.relay.transform.build_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_config</span></code></a>([opt_level, fallback_device, …])</p></td>
<td><p>Configure the build behavior by setting config variables.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.function_pass" title="tvm.relay.transform.function_pass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">function_pass</span></code></a>([pass_func, opt_level, name, …])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.function_pass" title="tvm.relay.transform.function_pass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">function_pass</span></code></a>([pass_func, opt_level, name, …])</p></td>
<td><p>Decorate a function pass.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.gradient" title="tvm.relay.transform.gradient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient</span></code></a>(expr[, mod, mode])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.gradient" title="tvm.relay.transform.gradient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient</span></code></a>(expr[, mod, mode])</p></td>
<td><p>Transform the input function, returning a function that calculate the original result, paired with gradient of the input.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.module_pass" title="tvm.relay.transform.module_pass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">module_pass</span></code></a>([pass_func, opt_level, name, …])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.module_pass" title="tvm.relay.transform.module_pass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">module_pass</span></code></a>([pass_func, opt_level, name, …])</p></td>
<td><p>Decorate a module pass.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.to_cps" title="tvm.relay.transform.to_cps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_cps</span></code></a>(func[, mod])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.to_cps" title="tvm.relay.transform.to_cps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_cps</span></code></a>(func[, mod])</p></td>
<td><p>Turn expression into CPS expression.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.relay.transform.un_cps" title="tvm.relay.transform.un_cps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">un_cps</span></code></a>(func)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.relay.transform.un_cps" title="tvm.relay.transform.un_cps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">un_cps</span></code></a>(func)</p></td>
<td><p>Turn an cps function into a Function without the continuation argument.</p></td>
</tr>
</tbody>
@@ -764,6 +767,20 @@ can be used to legalize the expr in a target-dependent manner.</p>
</dd></dl>
<dl class="function">
+<dt id="tvm.relay.transform.MergeCompilerRegions">
+<code class="sig-prename descclassname">tvm.relay.transform.</code><code class="sig-name descname">MergeCompilerRegions</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tvm.relay.transform.MergeCompilerRegions" title="Permalink to this definition">¶</a></dt>
+<dd><p>Merge together compiler regions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><strong>ret</strong> – The registered pass that merges compiler regions.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>tvm.relay.Pass</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
<dt id="tvm.relay.transform.MergeComposite">
<code class="sig-prename descclassname">tvm.relay.transform.</code><code class="sig-name descname">MergeComposite</code><span class="sig-paren">(</span><em class="sig-param">pattern_table</em><span class="sig-paren">)</span><a class="headerlink" href="#tvm.relay.transform.MergeComposite" title="Permalink to this definition">¶</a></dt>
<dd><p>Merge multiple operators into a single composite relay function.</p>
diff --git a/docs/api/python/te.html b/docs/api/python/te.html
index 5ce6ace..2061566 100644
--- a/docs/api/python/te.html
+++ b/docs/api/python/te.html
@@ -304,103 +304,106 @@
<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.fmod" title="tvm.te.fmod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fmod</span></code></a>(x, y)</p></td>
<td><p>Return the remainder of x divided by y with the same sign as x.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.if_then_else" title="tvm.te.if_then_else"><code class="xref py py-obj docutils literal notranslate"><span class="pre">if_then_else</span></code></a>(cond, t, f)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.gradient" title="tvm.te.gradient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient</span></code></a>(output, inputs[, head])</p></td>
+<td><p>Perform reverse-mode automatic differentiation.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.if_then_else" title="tvm.te.if_then_else"><code class="xref py py-obj docutils literal notranslate"><span class="pre">if_then_else</span></code></a>(cond, t, f)</p></td>
<td><p>Conditional selection expression.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.indexdiv" title="tvm.te.indexdiv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">indexdiv</span></code></a>(a, b)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.indexdiv" title="tvm.te.indexdiv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">indexdiv</span></code></a>(a, b)</p></td>
<td><p>Compute floor(a / b) where a and b are non-negative.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.indexmod" title="tvm.te.indexmod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">indexmod</span></code></a>(a, b)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.indexmod" title="tvm.te.indexmod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">indexmod</span></code></a>(a, b)</p></td>
<td><p>Compute the remainder of indexdiv.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.isfinite" title="tvm.te.isfinite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isfinite</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.isfinite" title="tvm.te.isfinite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isfinite</span></code></a>(x)</p></td>
<td><p>Check if input value is finite.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.isinf" title="tvm.te.isinf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isinf</span></code></a>(x)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.isinf" title="tvm.te.isinf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isinf</span></code></a>(x)</p></td>
<td><p>Check if input value is infinite.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.isnan" title="tvm.te.isnan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isnan</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.isnan" title="tvm.te.isnan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isnan</span></code></a>(x)</p></td>
<td><p>Check if input value is Nan.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.log" title="tvm.te.log"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code></a>(x)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.log" title="tvm.te.log"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code></a>(x)</p></td>
<td><p>Take log of input x.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.max" title="tvm.te.max"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max</span></code></a>(expr, axis[, where])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.max" title="tvm.te.max"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max</span></code></a>(expr, axis[, where])</p></td>
<td><p>Create a max expression over axis.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.max_value" title="tvm.te.max_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_value</span></code></a>(dtype)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.max_value" title="tvm.te.max_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_value</span></code></a>(dtype)</p></td>
<td><p>maximum value of dtype</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.min" title="tvm.te.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min</span></code></a>(expr, axis[, where])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.min" title="tvm.te.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min</span></code></a>(expr, axis[, where])</p></td>
<td><p>Create a min expression over axis.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.min_value" title="tvm.te.min_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_value</span></code></a>(dtype)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.min_value" title="tvm.te.min_value"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_value</span></code></a>(dtype)</p></td>
<td><p>minimum value of dtype</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.nearbyint" title="tvm.te.nearbyint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearbyint</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.nearbyint" title="tvm.te.nearbyint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearbyint</span></code></a>(x)</p></td>
<td><p>Round elements of the array to the nearest integer.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.placeholder" title="tvm.te.placeholder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">placeholder</span></code></a>(shape[, dtype, name])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.placeholder" title="tvm.te.placeholder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">placeholder</span></code></a>(shape[, dtype, name])</p></td>
<td><p>Construct an empty tensor object.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.popcount" title="tvm.te.popcount"><code class="xref py py-obj docutils literal notranslate"><span class="pre">popcount</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.popcount" title="tvm.te.popcount"><code class="xref py py-obj docutils literal notranslate"><span class="pre">popcount</span></code></a>(x)</p></td>
<td><p>Count the number of set bits in input x.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.power" title="tvm.te.power"><code class="xref py py-obj docutils literal notranslate"><span class="pre">power</span></code></a>(x, y)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.power" title="tvm.te.power"><code class="xref py py-obj docutils literal notranslate"><span class="pre">power</span></code></a>(x, y)</p></td>
<td><p>x power y</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.reduce_axis" title="tvm.te.reduce_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce_axis</span></code></a>(dom[, name])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.reduce_axis" title="tvm.te.reduce_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce_axis</span></code></a>(dom[, name])</p></td>
<td><p>Create a new IterVar for reduction.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.round" title="tvm.te.round"><code class="xref py py-obj docutils literal notranslate"><span class="pre">round</span></code></a>(x)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.round" title="tvm.te.round"><code class="xref py py-obj docutils literal notranslate"><span class="pre">round</span></code></a>(x)</p></td>
<td><p>Round elements of the array to the nearest integer.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.rsqrt" title="tvm.te.rsqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rsqrt</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.rsqrt" title="tvm.te.rsqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rsqrt</span></code></a>(x)</p></td>
<td><p>Take reciprocal of square root of input x.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.scan" title="tvm.te.scan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scan</span></code></a>(init, update, state_placeholder[, …])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.scan" title="tvm.te.scan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scan</span></code></a>(init, update, state_placeholder[, …])</p></td>
<td><p>Construct new tensors by scanning over axis.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.sigmoid" title="tvm.te.sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sigmoid</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.sigmoid" title="tvm.te.sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sigmoid</span></code></a>(x)</p></td>
<td><p>Quick function to get sigmoid</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.sin" title="tvm.te.sin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sin</span></code></a>(x)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.sin" title="tvm.te.sin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sin</span></code></a>(x)</p></td>
<td><p>Take sin of input x.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.size_var" title="tvm.te.size_var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">size_var</span></code></a>([name, dtype])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.size_var" title="tvm.te.size_var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">size_var</span></code></a>([name, dtype])</p></td>
<td><p>Create a new variable represents a tensor shape size, which is non-negative.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.sqrt" title="tvm.te.sqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sqrt</span></code></a>(x)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.sqrt" title="tvm.te.sqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sqrt</span></code></a>(x)</p></td>
<td><p>Take square root of input x.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.sum" title="tvm.te.sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum</span></code></a>(expr, axis[, where])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.sum" title="tvm.te.sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum</span></code></a>(expr, axis[, where])</p></td>
<td><p>Create a sum expression over axis.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.tag_scope" title="tvm.te.tag_scope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tag_scope</span></code></a>(tag)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.tag_scope" title="tvm.te.tag_scope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tag_scope</span></code></a>(tag)</p></td>
<td><p>The operator tag scope.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.tan" title="tvm.te.tan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tan</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.tan" title="tvm.te.tan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tan</span></code></a>(x)</p></td>
<td><p>Take tan of input x.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.tanh" title="tvm.te.tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tanh</span></code></a>(x)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.tanh" title="tvm.te.tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tanh</span></code></a>(x)</p></td>
<td><p>Take hyperbolic tanh of input x.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.thread_axis" title="tvm.te.thread_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">thread_axis</span></code></a>([dom, tag, name])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.thread_axis" title="tvm.te.thread_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">thread_axis</span></code></a>([dom, tag, name])</p></td>
<td><p>Create a new IterVar to represent thread index.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.trace" title="tvm.te.trace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trace</span></code></a>(args[, trace_action])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.trace" title="tvm.te.trace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trace</span></code></a>(args[, trace_action])</p></td>
<td><p>Trace tensor data at the runtime.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.trunc" title="tvm.te.trunc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trunc</span></code></a>(x)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.trunc" title="tvm.te.trunc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trunc</span></code></a>(x)</p></td>
<td><p>Get truncated value of the input.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.truncdiv" title="tvm.te.truncdiv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncdiv</span></code></a>(a, b)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.truncdiv" title="tvm.te.truncdiv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncdiv</span></code></a>(a, b)</p></td>
<td><p>Compute the truncdiv of two expressions.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.truncmod" title="tvm.te.truncmod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncmod</span></code></a>(a, b)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.truncmod" title="tvm.te.truncmod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncmod</span></code></a>(a, b)</p></td>
<td><p>Compute the truncmod of two expressions.</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.te.var" title="tvm.te.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>([name, dtype])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.te.var" title="tvm.te.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>([name, dtype])</p></td>
<td><p>Create a new variable with specified name and dtype</p></td>
</tr>
</tbody>
@@ -2335,6 +2338,48 @@ When str is passed, dom is set to None and str is used as tag</p></li>
</dd></dl>
+<dl class="function">
+<dt id="tvm.te.gradient">
+<code class="sig-prename descclassname">tvm.te.</code><code class="sig-name descname">gradient</code><span class="sig-paren">(</span><em class="sig-param">output</em>, <em class="sig-param">inputs</em>, <em class="sig-param">head=None</em><span class="sig-paren">)</span><a class="headerlink" href="#tvm.te.gradient" title="Permalink to this definition">¶</a></dt>
+<dd><p>Perform reverse-mode automatic differentiation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>output</strong> (<a class="reference internal" href="#tvm.te.Tensor" title="tvm.te.Tensor"><em>Tensor</em></a>) – The tensor to differentiate.</p></li>
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><a class="reference internal" href="#tvm.te.Tensor" title="tvm.te.Tensor"><em>Tensor</em></a><em>]</em>) – The list of input tensors to be differentiated wrt.</p></li>
+<li><p><strong>head</strong> (<a class="reference internal" href="#tvm.te.Tensor" title="tvm.te.Tensor"><em>Tensor</em></a>) – The adjoint of the output, in other words, some tensor, by which the Jacobians
+will be multiplied. Its shape must be of the form <cite>prefix + output.shape</cite>.
+If <cite>None</cite> is passed, the identity tensor of shape <cite>output.shape + output.shape</cite>
+will be used.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>tensors</strong> – The result gradient, in the same order as the inputs</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>List[<a class="reference internal" href="#tvm.te.Tensor" title="tvm.te.Tensor">Tensor</a>]</p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span cl [...]
+<span class="n">w1</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'w1'</span><span class="p">)</span>
+<span class="n">w2</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">placeholder</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'w2'</span><span class="p">)</span>
+<span class="n">z1</span> <span class="o">=</span> <span class="n">topi</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">z2</span> <span class="o">=</span> <span class="n">topi</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">z1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">topi</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">z2</span><span class="p">)</span>
+
+<span class="c1"># produce gradients</span>
+<span class="p">[</span><span class="n">dw1</span><span class="p">,</span> <span class="n">dw2</span><span class="p">]</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">[</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">])</span>
+
+<span class="c1"># produce Jacobians</span>
+<span class="p">[</span><span class="n">jw1</span><span class="p">,</span> <span class="n">jw2</span><span class="p">]</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">z2</span><span class="p">,</span> <span class="p">[</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">])</span>
+
+<span class="c1"># produce gradients, the head adjoint for z2 is provided manually</span>
+<span class="p">[</span><span class="n">dw1</span><span class="p">,</span> <span class="n">dw2</span><span class="p">]</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">z2</span><span class="p">,</span> <span class="p">[</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">],</span> <span class="n">topi</span><span class="o">.</span><span clas [...]
+</pre></div>
+</div>
+</dd></dl>
+
</div>
diff --git a/docs/api/python/tir.html b/docs/api/python/tir.html
index 3e1ac44..a5a8962 100644
--- a/docs/api/python/tir.html
+++ b/docs/api/python/tir.html
@@ -2858,7 +2858,16 @@ specified axis;</p></li>
<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.tir.transform.CombineContextCall" title="tvm.tir.transform.CombineContextCall"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CombineContextCall</span></code></a>()</p></td>
<td><p>Combine context calls in the host function.</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#tvm.tir.transform.prim_func_pass" title="tvm.tir.transform.prim_func_pass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prim_func_pass</span></code></a>([pass_func, opt_level, name, …])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.tir.transform.LowerDeviceStorageAccessInfo" title="tvm.tir.transform.LowerDeviceStorageAccessInfo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LowerDeviceStorageAccessInfo</span></code></a>()</p></td>
+<td><p>Lower attached storage access information on device.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.tir.transform.LowerIntrin" title="tvm.tir.transform.LowerIntrin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LowerIntrin</span></code></a>()</p></td>
+<td><p>Lower target specific intrinsic calls.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#tvm.tir.transform.LowerWarpMemory" title="tvm.tir.transform.LowerWarpMemory"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LowerWarpMemory</span></code></a>()</p></td>
+<td><p>Lower warp memory access to low-level device related function calls.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#tvm.tir.transform.prim_func_pass" title="tvm.tir.transform.prim_func_pass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prim_func_pass</span></code></a>([pass_func, opt_level, name, …])</p></td>
<td><p>Decorate a function pass.</p></td>
</tr>
</tbody>
@@ -2956,6 +2965,52 @@ pass class should be created through py:func:<cite>tvm.tir.transform.function_pa
</dl>
</dd></dl>
+<dl class="function">
+<dt id="tvm.tir.transform.LowerDeviceStorageAccessInfo">
+<code class="sig-prename descclassname">tvm.tir.transform.</code><code class="sig-name descname">LowerDeviceStorageAccessInfo</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tvm.tir.transform.LowerDeviceStorageAccessInfo" title="Permalink to this definition">¶</a></dt>
+<dd><p>Lower attached storage access information on device.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><strong>fpass</strong> – The result pass</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>tvm.ir.transform.Pass</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Run this pass after all storage access analysis finish.</p>
+</div>
+</dd></dl>
+
+<dl class="function">
+<dt id="tvm.tir.transform.LowerIntrin">
+<code class="sig-prename descclassname">tvm.tir.transform.</code><code class="sig-name descname">LowerIntrin</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tvm.tir.transform.LowerIntrin" title="Permalink to this definition">¶</a></dt>
+<dd><p>Lower target specific intrinsic calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><strong>fpass</strong> – The result pass</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>tvm.ir.transform.Pass</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="tvm.tir.transform.LowerWarpMemory">
+<code class="sig-prename descclassname">tvm.tir.transform.</code><code class="sig-name descname">LowerWarpMemory</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#tvm.tir.transform.LowerWarpMemory" title="Permalink to this definition">¶</a></dt>
+<dd><p>Lower warp memory access to low-level device related function calls.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><strong>fpass</strong> – The result pass</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>tvm.ir.transform.Pass</p>
+</dd>
+</dl>
+</dd></dl>
+
</div>
diff --git a/docs/doxygen/bound_8h.html b/docs/doxygen/autodiff_8h.html
similarity index 57%
copy from docs/doxygen/bound_8h.html
copy to docs/doxygen/autodiff_8h.html
index d11d8be..8ead41c 100644
--- a/docs/doxygen/bound_8h.html
+++ b/docs/doxygen/autodiff_8h.html
@@ -4,7 +4,7 @@
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.11"/>
-<title>tvm: include/tvm/arith/bound.h File Reference</title>
+<title>tvm: include/tvm/te/autodiff.h File Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
@@ -81,7 +81,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<div id="nav-path" class="navpath">
<ul>
-<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_b4c7d8e826c599ba55146c099a14beb5.html">tvm</a></li><li class="navelem"><a class="el" href="dir_194ecda214f05a38134392ac6a69b970.html">arith</a></li> </ul>
+<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_b4c7d8e826c599ba55146c099a14beb5.html">tvm</a></li><li class="navelem"><a class="el" href="dir_f97d855a3173728370e632aa77170e34.html">te</a></li> </ul>
</div>
</div><!-- top -->
<div class="header">
@@ -89,32 +89,23 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<a href="#namespaces">Namespaces</a> |
<a href="#func-members">Functions</a> </div>
<div class="headertitle">
-<div class="title">bound.h File Reference</div> </div>
+<div class="title">autodiff.h File Reference</div> </div>
</div><!--header-->
<div class="contents">
-<p>Bound deducers.
+<p>Automatic differentiation of tensor expressions.
<a href="#details">More...</a></p>
-<div class="textblock"><code>#include <<a class="el" href="node_2container_8h_source.html">tvm/node/container.h</a>></code><br />
-<code>#include <<a class="el" href="ir_2expr_8h_source.html">tvm/ir/expr.h</a>></code><br />
-<code>#include <<a class="el" href="int__set_8h_source.html">tvm/arith/int_set.h</a>></code><br />
+<div class="textblock"><code>#include <<a class="el" href="object_8h_source.html">tvm/runtime/object.h</a>></code><br />
<code>#include <<a class="el" href="tir_2expr_8h_source.html">tvm/tir/expr.h</a>></code><br />
-<code>#include <<a class="el" href="stmt_8h_source.html">tvm/tir/stmt.h</a>></code><br />
-<code>#include <unordered_map></code><br />
+<code>#include "<a class="el" href="tensor_8h_source.html">tensor.h</a>"</code><br />
</div><div class="textblock"><div class="dynheader">
-Include dependency graph for bound.h:</div>
+Include dependency graph for autodiff.h:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="bound_8h__incl.svg" width="3363" height="1202"><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="autodiff_8h__incl.svg" width="3678" height="1351"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
-</div><div class="textblock"><div class="dynheader">
-This graph shows which files directly or indirectly include this file:</div>
-<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="bound_8h__dep__incl.svg" width="4596" height="812"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
-</div>
</div>
-</div>
-<p><a href="bound_8h_source.html">Go to the source code of this file.</a></p>
+<p><a href="autodiff_8h_source.html">Go to the source code of this file.</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
Namespaces</h2></td></tr>
@@ -123,24 +114,24 @@ Namespaces</h2></td></tr>
<tr class="memitem:namespacetvm_1_1te"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1te.html">tvm::te</a></td></tr>
<tr class="memdesc:namespacetvm_1_1te"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> expression language DSL. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:namespacetvm_1_1arith"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1arith.html">tvm::arith</a></td></tr>
-<tr class="memdesc:namespacetvm_1_1arith"><td class="mdescLeft"> </td><td class="mdescRight">namespace of arithmetic analysis. <br /></td></tr>
-<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
-<tr class="memitem:af0860828b70ea8ff0d2f7c036c37afd5"><td class="memItemLeft" align="right" valign="top">IntSet </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1arith.html#af0860828b70ea8ff0d2f7c036c37afd5">tvm::arith::DeduceBound</a> (PrimExpr v, PrimExpr cond, const Map< Var, IntSet > &hint_map, const Map< Var, IntSet > &relax_map)</td></tr>
-<tr class="memdesc:af0860828b70ea8ff0d2f7c036c37afd5"><td class="mdescLeft"> </td><td class="mdescRight">Deduce the bound of the target variable in a expression, give the domain of each variables. Return undefined <a class="el" href="classtvm_1_1arith_1_1IntSet.html" title="Managed reference to IntSetNode. ">IntSet</a> to represent failure. <a href="namespacetvm_1_1arith.html#af0860828b70ea8ff0d2f7c036c37afd5">More...</a><br /></td></tr>
-<tr class="separator:af0860828b70ea8ff0d2f7c036c37afd5"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a3658d391f2333f405abdd54a9f174c9f"><td class="memItemLeft" align="right" valign="top">IntSet </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1arith.html#a3658d391f2333f405abdd54a9f174c9f">tvm::arith::DeduceBound</a> (PrimExpr v, PrimExpr cond, const std::unordered_map< const VarNode *, IntSet > &hint_map, const std::unordered_map< const VarNode *, IntSet > &relax_map)</td></tr>
-<tr class="memdesc:a3658d391f2333f405abdd54a9f174c9f"><td class="mdescLeft"> </td><td class="mdescRight">Same as DeduceBound with unordered_map signature. <a href="namespacetvm_1_1arith.html#a3658d391f2333f405abdd54a9f174c9f">More...</a><br /></td></tr>
-<tr class="separator:a3658d391f2333f405abdd54a9f174c9f"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a7f55bb82049e1b5f3a22fbd7b7ef3570"><td class="memItemLeft" align="right" valign="top">Domain </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1arith.html#a7f55bb82049e1b5f3a22fbd7b7ef3570">tvm::arith::DomainTouched</a> (Stmt body, const te::Tensor &tensor, bool consider_calls, bool consider_provides)</td></tr>
-<tr class="memdesc:a7f55bb82049e1b5f3a22fbd7b7ef3570"><td class="mdescLeft"> </td><td class="mdescRight">Infer a regular domain that covers all the calls or provides within the given statement. <a href="namespacetvm_1_1arith.html#a7f55bb82049e1b5f3a22fbd7b7ef3570">More...</a><br /></td></tr>
-<tr class="separator:a7f55bb82049e1b5f3a22fbd7b7ef3570"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5c207a8c51face2c69657fb93ecec9c8"><td class="memItemLeft" align="right" valign="top">PrimExpr </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1te.html#a5c207a8c51face2c69657fb93ecec9c8">tvm::te::Derivative</a> (const PrimExpr &expr, const Var &var)</td></tr>
+<tr class="memdesc:a5c207a8c51face2c69657fb93ecec9c8"><td class="mdescLeft"> </td><td class="mdescRight">Take the derivative of the expression with respect to the given variable. <a href="namespacetvm_1_1te.html#a5c207a8c51face2c69657fb93ecec9c8">More...</a><br /></td></tr>
+<tr class="separator:a5c207a8c51face2c69657fb93ecec9c8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a6298db2545a416ec1fd47c593b7217b7"><td class="memItemLeft" align="right" valign="top">Tensor </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1te.html#a6298db2545a416ec1fd47c593b7217b7">tvm::te::Jacobian</a> (const Tensor &output, const Tensor &input)</td></tr>
+<tr class="memdesc:a6298db2545a416ec1fd47c593b7217b7"><td class="mdescLeft"> </td><td class="mdescRight">Get the tensor representing the Jacobian of the output with respect to the input. <a href="namespacetvm_1_1te.html#a6298db2545a416ec1fd47c593b7217b7">More...</a><br /></td></tr>
+<tr class="separator:a6298db2545a416ec1fd47c593b7217b7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a547183f5a311af53ab598faba423fd64"><td class="memItemLeft" align="right" valign="top">Tensor </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1te.html#a547183f5a311af53ab598faba423fd64">tvm::te::VectorJacobianProduct</a> (const Tensor &output, const Tensor &input, const Tensor &head)</td></tr>
+<tr class="memdesc:a547183f5a311af53ab598faba423fd64"><td class="mdescLeft"> </td><td class="mdescRight">The building block for reverse-mode AD. <a href="namespacetvm_1_1te.html#a547183f5a311af53ab598faba423fd64">More...</a><br /></td></tr>
+<tr class="separator:a547183f5a311af53ab598faba423fd64"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0de1399717049f2b3582f0344b267d56"><td class="memItemLeft" align="right" valign="top">Array< Tensor > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1te.html#a0de1399717049f2b3582f0344b267d56">tvm::te::Gradient</a> (const Tensor &output, const Array< Tensor > &inputs, const Tensor &head=Tensor())</td></tr>
+<tr class="memdesc:a0de1399717049f2b3582f0344b267d56"><td class="mdescLeft"> </td><td class="mdescRight">Perform reverse mode automatic differentiation. <a href="namespacetvm_1_1te.html#a0de1399717049f2b3582f0344b267d56">More...</a><br /></td></tr>
+<tr class="separator:a0de1399717049f2b3582f0344b267d56"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
-<div class="textblock"><p>Bound deducers. </p>
+<div class="textblock"><p>Automatic differentiation of tensor expressions. </p>
</div></div><!-- contents -->
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<g id="edge36" class="edge"><title>Node24->Node19</title>
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<g id="edge37" class="edge"><title>Node24->Node25</title>
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diff --git a/docs/doxygen/classtvm_1_1IRModule-members.html b/docs/doxygen/classtvm_1_1IRModule-members.html
index 2f51b6a..8f085b9 100644
--- a/docs/doxygen/classtvm_1_1IRModule-members.html
+++ b/docs/doxygen/classtvm_1_1IRModule-members.html
@@ -94,12 +94,13 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
<p>This is the complete list of members for <a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a>, including all inherited members.</p>
<table class="directory">
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a59099426f65dbeac227e51f8864e322a">FromExpr</a>(const RelayExpr &expr, const Map< GlobalVar, BaseFunc > &global_funcs={}, const Map< GlobalTypeVar, TypeData > &type_definitions={})</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a499a18238ecdccf2738d7edb05408eee">FromText</a>(const std::string &text, const std::string &source_path)</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a53828dbf85c0755eb80ad794f5aa07c5">IRModule</a>(Map< GlobalVar, BaseFunc > functions, Map< GlobalTypeVar, TypeData > type_definitions={}, std::unordered_set< std::string > import_set={})</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">explicit</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#ad48ab78dc0e686cd722e2b677ccaa396">IRModule</a>()</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a69a94bf1f2e54191598b2271443d2696">IRModule</a>(ObjectPtr< Object > n)</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#aff20748321d5b23f6e2ed5c558b026e2">operator-></a>() const </td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a270cda2dde4e757b2457632f9a0db537">ContainerType</a> typedef</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a59099426f65dbeac227e51f8864e322a">FromExpr</a>(const RelayExpr &expr, const Map< GlobalVar, BaseFunc > &global_funcs={}, const Map< GlobalTypeVar, TypeData > &type_definitions={})</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a499a18238ecdccf2738d7edb05408eee">FromText</a>(const std::string &text, const std::string &source_path)</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">static</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a53828dbf85c0755eb80ad794f5aa07c5">IRModule</a>(Map< GlobalVar, BaseFunc > functions, Map< GlobalTypeVar, TypeData > type_definitions={}, std::unordered_set< std::string > import_set={})</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">explicit</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#ad48ab78dc0e686cd722e2b677ccaa396">IRModule</a>()</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#a69a94bf1f2e54191598b2271443d2696">IRModule</a>(ObjectPtr< Object > n)</td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">explicit</span></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1IRModule.html#aff20748321d5b23f6e2ed5c558b026e2">operator-></a>() const </td><td class="entry"><a class="el" href="classtvm_1_1IRModule.html">tvm::IRModule</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
</table></div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
diff --git a/docs/doxygen/classtvm_1_1IRModule.html b/docs/doxygen/classtvm_1_1IRModule.html
index b8b7135..7f2d819 100644
--- a/docs/doxygen/classtvm_1_1IRModule.html
+++ b/docs/doxygen/classtvm_1_1IRModule.html
@@ -88,6 +88,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
</div><!-- top -->
<div class="header">
<div class="summary">
+<a href="#pub-types">Public Types</a> |
<a href="#pub-methods">Public Member Functions</a> |
<a href="#pub-static-methods">Static Public Member Functions</a> |
<a href="classtvm_1_1IRModule-members.html">List of all members</a> </div>
@@ -113,6 +114,12 @@ Collaboration diagram for tvm::IRModule:</div>
</div>
</div>
<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
+Public Types</h2></td></tr>
+<tr class="memitem:a270cda2dde4e757b2457632f9a0db537"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1IRModule.html#a270cda2dde4e757b2457632f9a0db537">ContainerType</a> = <a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a></td></tr>
+<tr class="memdesc:a270cda2dde4e757b2457632f9a0db537"><td class="mdescLeft"> </td><td class="mdescRight">Declare the container type. <a href="#a270cda2dde4e757b2457632f9a0db537">More...</a><br /></td></tr>
+<tr class="separator:a270cda2dde4e757b2457632f9a0db537"><td class="memSeparator" colspan="2"> </td></tr>
+</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a53828dbf85c0755eb80ad794f5aa07c5"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1IRModule.html#a53828dbf85c0755eb80ad794f5aa07c5">IRModule</a> (<a class="el" href="classtvm_1_1Map.html">Map</a>< <a class="el" href="classtvm_1_1GlobalVar.html">GlobalVar</a>, <a class="el" href="classtvm_1_1BaseFunc.html">BaseFunc</a> > functions, <a class="el" href="classtvm_1_1Map.html">Map</ [...]
@@ -139,7 +146,22 @@ Static Public Member Functions</h2></td></tr>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a>. </p>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </dd></dl>
-</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
+</div><h2 class="groupheader">Member Typedef Documentation</h2>
+<a class="anchor" id="a270cda2dde4e757b2457632f9a0db537"></a>
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">using <a class="el" href="classtvm_1_1IRModule.html#a270cda2dde4e757b2457632f9a0db537">tvm::IRModule::ContainerType</a> = <a class="el" href="classtvm_1_1IRModuleNode.html">IRModuleNode</a></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Declare the container type. </p>
+
+</div>
+</div>
+<h2 class="groupheader">Constructor & Destructor Documentation</h2>
<a class="anchor" id="a53828dbf85c0755eb80ad794f5aa07c5"></a>
<div class="memitem">
<div class="memproto">
diff --git a/docs/doxygen/data__type_8h.html b/docs/doxygen/data__type_8h.html
index e8bb2d3..34b1953 100644
--- a/docs/doxygen/data__type_8h.html
+++ b/docs/doxygen/data__type_8h.html
@@ -106,7 +106,7 @@ Include dependency graph for data_type.h:</div>
</div><div class="textblock"><div class="dynheader">
This graph shows which files directly or indirectly include this file:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="data__type_8h__dep__incl.svg" width="3654" height="1304"><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="data__type_8h__dep__incl.svg" width="3371" height="1304"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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-<g id="edge77" class="edge"><title>Node36->Node15</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="868.238,-313.428 859.187,-318.936 869.697,-320.274 868.238,-313.428"/>
+<!-- Node36->Node14 -->
+<g id="edge71" class="edge"><title>Node36->Node14</title>
+<path fill="none" stroke="midnightblue" d="M1175.51,-316.091C1131,-303.232 1059.64,-282.618 1019.64,-271.064"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1174.54,-319.456 1185.12,-318.869 1176.49,-312.731 1174.54,-319.456"/>
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<!-- Node36->Node30 -->
-<g id="edge78" class="edge"><title>Node36->Node30</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="759.401,-320.667 769.853,-318.936 760.603,-313.771 759.401,-320.667"/>
+<g id="edge73" class="edge"><title>Node36->Node30</title>
+<path fill="none" stroke="midnightblue" d="M1150.27,-317.748C1137.6,-316.02 1124.4,-314.345 1112,-313 892.931,-289.223 631.754,-272.733 514.454,-265.953"/>
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<!-- Node36->Node31 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="849.372,-311.996 841.309,-318.869 851.898,-318.524 849.372,-311.996"/>
+<g id="edge74" class="edge"><title>Node36->Node31</title>
+<path fill="none" stroke="midnightblue" d="M1211.54,-308.739C1209.64,-298.487 1207.34,-286.001 1205.6,-276.589"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1208.12,-309.535 1213.38,-318.734 1215.01,-308.265 1208.12,-309.535"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="804.969,-312.234 813.336,-318.734 810.848,-308.435 804.969,-312.234"/>
+<g id="edge75" class="edge"><title>Node36->Node37</title>
+<path fill="none" stroke="midnightblue" d="M1240.25,-314.046C1260.76,-303.122 1289.59,-287.76 1310.44,-276.652"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1238.38,-311.077 1231.2,-318.869 1241.67,-317.255 1238.38,-311.077"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="883.17,-381.813 873.715,-386.595 884.088,-388.752 883.17,-381.813"/>
+<!-- Node38->Node13 -->
+<g id="edge78" class="edge"><title>Node38->Node13</title>
+<path fill="none" stroke="midnightblue" d="M935.557,-381.103C960.113,-368.231 996.495,-349.16 1017.47,-338.167"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="933.698,-378.126 926.466,-385.869 936.948,-384.326 933.698,-378.126"/>
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<!-- Node38->Node24 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="788.717,-383.978 799.129,-385.936 792.21,-377.912 788.717,-383.978"/>
+<g id="edge79" class="edge"><title>Node38->Node24</title>
+<path fill="none" stroke="midnightblue" d="M891.157,-379.705C863.014,-357.675 812.3,-318.265 803,-313 715.937,-263.715 602.846,-225.761 550.674,-209.565"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="850.263,-379.557 841.754,-385.869 852.342,-386.241 850.263,-379.557"/>
+<g id="edge82" class="edge"><title>Node38->Node33</title>
+<path fill="none" stroke="midnightblue" d="M877.648,-382.202C848.671,-371.188 806.196,-355.042 775.905,-343.528"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="876.701,-385.587 887.293,-385.869 879.189,-379.044 876.701,-385.587"/>
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-<polygon fill="midnightblue" stroke="midnightblue" points="743.817,-388.906 754.185,-386.724 744.72,-381.964 743.817,-388.906"/>
+<g id="edge80" class="edge"><title>Node38->Node34</title>
+<path fill="none" stroke="midnightblue" d="M840.269,-384.727C827.547,-383.061 814.384,-381.414 802,-380 631.13,-360.488 428.987,-342.936 328.798,-334.607"/>
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<!-- Node38->Node36 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="811.952,-375.495 814.674,-385.734 818.931,-376.032 811.952,-375.495"/>
+<g id="edge81" class="edge"><title>Node38->Node36</title>
+<path fill="none" stroke="midnightblue" d="M960.194,-383.803C1020.21,-371.012 1119.48,-349.858 1174.72,-338.083"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="959.238,-380.428 950.187,-385.936 960.697,-387.274 959.238,-380.428"/>
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<!-- Node40 -->
<g id="node40" class="node"><title>Node40</title>
<g id="a_node40"><a xlink:href="env__func_8h.html" target="_top" xlink:title="Serializable global function used in IR. ">
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-<text text-anchor="middle" x="2611" y="-320.5" font-family="Helvetica,sans-Serif" font-size="10.00">_func.h</text>
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+<text text-anchor="middle" x="2387" y="-320.5" font-family="Helvetica,sans-Serif" font-size="10.00">_func.h</text>
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+<g id="edge96" class="edge"><title>Node39->Node40</title>
+<path fill="none" stroke="midnightblue" d="M2364.21,-676.334C2389.69,-657.547 2428,-622.417 2428,-581 2428,-581 2428,-581 2428,-461.5 2428,-416.866 2406.36,-367.409 2394.52,-343.699"/>
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<!-- Node41 -->
<g id="node41" class="node"><title>Node41</title>
<g id="a_node41"><a xlink:href="node_8h.html" target="_top" xlink:title="Definitions and helper macros for IR/AST nodes. ">
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</a>
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<!-- Node39->Node41 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="2209.47,-680.903 2219.97,-682.324 2212.65,-674.666 2209.47,-680.903"/>
+<g id="edge99" class="edge"><title>Node39->Node41</title>
+<path fill="none" stroke="midnightblue" d="M2253.94,-683.318C2129.02,-672.42 1903.07,-652.709 1790.51,-642.889"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2253.96,-686.833 2264.23,-684.216 2254.57,-679.86 2253.96,-686.833"/>
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<!-- Node40->Node5 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="2554.03,-313.932 2564.61,-313.476 2556.05,-307.232 2554.03,-313.932"/>
+<g id="edge97" class="edge"><title>Node40->Node5</title>
+<path fill="none" stroke="midnightblue" d="M2334.39,-310.228C2302.36,-299.713 2261.96,-286.451 2231.88,-276.577"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2333.44,-313.602 2344.04,-313.396 2335.63,-306.951 2333.44,-313.602"/>
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+<!-- Node40->Node7 -->
+<g id="edge98" class="edge"><title>Node40->Node7</title>
+<path fill="none" stroke="midnightblue" d="M2370.91,-305.113C2358.34,-288.204 2340.04,-264.783 2322,-246 2308.93,-232.397 2292.19,-218.444 2281.28,-209.754"/>
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<!-- Node41->Node2 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="2169.27,-620.307 2160.63,-626.444 2171.21,-627.032 2169.27,-620.307"/>
+<g id="edge102" class="edge"><title>Node41->Node2</title>
+<path fill="none" stroke="midnightblue" d="M1770.92,-624.217C1816.27,-614.013 1883.44,-598.901 1925.15,-589.516"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1770.02,-620.834 1761.03,-626.444 1771.55,-627.663 1770.02,-620.834"/>
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<!-- Node41->Node3 -->
-<g id="edge105" class="edge"><title>Node41->Node3</title>
-<path fill="none" stroke="midnightblue" d="M2209.05,-633.195C2299.76,-627.547 2450.34,-606.837 2555,-534 2579.12,-517.214 2598.01,-487.061 2606.35,-472.227"/>
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+<g id="edge100" class="edge"><title>Node41->Node3</title>
+<path fill="none" stroke="midnightblue" d="M1753.65,-622.35C1772.06,-614.189 1795.6,-602.741 1815,-590 1872.92,-551.964 1932.98,-493.727 1954.62,-472.033"/>
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-<!-- Node41->Node10 -->
-<g id="edge106" class="edge"><title>Node41->Node10</title>
-<path fill="none" stroke="midnightblue" d="M2095.51,-623.241C2028.33,-600.983 1884.03,-553.172 1824.99,-533.61"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2094.63,-626.637 2105.22,-626.46 2096.83,-619.992 2094.63,-626.637"/>
+<!-- Node41->Node9 -->
+<g id="edge101" class="edge"><title>Node41->Node9</title>
+<path fill="none" stroke="midnightblue" d="M1715.64,-616.998C1705.82,-592.997 1688.83,-551.468 1681.51,-533.591"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1712.44,-618.438 1719.47,-626.368 1718.92,-615.788 1712.44,-618.438"/>
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<!-- Node41->Node27 -->
-<g id="edge109" class="edge"><title>Node41->Node27</title>
-<path fill="none" stroke="midnightblue" d="M2053.22,-626.566C1903.66,-610.167 1565.65,-572.227 1282,-534 1131.15,-513.67 953.554,-485.846 866.913,-472.019"/>
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+<g id="edge104" class="edge"><title>Node41->Node27</title>
+<path fill="none" stroke="midnightblue" d="M1674.44,-624.239C1531.87,-592.651 1116.52,-500.617 987.614,-472.056"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1674.1,-627.749 1684.62,-626.495 1675.61,-620.915 1674.1,-627.749"/>
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<!-- Node41->Node28 -->
-<g id="edge108" class="edge"><title>Node41->Node28</title>
-<path fill="none" stroke="midnightblue" d="M2137.76,-616.808C2145.17,-595.226 2156,-558.005 2156,-525 2156,-525 2156,-525 2156,-461.5 2156,-359.292 2023.28,-293.611 1969.37,-271.145"/>
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+<g id="edge103" class="edge"><title>Node41->Node28</title>
+<path fill="none" stroke="midnightblue" d="M1731.49,-617.205C1741.75,-593.113 1756.11,-549.097 1743,-514 1724.66,-464.907 1675.54,-422.743 1652.82,-405.199"/>
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<!-- Node42->Node22 -->
-<g id="edge111" class="edge"><title>Node42->Node22</title>
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+<g id="edge106" class="edge"><title>Node42->Node22</title>
+<path fill="none" stroke="midnightblue" d="M2013.01,-735.783C2087.66,-715.151 2198,-677.762 2198,-637 2198,-637 2198,-637 2198,-579 2198,-520.352 2271.58,-486.532 2313.55,-472.012"/>
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<!-- Node42->Node39 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="2041.18,-732.89 2032.17,-738.475 2042.69,-739.724 2041.18,-732.89"/>
+<g id="edge108" class="edge"><title>Node42->Node39</title>
+<path fill="none" stroke="midnightblue" d="M2030.24,-737.829C2122.33,-723.273 2217.61,-709.892 2278.67,-701.534"/>
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<!-- Node42->Node41 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="1994.97,-729.682 1988.86,-738.337 1999.04,-735.382 1994.97,-729.682"/>
+<g id="edge107" class="edge"><title>Node42->Node41</title>
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<!-- Node43->Node22 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="2113.53,-732.859 2122.7,-738.168 2118.85,-728.3 2113.53,-732.859"/>
+<g id="edge110" class="edge"><title>Node43->Node22</title>
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<!-- Node43->Node39 -->
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+<g id="edge112" class="edge"><title>Node43->Node39</title>
+<path fill="none" stroke="midnightblue" d="M2176.08,-735.922C2219.31,-724.74 2274.56,-710.447 2309.12,-701.506"/>
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<!-- Node43->Node41 -->
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