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Posted to commits@tvm.apache.org by tq...@apache.org on 2021/06/04 16:20:43 UTC
[tvm-site] branch asf-site updated: Docs build at Fri Jun 4
12:20:00 EDT 2021
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/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 26e6d19 Docs build at Fri Jun 4 12:20:00 EDT 2021
26e6d19 is described below
commit 26e6d19d2dd3d9aada8d714d35700613fb95e2ee
Author: tqchen <ti...@gmail.com>
AuthorDate: Fri Jun 4 12:20:01 2021 -0400
Docs build at Fri Jun 4 12:20:00 EDT 2021
---
.../tuple_inputs.ipynb | 122 +
.../from_tflite.py | 192 +
.../micro_reference_vm.ipynb | 43 +
.../tune_alu_vta.py | 320 +
.../from_tflite.ipynb | 151 +
.../tune_network_mali.ipynb | 154 +
.../auto_tuning_with_python.py | 475 +
.../matrix_multiply.py | 472 +
.../use_pass_infra.ipynb | 259 +
.../143c743c62f58570eabd77fd3395ca8c/scan.py | 154 +
.../tune_sparse_x86.py | 324 +
.../deploy_prequantized_tflite.ipynb | 319 +
.../tvmc_command_line_driver.py | 498 +
.../tune_conv2d_cuda.ipynb | 115 +
.../relay_quick_start.ipynb | 144 +
.../tune_relay_cuda.py | 389 +
.../tune_network_cuda.py | 312 +
.../tune_relay_mobile_gpu.ipynb | 175 +
.../tune_conv2d_cuda.py | 244 +
.../autotvm_matmul.py | 376 +
.../matrix_multiply_opt.ipynb | 176 +
.../from_keras.ipynb | 144 +
.../from_coreml.ipynb | 144 +
.../from_caffe2.ipynb | 133 +
.../deploy_sparse.py | 353 +
.../tensor_expr_get_started.py | 902 ++
.../deploy_model_on_android.ipynb | 190 +
.../deploy_model_on_android.py | 362 +
.../tune_matmul_x86.py | 214 +
.../opt_gemm.ipynb | 309 +
.../using_external_lib.py | 561 +
.../tune_relay_vta.ipynb | 186 +
.../4c010cd30d80efae3f2dfe5743a098a5/install.ipynb | 57 +
.../cross_compilation_and_rpc.ipynb | 179 +
.../4e9540fc014621d8d3bd14869c1ab227/scan.ipynb | 169 +
.../deploy_quantized.ipynb | 144 +
.../from_tensorflow.py | 237 +
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.../intro_topi.ipynb | 230 +
.../deploy_quantized.py | 165 +
.../5bd1bb9c6505ea40407fa19f01579414/reduction.py | 196 +
.../deploy_prequantized_tflite.py | 264 +
.../intrin_math.ipynb | 169 +
.../tune_relay_vta.py | 512 +
.../schedule_primitives.ipynb | 284 +
.../tensorize.ipynb | 241 +
.../opt_conv_cuda.ipynb | 151 +
.../tune_conv2d_layer_cuda.py | 209 +
.../matrix_multiply_opt.py | 372 +
.../tuple_inputs.py | 122 +
.../696dd37904ef92773435ca321ff41bfb/from_onnx.py | 133 +
.../relay_quick_start.py | 154 +
.../using_external_lib.ipynb | 147 +
.../from_pytorch.ipynb | 162 +
.../tensor_expr_get_started.ipynb | 592 +
.../70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py | 358 +
.../from_caffe2.py | 145 +
.../tune_relay_cuda.ipynb | 179 +
.../micro_reference_vm.py | 160 +
.../tune_network_mali.py | 363 +
.../deploy_prequantized.ipynb | 287 +
.../7ece74acc230c7d55086182cc8884b09/extern_op.py | 139 +
.../deploy_ssd_gluoncv.ipynb | 144 +
.../from_darknet.ipynb | 144 +
.../836dc3852acf09662e9eb37c4c5e1e1b/opt_gemm.py | 382 +
.../deploy_model_on_rasp.py | 230 +
.../tune_relay_x86.py | 261 +
.../extern_op.ipynb | 133 +
.../deploy_sparse.ipynb | 187 +
.../deploy_prequantized.py | 242 +
.../schedule_primitives.py | 208 +
.../95f64205f29091c5f31071f9072f3236/install.py | 49 +
.../tune_matmul_x86.ipynb | 205 +
.../opt_conv_tensorcore.ipynb | 165 +
.../9a950897eeef498440fbe2f0afe2601f/tedd.py | 160 +
.../9b0365fd5723f7c4d4e996637ab9a487/intro_topi.py | 149 +
.../introduction.py | 132 +
.../low_level_custom_pass.py | 171 +
.../use_pass_infra.py | 301 +
.../a2f661bf234a167b5458fa28d8fafedc/tedd.ipynb | 165 +
.../from_darknet.py | 202 +
.../tune_network_x86.ipynb | 147 +
.../tune_alu_vta.ipynb | 126 +
.../tune_network_x86.py | 346 +
.../opt_conv_cuda.py | 249 +
.../tune_network_arm.ipynb | 161 +
.../bring_your_own_datatypes.py | 411 +
.../tune_relay_x86.ipynb | 115 +
.../auto_tuning_with_python.ipynb | 305 +
.../baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py | 318 +
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.../vta_get_started.py | 403 +
.../tune_conv2d_layer_cuda.ipynb | 205 +
.../deploy_model_on_rasp.ipynb | 201 +
.../bring_your_own_datatypes.ipynb | 349 +
.../build_gcn.ipynb | 223 +
.../deploy_object_detection_pytorch.ipynb | 162 +
.../deploy_classification.ipynb | 133 +
.../convolution_opt.ipynb | 194 +
.../deploy_ssd_gluoncv.py | 131 +
.../convolution_opt.py | 455 +
.../micro_tflite.ipynb | 169 +
.../cross_compilation_and_rpc.py | 265 +
.../matrix_multiply.ipynb | 311 +
.../tune_network_cuda.ipynb | 147 +
.../tvmc_command_line_driver.ipynb | 134 +
.../from_tensorflow.ipynb | 216 +
.../opt_conv_tensorcore.py | 411 +
.../tune_relay_mobile_gpu.py | 419 +
.../from_coreml.py | 114 +
.../low_level_custom_pass.ipynb | 140 +
.../from_onnx.ipynb | 151 +
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docs/_sources/deploy/vitis_ai.rst.txt | 709 ++
docs/_sources/dev/benchmark.rst.txt | 137 +
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docs/_sources/dev/inferbound.rst.txt | 765 ++
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docs/_static/css/badge_only.css | 1 +
docs/_static/css/fonts/Roboto-Slab-Bold.woff | Bin 0 -> 87624 bytes
docs/_static/css/fonts/Roboto-Slab-Bold.woff2 | Bin 0 -> 67312 bytes
docs/_static/css/fonts/Roboto-Slab-Regular.woff | Bin 0 -> 86288 bytes
docs/_static/css/fonts/Roboto-Slab-Regular.woff2 | Bin 0 -> 66444 bytes
docs/_static/css/fonts/fontawesome-webfont.eot | Bin 0 -> 165742 bytes
docs/_static/css/fonts/fontawesome-webfont.svg | 2671 +++++
docs/_static/css/fonts/fontawesome-webfont.ttf | Bin 0 -> 165548 bytes
docs/_static/css/fonts/fontawesome-webfont.woff | Bin 0 -> 98024 bytes
docs/_static/css/fonts/fontawesome-webfont.woff2 | Bin 0 -> 77160 bytes
docs/_static/css/fonts/lato-bold-italic.woff | Bin 0 -> 323344 bytes
docs/_static/css/fonts/lato-bold-italic.woff2 | Bin 0 -> 193308 bytes
docs/_static/css/fonts/lato-bold.woff | Bin 0 -> 309728 bytes
docs/_static/css/fonts/lato-bold.woff2 | Bin 0 -> 184912 bytes
docs/_static/css/fonts/lato-normal-italic.woff | Bin 0 -> 328412 bytes
docs/_static/css/fonts/lato-normal-italic.woff2 | Bin 0 -> 195704 bytes
docs/_static/css/fonts/lato-normal.woff | Bin 0 -> 309192 bytes
docs/_static/css/fonts/lato-normal.woff2 | Bin 0 -> 182708 bytes
docs/_static/css/gallery.css | 192 +
docs/_static/css/theme.css | 4 +
docs/_static/css/tlcpack_theme.css | 1054 ++
docs/_static/css/tvm_theme.css | 43 +
docs/_static/doctools.js | 321 +
docs/_static/documentation_options.js | 12 +
docs/_static/file.png | Bin 0 -> 286 bytes
docs/_static/fonts/Inconsolata-Bold.ttf | Bin 0 -> 109948 bytes
docs/_static/fonts/Inconsolata-Regular.ttf | Bin 0 -> 96964 bytes
docs/_static/fonts/Inconsolata.ttf | Bin 0 -> 63184 bytes
docs/_static/fonts/Lato-Bold.ttf | Bin 0 -> 656544 bytes
docs/_static/fonts/Lato-Regular.ttf | Bin 0 -> 656568 bytes
docs/_static/fonts/Lato/lato-bold.eot | Bin 0 -> 256056 bytes
docs/_static/fonts/Lato/lato-bold.ttf | Bin 0 -> 600856 bytes
docs/_static/fonts/Lato/lato-bold.woff | Bin 0 -> 309728 bytes
docs/_static/fonts/Lato/lato-bold.woff2 | Bin 0 -> 184912 bytes
docs/_static/fonts/Lato/lato-bolditalic.eot | Bin 0 -> 266158 bytes
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docs/_static/fonts/Lato/lato-bolditalic.woff | Bin 0 -> 323344 bytes
docs/_static/fonts/Lato/lato-bolditalic.woff2 | Bin 0 -> 193308 bytes
docs/_static/fonts/Lato/lato-italic.eot | Bin 0 -> 268604 bytes
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docs/_static/fonts/Lato/lato-italic.woff | Bin 0 -> 328412 bytes
docs/_static/fonts/Lato/lato-italic.woff2 | Bin 0 -> 195704 bytes
docs/_static/fonts/Lato/lato-regular.eot | Bin 0 -> 253461 bytes
docs/_static/fonts/Lato/lato-regular.ttf | Bin 0 -> 607720 bytes
docs/_static/fonts/Lato/lato-regular.woff | Bin 0 -> 309192 bytes
docs/_static/fonts/Lato/lato-regular.woff2 | Bin 0 -> 182708 bytes
docs/_static/fonts/RobotoSlab-Bold.ttf | Bin 0 -> 170616 bytes
docs/_static/fonts/RobotoSlab-Regular.ttf | Bin 0 -> 169064 bytes
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docs/_static/fonts/fontawesome-webfont.eot | Bin 0 -> 165742 bytes
docs/_static/fonts/fontawesome-webfont.svg | 2671 +++++
docs/_static/fonts/fontawesome-webfont.ttf | Bin 0 -> 165548 bytes
docs/_static/fonts/fontawesome-webfont.woff | Bin 0 -> 98024 bytes
docs/_static/fonts/fontawesome-webfont.woff2 | Bin 0 -> 77160 bytes
docs/_static/gallery.css | 192 +
docs/_static/img/README | 2 +
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docs/_static/img/dwonloaddoc.svg | 5 +
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docs/_static/img/menu-icon.svg | 5 +
docs/_static/img/nextarrow.svg | 4 +
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docs/_static/img/pattern.svg | 114 +
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docs/_static/img/rightangle.svg | 3 +
docs/_static/img/show.svg | 4 +
docs/_static/img/source.svg | 5 +
docs/_static/img/tvm-logo-small.png | Bin 0 -> 6683 bytes
docs/_static/img/tvm-logo-square.png | Bin 0 -> 3453 bytes
docs/_static/jquery-3.5.1.js | 10872 +++++++++++++++++++
docs/_static/jquery.js | 2 +
docs/_static/js/badge_only.js | 1 +
docs/_static/js/html5shiv-printshiv.min.js | 4 +
docs/_static/js/html5shiv.min.js | 4 +
docs/_static/js/modernizr.min.js | 4 +
docs/_static/js/theme.js | 1 +
docs/_static/js/tlcpack_theme.js | 42 +
docs/_static/language_data.js | 297 +
docs/_static/minus.png | Bin 0 -> 90 bytes
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docs/_static/plus.png | Bin 0 -> 90 bytes
docs/_static/pygments.css | 69 +
docs/_static/searchtools.js | 522 +
docs/_static/tvm-logo-small.png | Bin 0 -> 6683 bytes
docs/_static/tvm-logo-square.png | Bin 0 -> 3453 bytes
docs/_static/underscore-1.12.0.js | 2027 ++++
docs/_static/underscore.js | 6 +
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docs/api/python/index.html | 491 +
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docs/api/python/ndarray.html | 573 +
docs/api/python/relay/analysis.html | 1154 ++
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docs/api/python/relay/dataflow_pattern.html | 1449 +++
docs/api/python/relay/frontend.html | 775 ++
docs/api/python/relay/image.html | 692 ++
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docs/contribute/code_review.html | 489 +
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docs/deploy/arm_compute_lib.html | 673 ++
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docs/deploy/vitis_ai.html | 1127 ++
docs/dev/benchmark.html | 594 +
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docs/dev/relay_add_pass.html | 734 ++
docs/dev/relay_bring_your_own_codegen.html | 1220 +++
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docs/dev/runtime.html | 650 ++
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docs/dev/security.html | 429 +
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docs/errors.html | 432 +
docs/faq.html | 436 +
docs/genindex.html | 5267 +++++++++
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docs/install/docker.html | 438 +
docs/install/from_source.html | 652 ++
docs/install/index.html | 410 +
docs/install/nnpack.html | 480 +
docs/langref/hybrid_script.html | 576 +
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docs/langref/relay_adt.html | 855 ++
docs/langref/relay_expr.html | 969 ++
docs/langref/relay_op.html | 895 ++
docs/langref/relay_pattern.html | 847 ++
docs/langref/relay_type.html | 707 ++
docs/microtvm/index.html | 435 +
docs/objects.inv | Bin 0 -> 21574 bytes
docs/py-modindex.html | 771 ++
docs/search.html | 389 +
docs/searchindex.js | 1 +
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.../auto_scheduler/tune_conv2d_layer_cuda.html | 787 ++
.../tutorials/auto_scheduler/tune_network_arm.html | 1007 ++
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.../tutorials/auto_scheduler/tune_network_x86.html | 994 ++
docs/tutorials/auto_scheduler/tune_sparse_x86.html | 803 ++
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docs/tutorials/autotvm/tune_conv2d_cuda.html | 663 ++
docs/tutorials/autotvm/tune_relay_arm.html | 777 ++
docs/tutorials/autotvm/tune_relay_cuda.html | 746 ++
docs/tutorials/autotvm/tune_relay_mobile_gpu.html | 777 ++
docs/tutorials/autotvm/tune_relay_x86.html | 641 ++
docs/tutorials/dev/bring_your_own_datatypes.html | 823 ++
docs/tutorials/dev/low_level_custom_pass.html | 582 +
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docs/tutorials/dev/use_pass_infra.html | 1105 ++
docs/tutorials/frontend/build_gcn.html | 822 ++
.../frontend/deploy_model_on_android.html | 755 ++
docs/tutorials/frontend/deploy_model_on_rasp.html | 633 ++
.../frontend/deploy_object_detection_pytorch.html | 588 +
docs/tutorials/frontend/deploy_prequantized.html | 675 ++
.../frontend/deploy_prequantized_tflite.html | 674 ++
docs/tutorials/frontend/deploy_quantized.html | 571 +
docs/tutorials/frontend/deploy_sparse.html | 756 ++
docs/tutorials/frontend/deploy_ssd_gluoncv.html | 540 +
docs/tutorials/frontend/from_caffe2.html | 563 +
docs/tutorials/frontend/from_coreml.html | 535 +
docs/tutorials/frontend/from_darknet.html | 641 ++
docs/tutorials/frontend/from_keras.html | 555 +
docs/tutorials/frontend/from_mxnet.html | 562 +
docs/tutorials/frontend/from_onnx.html | 557 +
docs/tutorials/frontend/from_pytorch.html | 591 +
docs/tutorials/frontend/from_tensorflow.html | 685 ++
docs/tutorials/frontend/from_tflite.html | 611 ++
docs/tutorials/frontend/sg_execution_times.html | 396 +
docs/tutorials/frontend/using_external_lib.html | 957 ++
.../get_started/auto_tuning_with_python.html | 968 ++
docs/tutorials/get_started/autotvm_matmul.html | 763 ++
.../get_started/cross_compilation_and_rpc.html | 650 ++
docs/tutorials/get_started/install.html | 445 +
docs/tutorials/get_started/introduction.html | 521 +
docs/tutorials/get_started/relay_quick_start.html | 715 ++
docs/tutorials/get_started/sg_execution_times.html | 386 +
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docs/tutorials/get_started/tune_matmul_x86.html | 704 ++
.../get_started/tvmc_command_line_driver.html | 827 ++
docs/tutorials/index.html | 933 ++
docs/tutorials/language/extern_op.html | 537 +
docs/tutorials/language/intrin_math.html | 676 ++
docs/tutorials/language/reduction.html | 744 ++
docs/tutorials/language/scan.html | 620 ++
docs/tutorials/language/schedule_primitives.html | 793 ++
docs/tutorials/language/sg_execution_times.html | 385 +
docs/tutorials/language/tedd.html | 530 +
docs/tutorials/language/tensorize.html | 855 ++
docs/tutorials/language/tuple_inputs.html | 574 +
docs/tutorials/micro/micro_reference_vm.html | 529 +
docs/tutorials/micro/micro_tflite.html | 671 ++
docs/tutorials/micro/sg_execution_times.html | 379 +
docs/tutorials/optimize/opt_conv_cuda.html | 614 ++
docs/tutorials/optimize/opt_conv_tensorcore.html | 958 ++
docs/tutorials/optimize/opt_gemm.html | 989 ++
docs/tutorials/optimize/sg_execution_times.html | 380 +
docs/tutorials/topi/intro_topi.html | 851 ++
docs/tutorials/topi/sg_execution_times.html | 378 +
docs/vta/dev/config.html | 490 +
docs/vta/dev/hardware.html | 666 ++
docs/vta/dev/index.html | 414 +
docs/vta/index.html | 416 +
docs/vta/install.html | 802 ++
docs/vta/tutorials/autotvm/sg_execution_times.html | 379 +
docs/vta/tutorials/autotvm/tune_alu_vta.html | 721 ++
docs/vta/tutorials/autotvm/tune_relay_vta.html | 981 ++
.../tutorials/frontend/deploy_classification.html | 737 ++
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docs/vta/tutorials/index.html | 479 +
docs/vta/tutorials/matrix_multiply.html | 985 ++
docs/vta/tutorials/optimize/convolution_opt.html | 1153 ++
.../tutorials/optimize/matrix_multiply_opt.html | 971 ++
.../vta/tutorials/optimize/sg_execution_times.html | 379 +
docs/vta/tutorials/sg_execution_times.html | 379 +
docs/vta/tutorials/vta_get_started.html | 851 ++
5452 files changed, 872260 insertions(+), 418618 deletions(-)
diff --git a/docs/_downloads/00a1355fcb7c30e9e70fc8fefc708f98/tuple_inputs.ipynb b/docs/_downloads/00a1355fcb7c30e9e70fc8fefc708f98/tuple_inputs.ipynb
new file mode 100644
index 0000000..c9fe241
--- /dev/null
+++ b/docs/_downloads/00a1355fcb7c30e9e70fc8fefc708f98/tuple_inputs.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nCompute and Reduce with Tuple Inputs\n=======================================\n**Author**: `Ziheng Jiang <https://github.com/ZihengJiang>`_\n\nOften we want to compute multiple outputs with the same shape within\na single loop or perform reduction that involves multiple values like\n:code:`argmax`. These problems can be addressed by tuple inputs.\n\nIn this tutorial, we will introduce the usage of tuple inputs in TVM.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, print_function\n\nimport tvm\nfrom tvm import te\nimport numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Describe Batchwise Computation\n------------------------------\nFor operators which have the same shape, we can put them together as\nthe inputs of :any:`te.compute`, if we want them to be scheduled\ntogether in the next schedule procedure.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nm = te.var(\"m\")\nA0 = te.placeholder((m, n), name=\"A0\")\nA1 = te.placeholder((m, n), name=\"A1\")\nB0, B1 = te.compute((m, n), lambda i, j: (A0[i, j] + 2, A1[i, j] * 3), name=\"B\")\n\n# The generated IR code would be:\ns = te.create_schedule(B0.op)\nprint(tvm.lower(s, [A0, A1, B0, B1], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nDescribe Reduction with Collaborative Inputs\n--------------------------------------------\nSometimes, we require multiple inputs to express some reduction\noperators, and the inputs will collaborate together, e.g. :code:`argmax`.\nIn the reduction procedure, :code:`argmax` need to compare the value of\noperands, also need to keep the index of operand. It can be expressed\nwith :py:func:`te.comm_reducer` as below:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# x and y are the operands of reduction, both of them is a tuple of index\n# and value.\ndef fcombine(x, y):\n lhs = tvm.tir.Select((x[1] >= y[1]), x[0], y[0])\n rhs = tvm.tir.Select((x[1] >= y[1]), x[1], y[1])\n return lhs, rhs\n\n\n# our identity element also need to be a tuple, so `fidentity` accepts\n# two types as inputs.\ndef fidentity(t0, t1):\n return tvm.tir.const(-1, t0), tvm.te.min_value(t1)\n\n\nargmax = te.comm_reducer(fcombine, fidentity, name=\"argmax\ [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>For ones who are not familiar with reduction, please refer to\n `general-reduction`.</p></div>\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Schedule Operation with Tuple Inputs\n------------------------------------\nIt is worth mentioning that although you will get multiple outputs\nwith one batch operation, but they can only be scheduled together\nin terms of operation.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nm = te.var(\"m\")\nA0 = te.placeholder((m, n), name=\"A0\")\nB0, B1 = te.compute((m, n), lambda i, j: (A0[i, j] + 2, A0[i, j] * 3), name=\"B\")\nA1 = te.placeholder((m, n), name=\"A1\")\nC = te.compute((m, n), lambda i, j: A1[i, j] + B0[i, j], name=\"C\")\n\ns = te.create_schedule(C.op)\ns[B0].compute_at(s[C], C.op.axis[0])\n# as you can see in the below generated IR code:\nprint(tvm.lower(s, [A0, A1, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\nThis tutorial introduces the usage of tuple inputs operation.\n\n- Describe normal batchwise computation.\n- Describe reduction operation with tuple inputs.\n- Notice that you can only schedule computation in terms of operation instead of tensor.\n\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py b/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
new file mode 100644
index 0000000..b720402
--- /dev/null
+++ b/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
@@ -0,0 +1,192 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Compile TFLite Models
+=====================
+**Author**: `Zhao Wu <https://github.com/FrozenGene>`_
+
+This article is an introductory tutorial to deploy TFLite models with Relay.
+
+To get started, TFLite package needs to be installed as prerequisite.
+
+.. code-block:: bash
+
+ # install tflite
+ pip install tflite==2.1.0 --user
+
+
+or you could generate TFLite package yourself. The steps are the following:
+
+.. code-block:: bash
+
+ # Get the flatc compiler.
+ # Please refer to https://github.com/google/flatbuffers for details
+ # and make sure it is properly installed.
+ flatc --version
+
+ # Get the TFLite schema.
+ wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+
+ # Generate TFLite package.
+ flatc --python schema.fbs
+
+ # Add current folder (which contains generated tflite module) to PYTHONPATH.
+ export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+
+
+Now please check if TFLite package is installed successfully, ``python -c "import tflite"``
+
+Below you can find an example on how to compile TFLite model using TVM.
+"""
+######################################################################
+# Utils for downloading and extracting zip files
+# ----------------------------------------------
+import os
+
+
+def extract(path):
+ import tarfile
+
+ if path.endswith("tgz") or path.endswith("gz"):
+ dir_path = os.path.dirname(path)
+ tar = tarfile.open(path)
+ tar.extractall(path=dir_path)
+ tar.close()
+ else:
+ raise RuntimeError("Could not decompress the file: " + path)
+
+
+######################################################################
+# Load pretrained TFLite model
+# ----------------------------
+# Load mobilenet V1 TFLite model provided by Google
+from tvm.contrib.download import download_testdata
+
+model_url = "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz"
+
+# Download model tar file and extract it to get mobilenet_v1_1.0_224.tflite
+model_path = download_testdata(model_url, "mobilenet_v1_1.0_224.tgz", module=["tf", "official"])
+model_dir = os.path.dirname(model_path)
+extract(model_path)
+
+# Now we can open mobilenet_v1_1.0_224.tflite
+tflite_model_file = os.path.join(model_dir, "mobilenet_v1_1.0_224.tflite")
+tflite_model_buf = open(tflite_model_file, "rb").read()
+
+# Get TFLite model from buffer
+try:
+ import tflite
+
+ tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
+except AttributeError:
+ import tflite.Model
+
+ tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Load a test image
+# -----------------
+# A single cat dominates the examples!
+from PIL import Image
+from matplotlib import pyplot as plt
+import numpy as np
+
+image_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+image_path = download_testdata(image_url, "cat.png", module="data")
+resized_image = Image.open(image_path).resize((224, 224))
+plt.imshow(resized_image)
+plt.show()
+image_data = np.asarray(resized_image).astype("float32")
+
+# Add a dimension to the image so that we have NHWC format layout
+image_data = np.expand_dims(image_data, axis=0)
+
+# Preprocess image as described here:
+# https://github.com/tensorflow/models/blob/edb6ed22a801665946c63d650ab9a0b23d98e1b1/research/slim/preprocessing/inception_preprocessing.py#L243
+image_data[:, :, :, 0] = 2.0 / 255.0 * image_data[:, :, :, 0] - 1
+image_data[:, :, :, 1] = 2.0 / 255.0 * image_data[:, :, :, 1] - 1
+image_data[:, :, :, 2] = 2.0 / 255.0 * image_data[:, :, :, 2] - 1
+print("input", image_data.shape)
+
+######################################################################
+# Compile the model with relay
+# ----------------------------
+
+# TFLite input tensor name, shape and type
+input_tensor = "input"
+input_shape = (1, 224, 224, 3)
+input_dtype = "float32"
+
+# Parse TFLite model and convert it to a Relay module
+from tvm import relay, transform
+
+mod, params = relay.frontend.from_tflite(
+ tflite_model, shape_dict={input_tensor: input_shape}, dtype_dict={input_tensor: input_dtype}
+)
+
+# Build the module against to x86 CPU
+target = "llvm"
+with transform.PassContext(opt_level=3):
+ lib = relay.build(mod, target, params=params)
+
+######################################################################
+# Execute on TVM
+# --------------
+import tvm
+from tvm import te
+from tvm.contrib import graph_executor as runtime
+
+# Create a runtime executor module
+module = runtime.GraphModule(lib["default"](tvm.cpu()))
+
+# Feed input data
+module.set_input(input_tensor, tvm.nd.array(image_data))
+
+# Run
+module.run()
+
+# Get output
+tvm_output = module.get_output(0).numpy()
+
+######################################################################
+# Display results
+# ---------------
+
+# Load label file
+label_file_url = "".join(
+ [
+ "https://raw.githubusercontent.com/",
+ "tensorflow/tensorflow/master/tensorflow/lite/java/demo/",
+ "app/src/main/assets/",
+ "labels_mobilenet_quant_v1_224.txt",
+ ]
+)
+label_file = "labels_mobilenet_quant_v1_224.txt"
+label_path = download_testdata(label_file_url, label_file, module="data")
+
+# List of 1001 classes
+with open(label_path) as f:
+ labels = f.readlines()
+
+# Convert result to 1D data
+predictions = np.squeeze(tvm_output)
+
+# Get top 1 prediction
+prediction = np.argmax(predictions)
+
+# Convert id to class name and show the result
+print("The image prediction result is: id " + str(prediction) + " name: " + labels[prediction])
diff --git a/docs/_downloads/08e39628455fe618afd9eb5b958a433e/micro_reference_vm.ipynb b/docs/_downloads/08e39628455fe618afd9eb5b958a433e/micro_reference_vm.ipynb
new file mode 100644
index 0000000..f141ae0
--- /dev/null
+++ b/docs/_downloads/08e39628455fe618afd9eb5b958a433e/micro_reference_vm.ipynb
@@ -0,0 +1,43 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n\n# microTVM Reference Virtual Machines\n\n**Author**: `Andrew Reusch <ar...@octoml.ai>`_\n\nThis tutorial explains how to launch microTVM Reference Virtual Machines. You can use these to\ndevelop on real physical hardware without needing to individually install the microTVM\ndependencies. These are also particularly useful when trying to reproduce behavior with\nmicroTVM, such as when filing bug reports.\n\nmicroTVM is the effort to allow TVM to build and execute models on [...]
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/09d0a1754546d603ffcd30e8e8a44cac/tune_alu_vta.py b/docs/_downloads/09d0a1754546d603ffcd30e8e8a44cac/tune_alu_vta.py
new file mode 100644
index 0000000..f2bf15b
--- /dev/null
+++ b/docs/_downloads/09d0a1754546d603ffcd30e8e8a44cac/tune_alu_vta.py
@@ -0,0 +1,320 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-tuning a ALU fused op on VTA
+"""
+
+import os
+from mxnet.gluon.model_zoo import vision
+import numpy as np
+from PIL import Image
+
+from tvm import topi
+import tvm
+from tvm import te
+from tvm import rpc, autotvm, relay
+from tvm.contrib import graph_runtime, download
+from tvm.autotvm.measure.measure_methods import request_remote
+from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
+from tvm.autotvm import record
+
+import vta
+from vta.testing import simulator
+from vta.top import graph_pack
+import copy
+
+
+#################################################################
+# Compile network
+# ---------------
+# Perform vta-specific compilation with Relay from a Gluon model
+def compile_network(env, target, model, start_pack, stop_pack):
+
+ # Populate the shape and data type dictionary
+ dtype_dict = {"data": "float32"}
+ shape_dict = {"data": (env.BATCH, 3, 224, 224)}
+
+ # Get off the shelf gluon model, and convert to relay
+ gluon_model = vision.get_model(model, pretrained=True)
+ mod, params = relay.frontend.from_mxnet(gluon_model, shape_dict)
+
+ # Update shape and type dictionary
+ shape_dict.update({k: v.shape for k, v in params.items()})
+ dtype_dict.update({k: str(v.dtype) for k, v in params.items()})
+
+ # Perform quantization in Relay
+ # Note: We set opt_level to 3 in order to fold batch norm
+ with relay.build_config(opt_level=3):
+ with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[0]):
+ mod = relay.quantize.quantize(mod, params=params)
+
+ # Perform graph packing and constant folding for VTA target
+ if target.device_name == "vta":
+ assert env.BLOCK_IN == env.BLOCK_OUT
+ relay_prog = graph_pack(
+ mod["main"],
+ env.BATCH,
+ env.BLOCK_OUT,
+ env.WGT_WIDTH,
+ start_name=start_pack,
+ stop_name=stop_pack,
+ )
+
+ return relay_prog, params
+
+
+###########################################
+# Set Tuning Options
+# ------------------
+# Before tuning, we should apply some configurations.
+# Here we use an Pynq-Z1 board as an example.
+
+# Tracker host and port can be set by your environment
+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/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.
+# Set ``device=arm_cpu`` to run inference on the CPU
+# or ``device=vta`` to run inference on the FPGA.
+device = "vta"
+target = env.target if device == "vta" else env.target_vta_cpu
+
+# Name of Gluon model to compile
+# The ``start_pack`` and ``stop_pack`` labels indicate where
+# to start and end the graph packing relay pass: in other words
+# where to start and finish offloading to VTA.
+network = "resnet50_v2"
+start_pack = "nn.max_pool2d"
+stop_pack = "nn.global_avg_pool2d"
+
+# Tuning option
+log_file = "%s.alu.%s.log" % (device, network)
+tuning_option = {
+ "log_filename": log_file,
+ "tuner": "random",
+ "n_trial": 1000,
+ "early_stopping": None,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(n_parallel=1),
+ runner=autotvm.RPCRunner(
+ env.TARGET,
+ host=tracker_host,
+ port=tracker_port,
+ number=5,
+ timeout=60,
+ # check_correctness=True, # TODO: re-enable when check_correctness works again.
+ ),
+ ),
+}
+
+
+def log_to_file(file_out, protocol="json"):
+ """Log the tuning records into file.
+ The rows of the log are stored in the format of autotvm.record.encode.
+ for lhs == rhs, we add an extra rhs = [] record
+
+ Parameters
+ ----------
+ file_out : str
+ The file to log to.
+ protocol: str, optional
+ The log protocol. Can be 'json' or 'pickle'
+
+ Returns
+ -------
+ callback : callable
+ Callback function to do the logging.
+ """
+
+ def _callback(_, inputs, results):
+ with open(file_out, "a") as f:
+ for inp, result in zip(inputs, results):
+ f.write(record.encode(inp, result, protocol) + "\n")
+
+ # we only consider task with same lhs and rhs
+ if inp.task.args[0] == inp.task.args[1]:
+ args = list(inp.task.args)
+ args[1] = (args[0][0], (), args[0][2])
+ inp_copy = copy.deepcopy(inp)
+ inp_copy.task.args = tuple(args)
+ f.write(record.encode(inp_copy, result, protocol) + "\n")
+
+ return _callback
+
+
+def tune_tasks(
+ tasks,
+ measure_option,
+ tuner="xgb",
+ n_trial=10,
+ early_stopping=None,
+ log_filename="tuning.log",
+ use_transfer_learning=True,
+):
+
+ # create tmp log file
+ tmp_log_file = log_filename + ".tmp"
+ if os.path.exists(tmp_log_file):
+ os.remove(tmp_log_file)
+
+ for i, tsk in enumerate(reversed(tasks)):
+ prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+ # create tuner
+ if tuner == "xgb" or tuner == "xgb-rank":
+ tuner_obj = XGBTuner(tsk, loss_type="rank")
+ elif tuner == "xgb_knob":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+ elif tuner == "ga":
+ tuner_obj = GATuner(tsk, pop_size=50)
+ elif tuner == "random":
+ tuner_obj = RandomTuner(tsk)
+ elif tuner == "gridsearch":
+ tuner_obj = GridSearchTuner(tsk)
+ else:
+ raise ValueError("Invalid tuner: " + tuner)
+
+ if use_transfer_learning:
+ if os.path.isfile(tmp_log_file):
+ tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
+
+ # do tuning
+ tsk_trial = min(n_trial, len(tsk.config_space))
+ tuner_obj.tune(
+ n_trial=tsk_trial,
+ early_stopping=early_stopping,
+ measure_option=measure_option,
+ callbacks=[
+ autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
+ log_to_file(tmp_log_file),
+ ],
+ )
+
+ # pick best records to a cache file
+ autotvm.record.pick_best(tmp_log_file, log_filename)
+ os.remove(tmp_log_file)
+
+
+########################################################################
+# Register VTA-specific tuning tasks
+def register_vta_tuning_tasks():
+ from tvm.autotvm.task import TaskExtractEnv
+
+ @tvm.te.tag_scope(tag=topi.tag.ELEMWISE)
+ def my_clip(x, a_min, a_max):
+ """Unlike topi's current clip, put min and max into two stages."""
+ const_min = tvm.tir.const(a_min, x.dtype)
+ const_max = tvm.tir.const(a_max, x.dtype)
+ x = te.compute(x.shape, lambda *i: tvm.te.min(x(*i), const_max), name="clipA")
+ x = te.compute(x.shape, lambda *i: tvm.te.max(x(*i), const_min), name="clipB")
+ return x
+
+ # init autotvm env to register VTA operator
+ TaskExtractEnv()
+
+ @autotvm.template("add.vta")
+ def _topi_add(*args, **kwargs):
+ assert not kwargs, "Do not support kwargs in template function call"
+ A, B = args[:2]
+
+ with tvm.target.vta():
+ res = vta.top.op.add_packed(*args, **kwargs)
+ res = my_clip(res, 0, 127)
+ res = topi.cast(res, "int8")
+
+ if tvm.target.Target.current().device_name == "vta":
+ s = vta.top.op.schedule_add_packed([res])
+ else:
+ s = te.create_schedule([res.op])
+ return s, [A, B, res]
+
+ @autotvm.template("multiply.vta")
+ def _topi_multiply(*args, **kwargs):
+ assert not kwargs, "Do not support kwargs in template function call"
+ A, B = args[:2]
+
+ with tvm.target.vta():
+ res = vta.top.op.multiply_packed(*args, **kwargs)
+ res = my_clip(res, 0, 127)
+ res = topi.cast(res, "int8")
+
+ if tvm.target.Target.current().device_name == "vta":
+ s = vta.top.op.schedule_multiply_packed([res])
+ else:
+ s = te.create_schedule([res.op])
+ return s, [A, B, res]
+
+
+########################################################################
+# Finally, we launch tuning jobs and evaluate the end-to-end performance.
+def tune_and_evaluate(tuning_opt):
+
+ if env.TARGET != "intelfocl":
+ print("ALU only op only available for intelfocl target")
+ return
+
+ # Register VTA tuning tasks
+ register_vta_tuning_tasks()
+
+ # Perform task extraction on Relay program
+ print("Extract tasks...")
+ relay_prog, params = compile_network(env, target, network, start_pack, stop_pack)
+ mod = tvm.IRModule.from_expr(relay_prog)
+ tasks = autotvm.task.extract_from_program(
+ mod,
+ params=params,
+ ops=(
+ relay.op.get("add"),
+ relay.op.get("multiply"),
+ ),
+ target=target,
+ target_host=env.target_host,
+ )
+
+ # filter out non-packed alu task
+ tasks = list(filter(lambda t: len(t.args[0][1]) > 4, tasks))
+ # filter out float alu task
+ tasks = list(filter(lambda t: t.args[0][2] != "float32", tasks))
+
+ # We should have extracted 10 convolution tasks
+ tasks_set = {}
+ print("Extracted {} alu tasks:".format(len(tasks)))
+ for tsk in tasks:
+ print("tsk = ", tsk)
+
+ if len(tsk.args[1][1]) == 0:
+ args = list(tsk.args)
+ args[1] = args[0]
+ tsk.args = tuple(args)
+
+ if (tsk.name, tsk.args) in tasks_set:
+ print("task {} already exists".format(tsk))
+ tasks_set[(tsk.name, tsk.args)] = tsk
+
+ tasks = list(tasks_set.values())
+ print("After merged, final #tasks={}, tasks = {}".format(len(tasks), tasks))
+
+ # run tuning tasks
+ print("Tuning...")
+ tune_tasks(tasks, **tuning_opt)
+
+
+# Run the tuning and evaluate the results
+tune_and_evaluate(tuning_option)
diff --git a/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb b/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
new file mode 100644
index 0000000..9e96def
--- /dev/null
+++ b/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nCompile TFLite Models\n=====================\n**Author**: `Zhao Wu <https://github.com/FrozenGene>`_\n\nThis article is an introductory tutorial to deploy TFLite models with Relay.\n\nTo get started, TFLite package needs to be installed as prerequisite.\n\n.. code-block:: bash\n\n # install tflite\n pip install tflite==2.1.0 --user\n\n\nor you could generate TFLite package yourself. The steps are the following:\n\n.. code-block:: bash\n\n # Get the flatc compiler.\n [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Utils for downloading and extracting zip files\n----------------------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n\n\ndef extract(path):\n import tarfile\n\n if path.endswith(\"tgz\") or path.endswith(\"gz\"):\n dir_path = os.path.dirname(path)\n tar = tarfile.open(path)\n tar.extractall(path=dir_path)\n tar.close()\n else:\n raise RuntimeError(\"Could not decompress the file: \" + path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load pretrained TFLite model\n----------------------------\nLoad mobilenet V1 TFLite model provided by Google\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from tvm.contrib.download import download_testdata\n\nmodel_url = \"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz\"\n\n# Download model tar file and extract it to get mobilenet_v1_1.0_224.tflite\nmodel_path = download_testdata(model_url, \"mobilenet_v1_1.0_224.tgz\", module=[\"tf\", \"official\"])\nmodel_dir = os.path.dirname(model_path)\nextract(model_path)\n\n# Now we can open mobilenet_v1_1.0_224.tflite\ntflite_model_file = os.path.joi [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a test image\n-----------------\nA single cat dominates the examples!\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from PIL import Image\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nimage_url = \"https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true\"\nimage_path = download_testdata(image_url, \"cat.png\", module=\"data\")\nresized_image = Image.open(image_path).resize((224, 224))\nplt.imshow(resized_image)\nplt.show()\nimage_data = np.asarray(resized_image).astype(\"float32\")\n\n# Add a dimension to the image so that we have NHWC format layout\nimage_data = np.ex [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Compile the model with relay\n----------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# TFLite input tensor name, shape and type\ninput_tensor = \"input\"\ninput_shape = (1, 224, 224, 3)\ninput_dtype = \"float32\"\n\n# Parse TFLite model and convert it to a Relay module\nfrom tvm import relay, transform\n\nmod, params = relay.frontend.from_tflite(\n tflite_model, shape_dict={input_tensor: input_shape}, dtype_dict={input_tensor: input_dtype}\n)\n\n# Build the module against to x86 CPU\ntarget = \"llvm\"\nwith transform.PassContext(opt_level=3):\n lib = relay [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Execute on TVM\n--------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import tvm\nfrom tvm import te\nfrom tvm.contrib import graph_executor as runtime\n\n# Create a runtime executor module\nmodule = runtime.GraphModule(lib[\"default\"](tvm.cpu()))\n\n# Feed input data\nmodule.set_input(input_tensor, tvm.nd.array(image_data))\n\n# Run\nmodule.run()\n\n# Get output\ntvm_output = module.get_output(0).numpy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Display results\n---------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Load label file\nlabel_file_url = \"\".join(\n [\n \"https://raw.githubusercontent.com/\",\n \"tensorflow/tensorflow/master/tensorflow/lite/java/demo/\",\n \"app/src/main/assets/\",\n \"labels_mobilenet_quant_v1_224.txt\",\n ]\n)\nlabel_file = \"labels_mobilenet_quant_v1_224.txt\"\nlabel_path = download_testdata(label_file_url, label_file, module=\"data\")\n\n# List of 1001 classes\nwith open(label_path) as f:\n labels = f.readlines()\n\n# [...]
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb b/docs/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
new file mode 100644
index 0000000..4b75bb6
--- /dev/null
+++ b/docs/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nAuto-scheduling a Neural Network for mali GPU\n=============================================\n**Author**: `Zhao Wu <https://github.com/FrozenGene>`_\n\nAuto-tuning for specific devices and workloads is critical for getting the\nbest performance. This is a tutorial on how to tune a whole neural\nnetwork for mali GPU with the auto-scheduler.\n\nTo auto-tune a neural network, we partition the network into small subgraphs and\ntune them independently. Each subgraph is treated as o [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n\nimport tvm\nfrom tvm import relay, auto_scheduler\nimport tvm.relay.testing\nfrom tvm.contrib import graph_executor\nimport os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define a Network\n----------------\nFirst, we need to define the network with relay frontend API.\nWe can load some pre-defined network from :code:`tvm.relay.testing`.\nWe can also load models from MXNet, ONNX, PyTorch, and TensorFlow\n(see `front end tutorials<tutorial-frontend>`).\n\nFor convolutional neural networks, although auto-scheduler can work correctly\nwith any layout, we found the best performance is typically achieved with NHWC layout.\nWe also implemented more opti [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def get_network(name, batch_size, layout=\"NHWC\", dtype=\"float32\"):\n \"\"\"Get the symbol definition and random weight of a network\"\"\"\n\n # auto-scheduler prefers NHWC layout\n if layout == \"NHWC\":\n image_shape = (224, 224, 3)\n elif layout == \"NCHW\":\n image_shape = (3, 224, 224)\n else:\n raise ValueError(\"Invalid layout: \" + layout)\n\n input_shape = (batch_size,) + image_shape\n output_shape = (batch_size, 1000)\n\n [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Start an RPC Tracker and Register Devices to the Tracker\n--------------------------------------------------------\nPlease refer to the \"Start RPC Tracker\" and \"Register Devices to RPC Tracker\" setions\nin this `tutorial <tutorials-autotvm-start-rpc-tracker>` to start an RPC tracker\nand register devices to the tracker.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Replace this with the device key in your tracker\ndevice_key = \"rk3399\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Extract Search Tasks\n--------------------\nNext, we extract the search tasks and their weights from a network.\nThe weight of a task is the number of appearances of the task's subgraph\nin the whole network.\nBy using the weight, we can approximate the end-to-end latency of the network\nas :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the\nlatency of a task and :code:`weight[t]` is the weight of the task.\nThe task scheduler will just optimize this objective.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Extract tasks from the network\nprint(\"Extract tasks...\")\nmod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)\ntasks, task_weights = auto_scheduler.extract_tasks(mod[\"main\"], params, target)\n\nfor idx, task in enumerate(tasks):\n print(\"========== Task %d (workload key: %s) ==========\" % (idx, task.workload_key))\n print(task.compute_dag)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>How to get the hardware parameters from remote device\n\n .. code-block:: python\n\n from tvm.auto_scheduler.utils import request_remote\n remote = request_remote(device_key, \"127.0.0.1\", 9190)\n dev = remote.cl()\n max_shared_memory_per_block = dev.max_shared_memory_per_block\n # There is no explicit local memory limition\n # so we can use INT32_MAX to disable the check on local_memory.\n max_local_memory_p [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tuning and Evaluate\n-------------------\nNow, we set some options for tuning, launch the search tasks and evaluate the end-to-end performance\n\n* :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.\n You can set it to a small number (e.g., 200) for a fast demonstrative run.\n In practice, we recommend setting it around :code:`800 * len(tasks)`,\n which is typically enough for the search to converge.\n For example, there are 29 tasks [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def tune_and_evaluate():\n print(\"Begin tuning...\")\n tuner = auto_scheduler.TaskScheduler(tasks, task_weights)\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=200, # change this to 20000 to achieve the best performance\n builder=auto_scheduler.LocalBuilder(build_func=\"ndk\" if use_ndk else \"default\"),\n runner=auto_scheduler.RPCRunner(\n device_key, host=\"127.0.0.1\", port=9190, repeat=3, timeout=50\n ),\ [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Explain the printed information during tuning\n\n During the tuning, a lot of information will be printed on the console.\n They are used for debugging purposes. The most important info is the output\n of the task scheduler. The following table is a sample output.\n\n .. code-block:: c\n\n ----------------------------------------------------------------------\n ------------------------------ [ Task Scheduler ]\n ----- [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Terminate the tuning earlier\n\n You can terminate the tuning earlier by forcibly killing this process.\n As long as you get at least one valid schedule for each task in the log file,\n you should be able to do the compilation (the secion below).</p></div>\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Other Tips\n----------\n1. During the tuning, the auto-scheduler needs to compile many programs and\n extract feature from them. This part is CPU-intensive,\n so a high-performance CPU with many cores is recommended for faster search.\n2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`\n to distill the large log file and only save the best useful records.\n3. You can resume a search from the previous log file. You just need to\n [...]
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/1087d6f8ab0d37227d1a26042de7ac88/auto_tuning_with_python.py b/docs/_downloads/1087d6f8ab0d37227d1a26042de7ac88/auto_tuning_with_python.py
new file mode 100644
index 0000000..848511c
--- /dev/null
+++ b/docs/_downloads/1087d6f8ab0d37227d1a26042de7ac88/auto_tuning_with_python.py
@@ -0,0 +1,475 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Compiling and Optimizing a Model with the Python AutoScheduler
+==============================================================
+**Author**:
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In the `TVMC Tutorial <tvmc_command_line_driver>`_, we covered how to compile, run, and tune a
+pre-trained vision model, ResNet-50-v2 using the command line interface for
+TVM, TVMC. TVM is more that just a command-line tool though, it is an
+optimizing framework with APIs available for a number of different languages
+that gives you tremendous flexibility in working with machine learning models.
+
+In this tutorial we will cover the same ground we did with TVMC, but show how
+it is done with the Python API. Upon completion of this section, we will have
+used the Python API for TVM to accomplish the following tasks:
+
+* Compile a pre-trained ResNet 50 v2 model for the TVM runtime.
+* Run a real image through the compiled model, and interpret the output and model
+ performance.
+* Tune the model that model on a CPU using TVM.
+* Re-compile an optimized model using the tuning data collected by TVM.
+* Run the image through the optimized model, and compare the output and model
+ performance.
+
+The goal of this section is to give you an overview of TVM's capabilites and
+how to use them through the Python API.
+"""
+
+################################################################################
+# TVM is a deep learning compiler framework, with a number of different modules
+# available for working with deep learning models and operators. In this
+# tutorial we will work through how to load, compile, and optimize a model
+# using the Python API.
+#
+# We begin by importing a number of dependencies, including ``onnx`` for
+# loading and converting the model, helper utilities for downloading test data,
+# the Python Image Library for working with the image data, ``numpy`` for pre
+# and post-processing of the image data, the TVM Relay framework, and the TVM
+# Graph Executor.
+
+import onnx
+from tvm.contrib.download import download_testdata
+from PIL import Image
+import numpy as np
+import tvm.relay as relay
+import tvm
+from tvm.contrib import graph_executor
+
+################################################################################
+# Downloading and Loading the ONNX Model
+# --------------------------------------
+#
+# For this tutorial, we will be working with ResNet-50 v2. ResNet-50 is a
+# convolutional neural network that is 50-layers deep and designed to classify
+# images. The model we will be using has been pre-trained on more than a
+# million images with 1000 different classifications. The network has an input
+# image size of 224x224. If you are interested exploring more of how the
+# ResNet-50 model is structured, we recommend downloading
+# `Netron <https://netron.app>`_, a freely available ML model viewer.
+#
+# TVM provides a helper library to download pre-trained models. By providing a
+# model URL, file name, and model type through the module, TVM will download
+# the model and save it to disk. For the instance of an ONNX model, you can
+# then load it into memory using the ONNX runtime.
+#
+# .. note:: Working with Other Model Formats
+#
+# TVM supports many popular model formats. A list can be found in the `Compile
+# Deep Learning Models
+# <https://tvm.apache.org/docs/tutorials/index.html#compile-deep-learning-models>`_
+# section of the TVM Documentation.
+
+model_url = "".join(
+ [
+ "https://github.com/onnx/models/raw/",
+ "master/vision/classification/resnet/model/",
+ "resnet50-v2-7.onnx",
+ ]
+)
+
+model_path = download_testdata(model_url, "resnet50-v2-7.onnx", module="onnx")
+onnx_model = onnx.load(model_path)
+
+################################################################################
+# Downloading, Preprocessing, and Loading the Test Image
+# ------------------------------------------------------
+#
+# Each model is particular when it comes to expected tensor shapes, formats and
+# data types. For this reason, most models require some pre and
+# post-processing, to ensure the input is valid and to interpret the output.
+# TVMC has adopted NumPy's ``.npz`` format for both input and output data.
+#
+# As input for this tutorial, we will use the image of a cat, but you can feel
+# free to substitute image for any of your choosing.
+#
+# .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg
+# :height: 224px
+# :width: 224px
+# :align: center
+#
+# Download the image data, then convert it to a numpy array to use as an input to the model.
+
+img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
+img_path = download_testdata(img_url, "imagenet_cat.png", module="data")
+
+# Resize it to 224x224
+resized_image = Image.open(img_path).resize((224, 224))
+img_data = np.asarray(resized_image).astype("float32")
+
+# Our input image is in HWC layout while ONNX expects CHW input, so convert the array
+img_data = np.transpose(img_data, (2, 0, 1))
+
+# Normalize according to the ImageNet input specification
+imagenet_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+imagenet_stddev = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+norm_img_data = (img_data / 255 - imagenet_mean) / imagenet_stddev
+
+# Add the batch dimension, as we are expecting 4-dimensional input: NCHW.
+img_data = np.expand_dims(norm_img_data, axis=0)
+
+###############################################################################
+# Compile the Model With Relay
+# ----------------------------
+#
+# The next step is to compile the ResNet model. We begin by importing the model
+# to relay using the `from_onnx` importer. We then build the model, with
+# standard optimizations, into a TVM library. Finally, we create a TVM graph
+# runtime module from the library.
+
+target = "llvm"
+
+######################################################################
+# .. note:: Defining the Correct Target
+#
+# Specifying the correct target can have a huge impact on the performance of
+# the compiled module, as it can take advantage of hardware features
+# available on the target. For more information, please refer to `Auto-tuning
+# a convolutional network for x86 CPU
+# <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network>`_.
+# We recommend identifying which CPU you are running, along with optional
+# features, and set the target appropriately. For example, for some
+# processors ``target = "llvm -mcpu=skylake"``, or ``target = "llvm
+# -mcpu=skylake-avx512"`` for processors with the AVX-512 vector instruction
+# set.
+#
+
+# The input name may vary across model types. You can use a tool
+# like netron to check input names
+input_name = "data"
+shape_dict = {input_name: img_data.shape}
+
+mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
+
+with tvm.transform.PassContext(opt_level=3):
+ lib = relay.build(mod, target=target, params=params)
+
+dev = tvm.device(str(target), 0)
+module = graph_executor.GraphModule(lib["default"](dev))
+
+######################################################################
+# Execute on the TVM Runtime
+# --------------------------
+# Now that we've compiled the model, we can use the TVM runtime to make
+# predictions with it. To use TVM to run the model and make predictions, we
+# need two things:
+#
+# - The compiled model, which we just produced.
+# - Valid input to the model to make predictions on.
+
+dtype = "float32"
+module.set_input(input_name, img_data)
+module.run()
+output_shape = (1, 1000)
+tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()
+
+################################################################################
+# Collect Basic Performance Data
+# ------------------------------
+# We want to collect some basic performance data associated with this
+# unoptimized model and compare it to a tuned model later. To help account for
+# CPU noise, we run the computation in multiple batches in multiple
+# repetitions, then gather some basis statistics on the mean, median, and
+# standard deviation.
+import timeit
+
+timing_number = 10
+timing_repeat = 10
+unoptimized = (
+ np.array(timeit.Timer(lambda: module.run()).repeat(repeat=timing_repeat, number=timing_number))
+ * 1000
+ / timing_number
+)
+unoptimized = {
+ "mean": np.mean(unoptimized),
+ "median": np.median(unoptimized),
+ "std": np.std(unoptimized),
+}
+
+print(unoptimized)
+
+################################################################################
+# Postprocess the output
+# ----------------------
+#
+# As previously mentioned, each model will have its own particular way of
+# providing output tensors.
+#
+# In our case, we need to run some post-processing to render the outputs from
+# ResNet-50-V2 into a more human-readable form, using the lookup-table provided
+# for the model.
+
+from scipy.special import softmax
+
+# Download a list of labels
+labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
+labels_path = download_testdata(labels_url, "synset.txt", module="data")
+
+with open(labels_path, "r") as f:
+ labels = [l.rstrip() for l in f]
+
+# Open the output and read the output tensor
+scores = softmax(tvm_output)
+scores = np.squeeze(scores)
+ranks = np.argsort(scores)[::-1]
+for rank in ranks[0:5]:
+ print("class='%s' with probability=%f" % (labels[rank], scores[rank]))
+
+################################################################################
+# This should produce the following output:
+#
+# .. code-block:: bash
+#
+# # class='n02123045 tabby, tabby cat' with probability=0.610553
+# # class='n02123159 tiger cat' with probability=0.367179
+# # class='n02124075 Egyptian cat' with probability=0.019365
+# # class='n02129604 tiger, Panthera tigris' with probability=0.001273
+# # class='n04040759 radiator' with probability=0.000261
+
+################################################################################
+# Tune the model
+# --------------
+# The previous model was compiled to work on the TVM runtime, but did not
+# include any platform specific optimization. In this section, we will show you
+# how to build an optimized model using TVM to target your working platform.
+#
+# In some cases, we might not get the expected performance when running
+# inferences using our compiled module. In cases like this, we can make use of
+# the auto-tuner, to find a better configuration for our model and get a boost
+# in performance. Tuning in TVM refers to the process by which a model is
+# optimized to run faster on a given target. This differs from training or
+# fine-tuning in that it does not affect the accuracy of the model, but only
+# the runtime performance. As part of the tuning process, TVM will try running
+# many different operator implementation variants to see which perform best.
+# The results of these runs are stored in a tuning records file.
+#
+# In the simplest form, tuning requires you to provide three things:
+#
+# - the target specification of the device you intend to run this model on
+# - the path to an output file in which the tuning records will be stored
+# - a path to the model to be tuned.
+#
+
+import tvm.auto_scheduler as auto_scheduler
+from tvm.autotvm.tuner import XGBTuner
+from tvm import autotvm
+
+# Set up some basic parameters for the runner. The runner takes compiled code
+# that is generated with a specific set of parameters and measures the
+# performance of it. ``number`` specifies the number of different
+# configurations that we will test, while ``repeat`` specifies how many
+# measurements we will take of each configuration. ``min_repeat_ms`` is a value
+# that specifies how long need to run configuration test. If the number of
+# repeats falls under this time, it will be increased. This option is necessary
+# for accurate tuning on GPUs, and is not required for CPU tuning. Setting this
+# value to 0 disables it. The ``timeout`` places an upper limit on how long to
+# run training code for each tested configuration.
+
+number = 10
+repeat = 1
+min_repeat_ms = 0 # since we're tuning on a CPU, can be set to 0
+timeout = 10 # in seconds
+
+# create a TVM runner
+runner = autotvm.LocalRunner(
+ number=number,
+ repeat=repeat,
+ timeout=timeout,
+ min_repeat_ms=min_repeat_ms,
+)
+
+# Create a simple structure for holding tuning options. We use an XGBoost
+# algorithim for guiding the search. For a production job, you will want to set
+# the number of trials to be larger than the value of 10 used here. For CPU we
+# recommend 1500, for GPU 3000-4000. The number of trials required can depend
+# on the particular model and processor, so it's worth spending some time
+# evaluating performance across a range of values to find the best balance
+# between tuning time and model optimization. Because running tuning is time
+# intensive we set number of trials to 10, but do not recommend a value this
+# small. The ``early_stopping`` parameter is the minimum number of trails to
+# run before a condition that stops the search early can be applied. The
+# measure option indicates where trial code will be built, and where it will be
+# run. In this case, we're using the ``LocalRunner`` we just created and a
+# ``LocalBuilder``. The ``tuning_records`` option specifies a file to write
+# the tuning data to.
+
+tuning_option = {
+ "tuner": "xgb",
+ "trials": 10,
+ "early_stopping": 100,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": "resnet-50-v2-autotuning.json",
+}
+
+################################################################################
+# .. note:: Defining the Tuning Search Algorithm
+#
+# By default this search is guided using an `XGBoost Grid` algorithm.
+# Depending on your model complexity and amount of time available, you might
+# want to choose a different algorithm.
+
+
+################################################################################
+# .. note:: Setting Tuning Parameters
+#
+# In this example, in the interest of time, we set the number of trials and
+# early stopping to 10. You will likely see more performance improvements if
+# you set these values to be higher but this comes at the expense of time
+# spent tuning. The number of trials required for convergence will vary
+# depending on the specifics of the model and the target platform.
+
+# begin by extracting the taks from the onnx model
+tasks = autotvm.task.extract_from_program(mod["main"], target=target, params=params)
+
+# Tune the extracted tasks sequentially.
+for i, task in enumerate(tasks):
+ prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+ tuner_obj = XGBTuner(task, loss_type="rank")
+ tuner_obj.tune(
+ n_trial=min(tuning_option["trials"], len(task.config_space)),
+ early_stopping=tuning_option["early_stopping"],
+ measure_option=tuning_option["measure_option"],
+ callbacks=[
+ autotvm.callback.progress_bar(tuning_option["trials"], prefix=prefix),
+ autotvm.callback.log_to_file(tuning_option["tuning_records"]),
+ ],
+ )
+
+################################################################################
+# The output from this tuning process will look something like this:
+#
+# .. code-block:: bash
+#
+# # [Task 1/24] Current/Best: 10.71/ 21.08 GFLOPS | Progress: (60/1000) | 111.77 s Done.
+# # [Task 1/24] Current/Best: 9.32/ 24.18 GFLOPS | Progress: (192/1000) | 365.02 s Done.
+# # [Task 2/24] Current/Best: 22.39/ 177.59 GFLOPS | Progress: (960/1000) | 976.17 s Done.
+# # [Task 3/24] Current/Best: 32.03/ 153.34 GFLOPS | Progress: (800/1000) | 776.84 s Done.
+# # [Task 4/24] Current/Best: 11.96/ 156.49 GFLOPS | Progress: (960/1000) | 632.26 s Done.
+# # [Task 5/24] Current/Best: 23.75/ 130.78 GFLOPS | Progress: (800/1000) | 739.29 s Done.
+# # [Task 6/24] Current/Best: 38.29/ 198.31 GFLOPS | Progress: (1000/1000) | 624.51 s Done.
+# # [Task 7/24] Current/Best: 4.31/ 210.78 GFLOPS | Progress: (1000/1000) | 701.03 s Done.
+# # [Task 8/24] Current/Best: 50.25/ 185.35 GFLOPS | Progress: (972/1000) | 538.55 s Done.
+# # [Task 9/24] Current/Best: 50.19/ 194.42 GFLOPS | Progress: (1000/1000) | 487.30 s Done.
+# # [Task 10/24] Current/Best: 12.90/ 172.60 GFLOPS | Progress: (972/1000) | 607.32 s Done.
+# # [Task 11/24] Current/Best: 62.71/ 203.46 GFLOPS | Progress: (1000/1000) | 581.92 s Done.
+# # [Task 12/24] Current/Best: 36.79/ 224.71 GFLOPS | Progress: (1000/1000) | 675.13 s Done.
+# # [Task 13/24] Current/Best: 7.76/ 219.72 GFLOPS | Progress: (1000/1000) | 519.06 s Done.
+# # [Task 14/24] Current/Best: 12.26/ 202.42 GFLOPS | Progress: (1000/1000) | 514.30 s Done.
+# # [Task 15/24] Current/Best: 31.59/ 197.61 GFLOPS | Progress: (1000/1000) | 558.54 s Done.
+# # [Task 16/24] Current/Best: 31.63/ 206.08 GFLOPS | Progress: (1000/1000) | 708.36 s Done.
+# # [Task 17/24] Current/Best: 41.18/ 204.45 GFLOPS | Progress: (1000/1000) | 736.08 s Done.
+# # [Task 18/24] Current/Best: 15.85/ 222.38 GFLOPS | Progress: (980/1000) | 516.73 s Done.
+# # [Task 19/24] Current/Best: 15.78/ 203.41 GFLOPS | Progress: (1000/1000) | 587.13 s Done.
+# # [Task 20/24] Current/Best: 30.47/ 205.92 GFLOPS | Progress: (980/1000) | 471.00 s Done.
+# # [Task 21/24] Current/Best: 46.91/ 227.99 GFLOPS | Progress: (308/1000) | 219.18 s Done.
+# # [Task 22/24] Current/Best: 13.33/ 207.66 GFLOPS | Progress: (1000/1000) | 761.74 s Done.
+# # [Task 23/24] Current/Best: 53.29/ 192.98 GFLOPS | Progress: (1000/1000) | 799.90 s Done.
+# # [Task 24/24] Current/Best: 25.03/ 146.14 GFLOPS | Progress: (1000/1000) | 1112.55 s Done.
+
+################################################################################
+# Compiling an Optimized Model with Tuning Data
+# ----------------------------------------------
+#
+# As an output of the tuning process above, we obtained the tuning records
+# stored in ``resnet-50-v2-autotuning.json``. The compiler will use the results to
+# generate high performance code for the model on your specified target.
+#
+# Now that tuning data for the model has been collected, we can re-compile the
+# model using optimized operators to speed up our computations.
+
+with autotvm.apply_history_best(tuning_option["tuning_records"]):
+ with tvm.transform.PassContext(opt_level=3, config={}):
+ lib = relay.build(mod, target=target, params=params)
+
+dev = tvm.device(str(target), 0)
+module = graph_executor.GraphModule(lib["default"](dev))
+
+################################################################################
+# Verify that the optimized model runs and produces the same results:
+
+dtype = "float32"
+module.set_input(input_name, img_data)
+module.run()
+output_shape = (1, 1000)
+tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()
+
+scores = softmax(tvm_output)
+scores = np.squeeze(scores)
+ranks = np.argsort(scores)[::-1]
+for rank in ranks[0:5]:
+ print("class='%s' with probability=%f" % (labels[rank], scores[rank]))
+
+# Verifying that the predictions are the same:
+#
+# .. code-block:: bash
+#
+# # class='n02123045 tabby, tabby cat' with probability=0.610550
+# # class='n02123159 tiger cat' with probability=0.367181
+# # class='n02124075 Egyptian cat' with probability=0.019365
+# # class='n02129604 tiger, Panthera tigris' with probability=0.001273
+# # class='n04040759 radiator' with probability=0.000261
+
+################################################################################
+# Comparing the Tuned and Untuned Models
+# --------------------------------------
+# We want to collect some basic performance data associated with this optimized
+# model to compare it to the unoptimized model. Depending on your underlying
+# hardware, number of iterations, and other factors, you should see a performance
+# improvement in comparing the optimized model to the unoptimized model.
+
+import timeit
+
+timing_number = 10
+timing_repeat = 10
+optimized = (
+ np.array(timeit.Timer(lambda: module.run()).repeat(repeat=timing_repeat, number=timing_number))
+ * 1000
+ / timing_number
+)
+optimized = {"mean": np.mean(optimized), "median": np.median(optimized), "std": np.std(optimized)}
+
+
+print("optimized: %s" % (optimized))
+print("unoptimized: %s" % (unoptimized))
+
+################################################################################
+# Final Remarks
+# -------------
+#
+# In this tutorial, we gave a short example of how to use the TVM Python API
+# to compile, run, and tune a model. We also discussed the need for pre and
+# post-processing of inputs and outputs. After the tuning process, we
+# demonstrated how to compare the performance of the unoptimized and optimize
+# models.
+#
+# Here we presented a simple example using ResNet 50 V2 locally. However, TVM
+# supports many more features including cross-compilation, remote execution and
+# profiling/benchmarking.
diff --git a/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py b/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
new file mode 100644
index 0000000..d802a87
--- /dev/null
+++ b/docs/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
@@ -0,0 +1,472 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+.. _basic-mat-mult:
+
+Simple Matrix Multiply
+======================
+**Author**: `Thierry Moreau <https://homes.cs.washington.edu/~moreau/>`_
+
+In this tutorial, we will build on top of the :ref:`vta-get-started` tutorial
+and introduce additional concepts required to implement matrix multiplication
+on VTA with the TVM workflow.
+"""
+
+######################################################################
+# RPC Setup
+# ---------
+# We start by programming the Pynq's FPGA and building its RPC runtime
+# as we did in the VTA introductory tutorial.
+
+from __future__ import absolute_import, print_function
+
+import os
+import tvm
+from tvm import te
+import vta
+import numpy as np
+from tvm import rpc
+from tvm.contrib import utils
+from vta.testing import simulator
+
+# 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
+host = os.environ.get("VTA_RPC_HOST", "192.168.2.99")
+port = int(os.environ.get("VTA_RPC_PORT", "9091"))
+
+# We configure both the bitstream and the runtime system on the Pynq
+# to match the VTA configuration specified by the vta_config.json file.
+if env.TARGET == "pynq" or env.TARGET == "de10nano":
+
+ # Make sure that TVM was compiled with RPC=1
+ assert tvm.runtime.enabled("rpc")
+ remote = rpc.connect(host, port)
+
+ # Reconfigure the JIT runtime
+ vta.reconfig_runtime(remote)
+
+ # Program the FPGA with a pre-compiled VTA bitstream.
+ # You can program the FPGA with your own custom bitstream
+ # by passing the path to the bitstream file instead of None.
+ vta.program_fpga(remote, bitstream=None)
+
+# In simulation mode, host the RPC server locally.
+elif env.TARGET in ["sim", "tsim"]:
+ remote = rpc.LocalSession()
+
+######################################################################
+# Computation Declaration
+# -----------------------
+# In this example we describe a simple matrix multiplication addition, which
+# requires multiple computation stages, as shown in the dataflow diagram below.
+# First we describe the input tensors :code:`A` and :code:`B` that are living
+# in main memory.
+# Second, we need to declare intermediate tensors :code:`A_buf` and
+# :code:`B_buf`, which will live in VTA's on-chip buffers.
+# Having this extra computational stage allows us to explicitly
+# stage cached reads and writes.
+# Third, we describe the matrix multiplication computation over
+# :code:`A_buf` and :code:`B_buf` to produce the product matrix :code:`C_buf`.
+# The last operation is a cast and copy back to DRAM, into results tensor
+# :code:`C`.
+#
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/gemm_dataflow.png
+# :align: center
+
+######################################################################
+# Data Layout
+# ~~~~~~~~~~~
+# We describe the placeholder tensors :code:`A`, and :code:`B` in a tiled data
+# format to match the data layout requirements imposed by the VTA tensor core.
+
+######################################################################
+# .. note::
+#
+# **Data Tiling**
+#
+# One source of complexity when targeting accelerators is to make sure
+# that the data layout matches the layout imposed by the accelerator design.
+# VTA is designed around a *tensor core* that performs, one matrix-matrix
+# operation per cycle between an activation matrix and a weight matrix,
+# adding the result matrix to an accumulator matrix, as shown in the
+# figure below.
+#
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/tensor_core.png
+# :align: center
+# :width: 480px
+#
+# The dimensions of that matrix-matrix multiplication are specified in
+# the :code:`vta_config.json` configuration file.
+# The activation matrix has a :code:`(BATCH, BLOCK_IN)` shape
+# and the transposed weight matrix has a :code:`(BLOCK_OUT, BLOCK_IN)` shape,
+# thus inferring that the resulting output matrix has a
+# :code:`(BATCH, BLOCK_OUT)` shape.
+# Consequently input and output tensors processed by VTA need to be
+# tiled according to these aforementioned dimension.
+#
+# The diagram below shows the impact of data tiling on a matrix that is
+# originally of shape (4, 8).
+# Tiling by a (2, 2) tile shape ensures that data within each tile is
+# contiguous.
+# The resulting tiled tensor has a shape of (2, 4, 2, 2).
+#
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/data_tiling.png
+# :align: center
+# :width: 480px
+#
+# We first define the variables :code:`m`, :code:`n`, :code:`o` to represent
+# the shape of the matrix multiplication. These variables are multiplicative
+# factors over the :code:`BLOCK_OUT`, :code:`BLOCK_IN`, and :code:`BATCH`
+# tensor dimensions respectively.
+# By default, the configuration file sets :code:`BATCH`, :code:`BLOCK_IN`, and
+# :code:`BLOCK_OUT` to be 1, 16 and 16 respectively (:code:`BATCH` being set to
+# 1 implies that our compute building block is vector-matrix multiply).
+#
+
+######################################################################
+# .. note::
+#
+# **Data Types**
+#
+# It's important to not only match the inner-tile
+# dimension of VTA's tensor core, but also to match the specific data types
+# expected by VTA.
+# VTA for now only supports fixed point data types, which integer width is
+# specified in the :code:`vta_config.json` file by :code:`INP_WIDTH` and
+# :code:`WGT_WIDTH` for the activations and weights data types respectively.
+# In addition, the accumulator data type integer width is specified by
+# :code:`ACC_WIDTH`.
+#
+# By default, the configuration file sets :code:`INP_WIDTH`
+# and :code:`WGT_WIDTH` to 8.
+# The accumulator width :code:`ACC_WIDTH` is set to 32, in order to avoid
+# overflow during accumulation.
+# As a result, :code:`env.inp_dtype` and :code:`env.wgt_dtype` are all
+# narrow 8-bit integers, while :code:`env.acc_dtype` is a standard 32-bit
+# integer.
+
+# Output channel factor m - total 16x16=256 output channels
+m = 16
+# Input channel factor n - total 16x16=256 input channels
+n = 16
+# Batch factor o (we use single batch inference)
+o = 1
+# A placeholder tensor in tiled data format
+A = te.placeholder((o, n, env.BATCH, env.BLOCK_IN), name="A", dtype=env.inp_dtype)
+# B placeholder tensor in tiled data format
+B = te.placeholder((m, n, env.BLOCK_OUT, env.BLOCK_IN), name="B", dtype=env.wgt_dtype)
+# A copy buffer
+A_buf = te.compute((o, n, env.BATCH, env.BLOCK_IN), lambda *i: A(*i), "A_buf")
+# B copy buffer
+B_buf = te.compute((m, n, env.BLOCK_OUT, env.BLOCK_IN), lambda *i: B(*i), "B_buf")
+
+######################################################################
+# Matrix Multiplication
+# ~~~~~~~~~~~~~~~~~~~~~
+# Now we're ready to describe the matrix multiplication result tensor :code:`C`,
+# with another compute operation.
+# The compute function takes the shape of the tensor, as well as a lambda
+# function that describes the computation rule for each position of the tensor.
+#
+# In order to implement matrix multiplication, the lambda function needs to
+# include a reduction formula over the input channel dimension axes.
+# To create a reduction formula, we can declare a reduction axis using
+# :code:`te.reduce_axis`, which takes in the range of reductions.
+# :code:`te.sum` takes in the expression to be reduced as well as
+# the reduction axes to compute the sum of value over all k in the declared
+# ranges.
+#
+# Note that the reduction needs to be performed over 32-bit :code:`env.acc_dtype`
+# accumulator data types.
+#
+# No computation happens during this phase, as we are only declaring how
+# the computation should be done.
+
+# Outer input feature reduction axis
+ko = te.reduce_axis((0, n), name="ko")
+# Inner input feature reduction axis
+ki = te.reduce_axis((0, env.BLOCK_IN), name="ki")
+# Describe the in-VTA matrix multiplication
+C_buf = te.compute(
+ (o, m, env.BATCH, env.BLOCK_OUT),
+ lambda bo, co, bi, ci: te.sum(
+ A_buf[bo, ko, bi, ki].astype(env.acc_dtype) * B_buf[co, ko, ci, ki].astype(env.acc_dtype),
+ axis=[ko, ki],
+ ),
+ name="C_buf",
+)
+
+######################################################################
+# Casting the Results
+# ~~~~~~~~~~~~~~~~~~~
+# After the computation is done, we'll need to send the results computed by VTA
+# back to main memory.
+
+######################################################################
+# .. note::
+#
+# **Memory Store Restrictions**
+#
+# One specificity of VTA is that it only supports DRAM stores in the narrow
+# :code:`env.inp_dtype` data type format.
+# This lets us reduce the data footprint for memory transfers, but also lets
+# us quantize the wide accumulator data type down to a data format that
+# matches the input activation data type.
+# This means that in the context of neural network inference, the outputs
+# of a given layer after activation can be consumed directly by the next
+# layer.
+#
+# We perform one last typecast operation to the narrow
+# input activation data format.
+
+# Cast to output type, and send to main memory
+C = te.compute(
+ (o, m, env.BATCH, env.BLOCK_OUT), lambda *i: C_buf(*i).astype(env.inp_dtype), name="C"
+)
+
+######################################################################
+# This concludes the computation declaration part of this tutorial.
+
+######################################################################
+# Scheduling the Computation
+# --------------------------
+# While the above lines describes the computation rule, we can obtain
+# :code:`C` in many ways.
+# TVM asks the user to provide an implementation of the computation called
+# *schedule*.
+#
+# A schedule is a set of transformations to an original computation that
+# transforms the implementation of the computation without affecting
+# correctness.
+# This simple VTA programming tutorial aims to demonstrate basic schedule
+# transformations that will map the original schedule down to VTA hardware
+# primitives.
+
+
+######################################################################
+# Default Schedule
+# ~~~~~~~~~~~~~~~~
+# After we construct the schedule, by default the schedule computes
+# :code:`C` in the following way:
+
+# Let's take a look at the generated schedule
+s = te.create_schedule(C.op)
+print(tvm.lower(s, [A, B, C], simple_mode=True))
+
+######################################################################
+# Although this schedule makes sense, it won't compile to VTA.
+# In order to obtain correct code generation, we need to apply scheduling
+# primitives and code annotation that will transform the schedule into
+# one that can be directly lowered onto VTA hardware intrinsics.
+# Those include:
+#
+# - DMA copy operations which will take globally-scoped tensors and copy
+# those into locally-scoped tensors.
+# - Tensor operations that will perform the matrix multiplication.
+
+######################################################################
+# Buffer Scopes
+# ~~~~~~~~~~~~~
+# First, we set the scope of the buffers to tell TVM that these buffers
+# will be living in the VTA's on-chip SRAM caches.
+# Below, we tell TVM that :code:`A_buf`, :code:`B_buf`, :code:`C_buf`
+# will respectively live in VTA's on-chip input, weight and accumulator
+# memory.
+
+######################################################################
+# .. note::
+#
+# **VTA's On-Chip SRAMs**
+#
+# VTA has three different memory scopes, each corresponding to different
+# on-chip SRAM buffers.
+#
+# - :code:`env.inp_scope`: Input buffer, which is a read-only SRAM buffer
+# that stores input matrices of shape :code:`(env.BATCH, env.BLOCK_IN)`
+# of type :code:`env.inp_dtype`. The input buffer contains
+# `2 ^ LOG_INP_BUFF_SIZE` matrix elements (as specified in the
+# :code:`vta_config.json` file).
+# - :code:`env.wgt_scope`: Weight buffer, which is a read-only SRAM buffer
+# that stores weight matrices of shape :code:`(env.BLOCK_OUT, env.BLOCK_IN)`
+# of type :code:`env.wgt_dtype`. The weight buffer contains
+# `2 ^ LOG_WGT_BUFF_SIZE` matrix elements.
+# - :code:`env.acc_scope`: Accumulator buffer, which is a read/write SRAM
+# buffer that stores accumulator matrices of shape
+# :code:`(env.BATCH, env.BLOCK_OUT)` of type :code:`env.acc_dtype`.
+# The accumulator buffer is VTA's general purpose register file: it holds
+# both intermediate results of convolutions and matrix multiplications
+# as well as intermediate results of pooling, batch normalization, and
+# activation layers. The accumulator buffer contains
+# `2 ^ LOG_ACC_BUFF_SIZE` matrix elements.
+
+# Set the intermediate tensor's scope to VTA's on-chip buffers
+s[A_buf].set_scope(env.inp_scope)
+s[B_buf].set_scope(env.wgt_scope)
+s[C_buf].set_scope(env.acc_scope)
+
+######################################################################
+# DMA Transfers
+# ~~~~~~~~~~~~~
+# We need to schedule DMA transfers to move data living in DRAM to
+# and from the VTA on-chip buffers.
+# This can be achieved using the :code:`compute_at` schedule primitive
+# which nests the copying of the buffers into the computation loop
+# that performs the matrix multiplication.
+#
+# We insert :code:`dma_copy` pragmas to indicate to the compiler
+# that the copy operations will be performed in bulk via DMA,
+# which is common in hardware accelerators.
+# Finally, we print the temporary schedule to observe the effects of
+# moving the copy operations into the matrix multiplication loop.
+
+# Move buffer copy into matrix multiply loop
+s[A_buf].compute_at(s[C_buf], ko)
+s[B_buf].compute_at(s[C_buf], ko)
+
+# Tag the buffer copies with the DMA pragma to insert a DMA transfer
+s[A_buf].pragma(s[A_buf].op.axis[0], env.dma_copy)
+s[B_buf].pragma(s[B_buf].op.axis[0], env.dma_copy)
+s[C].pragma(s[C].op.axis[0], env.dma_copy)
+
+# Let's take a look at the transformed schedule
+print(tvm.lower(s, [A, B, C], simple_mode=True))
+
+######################################################################
+# Tensorization
+# ~~~~~~~~~~~~~
+# The last step of the schedule transformation consists in applying
+# *tensorization* to our schedule.
+# Tensorization is analogous to vectorization, but extends the concept
+# to a higher-dimensional unit of computation.
+# Consequently, tensorization imposes data layout constraints as discussed
+# earlier when declaring the data layout input placeholders.
+# We've already arranged our tensors in a tiled format, so the next thing
+# we need to perform is loop reordering to accommodate for tensorization.
+#
+# Here we choose to move the outermost reduction axis all the way out.
+# This dictates that we first iterate over input channels, then batch
+# dimensions, and finally output channels.
+# Lastly, we apply the tensorization scheduling primitive :code:`tensorize`
+# along the outer axis of the inner-most matrix matrix multiplication tensor
+# block.
+# We print the finalized schedule that is ready for code-generation
+# by the VTA runtime JIT compiler.
+
+s[C_buf].reorder(
+ ko, s[C_buf].op.axis[0], s[C_buf].op.axis[1], s[C_buf].op.axis[2], s[C_buf].op.axis[3], ki
+)
+s[C_buf].tensorize(s[C_buf].op.axis[2], env.gemm)
+
+# Let's take a look at the finalized schedule
+print(vta.lower(s, [A, B, C], simple_mode=True))
+
+######################################################################
+# This concludes the scheduling portion of this tutorial.
+
+######################################################################
+# TVM Compilation
+# ---------------
+# After we have finished specifying the schedule, we can compile it
+# into a TVM function.
+
+# Build GEMM VTA kernel
+my_gemm = vta.build(s, [A, B, C], "ext_dev", env.target_host, name="my_gemm")
+
+# Write the compiled module into an object file.
+temp = utils.tempdir()
+my_gemm.save(temp.relpath("gemm.o"))
+
+# Send the executable over RPC
+remote.upload(temp.relpath("gemm.o"))
+
+# Load the compiled module
+f = remote.load_module("gemm.o")
+
+######################################################################
+# Running the Function
+# --------------------
+# The compiled TVM function uses a concise C API and can be invoked from
+# code language.
+#
+# TVM provides an array API in python to aid quick testing and prototyping.
+# The array API is based on `DLPack <https://github.com/dmlc/dlpack>`_ standard.
+#
+# - We first create a remote context (for remote execution on the Pynq).
+# - Then :code:`tvm.nd.array` formats the data accordingly.
+# - :code:`f()` runs the actual computation.
+# - :code:`numpy()` copies the result array back in a format that can be
+# interpreted.
+#
+
+# Get the remote device context
+ctx = remote.ext_dev(0)
+
+# Initialize the A and B arrays randomly in the int range of (-128, 128]
+A_orig = np.random.randint(-128, 128, size=(o * env.BATCH, n * env.BLOCK_IN)).astype(A.dtype)
+B_orig = np.random.randint(-128, 128, size=(m * env.BLOCK_OUT, n * env.BLOCK_IN)).astype(B.dtype)
+
+# Apply packing to the A and B arrays from a 2D to a 4D packed layout
+A_packed = A_orig.reshape(o, env.BATCH, n, env.BLOCK_IN).transpose((0, 2, 1, 3))
+B_packed = B_orig.reshape(m, env.BLOCK_OUT, n, env.BLOCK_IN).transpose((0, 2, 1, 3))
+
+# Format the input/output arrays with tvm.nd.array to the DLPack standard
+A_nd = tvm.nd.array(A_packed, ctx)
+B_nd = tvm.nd.array(B_packed, ctx)
+C_nd = tvm.nd.array(np.zeros((o, m, env.BATCH, env.BLOCK_OUT)).astype(C.dtype), ctx)
+
+# Clear stats
+if env.TARGET in ["sim", "tsim"]:
+ simulator.clear_stats()
+
+# Invoke the module to perform the computation
+f(A_nd, B_nd, C_nd)
+
+######################################################################
+# Verifying Correctness
+# ---------------------
+# Compute the reference result with numpy and assert that the output of the
+# matrix multiplication indeed is correct
+
+# Compute reference result with numpy
+C_ref = np.dot(A_orig.astype(env.acc_dtype), B_orig.T.astype(env.acc_dtype)).astype(C.dtype)
+C_ref = C_ref.reshape(o, env.BATCH, m, env.BLOCK_OUT).transpose((0, 2, 1, 3))
+np.testing.assert_equal(C_ref, C_nd.numpy())
+
+# Print stats
+if env.TARGET in ["sim", "tsim"]:
+ sim_stats = simulator.stats()
+ print("Execution statistics:")
+ for k, v in sim_stats.items():
+ print("\t{:<16}: {:>16}".format(k, v))
+
+print("Successful matrix multiply test!")
+
+######################################################################
+# Summary
+# -------
+# This tutorial showcases the TVM workflow to implement a simple matrix
+# multiplication example on VTA.
+# The general workflow includes:
+#
+# - Programming the FPGA with the VTA bitstream over RPC.
+# - Describing matrix multiplication via a series of computations.
+# - Describing how we want to perform the computation using schedule primitives.
+# - Compiling the function to the VTA target.
+# - Running the compiled module and verifying it against a numpy implementation.
+#
diff --git a/docs/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb b/docs/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
new file mode 100644
index 0000000..21c0235
--- /dev/null
+++ b/docs/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n\nHow to Use TVM Pass Infra\n=========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay/tir, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes and make it\napplicable to different layers of the IR in the TVM stack.\n\nThe optimizations of a Relay/tir program could be applied at various [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\nimport tvm\nfrom tvm import te\nimport tvm.relay as relay"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create An Example Relay Program\n-------------------------------\nFirst of all, we create a simple Relay program for the tutorial. This program\nwill be used by various optimizations of the examples in this tutorial.\nSimilarly, users can write a tir primitive function and apply the tir passes.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def example():\n shape = (1, 64, 54, 54)\n c_data = np.empty(shape).astype(\"float32\")\n c = relay.const(c_data)\n weight = relay.var(\"weight\", shape=(64, 64, 3, 3))\n x = relay.var(\"x\", relay.TensorType((1, 64, 56, 56), \"float32\"))\n conv = relay.nn.conv2d(x, weight)\n y = relay.add(c, c)\n y = relay.multiply(y, relay.const(2, \"float32\"))\n y = relay.add(conv, y)\n z = relay.add(y, c)\n z1 = relay.add(y, c)\n z2 = relay.add(z, z1)\n [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us register layout alteration for a conv2d op so that we can apply the\nlayout alteration pass on the example. How alter layout pass works is out\nthe scope of this tutorial.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "@relay.op.register_alter_op_layout(\"nn.conv2d\", level=101)\ndef alter_conv2d(attrs, inputs, tinfos, out_type):\n data, weight = inputs\n new_attrs = dict(attrs)\n new_attrs[\"data_layout\"] = \"NCHW16c\"\n return relay.nn.conv2d(data, weight, **new_attrs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Optimize the Program\n--------------------\nNow we would like to optimize the program. Relay features a host of\noptimizations. We will select some of them to apply on this example program.\n\nThere are multiple ways to optimize a Relay program. Below we will provide\nexamples for each of them.\n\nManually Apply Optimization Passes\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Let's first create a relay Module which contains one or multiple Relay\n# functions for optimization.\nf = example()\nmod = tvm.IRModule.from_expr(f)\n\n# Now we can apply constant folding on the module.\n# fold_const here is a callback that doesn't take any parameters.\nfold_const = relay.transform.FoldConstant()\n# Then, we can invoke the pass on the given module. Note that the constant\n# folding pass works at the function-level. That being said, each function in\n# the mod [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "More optimizations can be applied in the similar manner. For instance, we can\neliminate the common expressions that used by `z` and `z1`.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "mod = relay.transform.EliminateCommonSubexpr()(mod)\nprint(mod)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Some optimizations, such as fusion, are parameteric as well. For example,\nopt level 0 will not allow operators to be fused together. Users can pass the\n`fuse_opt_level` to enable this.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "mod = relay.transform.FuseOps(fuse_opt_level=0)(mod)\n\n# We can observe that the optimized module contains functions that only have\n# a signle primitive op.\nprint(mod)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use Sequential to Apply a Sequence of Passes\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nApplying passes as above is actually tedious and it may require users to have\nbetter understanding about the dependencies between them. For example, fusion\ncurrently doesn't work well on let bindings. Therefore, we would not be able\nto fuse operators that were fusable if :py:func:`relay.transform.ToANormalForm` is applied before\nfusion, as this pass generates let bindings for each exp [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Now let's execute some passes through :py:class:`tvm.transform.Sequential`\nf = example()\nmod = tvm.IRModule.from_expr(f)\n# Glob the interested passes.\nseq = tvm.transform.Sequential(\n [\n relay.transform.FoldConstant(),\n relay.transform.EliminateCommonSubexpr(),\n relay.transform.FuseOps(fuse_opt_level=2),\n ]\n)\nmod1 = seq(mod)\nprint(mod1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the transformed Relay program, we can see that there are still two\nidentical addition operations. This is because ``EliminateCommonSubexpr``\nwas not actually performed. The reason is because only the passes that have\noptimization level less or equal to 2 will be executed by default under\n:py:class:`tvm.transform.Sequential`. The pass infra,\nhowever, provides a configuration interface\nfor users to customize the optimization level that they want to execute.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "with tvm.transform.PassContext(opt_level=3):\n mod2 = seq(mod)\nprint(mod2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can see that only one of the two identical additions is kept.\n\nIn addition, users can selectively disable some passes using the\n`disabled_pass` config, which is similar to the `-fno-xxx` option used the\ngeneral purpose compilers, such as Clang and GCC. For example, we can disable\nEliminateCommonSubexpr as following. The printed module will again show two\nidentical addition operations.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "with tvm.transform.PassContext(opt_level=3, disabled_pass=[\"EliminateCommonSubexpr\"]):\n mod3 = seq(mod)\nprint(mod3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The passes applied so far are target independent. The pass infra also\nprovides a means to make pass target-aware. For example, the layout\nalteration pass falls in such category.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "with tvm.transform.PassContext(opt_level=3):\n mod4 = seq(mod)\nprint(mod4)\n\nseq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])\nwith tvm.transform.PassContext(opt_level=3):\n with tvm.target.Target(\"llvm\"):\n mod5 = seq1(mod)\nprint(mod5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Implement a Pass Using Python Decorator\n------------------------------------------\nThe next example illustrates how we can orchestrate a customized optimization\npipeline through the pass infra using Python decorators. This functionality\ngreatly eases the implementation of passes. For example, users can simply\ndefine a decorated class to do function-level optimizations as the following\nexample shows. `transform_function` wraps a class to replace all constants\nwith a multip [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "@relay.transform.function_pass(opt_level=1)\nclass CustomPipeline:\n \"\"\"Simple test function to replace one argument to another.\"\"\"\n\n def __init__(self, multiplier):\n self.multiplier = multiplier\n\n # This function can define a pass.\n def transform_function(self, func, mod, ctx):\n obj = self\n\n class ReplaceConstant(tvm.relay.ExprMutator):\n def visit_constant(self, c):\n return relay.multiply(obj.multiplier [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Debug a Pass\n------------\nTVM provides users a plug-and-play style debugging pass that print the IR\nafter a certain pass is done through a special pass (``PrintIR``) to dump the IR of the\nwhole module. A slightly modified version of the sequential pass example\ncould be like the following to enable IR dumping for ``FoldConstant`` optimization.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "f = example()\nmod = tvm.IRModule.from_expr(f)\nseq = tvm.transform.Sequential(\n [\n relay.transform.FoldConstant(),\n tvm.transform.PrintIR(),\n relay.transform.EliminateCommonSubexpr(),\n relay.transform.FuseOps(),\n relay.transform.AlterOpLayout(),\n ]\n)\n\n# By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will\n# dump out the module IR when ``FoldConstant`` is done. Users can plug in this\n# pass after any p [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\nThis tutorial has covered how we can write and invoke passes in TVM more\nconveniently using the pass infra. Different ways of invoking a pass are also\ndisucssed. Using :py:class:`tvm.transform.Sequential` can largely help\nusers to ease the work of handling multiple optimization passes and their\ndependencies. In addition, an example is provided to illustrate\nhow we can debug a pass using the ``PrintIR`` and tracing.\n\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/143c743c62f58570eabd77fd3395ca8c/scan.py b/docs/_downloads/143c743c62f58570eabd77fd3395ca8c/scan.py
new file mode 100644
index 0000000..ba8b5a9
--- /dev/null
+++ b/docs/_downloads/143c743c62f58570eabd77fd3395ca8c/scan.py
@@ -0,0 +1,154 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Scan and Recurrent Kernel
+=========================
+**Author**: `Tianqi Chen <https://tqchen.github.io>`_
+
+This is an introduction material on how to do recurrent computing in TVM.
+Recurrent computing is a typical pattern in neural networks.
+"""
+from __future__ import absolute_import, print_function
+
+import tvm
+import tvm.testing
+from tvm import te
+import numpy as np
+
+######################################################################
+# TVM supports a scan operator to describe symbolic loop.
+# The following scan op computes cumsum over columns of X.
+#
+# The scan is carried over the highest dimension of the tensor.
+# :code:`s_state` is a placeholder that describes the transition state of the scan.
+# :code:`s_init` describes how we can initialize the first k timesteps.
+# Here since s_init's first dimension is 1, it describes how we initialize
+# The state at first timestep.
+#
+# :code:`s_update` describes how to update the value at timestep t. The update
+# value can refer back to the values of previous timestep via state placeholder.
+# Note that while it is invalid to refer to :code:`s_state` at current or later timestep.
+#
+# The scan takes in state placeholder, initial value and update description.
+# It is also recommended(although not necessary) to list the inputs to the scan cell.
+# The result of the scan is a tensor, giving the result of :code:`s_state` after the
+# update over the time domain.
+#
+m = te.var("m")
+n = te.var("n")
+X = te.placeholder((m, n), name="X")
+s_state = te.placeholder((m, n))
+s_init = te.compute((1, n), lambda _, i: X[0, i])
+s_update = te.compute((m, n), lambda t, i: s_state[t - 1, i] + X[t, i])
+s_scan = tvm.te.scan(s_init, s_update, s_state, inputs=[X])
+
+######################################################################
+# Schedule the Scan Cell
+# ----------------------
+# We can schedule the body of the scan by scheduling the update and
+# init part seperately. Note that it is invalid to schedule the
+# first iteration dimension of the update part.
+# To split on the time iteration, user can schedule on scan_op.scan_axis instead.
+#
+s = te.create_schedule(s_scan.op)
+num_thread = 256
+block_x = te.thread_axis("blockIdx.x")
+thread_x = te.thread_axis("threadIdx.x")
+xo, xi = s[s_init].split(s_init.op.axis[1], factor=num_thread)
+s[s_init].bind(xo, block_x)
+s[s_init].bind(xi, thread_x)
+xo, xi = s[s_update].split(s_update.op.axis[1], factor=num_thread)
+s[s_update].bind(xo, block_x)
+s[s_update].bind(xi, thread_x)
+print(tvm.lower(s, [X, s_scan], simple_mode=True))
+
+######################################################################
+# Build and Verify
+# ----------------
+# We can build the scan kernel like other TVM kernels, here we use
+# numpy to verify the correctness of the result.
+#
+fscan = tvm.build(s, [X, s_scan], "cuda", name="myscan")
+dev = tvm.cuda(0)
+n = 1024
+m = 10
+a_np = np.random.uniform(size=(m, n)).astype(s_scan.dtype)
+a = tvm.nd.array(a_np, dev)
+b = tvm.nd.array(np.zeros((m, n), dtype=s_scan.dtype), dev)
+fscan(a, b)
+tvm.testing.assert_allclose(b.numpy(), np.cumsum(a_np, axis=0))
+
+######################################################################
+# Multi-Stage Scan Cell
+# ---------------------
+# In the above example we described the scan cell using one Tensor
+# computation stage in s_update. It is possible to use multiple
+# Tensor stages in the scan cell.
+#
+# The following lines demonstrate a scan with two stage operations
+# in the scan cell.
+#
+m = te.var("m")
+n = te.var("n")
+X = te.placeholder((m, n), name="X")
+s_state = te.placeholder((m, n))
+s_init = te.compute((1, n), lambda _, i: X[0, i])
+s_update_s1 = te.compute((m, n), lambda t, i: s_state[t - 1, i] * 2, name="s1")
+s_update_s2 = te.compute((m, n), lambda t, i: s_update_s1[t, i] + X[t, i], name="s2")
+s_scan = tvm.te.scan(s_init, s_update_s2, s_state, inputs=[X])
+
+######################################################################
+# These intermediate tensors can also be scheduled normally.
+# To ensure correctness, TVM creates a group constraint to forbid
+# the body of scan to be compute_at locations outside the scan loop.
+#
+s = te.create_schedule(s_scan.op)
+xo, xi = s[s_update_s2].split(s_update_s2.op.axis[1], factor=32)
+s[s_update_s1].compute_at(s[s_update_s2], xo)
+print(tvm.lower(s, [X, s_scan], simple_mode=True))
+
+######################################################################
+# Multiple States
+# ---------------
+# For complicated applications like RNN, we might need more than one
+# recurrent state. Scan support multiple recurrent states.
+# The following example demonstrates how we can build recurrence with two states.
+#
+m = te.var("m")
+n = te.var("n")
+l = te.var("l")
+X = te.placeholder((m, n), name="X")
+s_state1 = te.placeholder((m, n))
+s_state2 = te.placeholder((m, l))
+s_init1 = te.compute((1, n), lambda _, i: X[0, i])
+s_init2 = te.compute((1, l), lambda _, i: 0.0)
+s_update1 = te.compute((m, n), lambda t, i: s_state1[t - 1, i] + X[t, i])
+s_update2 = te.compute((m, l), lambda t, i: s_state2[t - 1, i] + s_state1[t - 1, 0])
+s_scan1, s_scan2 = tvm.te.scan(
+ [s_init1, s_init2], [s_update1, s_update2], [s_state1, s_state2], inputs=[X]
+)
+s = te.create_schedule(s_scan1.op)
+print(tvm.lower(s, [X, s_scan1, s_scan2], simple_mode=True))
+
+######################################################################
+# Summary
+# -------
+# This tutorial provides a walk through of scan primitive.
+#
+# - Describe scan with init and update.
+# - Schedule the scan cells as normal schedule.
+# - For complicated workload, use multiple states and steps in scan cell.
diff --git a/docs/_downloads/15c94550bd86853c42df8e5d139d700a/tune_sparse_x86.py b/docs/_downloads/15c94550bd86853c42df8e5d139d700a/tune_sparse_x86.py
new file mode 100644
index 0000000..55ee76e
--- /dev/null
+++ b/docs/_downloads/15c94550bd86853c42df8e5d139d700a/tune_sparse_x86.py
@@ -0,0 +1,324 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule
+===========================================================================
+**Author**: `Chengfan Jia <https://github.com/jcf94/>`_
+
+This is a tutorial on how to use the auto-scheduler to tune a sparse matrix multiplication for
+CPUs.
+
+Auto-scheduler is designed to explore the schedule with best performance for a given computation
+declaration automatically. While sometimes, we may have a demand to try some special ops which may
+not been well-supported by auto-scheduler's default sketch rules and result in poor performance.
+Fortunately, auto-scheduler currently allows user to provide a CustomSketch to cover these cases.
+
+We use sparse matrix multiplication as an example in this tutorial to demonstrate how to implement
+and plug a custom sketch rule to the auto-scheduler's search policy.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+import os
+
+import numpy as np
+import tvm
+import tvm.testing
+from tvm import te, auto_scheduler, runtime, topi
+from tvm.auto_scheduler import _ffi_api
+from tvm.topi.utils import get_const_tuple
+from tvm.topi.sparse.utils import random_bsr_matrix
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, let us define the computation of a sparse matmul with several relu and bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def sparse_dense(M, N, K, w_data_shape, w_indices_shape, w_indptr_shape, dtype):
+ X = te.placeholder(shape=(M, K), dtype=dtype)
+ W_data = te.placeholder(shape=w_data_shape, dtype=dtype)
+ W_indices = te.placeholder(shape=w_indices_shape, dtype="int32")
+ W_indptr = te.placeholder(shape=w_indptr_shape, dtype="int32")
+ B = te.placeholder(shape=(M, N), dtype=dtype)
+
+ out = topi.nn.sparse_dense(topi.nn.relu(X), W_data, W_indices, W_indptr)
+ out = te.compute((M, N), lambda i, j: out[i, j] + B[i, j], name="BiasAdd")
+ out = topi.nn.relu(out)
+
+ return [X, W_data, W_indices, W_indptr, B, out]
+
+
+######################################################################
+# Special step for sparse workload
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# During schedule tuning, auto-scheduler will use random inputs to measure the performance of a
+# generated schedule. While we cannot directly use a random array as the input of a sparse op, for
+# the "indices" and "indptr" array are meaningful for the computation.
+#
+# To solve this problem, we register these as special buffers, and load them when process program
+# measuring.
+# See the `tvm.auto_scheduler.measure.py` for more details.
+
+# Define the basic shapes of this sparse computation
+M = 128
+K = 256
+N = 512
+BS_R = 16
+BS_C = 1
+density = 0.6
+
+# Generate the test data with numpy
+X_np = np.random.randn(M, K).astype("float32")
+X_np = np.maximum(np.zeros((M, K), dtype="float32"), X_np) # Relu
+W_sp_np = random_bsr_matrix(N, K, BS_R, BS_C, density=density, dtype="float32")
+W_np = W_sp_np.todense()
+Y_np = X_np @ W_np.T # Process the matrix multiplication
+B_np = np.random.randn(M, N).astype("float32")
+Y_np = Y_np + B_np # Bias add
+Y_np = np.maximum(np.zeros((M, N), dtype="float32"), Y_np) # Relu
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create a search task with M=N=K=512 and dtype="float32"
+# If your machine supports avx instructions, you can
+#
+# - replace "llvm" below with "llvm -mcpu=core-avx2" to enable AVX2
+# - replace "llvm" below with "llvm -mcpu=skylake-avx512" to enable AVX-512
+
+target = tvm.target.Target("llvm")
+
+# Register the sparse data to task inputs
+prefix = "sparse_dense_bsr_%d_%d_%d_%d_%d_%d_" % (
+ N,
+ K,
+ BS_R,
+ BS_C,
+ W_sp_np.indices.shape[0],
+ W_sp_np.indptr.shape[0],
+)
+task = tvm.auto_scheduler.SearchTask(
+ func=sparse_dense,
+ args=(M, N, K, W_sp_np.data.shape, W_sp_np.indices.shape, W_sp_np.indptr.shape, "float32"),
+ target=target,
+ task_inputs={
+ prefix + "W_data": runtime.ndarray.array(W_sp_np.data),
+ prefix + "W_indices": runtime.ndarray.array(W_sp_np.indices),
+ prefix + "W_indptr": runtime.ndarray.array(W_sp_np.indptr),
+ },
+ task_inputs_save_to_file=True,
+)
+
+# Inspect the computational graph
+print("Computational DAG:")
+print(task.compute_dag)
+
+######################################################################
+# Write the custom sketch for sparse dense op
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before tuning, we will need to define the CustomSketchRule for the sparse dense op.
+#
+# CustomSketchRule consists of two parts: the condition function and the apply function.
+#
+# - condition function: describe when to apply this sketch rule. For example, we can only apply
+# the rule to the sparse ops by matching their name and tag.
+# - apply function: describe how to generate the initial sketch. You can implement it using
+# auto-scheduler provided loop state APIs.
+
+
+def meet_condition_func(search_policy, state, stage_id):
+ state = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+ if state.stages[stage_id].op.tag in [
+ "sparse_dense_sp_rhs_bsrmm",
+ "sparse_dense_sp_rhs_bsrmm_block",
+ ]:
+ return auto_scheduler.PreloadCustomSketchRule.APPLY_AND_SKIP_REST
+ else:
+ return auto_scheduler.PreloadCustomSketchRule.PASS
+
+
+def apply_func(search_policy, state, stage_id):
+ ret = []
+ s0 = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+ if s0.stages[stage_id].op.tag == "sparse_dense_sp_rhs_bsrmm_block":
+ return [s0.state_object, stage_id - 1]
+
+ sparse_dense = s0.stages[stage_id].op
+ sparse_dense_block = s0.stages[stage_id - 1].op
+ assert sparse_dense.tag == "sparse_dense_sp_rhs_bsrmm"
+ assert sparse_dense_block.tag == "sparse_dense_sp_rhs_bsrmm_block"
+
+ # Set the default consumer of compute block
+ consumer = sparse_dense
+
+ # If sparse dense has a single elementwise consumer
+ # We can compute inline the sparse_dense output stage
+ consumers = _ffi_api.SearchPolicyUtilsGetConsumers(
+ search_policy.search_task, s0.state_object, stage_id
+ )
+ if len(consumers) == 1:
+ consumer_id = int(consumers.items()[0][0])
+ if _ffi_api.SearchPolicyUtilsIsElementwiseMatch(
+ search_policy.search_task, s0.state_object, stage_id, consumer_id
+ ):
+ consumer = s0.stages[consumer_id].op
+ s0.compute_inline(sparse_dense)
+
+ i, nb_j, j, row_offset, c = s0[sparse_dense_block].iters
+ m, n = s0[consumer].iters
+ i0, i1, i2 = s0.split(sparse_dense_block, i, [None, None])
+ m0, m1 = s0.follow_split(consumer, m, len(s0.transform_steps) - 1, 1)
+ j0, j1 = s0.split(sparse_dense_block, nb_j, [None])
+ n0, n1 = s0.follow_split(consumer, n, len(s0.transform_steps) - 1, 1)
+ s0.reorder(sparse_dense_block, [i0, j0, i1, j1, row_offset, i2, j, c])
+ s0.reorder(consumer, [m0, n0, m1, n1])
+ s0.compute_at(sparse_dense_block, consumer, n0)
+
+ ret.append([s0.state_object, stage_id - 2])
+
+ return ret
+
+
+######################################################################
+# Next, we set parameters for the auto-scheduler with the custom sketch plugged in.
+#
+# * :code:`num_measure_trials` is the number of measurement trials we can use during the search.
+# We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+# good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use :code:`RecordToFile` to dump measurement records into a file
+# `sparse_dense.json`.
+# The measurement records can be used to query the history best, resume the search,
+# and do more analyses later.
+# * see :any:`auto_scheduler.TuningOptions` for more parameters
+# * Here, we need to create a :code:`auto_scheduler.SketchPolicy` object, and add the custom sketch
+# rule as a `init_search_callbacks`.
+
+log_file = "sparse_dense.json"
+tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=10,
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+ verbose=2,
+)
+
+search_policy = auto_scheduler.SketchPolicy(
+ task,
+ program_cost_model=auto_scheduler.XGBModel(),
+ init_search_callbacks=[
+ auto_scheduler.PreloadCustomSketchRule(meet_condition_func, apply_func, "SparseDense")
+ ],
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready.
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, we can load the best schedule from the log
+# file and apply it.
+
+# Run auto-tuning (search)
+# Notice: We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+task.tune(tune_option, search_policy)
+
+# Apply the best schedule
+sch, args = task.apply_best(log_file)
+
+######################################################################
+# We can lower the schedule to see the IR after auto-scheduling.
+# The auto-scheduler correctly performs optimizations including multi-level tiling,
+# layout transformation, parallelization, vectorization, unrolling, and operator fusion.
+
+print("Lowered TIR:")
+print(tvm.lower(sch, args, simple_mode=True))
+
+######################################################################
+# Check correctness and evaluate performance
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# We build the binary and check its correctness and performance.
+
+func = tvm.build(sch, args, target)
+
+dev = tvm.cpu()
+
+X_tvm = tvm.nd.array(X_np, device=dev)
+W_data_tvm = tvm.nd.array(W_sp_np.data, device=dev)
+W_indices_tvm = tvm.nd.array(W_sp_np.indices, device=dev)
+W_indptr_tvm = tvm.nd.array(W_sp_np.indptr, device=dev)
+B_tvm = tvm.nd.array(B_np, device=dev)
+Y_tvm = tvm.nd.empty(Y_np.shape, device=dev)
+
+func(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm)
+
+# Check results
+tvm.testing.assert_allclose(Y_np, Y_tvm.numpy(), atol=1e-4, rtol=1e-4)
+
+# Evaluate execution time.
+evaluator = func.time_evaluator(func.entry_name, dev, min_repeat_ms=500)
+print(
+ "Execution time of this operator: %.3f ms"
+ % (
+ np.median(evaluator(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm).results)
+ * 1000
+ )
+)
+
+######################################################################
+# .. note:: Tuning result example
+#
+# .. code-block:: c
+#
+# ----------------------------------------------------------------------
+# Lowered TIR:
+# primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> ()
+# attr = {"global_symbol": "main", "tir.noalias": True}
+# buffers = {placeholder_2: Buffer(placeholder_10: Pointer(float32), float32, [9831, 16, 1], []),
+# placeholder_4: Buffer(placeholder_11: Pointer(int32), int32, [33], []),
+# placeholder_3: Buffer(placeholder_12: Pointer(float32), float32, [512, 512], []),
+# compute: Buffer(compute_2: Pointer(float32), float32, [512, 512], []),
+# placeholder_1: Buffer(placeholder_13: Pointer(float32), float32, [512, 512], []),
+# placeholder: Buffer(placeholder_14: Pointer(int32), int32, [9831], [])}
+# buffer_map = {placeholder_7: placeholder, placeholder_9: placeholder_1, placeholder_6: placeholder_2, compute_1: compute, placeholder_5: placeholder_3, placeholder_8: placeholder_4} {
+# for (i0.outer.i1.outer.fused: int32, 0, 1024) "parallel" {
+# attr [compute_3: Pointer(float32)] "storage_scope" = "global";
+# allocate(compute_3, float32, [256]) {
+# for (nb_j.inner: int32, 0, 2) {
+# for (i.inner.init: int32, 0, 8) {
+# for (j.init: int32, 0, 16) {
+# compute_3[(((i.inner.init*32) + (nb_j.inner*16)) + j.init)] = 0f32
+# }
+# }
+# for (elem_idx: int32, 0, ((int32*)placeholder_11[(((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) + 1)] - (int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)])) {
+# for (i.inner: int32, 0, 8) {
+# for (j: int32, 0, 16) {
+# compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] = ((float32*)compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] + ((float32*)placeholder_10[((((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_12[(((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i.inner*512)) + (int32*)placeholder_14[((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+# }
+# }
+# }
+# }
+# for (i0.inner: int32, 0, 8) {
+# compute_2[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)] = max(((float32x32*)compute_3[ramp((i0.inner*32), 1, 32)] + (float32x32*)placeholder_13[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)]), broadcast(0f32, 32))
+# }
+# }
+# }
+# }
diff --git a/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb b/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
new file mode 100644
index 0000000..da3c2b4
--- /dev/null
+++ b/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nDeploy a Framework-prequantized Model with TVM - Part 3 (TFLite)\n================================================================\n**Author**: `Siju Samuel <https://github.com/siju-samuel>`_\n\nWelcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.\nIn this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.\n\n\nFor more details on quantizing the model using TFLite, readers are encouraged to\ngo through `Convert [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Necessary imports\n-----------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n\nimport numpy as np\nimport tflite\n\nimport tvm\nfrom tvm import relay"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Download pretrained Quantized TFLite model\n------------------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Download mobilenet V2 TFLite model provided by Google\nfrom tvm.contrib.download import download_testdata\n\nmodel_url = (\n \"https://storage.googleapis.com/download.tensorflow.org/models/\"\n \"tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz\"\n)\n\n# Download model tar file and extract it to get mobilenet_v2_1.0_224.tflite\nmodel_path = download_testdata(\n model_url, \"mobilenet_v2_1.0_224_quant.tgz\", module=[\"tf\", \"official\"]\n)\nmodel_dir = os.path.dirname(m [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Utils for downloading and extracting zip files\n----------------------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def extract(path):\n import tarfile\n\n if path.endswith(\"tgz\") or path.endswith(\"gz\"):\n dir_path = os.path.dirname(path)\n tar = tarfile.open(path)\n tar.extractall(path=dir_path)\n tar.close()\n else:\n raise RuntimeError(\"Could not decompress the file: \" + path)\n\n\nextract(model_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a test image\n-----------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Get a real image for e2e testing\n--------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def get_real_image(im_height, im_width):\n from PIL import Image\n\n repo_base = \"https://github.com/dmlc/web-data/raw/main/tensorflow/models/InceptionV1/\"\n img_name = \"elephant-299.jpg\"\n image_url = os.path.join(repo_base, img_name)\n img_path = download_testdata(image_url, img_name, module=\"data\")\n image = Image.open(img_path).resize((im_height, im_width))\n x = np.array(image).astype(\"uint8\")\n data = np.reshape(x, (1, im_height, im_width, 3 [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a tflite model\n-------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can open mobilenet_v2_1.0_224.tflite\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tflite_model_file = os.path.join(model_dir, \"mobilenet_v2_1.0_224_quant.tflite\")\ntflite_model_buf = open(tflite_model_file, \"rb\").read()\n\n# Get TFLite model from buffer\ntry:\n import tflite\n\n tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)\nexcept AttributeError:\n import tflite.Model\n\n tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets run TFLite pre-quantized model inference and get the TFLite prediction.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def run_tflite_model(tflite_model_buf, input_data):\n \"\"\"Generic function to execute TFLite\"\"\"\n try:\n from tensorflow import lite as interpreter_wrapper\n except ImportError:\n from tensorflow.contrib import lite as interpreter_wrapper\n\n input_data = input_data if isinstance(input_data, list) else [input_data]\n\n interpreter = interpreter_wrapper.Interpreter(model_content=tflite_model_buf)\n interpreter.allocate_tensors()\n\n input_d [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets run TVM compiled pre-quantized model inference and get the TVM prediction.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def run_tvm(lib):\n from tvm.contrib import graph_executor\n\n rt_mod = graph_executor.GraphModule(lib[\"default\"](tvm.cpu(0)))\n rt_mod.set_input(\"input\", data)\n rt_mod.run()\n tvm_res = rt_mod.get_output(0).numpy()\n tvm_pred = np.squeeze(tvm_res).argsort()[-5:][::-1]\n return tvm_pred, rt_mod"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TFLite inference\n----------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run TFLite inference on the quantized model.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tflite_res = run_tflite_model(tflite_model_buf, data)\ntflite_pred = np.squeeze(tflite_res).argsort()[-5:][::-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TVM compilation and inference\n-----------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We use the TFLite-Relay parser to convert the TFLite pre-quantized graph into Relay IR. Note that\nfrontend parser call for a pre-quantized model is exactly same as frontend parser call for a FP32\nmodel. We encourage you to remove the comment from print(mod) and inspect the Relay module. You\nwill see many QNN operators, like, Requantize, Quantize and QNN Conv2D.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "dtype_dict = {\"input\": data.dtype.name}\nshape_dict = {\"input\": data.shape}\n\nmod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict)\n# print(mod)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets now the compile the Relay module. We use the \"llvm\" target here. Please replace it with the\ntarget platform that you are interested in.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "target = \"llvm\"\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build_module.build(mod, target=target, params=params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, lets call inference on the TVM compiled module.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tvm_pred, rt_mod = run_tvm(lib)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy comparison\n-------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Print the top-5 labels for MXNet and TVM inference.\nChecking the labels because the requantize implementation is different between\nTFLite and Relay. This cause final output numbers to mismatch. So, testing accuracy via labels.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(\"TVM Top-5 labels:\", tvm_pred)\nprint(\"TFLite Top-5 labels:\", tflite_pred)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Measure performance\n-------------------\nHere we give an example of how to measure performance of TVM compiled models.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n_repeat = 100 # should be bigger to make the measurement more accurate\ndev = tvm.cpu(0)\nftimer = rt_mod.module.time_evaluator(\"run\", dev, number=1, repeat=n_repeat)\nprof_res = np.array(ftimer().results) * 1e3\nprint(\"Elapsed average ms:\", np.mean(prof_res))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Unless the hardware has special support for fast 8 bit instructions, quantized models are\n not expected to be any faster than FP32 models. Without fast 8 bit instructions, TVM does\n quantized convolution in 16 bit, even if the model itself is 8 bit.\n\n For x86, the best performance can be achieved on CPUs with AVX512 instructions set.\n In this case, TVM utilizes the fastest available 8 bit instructions for the given target. [...]
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py b/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
new file mode 100644
index 0000000..fffbfbf
--- /dev/null
+++ b/docs/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
@@ -0,0 +1,498 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Compiling and Optimizing a Model with TVMC
+==========================================
+**Authors**:
+`Leandro Nunes <https://github.com/leandron>`_,
+`Matthew Barrett <https://github.com/mbaret>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In this section, we will work with TVMC, the TVM command line driver. TVMC is a
+tool that exposes TVM features such as auto-tuning, compiling, profiling and
+execution of models through a command line interface.
+
+Upon completion of this section, we will have used TVMC to accomplish the
+following tasks:
+
+* Compile a pre-trained ResNet 50 v2 model for the TVM runtime.
+* Run a real image through the compiled model, and interpret the output and
+ model performance.
+* Tune the model on a CPU using TVM.
+* Re-compile an optimized model using the tuning data collected by TVM.
+* Run the image through the optimized model, and compare the output and model
+ performance.
+
+The goal of this section is to give you an overview of TVM and TVMC's
+capabilities, and set the stage for understanding how TVM works.
+"""
+
+################################################################################
+# Using TVMC
+# ----------
+#
+# TVMC is a Python application, part of the TVM Python package.
+# When you install TVM using a Python package, you will get TVMC as
+# as a command line application called ``tvmc``.
+#
+# Alternatively, if you have TVM as a Python module on your
+# ``$PYTHONPATH``,you can access the command line driver functionality
+# via the executable python module, ``python -m tvm.driver.tvmc``.
+#
+# For simplicity, this tutorial will mention TVMC command line using
+# ``tvmc <options>``, but the same results can be obtained with
+# ``python -m tvm.driver.tvmc <options>``.
+#
+# You can check the help page using:
+#
+# .. code-block:: bash
+#
+# tvmc --help
+#
+# The main features of TVM available to ``tvmc`` are from subcommands
+# ``compile``, and ``run``, and ``tune``. To read about specific options under
+# a given subcommand, use ``tvmc <subcommand> --help``. We will cover each of
+# these commands in this tutorial, but first we need to download a pre-trained
+# model to work with.
+#
+
+
+################################################################################
+# Obtaining the Model
+# -------------------
+#
+# For this tutorial, we will be working with ResNet-50 v2. ResNet-50 is a
+# convolutional neural network that is 50-layers deep and designed to classify
+# images. The model we will be using has been pre-trained on more than a
+# million images with 1000 different classifications. The network has an input
+# image size of 224x224. If you are interested exploring more of how the
+# ResNet-50 model is structured, we recommend downloading `Netron
+# <https://netron.app>`, a freely available ML model viewer.
+#
+# For this tutorial we will be using the model in ONNX format.
+#
+# .. code-block:: bash
+#
+# wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+#
+
+
+################################################################################
+# .. note:: Supported model formats
+#
+# TVMC supports models created with Keras, ONNX, TensorFlow, TFLite
+# and Torch. Use the option``--model-format`` if you need to
+# explicitly provide the model format you are using. See ``tvmc
+# compile --help`` for more information.
+#
+
+
+################################################################################
+# Compiling an ONNX Model to the TVM Runtime
+# ------------------------------------------
+#
+# Once we've downloaded the ResNet-50 model, the next step is to compile it. To
+# accomplish that, we are going to use ``tvmc compile``. The output we get from
+# the compilation process is a TAR package of the model compiled to a dynamic
+# library for our target platform. We can run that model on our target device
+# using the TVM runtime.
+#
+# .. code-block:: bash
+#
+# tvmc compile \
+# --target "llvm" \
+# --output resnet50-v2-7-tvm.tar \
+# resnet50-v2-7.onnx
+#
+# Let's take a look at the files that ``tvmc compile`` creates in the module:
+#
+# .. code-block:: bash
+#
+# mkdir model
+# tar -xvf resnet50-v2-7-tvm.tar -C model
+# ls model
+#
+# You will see three files listed.
+#
+# * ``mod.so`` is the model, represented as a C++ library, that can be loaded
+# by the TVM runtime.
+# * ``mod.json`` is a text representation of the TVM Relay computation graph.
+# * ``mod.params`` is a file containing the parameters for the pre-trained
+# model.
+#
+# This module can be directly loaded by your application, and the model can be
+# run via the TVM runtime APIs.
+
+
+################################################################################
+# .. note:: Defining the Correct Target
+#
+# Specifying the correct target (option ``--target``) can have a huge
+# impact on the performance of the compiled module, as it can take
+# advantage of hardware features available on the target. For more
+# information, please refer to `Auto-tuning a convolutional network
+# for x86 CPU <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network>`_.
+# We recommend identifying which CPU you are running, along with optional features,
+# and set the target appropriately.
+#
+
+################################################################################
+# Running the Model from The Compiled Module with TVMC
+# ----------------------------------------------------
+#
+# Now that we've compiled the model to this module, we can use the TVM runtime
+# to make predictions with it. TVMC has the TVM runtime built in to it,
+# allowing you to run compiled TVM models. To use TVMC to run the model and
+# make predictions, we need two things:
+#
+# - The compiled module, which we just produced.
+# - Valid input to the model to make predictions on.
+#
+# Each model is particular when it comes to expected tensor shapes, formats and
+# data types. For this reason, most models require some pre and
+# post-processing, to ensure the input is valid and to interpret the output.
+# TVMC has adopted NumPy's ``.npz`` format for both input and output data. This
+# is a well-supported NumPy format to serialize multiple arrays into a file
+#
+# As input for this tutorial, we will use the image of a cat, but you can feel
+# free to substitute image for any of your choosing.
+#
+# .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg
+# :height: 224px
+# :width: 224px
+# :align: center
+
+
+################################################################################
+# Input pre-processing
+# ~~~~~~~~~~~~~~~~~~~~
+#
+# For our ResNet 50 V2 model, the input is expected to be in ImageNet format.
+# Here is an example of a script to pre-process an image for ResNet 50 V2.
+#
+# .. code-block:: python
+# :caption: preprocess.py
+# :name: preprocess.py
+#
+# #!python ./preprocess.py
+# from tvm.contrib.download import download_testdata
+# from PIL import Image
+# import numpy as np
+#
+# img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
+# img_path = download_testdata(img_url, "imagenet_cat.png", module="data")
+#
+# # Resize it to 224x224
+# resized_image = Image.open(img_path).resize((224, 224))
+# img_data = np.asarray(resized_image).astype("float32")
+#
+# # ONNX expects NCHW input, so convert the array
+# img_data = np.transpose(img_data, (2, 0, 1))
+#
+# # Normalize according to ImageNet
+# imagenet_mean = np.array([0.485, 0.456, 0.406])
+# imagenet_stddev = np.array([0.229, 0.224, 0.225])
+# norm_img_data = np.zeros(img_data.shape).astype("float32")
+# for i in range(img_data.shape[0]):
+# norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i]
+#
+# # Add batch dimension
+# img_data = np.expand_dims(norm_img_data, axis=0)
+#
+# # Save to .npz (outputs imagenet_cat.npz)
+# np.savez("imagenet_cat", data=img_data)
+#
+
+################################################################################
+# Running the Compiled Module
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# With both the model and input data in hand, we can now run TVMC to make a
+# prediction:
+#
+# .. code-block:: bash
+#
+# tvmc run \
+# --inputs imagenet_cat.npz \
+# --output predictions.npz \
+# resnet50-v2-7-tvm.tar
+#
+# Recall that the `.tar` model file includes a C++ library, a description of
+# the Relay model, and the parameters for the model. TVMC includes the TVM
+# runtime, which can load the model and make predictions against input. When
+# running the above command, TVMC outputs a new file, ``predictions.npz``, that
+# contains the model output tensors in NumPy format.
+#
+# In this example, we are running the model on the same machine that we used
+# for compilation. In some cases we might want to run it remotely via an RPC
+# Tracker. To read more about these options please check ``tvmc run --help``.
+
+################################################################################
+# Output Post-Processing
+# ~~~~~~~~~~~~~~~~~~~~~~
+#
+# As previously mentioned, each model will have its own particular way of
+# providing output tensors.
+#
+# In our case, we need to run some post-processing to render the outputs from
+# ResNet 50 V2 into a more human-readable form, using the lookup-table provided
+# for the model.
+#
+# The script below shows an example of the post-processing to extract labels
+# from the output of our compiled module.
+#
+# .. code-block:: python
+# :caption: postprocess.py
+# :name: postprocess.py
+#
+# #!python ./postprocess.py
+# import os.path
+# import numpy as np
+#
+# from scipy.special import softmax
+#
+# from tvm.contrib.download import download_testdata
+#
+# # Download a list of labels
+# labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
+# labels_path = download_testdata(labels_url, "synset.txt", module="data")
+#
+# with open(labels_path, "r") as f:
+# labels = [l.rstrip() for l in f]
+#
+# output_file = "predictions.npz"
+#
+# # Open the output and read the output tensor
+# if os.path.exists(output_file):
+# with np.load(output_file) as data:
+# scores = softmax(data["output_0"])
+# scores = np.squeeze(scores)
+# ranks = np.argsort(scores)[::-1]
+#
+# for rank in ranks[0:5]:
+# print("class='%s' with probability=%f" % (labels[rank], scores[rank]))
+#
+# Running this script should produce the following output:
+#
+# .. code-block:: bash
+#
+# python postprocess.py
+#
+# # class='n02123045 tabby, tabby cat' with probability=0.610553
+# # class='n02123159 tiger cat' with probability=0.367179
+# # class='n02124075 Egyptian cat' with probability=0.019365
+# # class='n02129604 tiger, Panthera tigris' with probability=0.001273
+# # class='n04040759 radiator' with probability=0.000261
+#
+# Try replacing the cat image with other images, and see what sort of
+# predictions the ResNet model makes.
+
+################################################################################
+# Automatically Tuning the ResNet Model
+# -------------------------------------
+#
+# The previous model was compiled to work on the TVM runtime, but did not
+# include any platform specific optimization. In this section, we will show you
+# how to build an optimized model using TVMC to target your working platform.
+#
+# In some cases, we might not get the expected performance when running
+# inferences using our compiled module. In cases like this, we can make use of
+# the auto-tuner, to find a better configuration for our model and get a boost
+# in performance. Tuning in TVM refers to the process by which a model is
+# optimized to run faster on a given target. This differs from training or
+# fine-tuning in that it does not affect the accuracy of the model, but only
+# the runtime performance. As part of the tuning process, TVM will try running
+# many different operator implementation variants to see which perform best.
+# The results of these runs are stored in a tuning records file, which is
+# ultimately the output of the ``tune`` subcommand.
+#
+# In the simplest form, tuning requires you to provide three things:
+#
+# - the target specification of the device you intend to run this model on
+# - the path to an output file in which the tuning records will be stored, and
+# finally
+# - a path to the model to be tuned.
+#
+# The example below demonstrates how that works in practice:
+#
+# .. code-block:: bash
+#
+# tvmc tune \
+# --target "llvm" \
+# --output resnet50-v2-7-autotuner_records.json \
+# resnet50-v2-7.onnx
+#
+# In this example, you will see better results if you indicate a more specific
+# target for the `--target` flag. For example, on an Intel i7 processor you
+# could use `--target llvm -mcpu=skylake`. For this tuning example, we are
+# tuning locally on the CPU using LLVM as the compiler for the specified
+# achitecture.
+#
+# TVMC will perform a search against the parameter space for the model, trying
+# out different configurations for operators and choosing the one that runs
+# fastest on your platform. Although this is a guided search based on the CPU
+# and model operations, it can still take several hours to complete the search.
+# The output of this search will be saved to the
+# `resnet50-v2-7-autotuner_records.json` file, which will later be used to
+# compile an optimized model.
+#
+# .. note:: Defining the Tuning Search Algorithm
+#
+# By default this search is guided using an `XGBoost Grid` algorithm.
+# Depending on your model complexity and amount of time avilable, you might
+# want to choose a different algorithm. A full list is available by
+# consulting ``tvmc tune --help``.
+#
+# The output will look something like this for a consumer-level Skylake CPU:
+#
+# .. code-block:: bash
+#
+# tvmc tune --target "llvm -mcpu=broadwell" --output resnet50-v2-7-autotuner_records.json resnet50-v2-7.onnx
+# # [Task 1/24] Current/Best: 9.65/ 23.16 GFLOPS | Progress: (60/1000) | 130.74 s Done.
+# # [Task 1/24] Current/Best: 3.56/ 23.16 GFLOPS | Progress: (192/1000) | 381.32 s Done.
+# # [Task 2/24] Current/Best: 13.13/ 58.61 GFLOPS | Progress: (960/1000) | 1190.59 s Done.
+# # [Task 3/24] Current/Best: 31.93/ 59.52 GFLOPS | Progress: (800/1000) | 727.85 s Done.
+# # [Task 4/24] Current/Best: 16.42/ 57.80 GFLOPS | Progress: (960/1000) | 559.74 s Done.
+# # [Task 5/24] Current/Best: 12.42/ 57.92 GFLOPS | Progress: (800/1000) | 766.63 s Done.
+# # [Task 6/24] Current/Best: 20.66/ 59.25 GFLOPS | Progress: (1000/1000) | 673.61 s Done.
+# # [Task 7/24] Current/Best: 15.48/ 59.60 GFLOPS | Progress: (1000/1000) | 953.04 s Done.
+# # [Task 8/24] Current/Best: 31.97/ 59.33 GFLOPS | Progress: (972/1000) | 559.57 s Done.
+# # [Task 9/24] Current/Best: 34.14/ 60.09 GFLOPS | Progress: (1000/1000) | 479.32 s Done.
+# # [Task 10/24] Current/Best: 12.53/ 58.97 GFLOPS | Progress: (972/1000) | 642.34 s Done.
+# # [Task 11/24] Current/Best: 30.94/ 58.47 GFLOPS | Progress: (1000/1000) | 648.26 s Done.
+# # [Task 12/24] Current/Best: 23.66/ 58.63 GFLOPS | Progress: (1000/1000) | 851.59 s Done.
+# # [Task 13/24] Current/Best: 25.44/ 59.76 GFLOPS | Progress: (1000/1000) | 534.58 s Done.
+# # [Task 14/24] Current/Best: 26.83/ 58.51 GFLOPS | Progress: (1000/1000) | 491.67 s Done.
+# # [Task 15/24] Current/Best: 33.64/ 58.55 GFLOPS | Progress: (1000/1000) | 529.85 s Done.
+# # [Task 16/24] Current/Best: 14.93/ 57.94 GFLOPS | Progress: (1000/1000) | 645.55 s Done.
+# # [Task 17/24] Current/Best: 28.70/ 58.19 GFLOPS | Progress: (1000/1000) | 756.88 s Done.
+# # [Task 18/24] Current/Best: 19.01/ 60.43 GFLOPS | Progress: (980/1000) | 514.69 s Done.
+# # [Task 19/24] Current/Best: 14.61/ 57.30 GFLOPS | Progress: (1000/1000) | 614.44 s Done.
+# # [Task 20/24] Current/Best: 10.47/ 57.68 GFLOPS | Progress: (980/1000) | 479.80 s Done.
+# # [Task 21/24] Current/Best: 34.37/ 58.28 GFLOPS | Progress: (308/1000) | 225.37 s Done.
+# # [Task 22/24] Current/Best: 15.75/ 57.71 GFLOPS | Progress: (1000/1000) | 1024.05 s Done.
+# # [Task 23/24] Current/Best: 23.23/ 58.92 GFLOPS | Progress: (1000/1000) | 999.34 s Done.
+# # [Task 24/24] Current/Best: 17.27/ 55.25 GFLOPS | Progress: (1000/1000) | 1428.74 s Done.
+#
+# Tuning sessions can take a long time, so ``tvmc tune`` offers many options to customize your tuning
+# process, in terms of number of repetitions (``--repeat`` and ``--number``, for example), the tuning
+# algorithm to be used, and so on. Check ``tvmc tune --help`` for more information.
+#
+
+################################################################################
+# Compiling an Optimized Model with Tuning Data
+# ----------------------------------------------
+#
+# As an output of the tuning process above, we obtained the tuning records
+# stored in ``resnet50-v2-7-autotuner_records.json``. This file can be used in
+# two ways:
+#
+# - As input to further tuning (via ``tvmc tune --tuning-records``).
+# - As input to the compiler
+#
+# The compiler will use the results to generate high performance code for the
+# model on your specified target. To do that we can use ``tvmc compile
+# --tuning-records``. Check ``tvmc compile --help`` for more information.
+#
+# Now that tuning data for the model has been collected, we can re-compile the
+# model using optimized operators to speed up our computations.
+#
+# .. code-block:: bash
+#
+# tvmc compile \
+# --target "llvm" \
+# --tuning-records resnet50-v2-7-autotuner_records.json \
+# --output resnet50-v2-7-tvm_autotuned.tar \
+# resnet50-v2-7.onnx
+#
+# Verify that the optimized model runs and produces the same results:
+#
+# .. code-block:: bash
+#
+# tvmc run \
+# --inputs imagenet_cat.npz \
+# --output predictions.npz \
+# resnet50-v2-7-tvm_autotuned.tar
+#
+# python postproccess.py
+#
+# Verifying that the predictions are the same:
+#
+# .. code-block:: bash
+#
+# # class='n02123045 tabby, tabby cat' with probability=0.610550
+# # class='n02123159 tiger cat' with probability=0.367181
+# # class='n02124075 Egyptian cat' with probability=0.019365
+# # class='n02129604 tiger, Panthera tigris' with probability=0.001273
+# # class='n04040759 radiator' with probability=0.000261
+
+################################################################################
+# Comparing the Tuned and Untuned Models
+# --------------------------------------
+#
+# TVMC gives you tools for basic performance benchmarking between the models.
+# You can specify a number of repetitions and that TVMC report on the model run
+# time (independent of runtime startup). We can get a rough idea of how much
+# tuning has improved the model performance. For example, on a test Intel i7
+# system, we see that the tuned model runs 47% faster than the untuned model:
+#
+# .. code-block:: bash
+#
+# tvmc run \
+# --inputs imagenet_cat.npz \
+# --output predictions.npz \
+# --print-time \
+# --repeat 100 \
+# resnet50-v2-7-tvm_autotuned.tar
+#
+# # Execution time summary:
+# # mean (s) max (s) min (s) std (s)
+# # 0.09219 0.11573 0.08985 0.00315
+#
+# tvmc run \
+# --inputs imagenet_cat.npz \
+# --output predictions.npz \
+# --print-time \
+# --repeat 100 \
+# resnet50-v2-7-tvm.tar
+#
+# # Execution time summary:
+# # mean (s) max (s) min (s) std (s)
+# # 0.19332 0.21997 0.18504 0.00711
+#
+
+
+################################################################################
+# Final Remarks
+# -------------
+#
+# In this tutorial, we presented TVMC, a command line driver for TVM. We
+# demonstrated how to compile, run, and tune a model. We also discussed the
+# need for pre and post-processing of inputs and outputs. After the tuning
+# process, we demonstrated how to compare the performance of the unoptimized
+# and optimize models.
+#
+# Here we presented a simple example using ResNet 50 V2 locally. However, TVMC
+# supports many more features including cross-compilation, remote execution and
+# profiling/benchmarking.
+#
+# To see what other options are available, please have a look at ``tvmc
+# --help``.
+#
+# In the next tutorial, `Compiling and Optimizing a Model with the Python
+# AutoScheduler <auto_tuning_with_pyton>`_, we will cover the same compilation
+# and optimization steps using the Python interface.
diff --git a/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
new file mode 100644
index 0000000..e8d0fee
--- /dev/null
+++ b/docs/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
@@ -0,0 +1,115 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nTuning High Performance Convolution on NVIDIA GPUs\n=========================================================================\n**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_\n\nThis is an advanced tutorial for writing high performance tunable template for\nNVIDIA GPU. By running auto-tuner on this template, we can outperform the\nvendor provided library CuDNN in many cases.\n\nNote that this tutorial will not run on Windows or recent versions of macOS. To\nget it [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Install dependencies\n--------------------\nTo use autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost tornado cloudpickle\n\nTo make TVM run faster in tuning, it is recommended to use cython\nas FFI of tvm. In the root directory of tvm, execute\n\n.. code-block:: bash\n\n pip3 install --user cython\n sudo make cython3\n\nNow return to python code. Impor [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import logging\nimport sys\nimport numpy as np\n\nimport tvm\nfrom tvm import te, topi, testing\nfrom tvm.topi.testing import conv2d_nchw_python\nimport tvm.testing\n\nfrom tvm import autotvm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Step 1: Define the search space\n--------------------------------\nThere are plenty of useful schedule primitives in tvm. You can also find\nsome tutorials that describe them in more details, such as\n(1). `opt-conv-gpu`\n(2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example>`_\n\nHowever, their implementations are manually tuned for some special input\nshapes. In this sectio [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "@autotvm.template(\"tutorial/conv2d_no_batching\")\ndef conv2d_no_batching(N, H, W, CO, CI, KH, KW, stride, padding):\n assert N == 1, \"Only consider batch_size = 1 in this template\"\n\n data = te.placeholder((N, CI, H, W), name=\"data\")\n kernel = te.placeholder((CO, CI, KH, KW), name=\"kernel\")\n conv = topi.nn.conv2d_nchw(data, kernel, stride, padding, dilation=1, out_dtype=\"float32\")\n s = te.create_schedule([conv.op])\n\n ##### space definition begin [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Step 2: Search through the space\n---------------------------------\nWe pick the last layer on resnet as test case.\nSince our space is very large, :code:`XGBoostTuner` is most suitable\nfor our case. Here we only do 20 trials for demonstration.\nIn practice, making 1000 trials usually can find some good kernels\nfor this template\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# logging config (for printing tuning log to screen)\nlogging.getLogger(\"autotvm\").setLevel(logging.DEBUG)\nlogging.getLogger(\"autotvm\").addHandler(logging.StreamHandler(sys.stdout))\n\n# the last layer in resnet\nN, H, W, CO, CI, KH, KW, strides, padding = 1, 7, 7, 512, 512, 3, 3, (1, 1), (1, 1)\ntask = autotvm.task.create(\n \"tutorial/conv2d_no_batching\", args=(N, H, W, CO, CI, KH, KW, strides, padding), target=\"cuda\"\n)\nprint(task.config_space)\n\n# Use local gpu, [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally we can inspect the best config from log file, check correctness,\nand measure running time.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# inspect the best config\ndispatch_context = autotvm.apply_history_best(\"conv2d.log\")\nbest_config = dispatch_context.query(task.target, task.workload)\nprint(\"\\nBest config:\")\nprint(best_config)\n\n# apply history best from log file\nwith autotvm.apply_history_best(\"conv2d.log\"):\n with tvm.target.Target(\"cuda\"):\n s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides, padding)\n func = tvm.build(s, arg_bufs)\n\n# check correctness\na_np [...]
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb b/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
new file mode 100644
index 0000000..2ab30e9
--- /dev/null
+++ b/docs/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n\nQuick Start Tutorial for Compiling Deep Learning Models\n=======================================================\n**Author**: `Yao Wang <https://github.com/kevinthesun>`_, `Truman Tian <https://github.com/SiNZeRo>`_\n\nThis example shows how to build a neural network with Relay python frontend and\ngenerates a runtime library for Nvidia GPU with TVM.\nNotice that you need to build TVM with cuda and llvm enabled.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Overview for Supported Hardware Backend of TVM\n----------------------------------------------\nThe image below shows hardware backend currently supported by TVM:\n\n\n\n :align: center\n\nIn this tutorial, we'll choose cuda and llvm as target backends.\nTo begin with, let's import Relay and TVM.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n\nfrom tvm import relay\nfrom tvm.relay import testing\nimport tvm\nfrom tvm import te\nfrom tvm.contrib import graph_executor\nimport tvm.testing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define Neural Network in Relay\n------------------------------\nFirst, let's define a neural network with relay python frontend.\nFor simplicity, we'll use pre-defined resnet-18 network in Relay.\nParameters are initialized with Xavier initializer.\nRelay also supports other model formats such as MXNet, CoreML, ONNX and\nTensorflow.\n\nIn this tutorial, we assume we will do inference on our device\nand the batch size is set to be 1. Input images are RGB color\nimages of size 224 [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 1\nnum_class = 1000\nimage_shape = (3, 224, 224)\ndata_shape = (batch_size,) + image_shape\nout_shape = (batch_size, num_class)\n\nmod, params = relay.testing.resnet.get_workload(\n num_layers=18, batch_size=batch_size, image_shape=image_shape\n)\n\n# set show_meta_data=True if you want to show meta data\nprint(mod.astext(show_meta_data=False))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Compilation\n-----------\nNext step is to compile the model using the Relay/TVM pipeline.\nUsers can specify the optimization level of the compilation.\nCurrently this value can be 0 to 3. The optimization passes include\noperator fusion, pre-computation, layout transformation and so on.\n\n:py:func:`relay.build` returns three components: the execution graph in\njson format, the TVM module library of compiled functions specifically\nfor this graph on the target hardware, and the [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "opt_level = 3\ntarget = tvm.target.cuda()\nwith tvm.transform.PassContext(opt_level=opt_level):\n lib = relay.build(mod, target, params=params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the generate library\n------------------------\nNow we can create graph executor and run the module on Nvidia GPU.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# create random input\ndev = tvm.cuda()\ndata = np.random.uniform(-1, 1, size=data_shape).astype(\"float32\")\n# create module\nmodule = graph_executor.GraphModule(lib[\"default\"](dev))\n# set input and parameters\nmodule.set_input(\"data\", data)\n# run\nmodule.run()\n# get output\nout = module.get_output(0, tvm.nd.empty(out_shape)).numpy()\n\n# Print first 10 elements of output\nprint(out.flatten()[0:10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Save and Load Compiled Module\n-----------------------------\nWe can also save the graph, lib and parameters into files and load them\nback in deploy environment.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# save the graph, lib and params into separate files\nfrom tvm.contrib import utils\n\ntemp = utils.tempdir()\npath_lib = temp.relpath(\"deploy_lib.tar\")\nlib.export_library(path_lib)\nprint(temp.listdir())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# load the module back.\nloaded_lib = tvm.runtime.load_module(path_lib)\ninput_data = tvm.nd.array(data)\n\nmodule = graph_executor.GraphModule(loaded_lib[\"default\"](dev))\nmodule.run(data=input_data)\nout_deploy = module.get_output(0).numpy()\n\n# Print first 10 elements of output\nprint(out_deploy.flatten()[0:10])\n\n# check whether the output from deployed module is consistent with original one\ntvm.testing.assert_allclose(out_deploy, out, atol=1e-5)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
new file mode 100644
index 0000000..65991cc
--- /dev/null
+++ b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
@@ -0,0 +1,389 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-tuning a Convolutional Network for NVIDIA GPU
+==================================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Eddie Yan <https://github.com/eqy/>`_
+
+Auto-tuning for specific devices and workloads is critical for getting the
+best performance. This is a tutorial on how to tune a whole convolutional
+network for NVIDIA GPU.
+
+The operator implementation for NVIDIA GPU in TVM is written in template form.
+The template has many tunable knobs (tile factor, unrolling, etc).
+We will tune all convolution and depthwise convolution operators
+in the neural network. After tuning, we produce a log file which stores
+the best knob values for all required operators. When the TVM compiler compiles
+these operators, it will query this log file to get the best knob values.
+
+We also released pre-tuned parameters for some NVIDIA GPUs. You can go to
+`NVIDIA GPU Benchmark <https://github.com/apache/tvm/wiki/Benchmark#nvidia-gpu>`_
+to see the results.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+######################################################################
+# Install dependencies
+# --------------------
+# To use the autotvm package in tvm, we need to install some extra dependencies.
+# (change "3" to "2" if you use python2):
+#
+# .. code-block:: bash
+#
+# pip3 install --user psutil xgboost tornado cloudpickle
+#
+# To make TVM run faster during tuning, it is recommended to use cython
+# as FFI of tvm. In the root directory of tvm, execute:
+#
+# .. code-block:: bash
+#
+# pip3 install --user cython
+# sudo make cython3
+#
+# Now return to python code. Import packages.
+
+import os
+
+import numpy as np
+
+import tvm
+from tvm import relay, autotvm
+import tvm.relay.testing
+from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
+import tvm.contrib.graph_executor as runtime
+
+#################################################################
+# Define Network
+# --------------
+# First we need to define the network in relay frontend API.
+# We can load some pre-defined network from :code:`tvm.relay.testing`.
+# We can also load models from MXNet, ONNX and TensorFlow.
+
+
+def get_network(name, batch_size):
+ """Get the symbol definition and random weight of a network"""
+ input_shape = (batch_size, 3, 224, 224)
+ output_shape = (batch_size, 1000)
+
+ if "resnet" in name:
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.resnet.get_workload(
+ num_layers=n_layer, batch_size=batch_size, dtype=dtype
+ )
+ elif "vgg" in name:
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.vgg.get_workload(
+ num_layers=n_layer, batch_size=batch_size, dtype=dtype
+ )
+ elif name == "mobilenet":
+ mod, params = relay.testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype)
+ elif name == "squeezenet_v1.1":
+ mod, params = relay.testing.squeezenet.get_workload(
+ batch_size=batch_size, version="1.1", dtype=dtype
+ )
+ elif name == "inception_v3":
+ input_shape = (batch_size, 3, 299, 299)
+ mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
+ elif name == "mxnet":
+ # an example for mxnet model
+ from mxnet.gluon.model_zoo.vision import get_model
+
+ block = get_model("resnet18_v1", pretrained=True)
+ mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)
+ net = mod["main"]
+ net = relay.Function(
+ net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
+ )
+ mod = tvm.IRModule.from_expr(net)
+ else:
+ raise ValueError("Unsupported network: " + name)
+
+ return mod, params, input_shape, output_shape
+
+
+###########################################
+# Set Tuning Options
+# ------------------
+# Before tuning, we apply some configurations.
+
+#### DEVICE CONFIG ####
+target = tvm.target.cuda()
+
+#### TUNING OPTION ####
+network = "resnet-18"
+log_file = "%s.log" % network
+dtype = "float32"
+
+tuning_option = {
+ "log_filename": log_file,
+ "tuner": "xgb",
+ "n_trial": 2000,
+ "early_stopping": 600,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(timeout=10),
+ runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
+ ),
+}
+
+####################################################################
+#
+# .. note:: How to set tuning options
+#
+# In general, the default value provided here works well.
+#
+# If you have large time budget, you can set :code:`n_trial`, :code:`early_stopping` larger,
+# which makes the tuning runs longer.
+#
+# If you have multiple devices, you can use all of them for measurement to
+# accelerate the tuning process. (see the 'Scale up measurement` section below).
+#
+
+###################################################################
+# Begin Tuning
+# ------------
+# Now we can extract tuning tasks from the network and begin tuning.
+# Here, we provide a simple utility function to tune a list of tasks.
+# This function is just an initial implementation which tunes them in sequential order.
+# We will introduce a more sophisticated tuning scheduler in the future.
+
+# You can skip the implementation of this function for this tutorial.
+def tune_tasks(
+ tasks,
+ measure_option,
+ tuner="xgb",
+ n_trial=1000,
+ early_stopping=None,
+ log_filename="tuning.log",
+ use_transfer_learning=True,
+):
+ # create tmp log file
+ tmp_log_file = log_filename + ".tmp"
+ if os.path.exists(tmp_log_file):
+ os.remove(tmp_log_file)
+
+ for i, tsk in enumerate(reversed(tasks)):
+ prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+ # create tuner
+ if tuner == "xgb" or tuner == "xgb-rank":
+ tuner_obj = XGBTuner(tsk, loss_type="rank")
+ elif tuner == "ga":
+ tuner_obj = GATuner(tsk, pop_size=100)
+ elif tuner == "random":
+ tuner_obj = RandomTuner(tsk)
+ elif tuner == "gridsearch":
+ tuner_obj = GridSearchTuner(tsk)
+ else:
+ raise ValueError("Invalid tuner: " + tuner)
+
+ if use_transfer_learning:
+ if os.path.isfile(tmp_log_file):
+ tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
+
+ # do tuning
+ tsk_trial = min(n_trial, len(tsk.config_space))
+ tuner_obj.tune(
+ n_trial=tsk_trial,
+ early_stopping=early_stopping,
+ measure_option=measure_option,
+ callbacks=[
+ autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
+ autotvm.callback.log_to_file(tmp_log_file),
+ ],
+ )
+
+ # pick best records to a cache file
+ autotvm.record.pick_best(tmp_log_file, log_filename)
+ os.remove(tmp_log_file)
+
+
+########################################################################
+# Finally, we launch tuning jobs and evaluate the end-to-end performance.
+
+
+def tune_and_evaluate(tuning_opt):
+ # extract workloads from relay program
+ print("Extract tasks...")
+ mod, params, input_shape, out_shape = get_network(network, batch_size=1)
+ tasks = autotvm.task.extract_from_program(
+ mod["main"], target=target, params=params, ops=(relay.op.get("nn.conv2d"),)
+ )
+
+ # run tuning tasks
+ print("Tuning...")
+ tune_tasks(tasks, **tuning_opt)
+
+ # compile kernels with history best records
+ with autotvm.apply_history_best(log_file):
+ print("Compile...")
+ with tvm.transform.PassContext(opt_level=3):
+ lib = relay.build_module.build(mod, target=target, params=params)
+
+ # load parameters
+ dev = tvm.device(str(target), 0)
+ module = runtime.GraphModule(lib["default"](dev))
+ data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
+ module.set_input("data", data_tvm)
+
+ # evaluate
+ print("Evaluate inference time cost...")
+ ftimer = module.module.time_evaluator("run", dev, number=1, repeat=600)
+ prof_res = np.array(ftimer().results) * 1000 # convert to millisecond
+ print(
+ "Mean inference time (std dev): %.2f ms (%.2f ms)"
+ % (np.mean(prof_res), np.std(prof_res))
+ )
+
+
+# We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+
+# tune_and_evaluate(tuning_option)
+
+######################################################################
+# Sample Output
+# -------------
+# The tuning needs to compile many programs and extract feature from them.
+# So a high performance CPU is recommended. One sample output is listed below.
+# It takes about 4 hours to get the following output on a 32T AMD Ryzen Threadripper.
+# The tuning target is NVIDIA 1080 Ti.
+# (You can see some errors during compilation. If the tuning is not stuck, it is okay.)
+#
+# .. code-block:: bash
+#
+# Extract tasks...
+# Tuning...
+# [Task 1/12] Current/Best: 541.83/3570.66 GFLOPS | Progress: (960/2000) | 1001.31 s Done.
+# [Task 2/12] Current/Best: 0.56/ 803.33 GFLOPS | Progress: (704/2000) | 608.08 s Done.
+# [Task 3/12] Current/Best: 103.69/1141.25 GFLOPS | Progress: (768/2000) | 702.13 s Done.
+# [Task 4/12] Current/Best: 2905.03/3925.15 GFLOPS | Progress: (864/2000) | 745.94 sterminate called without an active exception
+# [Task 4/12] Current/Best: 2789.36/3925.15 GFLOPS | Progress: (1056/2000) | 929.40 s Done.
+# [Task 5/12] Current/Best: 89.06/1076.24 GFLOPS | Progress: (704/2000) | 601.73 s Done.
+# [Task 6/12] Current/Best: 40.39/2129.02 GFLOPS | Progress: (1088/2000) | 1125.76 s Done.
+# [Task 7/12] Current/Best: 4090.53/5007.02 GFLOPS | Progress: (800/2000) | 903.90 s Done.
+# [Task 8/12] Current/Best: 4.78/1272.28 GFLOPS | Progress: (768/2000) | 749.14 s Done.
+# [Task 9/12] Current/Best: 1391.45/2325.08 GFLOPS | Progress: (992/2000) | 1084.87 s Done.
+# [Task 10/12] Current/Best: 1995.44/2383.59 GFLOPS | Progress: (864/2000) | 862.60 s Done.
+# [Task 11/12] Current/Best: 4093.94/4899.80 GFLOPS | Progress: (224/2000) | 240.92 sterminate called without an active exception
+# [Task 11/12] Current/Best: 3487.98/4909.91 GFLOPS | Progress: (480/2000) | 534.96 sterminate called without an active exception
+# [Task 11/12] Current/Best: 4636.84/4912.17 GFLOPS | Progress: (1184/2000) | 1381.16 sterminate called without an active exception
+# [Task 11/12] Current/Best: 50.12/4912.17 GFLOPS | Progress: (1344/2000) | 1602.81 s Done.
+# [Task 12/12] Current/Best: 3581.31/4286.30 GFLOPS | Progress: (736/2000) | 943.52 s Done.
+# Compile...
+# Evaluate inference time cost...
+# Mean inference time (std dev): 1.07 ms (0.05 ms)
+#
+# As a reference baseline, the time cost of MXNet + TensorRT on resnet-18 is 1.30ms. So we are a little faster.
+
+######################################################################
+#
+# .. note:: **Experiencing Difficulties?**
+#
+# The auto tuning module is error-prone. If you always see " 0.00/ 0.00 GFLOPS",
+# then there must be something wrong.
+#
+# First, make sure you set the correct configuration of your device.
+# Then, you can print debug information by adding these lines in the beginning
+# of the script. It will print every measurement result, where you can find useful
+# error messages.
+#
+# .. code-block:: python
+#
+# import logging
+# logging.getLogger('autotvm').setLevel(logging.DEBUG)
+#
+# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
+
+#################################################################
+# .. _tutorials-autotvm-scale-up-rpc-tracker:
+
+#################################################################
+# Scale up measurement by using multiple devices
+# ----------------------------------------------
+# If you have multiple devices, you can use all of them for measurement.
+# TVM uses the RPC Tracker to manage distributed devices.
+# The RPC Tracker is a centralized controller node. We can register all devices to
+# the tracker. For example, if we have 10 GPU cards, we can register all of them
+# to the tracker, and run 10 measurements in parallel, accelerating the tuning process.
+#
+# To start an RPC tracker, run this command on the host machine. The tracker is
+# required during the whole tuning process, so we need to open a new terminal for
+# this command:
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.rpc_tracker --host=0.0.0.0 --port=9190
+#
+# The expected output is
+#
+# .. code-block:: bash
+#
+# INFO:RPCTracker:bind to 0.0.0.0:9190
+#
+# Then open another new terminal for the RPC server. We need to start one dedicated server
+# for each device. We use a string key to distinguish the types of devices.
+# You can pick a name you like.
+# (Note: For rocm backend, there are some internal errors with the compiler,
+# we need to add `--no-fork` to the argument list.)
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.rpc_server --tracker=127.0.0.1:9190 --key=1080ti
+#
+# After registering devices, we can confirm it by querying rpc_tracker
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.query_rpc_tracker --host=127.0.0.1 --port=9190
+#
+# For example, if we have four 1080ti, two titanx and one gfx900, the output can be
+#
+# .. code-block:: bash
+#
+# Queue Status
+# ----------------------------------
+# key total free pending
+# ----------------------------------
+# 1080ti 4 4 0
+# titanx 2 2 0
+# gfx900 1 1 0
+# ----------------------------------
+#
+# Finally, we need to change the tuning option to use RPCRunner. Use the code below
+# to replace the corresponding part above.
+
+tuning_option = {
+ "log_filename": log_file,
+ "tuner": "xgb",
+ "n_trial": 2000,
+ "early_stopping": 600,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(timeout=10),
+ runner=autotvm.RPCRunner(
+ "1080ti", # change the device key to your key
+ "127.0.0.1",
+ 9190,
+ number=20,
+ repeat=3,
+ timeout=4,
+ min_repeat_ms=150,
+ ),
+ ),
+}
diff --git a/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py b/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
new file mode 100644
index 0000000..7b5619c
--- /dev/null
+++ b/docs/_downloads/2771a7fc8bf8eeb7788823ff349aacc0/tune_network_cuda.py
@@ -0,0 +1,312 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-scheduling a Neural Network for NVIDIA GPU
+===============================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_
+
+Auto-tuning for specific devices and workloads is critical for getting the
+best performance. This is a tutorial on how to tune a whole neural
+network for NVIDIA GPU with the auto-scheduler.
+
+To auto-tune a neural network, we partition the network into small subgraphs and
+tune them independently. Each subgraph is treated as one search task.
+A task scheduler slices the time and dynamically allocates time resources to
+these tasks. The task scheduler predicts the impact of each task on the end-to-end
+execution time and prioritizes the one that can reduce the execution time the most.
+
+For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to
+get the computational DAG in the tensor expression form.
+We then use the auto-scheduler to construct a search space of this DAG and search
+for good schedules (low-level optimizations).
+
+Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any
+schedule templates. In other words, the auto-scheduler only uses the compute declarations
+in :code:`tvm/python/topi` and does not use existing schedule templates.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+import numpy as np
+
+import tvm
+from tvm import relay, auto_scheduler
+import tvm.relay.testing
+from tvm.contrib import graph_executor
+
+#################################################################
+# Define a Network
+# ----------------
+# First, we need to define the network with relay frontend API.
+# We can load some pre-defined network from :code:`tvm.relay.testing`.
+# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow
+# (see :ref:`front end tutorials<tutorial-frontend>`).
+#
+# For convolutional neural networks, although auto-scheduler can work correctly
+# with any layout, we found the best performance is typically achieved with NHWC layout.
+# We also implemented more optimizations for NHWC layout with the auto-scheduler.
+# So it is recommended to convert your models to NHWC layout to use the auto-scheduler.
+# You can use :ref:`ConvertLayout <convert-layout-usage>` pass to do the layout conversion in TVM.
+
+
+def get_network(name, batch_size, layout="NHWC", dtype="float32"):
+ """Get the symbol definition and random weight of a network"""
+
+ # auto-scheduler prefers NHWC layout
+ if layout == "NHWC":
+ image_shape = (224, 224, 3)
+ elif layout == "NCHW":
+ image_shape = (3, 224, 224)
+ else:
+ raise ValueError("Invalid layout: " + layout)
+
+ input_shape = (batch_size,) + image_shape
+ output_shape = (batch_size, 1000)
+
+ if name.startswith("resnet-"):
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.resnet.get_workload(
+ num_layers=n_layer,
+ batch_size=batch_size,
+ layout=layout,
+ dtype=dtype,
+ image_shape=image_shape,
+ )
+ elif name.startswith("resnet3d-"):
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.resnet.get_workload(
+ num_layers=n_layer,
+ batch_size=batch_size,
+ layout=layout,
+ dtype=dtype,
+ image_shape=image_shape,
+ )
+ elif name == "mobilenet":
+ mod, params = relay.testing.mobilenet.get_workload(
+ batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape
+ )
+ elif name == "squeezenet_v1.1":
+ assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"
+ mod, params = relay.testing.squeezenet.get_workload(
+ version="1.1",
+ batch_size=batch_size,
+ dtype=dtype,
+ image_shape=image_shape,
+ )
+ elif name == "inception_v3":
+ input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3)
+ mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
+ elif name == "mxnet":
+ # an example for mxnet model
+ from mxnet.gluon.model_zoo.vision import get_model
+
+ assert layout == "NCHW"
+
+ block = get_model("resnet18_v1", pretrained=True)
+ mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)
+ net = mod["main"]
+ net = relay.Function(
+ net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
+ )
+ mod = tvm.IRModule.from_expr(net)
+
+ return mod, params, input_shape, output_shape
+
+
+# Define the neural network and compilation target
+network = "resnet-18"
+batch_size = 1
+layout = "NHWC"
+target = tvm.target.Target("cuda")
+dtype = "float32"
+log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
+
+#################################################################
+# Extract Search Tasks
+# --------------------
+# Next, we extract the search tasks and their weights from a network.
+# The weight of a task is the number of appearances of the task's subgraph
+# in the whole network.
+# By using the weight, we can approximate the end-to-end latency of the network
+# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the
+# latency of a task and :code:`weight[t]` is the weight of the task.
+# The task scheduler will just optimize this objective.
+
+# Extract tasks from the network
+print("Extract tasks...")
+mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)
+tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
+
+for idx, task in enumerate(tasks):
+ print("========== Task %d (workload key: %s) ==========" % (idx, task.workload_key))
+ print(task.compute_dag)
+
+#################################################################
+# Begin Tuning
+# ------------
+# Now, we set some options for tuning and launch the search tasks
+#
+# * :code:`measure_ctx` launches a different process for measurement to
+# provide isolation. It can protect the master process from GPU crashes
+# during measurement and avoid other runtime conflicts.
+# * :code:`min_repeat_ms` defines the minimum duration of one "repeat" in every measurement.
+# This can warmup the GPU, which is necessary to get accurate measurement results.
+# Typically, we recommend a value >= 300 ms.
+# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.
+# You can set it to a small number (e.g., 200) for a fast demonstrative run.
+# In practice, we recommend setting it around :code:`900 * len(tasks)`,
+# which is typically enough for the search to converge.
+# For example, there are 24 tasks in resnet-18, so we can set it as 20000.
+# You can adjust this parameter according to your time budget.
+# * In addition, we use :code:`RecordToFile` to dump measurement records into a log file,
+# The measurement records can be used to query the history best, resume the search,
+# and do more analyses later.
+# * see :any:`auto_scheduler.TuningOptions`,
+# :any:`auto_scheduler.LocalRPCMeasureContext` for more parameters.
+#
+
+
+def run_tuning():
+ print("Begin tuning...")
+ measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=300, timeout=10)
+
+ tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
+ tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=200, # change this to 20000 to achieve the best performance
+ runner=measure_ctx.runner,
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+ )
+
+ tuner.tune(tune_option)
+
+
+# We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+
+# run_tuning()
+
+
+######################################################################
+# .. note:: Explain the printed information during tuning
+#
+# During the tuning, a lot of information will be printed on the console.
+# They are used for debugging purposes. The most important info is the output
+# of the task scheduler. The following table is a sample output.
+#
+# .. code-block:: c
+#
+# ----------------------------------------------------------------------
+# ------------------------------ [ Task Scheduler ]
+# ----------------------------------------------------------------------
+# | ID | Latency (ms) | Speed (GFLOPS) | Trials |
+# -------------------------------------------------
+# | 0 | 0.005 | 0.88 | 64 |
+# | 1 | 0.010 | 99.10 | 64 |
+# | 2 | 0.006 | 0.00 | 64 |
+# | 3 | 0.145 | 979.78 | 384 |
+# | 4 | 0.130 | 1097.02 | 384 |
+# | 5 | 0.143 | 992.69 | 384 |
+# | 6 | 0.076 | 1526.86 | 192 |
+# | 7 | 0.115 | 999.44 | 320 |
+# | 8 | 0.079 | 1449.39 | 320 |
+# | 9 | 0.122 | 938.73 | 384 |
+# | 10 | 0.063 | 1832.98 | 192 |
+# | 11 | 0.072 | 1763.62 | 256 |
+# | 12 | 0.062 | 2036.40 | 192 |
+# | 13 | 0.068 | 1874.44 | 192 |
+# | 14 | 0.049 | 2346.50 | 128 |
+# | 15 | 0.076 | 1694.31 | 256 |
+# | 16 | 0.067 | 1933.30 | 448 |
+# | 17 | 0.076 | 1680.90 | 256 |
+# | 18 | 0.022 | 98.43 | 64 |
+# | 19 | 0.076 | 3112.55 | 192 |
+# | 20 | 0.013 | 2026.44 | 64 |
+# | 21 | 0.011 | 1136.69 | 64 |
+# | 22 | 0.013 | 992.47 | 64 |
+# | 23 | 0.020 | 627.56 | 64 |
+# -------------------------------------------------
+# Estimated total latency: 1.587 ms Trials: 4992 Used time : 13296 s Next ID: 3
+#
+# This table lists the latency and (estimated) speed of all tasks.
+# It also lists the allocation of measurement trials for all tasks.
+# The last line prints the total weighted latency of these tasks,
+# which can be a rough estimation of the end-to-end execution time
+# of the network.
+# The last line also prints the total number of measurement trials,
+# total time spent on auto-tuning and the id of the next task to tune.
+#
+# There will also be some "tvm::Error"s and CUDA errors, because the
+# auto-scheduler will try some invalid schedules.
+# You can safely ignore them if the tuning can continue, because these
+# errors are isolated from the main process.
+#
+
+######################################################################
+# .. note:: Terminate the tuning earlier
+#
+# You can terminate the tuning earlier by forcibly killing this process.
+# As long as you get at least one valid schedule for each task in the log file,
+# you should be able to do the compilation (the secion below).
+#
+
+
+#################################################################
+# Compile and Evaluate
+# --------------------
+# After auto-tuning, we can compile the network with the best schedules we found.
+# All measurement records are dumped into the log file during auto-tuning,
+# so we can read the log file and load the best schedules.
+
+# Compile with the history best
+print("Compile...")
+with auto_scheduler.ApplyHistoryBest(log_file):
+ with tvm.transform.PassContext(opt_level=3, config={"relay.backend.use_auto_scheduler": True}):
+ lib = relay.build(mod, target=target, params=params)
+
+# Create graph executor
+dev = tvm.device(str(target), 0)
+module = graph_executor.GraphModule(lib["default"](dev))
+data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
+module.set_input("data", data_tvm)
+
+# Evaluate
+print("Evaluate inference time cost...")
+ftimer = module.module.time_evaluator("run", dev, repeat=3, min_repeat_ms=500)
+prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
+print("Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), np.std(prof_res)))
+
+
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