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Posted to commits@tvm.apache.org by ar...@apache.org on 2021/12/15 18:02:17 UTC
[tvm-site] branch asf-site updated: Docs build at Wed Dec 15 10:02:02 PST 2021
This is an automated email from the ASF dual-hosted git repository.
areusch 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 cbcaeea Docs build at Wed Dec 15 10:02:02 PST 2021
cbcaeea is described below
commit cbcaeeaf61ef2c6e054991689dcc9025b56934c2
Author: Andrew Reusch <ar...@octoml.ai>
AuthorDate: Wed Dec 15 10:02:03 2021 -0800
Docs build at Wed Dec 15 10:02:02 PST 2021
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diff --git a/.gitignore b/.gitignore
index 72a3cf5..cf6401d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,3 @@ website.tgz
.jekyll-cache
docs.tgz
Gemfile.lock
-.bundle/
-vendor/
-
diff --git a/docs-tgzz/_downloads/00a1355fcb7c30e9e70fc8fefc708f98/tuple_inputs.ipynb b/docs-tgzz/_downloads/00a1355fcb7c30e9e70fc8fefc708f98/tuple_inputs.ipynb
deleted file mode 100644
index c9fe241..0000000
--- a/docs-tgzz/_downloads/00a1355fcb7c30e9e70fc8fefc708f98/tuple_inputs.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "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-tgzz/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py b/docs-tgzz/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
deleted file mode 100644
index b720402..0000000
--- a/docs-tgzz/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# 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-tgzz/_downloads/08e39628455fe618afd9eb5b958a433e/micro_reference_vm.ipynb b/docs-tgzz/_downloads/08e39628455fe618afd9eb5b958a433e/micro_reference_vm.ipynb
deleted file mode 100644
index fc30179..0000000
--- a/docs-tgzz/_downloads/08e39628455fe618afd9eb5b958a433e/micro_reference_vm.ipynb
+++ /dev/null
@@ -1,43 +0,0 @@
-{
- "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-tgzz/_downloads/09d0a1754546d603ffcd30e8e8a44cac/tune_alu_vta.py b/docs-tgzz/_downloads/09d0a1754546d603ffcd30e8e8a44cac/tune_alu_vta.py
deleted file mode 100644
index 3d38bbb..0000000
--- a/docs-tgzz/_downloads/09d0a1754546d603ffcd30e8e8a44cac/tune_alu_vta.py
+++ /dev/null
@@ -1,321 +0,0 @@
-# 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 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-tgzz/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb b/docs-tgzz/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
deleted file mode 100644
index 9e96def..0000000
--- a/docs-tgzz/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
- "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-tgzz/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb b/docs-tgzz/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
deleted file mode 100644
index a1b6c49..0000000
--- a/docs-tgzz/_downloads/0c8b1cb0bb1d1dff7899c341215a0f35/tune_network_mali.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "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-tgzz/_downloads/109bfb3877299cad725a4180ee2685d9/micro_autotune.ipynb b/docs-tgzz/_downloads/109bfb3877299cad725a4180ee2685d9/micro_autotune.ipynb
deleted file mode 100644
index 40f8461..0000000
--- a/docs-tgzz/_downloads/109bfb3877299cad725a4180ee2685d9/micro_autotune.ipynb
+++ /dev/null
@@ -1,162 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n\nAutotuning with micro TVM\n=========================\n**Author**: `Andrew Reusch <https://github.com/areusch>`_, `Mehrdad Hessar <https://github.com/mehrdadh>`\n\nThis tutorial explains how to autotune a model using the C runtime.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\nimport subprocess\nimport pathlib\n\nimport tvm"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Defining the model\n###################\n\n To begin with, define a model in Relay to be executed on-device. Then create an IRModule from relay model and\n fill parameters with random numbers.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "data_shape = (1, 3, 10, 10)\nweight_shape = (6, 3, 5, 5)\n\ndata = tvm.relay.var(\"data\", tvm.relay.TensorType(data_shape, \"float32\"))\nweight = tvm.relay.var(\"weight\", tvm.relay.TensorType(weight_shape, \"float32\"))\n\ny = tvm.relay.nn.conv2d(\n data,\n weight,\n padding=(2, 2),\n kernel_size=(5, 5),\n kernel_layout=\"OIHW\",\n out_dtype=\"float32\",\n)\nf = tvm.relay.Function([data, weight], y)\n\nrelay_mod = tvm.IRModule.from_expr(f)\nrelay_mod = tvm.r [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Defining the target #\n######################\n Now we define the TVM target that describes the execution environment. This looks very similar\n to target definitions from other microTVM tutorials.\n\n When running on physical hardware, choose a target and a board that\n describe the hardware. There are multiple hardware targets that could be selected from\n PLATFORM list in this tutorial. You can chose the platform by passing --platform argument when running\n this tutorial.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "TARGET = tvm.target.target.micro(\"host\")\n\n# Compiling for physical hardware\n# --------------------------------------------------------------------------\n# When running on physical hardware, choose a TARGET and a BOARD that describe the hardware. The\n# STM32L4R5ZI Nucleo target and board is chosen in the example below.\n#\n# TARGET = tvm.target.target.micro(\"stm32l4r5zi\")\n# BOARD = \"nucleo_l4r5zi\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Extracting tuning tasks\n########################\n Not all operators in the Relay program printed above can be tuned. Some are so trivial that only\n a single implementation is defined; others don't make sense as tuning tasks. Using\n `extract_from_program`, you can produce a list of tunable tasks.\n\n Because task extraction involves running the compiler, we first configure the compiler's\n transformation passes; we'll apply the same configuration later on during autotuning.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pass_context = tvm.transform.PassContext(opt_level=3, config={\"tir.disable_vectorize\": True})\nwith pass_context:\n tasks = tvm.autotvm.task.extract_from_program(relay_mod[\"main\"], {}, TARGET)\nassert len(tasks) > 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Configuring microTVM\n#####################\n Before autotuning, we need to define a module loader and then pass that to\n a `tvm.autotvm.LocalBuilder`. Then we create a `tvm.autotvm.LocalRunner` and use\n both builder and runner to generates multiple measurements for auto tunner.\n\n In this tutorial, we have the option to use x86 host as an example or use different targets\n from Zephyr RTOS. If you choose pass `--platform=host` to this tutorial it will uses x86. You can\n cho [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "repo_root = pathlib.Path(\n subprocess.check_output([\"git\", \"rev-parse\", \"--show-toplevel\"], encoding=\"utf-8\").strip()\n)\n\nmodule_loader = tvm.micro.AutoTvmModuleLoader(\n template_project_dir=repo_root / \"src\" / \"runtime\" / \"crt\" / \"host\",\n project_options={},\n)\nbuilder = tvm.autotvm.LocalBuilder(\n n_parallel=1,\n build_kwargs={\"build_option\": {\"tir.disable_vectorize\": True}},\n do_fork=True,\n build_func=tvm.micro.autotvm_build_fu [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Timing the untuned program\n###########################\n For comparison, let's compile and run the graph without imposing any autotuning schedules. TVM\n will select a randomly-tuned implementation for each operator, which should not perform as well as\n the tuned operator.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "with pass_context:\n lowered = tvm.relay.build(relay_mod, target=TARGET, params=params)\n\ntemp_dir = tvm.contrib.utils.tempdir()\n\nproject = tvm.micro.generate_project(\n str(repo_root / \"src\" / \"runtime\" / \"crt\" / \"host\"), lowered, temp_dir / \"project\"\n)\n\n# Compiling for physical hardware\n# --------------------------------------------------------------------------\n# project = tvm.micro.generate_project(\n# str(repo_root / \"apps\" / \"microtvm\" [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Timing the tuned program\n#########################\n Once autotuning completes, you can time execution of the entire program using the Debug Runtime:\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "with tvm.autotvm.apply_history_best(\"microtvm_autotune.log.txt\"):\n with pass_context:\n lowered_tuned = tvm.relay.build(relay_mod, target=TARGET, params=params)\n\ntemp_dir = tvm.contrib.utils.tempdir()\n\nproject = tvm.micro.generate_project(\n str(repo_root / \"src\" / \"runtime\" / \"crt\" / \"host\"), lowered_tuned, temp_dir / \"project\"\n)\n\n# Compiling for physical hardware\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-tgzz/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py b/docs-tgzz/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
deleted file mode 100644
index d802a87..0000000
--- a/docs-tgzz/_downloads/1195277fef6a622db64b78b4ea799ed4/matrix_multiply.py
+++ /dev/null
@@ -1,472 +0,0 @@
-# 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-tgzz/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb b/docs-tgzz/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
deleted file mode 100644
index 013615c..0000000
--- a/docs-tgzz/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
+++ /dev/null
@@ -1,277 +0,0 @@
-{
- "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)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will\ndump out the module IR when ``FoldConstant`` is done. Users can plug in this\npass after any pass they want to debug for viewing the optimization effect.\n\nThere is a more flexible debugging mechanism. One can implement a ``PassInstrument``\nclass to execute arbitrary code not only before and/or after each pass but also\nat entering/exiting ``PassContext``. See `pass_instrument_cpp_backend`\nfor more [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "@tvm.instrument.pass_instrument\nclass PrintIR:\n \"\"\"Print the name of the pass, the IR, only before passes execute.\"\"\"\n\n def run_before_pass(self, mod, info):\n print(\"Running pass: {}\", info)\n print(mod)\n\n\nwith tvm.transform.PassContext(opt_level=3, instruments=[PrintIR()]):\n with tvm.target.Target(\"llvm\"):\n # Perform the optimizations.\n mod = seq(mod)\nprint(mod)\n\nprint(\"done\")"
- ]
- },
- {
- "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-tgzz/_downloads/143c743c62f58570eabd77fd3395ca8c/scan.py b/docs-tgzz/_downloads/143c743c62f58570eabd77fd3395ca8c/scan.py
deleted file mode 100644
index ba8b5a9..0000000
--- a/docs-tgzz/_downloads/143c743c62f58570eabd77fd3395ca8c/scan.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# 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-tgzz/_downloads/15c94550bd86853c42df8e5d139d700a/tune_sparse_x86.py b/docs-tgzz/_downloads/15c94550bd86853c42df8e5d139d700a/tune_sparse_x86.py
deleted file mode 100644
index 55ee76e..0000000
--- a/docs-tgzz/_downloads/15c94550bd86853c42df8e5d139d700a/tune_sparse_x86.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# 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-tgzz/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb b/docs-tgzz/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
deleted file mode 100644
index fe2bffa..0000000
--- a/docs-tgzz/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "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)\nprint(rt_mod.benchmark(dev, number=1, repeat=n_repeat))"
- ]
- },
- {
- "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-tgzz/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py b/docs-tgzz/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
deleted file mode 100644
index c729b86..0000000
--- a/docs-tgzz/_downloads/18fb1ab3ed0a0c9f304520f2beaf4fd6/tvmc_command_line_driver.py
+++ /dev/null
@@ -1,511 +0,0 @@
-# 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``. The location of this command
-# will vary depending on your platform and installation method.
-#
-# 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.
-#
-
-################################################################################
-# .. note:: Adding ONNX Support to TVM
-#
-# TVM relies on the ONNX python library being available on your system. You
-# can install ONNX using the command ``pip3 install --user onnx``. You may
-# remove the ``--user`` option if you have root access and want to install
-# ONNX globally.
-#
-
-################################################################################
-# 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.
-#
-# You will need to have a supported version of the Python Image Library
-# installed. You can use ``pip3 install --user pillow`` to satisfy this
-# requirement for the script.
-#
-# .. 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 postprocess.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 (ms) max (ms) min (ms) std (ms)
-# # 92.19 115.73 89.85 3.15
-#
-# tvmc run \
-# --inputs imagenet_cat.npz \
-# --output predictions.npz \
-# --print-time \
-# --repeat 100 \
-# resnet50-v2-7-tvm.tar
-#
-# # Execution time summary:
-# # mean (ms) max (ms) min (ms) std (ms)
-# # 193.32 219.97 185.04 7.11
-#
-
-
-################################################################################
-# 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
-# Interface <auto_tuning_with_pyton>`_, we will cover the same compilation
-# and optimization steps using the Python interface.
diff --git a/docs-tgzz/_downloads/1c56ea64f39bea4084f04ec0959030ed/autotvm_matmul_x86.ipynb b/docs-tgzz/_downloads/1c56ea64f39bea4084f04ec0959030ed/autotvm_matmul_x86.ipynb
deleted file mode 100644
index 5473894..0000000
--- a/docs-tgzz/_downloads/1c56ea64f39bea4084f04ec0959030ed/autotvm_matmul_x86.ipynb
+++ /dev/null
@@ -1,222 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n\nOptimizing Operators with Schedule Templates and AutoTVM\n========================================================\n**Authors**:\n`Lianmin Zheng <https://github.com/merrymercy>`_,\n`Chris Hoge <https://github.com/hogepodge>`_\n\nIn this tutorial, we show how the TVM Tensor Expression (TE) language\ncan be used to write schedule templates that can be searched by AutoTVM to\nfind the optimal schedule. This process is called Auto-Tuning, which helps\nautomate the process of opt [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Install dependencies\n--------------------\nTo use autotvm package in TVM, we need to install some extra dependencies.\n\n.. code-block:: bash\n\n pip3 install --user psutil xgboost cloudpickle\n\nTo make TVM run faster in tuning, it is recommended to use cython as FFI of\nTVM. 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. Begin by importing the required packages.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import logging\nimport sys\n\nimport numpy as np\nimport tvm\nfrom tvm import te\nimport tvm.testing\n\n# the module is called `autotvm`\nfrom tvm import autotvm"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Basic Matrix Multiplication with TE\n-----------------------------------\nRecall the basic implementation of matrix multiplication using TE. We write\nit down here with a few changes. We will wrap the multiplication in a python\nfunction definition. For simplicity, we will focus our attention on a split\noptimization, using a fixed value that defines the block size of the\nreordering.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def matmul_basic(N, L, M, dtype):\n\n A = te.placeholder((N, L), name=\"A\", dtype=dtype)\n B = te.placeholder((L, M), name=\"B\", dtype=dtype)\n\n k = te.reduce_axis((0, L), name=\"k\")\n C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name=\"C\")\n s = te.create_schedule(C.op)\n\n # schedule\n y, x = s[C].op.axis\n k = s[C].op.reduce_axis[0]\n\n yo, yi = s[C].split(y, 8)\n xo, xi = s[C].split(x, 8)\n\n s[C].reorder(yo, xo [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Matrix Multiplication with AutoTVM\n----------------------------------\nIn the previous schedule code, we use a constant \"8\" as the tiling factor.\nHowever, it might not be the best one because the best tiling factor depends\non real hardware environment and input shape.\n\nIf you want the schedule code to be portable across a wider range of input\nshapes and target hardware, it is better to define a set of candidate values\nand pick the best one according to the measurement r [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A Basic Matrix Multiplication Template\n--------------------------------------\nWe begin with an example of how to create a tunable parameter set for the\nblock size of the `split` scheduling operation.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Matmul V1: List candidate values\n@autotvm.template(\"tutorial/matmul_v1\") # 1. use a decorator\ndef matmul_v1(N, L, M, dtype):\n A = te.placeholder((N, L), name=\"A\", dtype=dtype)\n B = te.placeholder((L, M), name=\"B\", dtype=dtype)\n\n k = te.reduce_axis((0, L), name=\"k\")\n C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name=\"C\")\n s = te.create_schedule(C.op)\n\n # schedule\n y, x = s[C].op.axis\n k = s[C].op.reduce_ax [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here we make four modifications to the previous schedule code and get a\ntunable \"template\". We can explain the modifications one by one.\n\n1. Use a decorator to mark this function as a simple template.\n2. Get a config object: You can regard this :code:`cfg` as an argument of\n this function but we obtain it in a different way. With this argument, this\n function is no longer a deterministic schedule. Instead, we can pass\n different configurations to this function and [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A Matrix Multiplication Template with the Advanced Parameter API\n----------------------------------------------------------------\nIn the previous template, we manually listed all of the possible values for a\nknob. This is the lowest level API to define the space, and gives an explicit\nenumeration of the parameter space to search. However, we also provide\nanother set of APIs that can make the definition of the search space easier\nand smarter. Where possible, we receomment y [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "@autotvm.template(\"tutorial/matmul\")\ndef matmul(N, L, M, dtype):\n A = te.placeholder((N, L), name=\"A\", dtype=dtype)\n B = te.placeholder((L, M), name=\"B\", dtype=dtype)\n\n k = te.reduce_axis((0, L), name=\"k\")\n C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name=\"C\")\n s = te.create_schedule(C.op)\n\n # schedule\n y, x = s[C].op.axis\n k = s[C].op.reduce_axis[0]\n\n ##### define space begin #####\n cfg = autotvm.g [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<div class=\"alert alert-info\"><h4>Note</h4><p>More Explanation on :code:`cfg.define_split`</p></div>\n\n In this template, :code:`cfg.define_split(\"tile_y\", y, num_outputs=2)` will\n enumerate all possible combinations that can split axis y into two axes with\n factors of the length of y. For example, if the length of y is 32 and we\n want to split it into two axes using factors of 32, then there are 6\n possible values for (length of outer axis, length of inner axis) pair,\ [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Step 2: Use AutoTVM to Optimize the Matrix Multiplication\n---------------------------------------------------------\nIn Step 1, we wrote a matrix multiplication template that allowed us to\nparamaterize the block size used in the `split` schedule. We can now conduct\na search over this parameter space. The next step is to pick a tuner to guide\nthe exploration of this space.\n\nAuto-tuners in TVM\n~~~~~~~~~~~~~~~~~~\nThe job for a tuner can be described by following pseudo code [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Begin tuning\n~~~~~~~~~~~~\nHere we continue our matrix multiplication example. First we create a tuning\ntask. We can also inspect the initialized search space. In this case, for a\n512x512 square matrix multiplication, the space size is 10x10=100 Note that\nthe task and search space are independent of the tuner picked.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "N, L, M = 512, 512, 512\ntask = autotvm.task.create(\"tutorial/matmul\", args=(N, L, M, \"float32\"), target=\"llvm\")\nprint(task.config_space)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then we need to define how to measure the generated code and pick a tuner.\nSince our space is small, a random tuner is just okay.\n\nWe only make 10 trials in this tutorial for demonstration. In practice, you\ncan do more trials according to your time budget. We will log the tuning\nresults into a log file. This file can be used to choose the best\nconfiguration discovered by the tuner later.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# logging config (for printing tuning log to the screen)\nlogging.getLogger(\"autotvm\").setLevel(logging.DEBUG)\nlogging.getLogger(\"autotvm\").addHandler(logging.StreamHandler(sys.stdout))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There are two steps for measuring a config: build and run. By default, we use\nall CPU cores to compile program. We then measure them sequentially. To help\nreduce variance, we take 5 measurements and average them.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "measure_option = autotvm.measure_option(builder=\"local\", runner=autotvm.LocalRunner(number=5))\n\n# Begin tuning with RandomTuner, log records to file `matmul.log`\n# You can use alternatives like XGBTuner.\ntuner = autotvm.tuner.RandomTuner(task)\ntuner.tune(\n n_trial=10,\n measure_option=measure_option,\n callbacks=[autotvm.callback.log_to_file(\"matmul.log\")],\n)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With tuning completed, we can choose the configuration from the log file that\nhas the best measured performance and compile the schedule with the\ncorresponding parameters. We also do a quick verfication that the schedule is\nproducing correct answers. We can call the function :code:`matmul` directly\nunder the :any:`autotvm.apply_history_best` context. When we call this\nfunction, it will query the dispatch context with its argument and get the\nbest config with the same argu [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# apply history best from log file\nwith autotvm.apply_history_best(\"matmul.log\"):\n with tvm.target.Target(\"llvm\"):\n s, arg_bufs = matmul(N, L, M, \"float32\")\n func = tvm.build(s, arg_bufs)\n\n# check correctness\na_np = np.random.uniform(size=(N, L)).astype(np.float32)\nb_np = np.random.uniform(size=(L, M)).astype(np.float32)\nc_np = a_np.dot(b_np)\n\nc_tvm = tvm.nd.empty(c_np.shape)\nfunc(tvm.nd.array(a_np), tvm.nd.array(b_np), c_tvm)\n\ntvm.testing.as [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Final Notes and Summary\n-----------------------\nIn this tutorial, we have shown how to build operator templates that allow\nTVM to search a parameter space and choose optimized schedule configurations.\nTo gain a deeper understanding of how this works, we recommend expanding on\nthis example by adding new search parameters to the schedule based on\nschedule operations demonstated in the `Getting Started With Tensor\nExpressions <tensor_expr_get_started>_` tutorial. In the upco [...]
- ]
- }
- ],
- "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-tgzz/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb b/docs-tgzz/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
deleted file mode 100644
index f5a049b..0000000
--- a/docs-tgzz/_downloads/2354a24ad8bc07194943c49f2fb48874/tune_conv2d_cuda.ipynb
+++ /dev/null
@@ -1,115 +0,0 @@
-{
- "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-tgzz/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb b/docs-tgzz/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
deleted file mode 100644
index aeb371d..0000000
--- a/docs-tgzz/_downloads/24a7471da81b18c4ba77d215289aed2f/relay_quick_start.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
... 1566543 lines suppressed ...