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A Python implementation of the UVM using cocotb

Project description

Description

pyuvm is the Universal Verification Methodology implemented in Python instead of SystemVerilog. pyuvm uses cocotb to interact with the simulator and schedule simulation events.

pyuvm implements the most often-used parts of the UVM while taking advantage of the fact that Python does not have strict typing and does not require parameterized classes. The project refactors pieces of the UVM that were either overly complicated due to typing or legacy code.

The code is based in the IEEE 1800.2 specification and most classes and methods have the specification references in the comments.

The following IEEE 1800.2 sections have been implemented:

Section Name Description
5 Base Classes uvm_object does not capture transaction timing information
6 Reporting Classes Leverages logging, controlled using UVM hierarchy
8 Factory Classes All uvm_void classes automatically registered
9 Phasing Simplified to only common phases. Supports objection system
12 UVM TLM Interfaces Fully implemented
13 Predefined Component Classes Implements uvm_component with hierarchy, uvm_root singleton,run_test(), simplified ConfigDB, uvm_driver, etc
14 & 15 Sequences, sequencer, sequence_item Refactored sequencer functionality leveraging Python language capabilities. Simpler and more direct implementation

Installation

You can install pyuvm using pip. This will also install cocotb as a requirement for pyuvm.

% pip install pyuvm

Then you can run a simple test:

% python
>>> from pyuvm import *
>>> my_object = uvm_object("my_object")
>>> type(my_object)
<class 's05_base_classes.uvm_object'>
>>> print("object name:", my_object.get_name())
object name: my_object

Running from the repository

You can run pyuvm from a cloned repository by installing the cloned repository using pip.

% cd <pyuvm repo directory>
% pip install -e .

Usage

This section demonstrates running an example simulation and then shows how the example has been put together demonstrating what the UVM looks like in Python.

Running the simulation

The TinyALU is, as its name implies, a tiny ALU. It has four operations: ADD, AND, NOT, and MUL. This example shows us running the Verilog version of the design, but there is also a VHDL version.

cocotb uses a Makefile to run its simulation. We see it in examples/TinyALU:

CWD=$(shell pwd)
COCOTB_REDUCED_LOG_FMT = True
SIM ?= icarus
VERILOG_SOURCES =$(CWD)/hdl/verilog/tinyalu.sv
MODULE := testbench
TOPLEVEL=tinyalu
TOPLEVEL_LANG=verilog
COCOTB_HDL_TIMEUNIT=1us
COCOTB_HDL_TIMEPRECISION=1us
include $(shell cocotb-config --makefiles)/Makefile.sim

You can learn more about the Makefile targets at cocotb.org. The cocotb-config command on the last line points to the cocotb Makefile locations and launches the sim target.

Modify the SIM variable to match your simulator. All the simulator types are in cocotb/share/makefiles/simulators/makefile.$(SIM).

You should be able to run the simulation like this:

% cd <path>/pyuvm/examples/TinyALU
% make sim

cocotb will present a lot of messages, but in the middle of them you will see these UVM messages. It runs four examples with one for each command with randomized operands.

250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0x34 ADD 0x23 = 0x0057
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0xf9 AND 0x29 = 0x0029
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0x71 XOR 0x01 = 0x0070
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0xb8 MUL 0x47 = 0x3308
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0xff ADD 0xff = 0x01fe
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0xff AND 0xff = 0x00ff
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0xff XOR 0xff = 0x0000
250000.00ns INFO     testbench.py(209)[uvm_test_top.env.scoreboard]: PASSED: 0xff MUL 0xff = 0xfe01

The TinyAluBfm in tinyalu_utils.py

The TinyAluBfm is a singleton that uses cocotb to communicate with the TinyALU. The BFM exposes three coroutines to the user: send_op(), get_cmd(), and get_result().

The singleton uses the cocotb.top variable to get the handle to the DUT. This is the handle that we normally pass to a cocotb.test() coroutine.

The TinyAluBfm is defined in tinyalu_utils.py and imported into our testbench.

The pyuvm testbench

The testbench.py contains the entire UVM testbench and connects to the TinyALU through a TinyAluBfm object defined in tinyalu_utils.py. We'll examine the testbench.py file and enough of the cocotb test too run the simlation

Importing pyuvm

Testbenches written in the SystemVerilog UVM usually import the package like this:

import uvm_pkg::*;

This gives you access to the class names without needing a package path. To get similar behavior with pyuvm us the from import syntax. We import pyuvm to distinguish the @pyuvm.test() decorator from the @cocotb.test() decorator:

import pyuvm
from pyuvm import *

The AluTest classes

We're going to examine the UVM classes from the top, the test, to the bottom, the sequences.

pyuvm names the UVM classes as they are named in the specification. Therefore **pyvu use underscore naming as is done in SystemVerilog and not camel-casing.

We extend uvm_test to create the AluTest, using camel-casing in our code even if pyuvm does not use it:

You'll see the following in the test:

  • We define a class that extends uvm_test.

  • We use the @pyuvm.test() decorator to notify cocotb that this is a test.

  • There is no uvm_component_utils() macro. pyuvm automatically registers classes that extend uvm_void with the factory.

  • The phases do not have a phase variable. Phasing has been refactored to support only the common phases as described in the specification.

  • We create the environment using the create() method and the factory. Notice that create() is now a simple class method. There is no typing-driven incantation.

  • raise_objection() is now a uvm_component method. There is no longer a phase variable.

  • The ConfigDB() singleton acts the same way as the uvm_config_db interface in the SystemVerilog UVM. pyuvm refactored away the uvm_resource_db as there are no issues with classes to manage.

  • pyuvm leverages the Python logging system and does not implement the UVM reporting system. Every descendent of uvm_report_object has a logger data member.

  • Sequences work as they do in the SystemVerilog UVM.

@pyuvm.test()
class AluTest(uvm_test):
    def build_phase(self):
        self.env = AluEnv("env", self)

    def end_of_elaboration_phase(self):
        self.test_all = TestAllSeq.create("test_all")

    async def run_phase(self):
        self.raise_objection()
        await self.test_all.start()
        self.drop_objection()

We extend the AluTest class to create a parallel version of the test and a Fibonacci program. You can find these sequences in testbench.py

@pyuvm.test()
class ParallelTest(AluTest):
    def build_phase(self):
        uvm_factory().set_type_override_by_type(TestAllSeq, TestAllForkSeq)
        super().build_phase()

@pyuvm.test()
class FibonacciTest(AluTest):
    def build_phase(self):
        ConfigDB().set(None, "*", "DISABLE_COVERAGE_ERRORS", True)
        uvm_factory().set_type_override_by_type(TestAllSeq, FibonacciSeq)
        return super().build_phase()


All the familiar pieces of a UVM testbench are in pyuvm.

The ALUEnv Class

The uvm_env class is a container for the components that make up the testbench. There are four component classes instantiated:

  • Monitor—There are actually two monitors instantiated, one to monitor commands (self.cmd_mod) and the other to monitor results (self.result_mon). The Monitor code is the same for both. We pass them the name of the proxy function that gets the data they monitor.
  • Scoreboard—The scoreboard gathers all the commands and results and compares predicted results to actual results.
  • Coverage—The coverage class checks that we've covered all the kinds of operations and issues an error if we did not.
  • Driver—This uvm_driver processes sequences items.
  • uvm_sequencer—The uvm_sequencer queues sequence items and passes them to the Driver
  • We store self.seqr in the ConfigDB() so test can get it as we see above.

The AluEnv creates all these components in build_phase() and connects the exports to the ports in connect_phase(). The build_phase() is a top-down phase and the connect_phase() is a bottom up phase.

class AluEnv(uvm_env):

    def build_phase(self):
        self.seqr = uvm_sequencer("seqr", self)
        ConfigDB().set(None, "*", "SEQR", self.seqr)
        self.driver = Driver.create("driver", self)
        self.cmd_mon = Monitor("cmd_mon", self, "get_cmd")
        self.coverage = Coverage("coverage", self)
        self.scoreboard = Scoreboard("scoreboard", self)

    def connect_phase(self):
        self.driver.seq_item_port.connect(self.seqr.seq_item_export)
        self.cmd_mon.ap.connect(self.scoreboard.cmd_export)
        self.cmd_mon.ap.connect(self.coverage.analysis_export)
        self.driver.ap.connect(self.scoreboard.result_export)

The Monitor

The Monitor extends uvm_component. Takes the name of a CocotProxy method name as an argument. It uses the name to find the method in the proxy and then calls the method. You cannot do this in SystemVerilog as there is no introspection.

The Monitor creates an analysis port and writes the data it gets into the analysis port.

Notice in the run_phase() we use the await keyword to wait for the get_cmd coroutine. Unlike SystemVerilog, Python makes it clear when you are calling a time-consuming task vs a function. Also notice that the run_phase() has the async keyword to designate that it is a coroutine. (A task in SystemVerilog.)

class Monitor(uvm_component):
    def __init__(self, name, parent, method_name):
        super().__init__(name, parent)
        self.method_name = method_name

    def build_phase(self):
        self.ap = uvm_analysis_port("ap", self)
        self.bfm = TinyAluBfm()
        self.get_method = getattr(self.bfm, self.method_name)

    async def run_phase(self):
        while True:
            datum = await self.get_method()
            self.logger.debug(f"MONITORED {datum}")
            self.ap.write(datum)

The Scoreboard

The scoreboard receives commands from the command monitor and results from the results monitor in the same order. It uses the commands to predict the results and compares them.

  • The build_phase uses uvm_tlm_analysis_fifos to receive data from the monitors and store it.
  • The scoreboard exposes the FIFO exports by copying them into class data members. As we see in the environment above, this allows us to connect the exports without reaching into the Scoreboard's inner workings.
  • We connect the exports in the connect_phase()
  • The check_phase() runs after the run_phase()`. At this point the scoreboard has all operations and results. It loops through the operations and predicts the result, then it compares the predicted and actual result.
  • Notice that we do not use UVM reporting. Instead we us the Python logging module. Every uvm_report_object and its children has its own logger stored in self.logger.
class Scoreboard(uvm_component):

    def build_phase(self):
        self.cmd_fifo = uvm_tlm_analysis_fifo("cmd_fifo", self)
        self.result_fifo = uvm_tlm_analysis_fifo("result_fifo", self)
        self.cmd_get_port = uvm_get_port("cmd_get_port", self)
        self.result_get_port = uvm_get_port("result_get_port", self)
        self.cmd_export = self.cmd_fifo.analysis_export
        self.result_export = self.result_fifo.analysis_export

    def connect_phase(self):
        self.cmd_get_port.connect(self.cmd_fifo.get_export)
        self.result_get_port.connect(self.result_fifo.get_export)

    def check_phase(self):
        while self.result_get_port.can_get():
            _, actual_result = self.result_get_port.try_get()
            cmd_success, cmd = self.cmd_get_port.try_get()
            if not cmd_success:
                self.logger.critical(f"result {actual_result} had no command")
            else:
                (A, B, op_numb) = cmd
                op = Ops(op_numb)
                predicted_result = alu_prediction(A, B, op)
                if predicted_result == actual_result:
                    self.logger.info(f"PASSED: 0x{A:02x} {op.name} 0x{B:02x} ="
                                     f" 0x{actual_result:04x}")
                else:
                    self.logger.error(f"FAILED: 0x{A:02x} {op.name} 0x{B:02x} "
                                      f"= 0x{actual_result:04x} "
                                      f"expected 0x{predicted_result:04x}")

Coverage

The Coverage Class extends uvm_subscriber which extends uvm_analysis_export. As we see in the AluEnv above, this allows us to pass the object directly to the connect() method to connect it to an analysis port.

The Coverage Class overrides the write() method expected of a uvm_subscriber. If it didn't you'd get a runtime error. The Coverage class uses a set to store all the operations seen. Then it subtracts that from the set of all operations. If the result has a length longer than 0 it issues an error.

Since this tesbench loops through all the operations you will not see this error.

class Coverage(uvm_subscriber):

    def end_of_elaboration_phase(self):
        self.cvg = set()

    def write(self, cmd):
        (_, _, op) = cmd
        self.cvg.add(op)

    def report_phase(self):
        try:
            disable_errors = ConfigDB().get(
                self, "", "DISABLE_COVERAGE_ERRORS")
        except UVMConfigItemNotFound:
            disable_errors = False
        if not disable_errors:
            if len(set(Ops) - self.cvg) > 0:
                self.logger.error(
                    f"Functional coverage error. Missed: {set(Ops)-self.cvg}")
                assert False
            else:
                self.logger.info("Covered all operations")
                assert True

Driver

The Driver extends uvm_driver and so it works with sequences and sequence items.

The connect_phase() gets the proxy from the ConfigDB() and the run_phase() uses it to get items and process them by calling send_op. We use while True because we do this forever. cocotb will shut down the run_phase coroutine at the end of simulation.

class Driver(uvm_driver):
    def build_phase(self):
        self.ap = uvm_analysis_port("ap", self)

    def start_of_simulation_phase(self):
        self.bfm = TinyAluBfm()

    async def launch_tb(self):
        await self.bfm.reset()
        self.bfm.start_tasks()

    async def run_phase(self):
        await self.launch_tb()
        while True:
            cmd = await self.seq_item_port.get_next_item()
            await self.bfm.send_op(cmd.A, cmd.B, cmd.op)
            result = await self.bfm.get_result()
            self.ap.write(result)
            cmd.result = result
            self.seq_item_port.item_done()

The ALU Sequence

The ALU Sequence creates sequence items, randomizes them and sends them to the Driver. It inherits start_item and finish_item from uvm_sequence.

It is clear that start_item and finish_item block because we call them using the await keyword. start_item waits for it's turn to use the sequencer, and finish_item sends the sequence_item to the driver and returns when the driver calls item_done()

class TestAllSeq(uvm_sequence):

    async def body(self):
        seqr = ConfigDB().get(None, "", "SEQR")
        random = RandomSeq("random")
        max = MaxSeq("max")
        await random.start(seqr)
        await max.start(seqr)

This virtual sequence launches two other sequences: RandomSeq and MaxSeq. RandomSeq randomizes the operands.

class RandomSeq(uvm_sequence):
    async def body(self):
        for op in list(Ops):
            cmd_tr = AluSeqItem("cmd_tr", None, None, op)
            await self.start_item(cmd_tr)
            cmd_tr.randomize_operands()
            await self.finish_item(cmd_tr)

MaxSeq sets the operands to 0xff:

class MaxSeq(uvm_sequence):
    async def body(self):
        for op in list(Ops):
            cmd_tr = AluSeqItem("cmd_tr", 0xff, 0xff, op)
            await self.start_item(cmd_tr)
            await self.finish_item(cmd_tr)

ALU Sequence Item

The AluSeqItem contains the TinyALU commands. It has two operands and an operation.

The SystemVerilog uvm_sequence_item class uses convert2string() to convert the item to a string and do_compare() to compare the item to another item. We do not use these in pyuvm because Python has magic methods that do these functions.

__eq__()—This does the same thing as do_compare() It returns True if the items are equal. This method works with the == operator.

__str__()—This does the same thing as convert2string(). It returns a string version of the item. The print function calls this method automatically.

class AluSeqItem(uvm_sequence_item):

    def __init__(self, name, aa, bb, op):
        super().__init__(name)
        self.A = aa
        self.B = bb
        self.op = Ops(op)

    def randomize_operands(self):
        self.A = random.randint(0, 255)
        self.B = random.randint(0, 255)

    def randomize(self):
        self.randomize_operands()
        self.op = random.choice(list(Ops))

    def __eq__(self, other):
        same = self.A == other.A and self.B == other.B and self.op == other.op
        return same

    def __str__(self):
        return f"{self.get_name()} : A: 0x{self.A:02x} \
        OP: {self.op.name} ({self.op.value}) B: 0x{self.B:02x}"

Now that we've got the UVM testbench we can call it from a cocotb test.

The Cocotb Tests

cocotb finds uvm_test classes identified with the @pyuvm.test() decorator and launches them as coroutines. Our test does the following:

Contributing

You can contribute to pyuvm by forking this repository and submitting pull requests.

The repository runs all needed tests using tox. The test runs flake8 and fails if that linter finds any issues. Visual Studio Code can be set up to automatically check flake8 issues. The repository ignores F403 and F405 issues from flake8.

There are three sets of pytest tests that test features that don't use coroutines. The rest of the tests are in tests/cocotb_tests and need a simulator to run.

Credits:

  • Ray Salemi—Original author, created as an employee of Siemens.
  • IEEE 1800.2 Specification
  • Siemens for supporting me in this effort.

License

Copyright 2020 Siemens EDA

Licensed 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.

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