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"""
.. _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
import vta
import numpy as np
from tvm import rpc
from tvm.contrib import util
from vta.testing import simulator

# Load VTA parameters from the vta/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_PYNQ_RPC_HOST", "192.168.2.99")
port = int(os.environ.get("VTA_PYNQ_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":

    # Make sure that TVM was compiled with RPC=1
    assert tvm.module.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/uwsaml/web-data/master/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/uwsaml/web-data/master/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/uwsaml/web-data/master/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 = tvm.placeholder((o, n, env.BATCH, env.BLOCK_IN), name="A", dtype=env.inp_dtype)
# B placeholder tensor in tiled data format
B = tvm.placeholder((m, n, env.BLOCK_OUT, env.BLOCK_IN), name="B", dtype=env.wgt_dtype)
# A copy buffer
A_buf = tvm.compute((o, n, env.BATCH, env.BLOCK_IN), lambda *i: A(*i), "A_buf")
# B copy buffer
B_buf = tvm.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:`tvm.reduce_axis`, which takes in the range of reductions.
# :code:`tvm.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 = tvm.reduce_axis((0, n), name="ko")
# Inner input feature reduction axis
ki = tvm.reduce_axis((0, env.BLOCK_IN), name="ki")
# Describe the in-VTA matrix multiplication
C_buf = tvm.compute(
    (o, m, env.BATCH, env.BLOCK_OUT),
    lambda bo, co, bi, ci:
        tvm.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 = tvm.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 = tvm.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 = util.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:`asnumpy()` 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.asnumpy())

# 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.
#
