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[VTA] Bug fix for padded load with large inputs (apache#4293)
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* bug fix for padded load with large inputs

* Update TensorLoad.scala

* Update test_vta_insn.py
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@liangfu
liangfu authored and Xingyu Zhou committed Nov 15, 2019
1 parent 8bf20c4 commit 0be8e56
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Showing 2 changed files with 75 additions and 70 deletions.
29 changes: 14 additions & 15 deletions vta/hardware/chisel/src/main/scala/core/TensorLoad.scala
View file
Original file line number Diff line number Diff line change
Expand Up @@ -103,20 +103,21 @@ class TensorLoad(tensorType: String = "none", debug: Boolean = false)(
when(dec.xpad_1 =/= 0.U) {
state := sXPad1
}.elsewhen(dec.ypad_1 =/= 0.U) {
state := sYPad1
}
.otherwise {
state := sIdle
}
}.elsewhen(dataCtrl.io.stride || dataCtrl.io.split) {
state := sYPad1
}
.otherwise {
state := sIdle
}
}.elsewhen(dataCtrl.io.stride) {
when(dec.xpad_1 =/= 0.U) {
state := sXPad1
}.elsewhen(dec.xpad_0 =/= 0.U) {
state := sXPad0
}
.otherwise {
state := sReadCmd
}
state := sXPad0
}.otherwise {
state := sReadCmd
}
}.elsewhen(dataCtrl.io.split) {
state := sReadCmd
}
}
}
Expand Down Expand Up @@ -168,13 +169,11 @@ class TensorLoad(tensorType: String = "none", debug: Boolean = false)(
xPadCtrl0.io.start := dec.xpad_0 =/= 0.U &
((state === sIdle & io.start) |
(state === sYPad0 & yPadCtrl0.io.done) |
(io.vme_rd.data
.fire() & ~dataCtrlDone & (dataCtrl.io.stride | dataCtrl.io.split) & dec.xpad_1 === 0.U) |
(io.vme_rd.data.fire() & ~dataCtrlDone & dataCtrl.io.stride & dec.xpad_1 === 0.U) |
(state === sXPad1 & xPadCtrl1.io.done & ~dataCtrlDone))

xPadCtrl1.io.start := dec.xpad_1 =/= 0.U & io.vme_rd.data.fire() &
((dataCtrl.io.done) |
(~dataCtrl.io.done & (dataCtrl.io.stride | dataCtrl.io.split) & dec.xpad_1 =/= 0.U))
((dataCtrl.io.done) | (~dataCtrl.io.done & dataCtrl.io.stride & dec.xpad_1 =/= 0.U))

yPadCtrl0.io.inst := io.inst
yPadCtrl1.io.inst := io.inst
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116 changes: 61 additions & 55 deletions vta/tests/python/unittest/test_vta_insn.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
import vta.testing
from vta.testing import simulator

np.random.seed(0xdeadb)

def test_save_load_out():
"""Test save/store output command"""
Expand Down Expand Up @@ -88,68 +89,73 @@ def _run(env, remote):
def test_padded_load():
"""Test padded load."""
def _run(env, remote):
# declare
n = 3
m = 5
pad_before = [2, 1, 0, 0]
pad_after = [1, 2, 0, 0]
x = tvm.placeholder(
(n, m, env.BATCH, env.BLOCK_OUT),
name="x",
dtype=env.acc_dtype)
x_buf = topi.nn.pad(x, pad_before, pad_after, name="y")
# insert no-op that won't be optimized away
y_buf = tvm.compute((n + pad_before[0] + pad_after[0],
def check_padded_load(pad_before, pad_after, test_name=None):
# declare
n = 3
m = 5
x = tvm.placeholder(
(n, m, env.BATCH, env.BLOCK_OUT),
name="x",
dtype=env.acc_dtype)
x_buf = topi.nn.pad(x, pad_before, pad_after, name="y")
# insert no-op that won't be optimized away
y_buf = tvm.compute((n + pad_before[0] + pad_after[0],
m + pad_before[1] + pad_after[1],
env.BATCH,
env.BLOCK_OUT), lambda *i: x_buf(*i)>>0, "y_buf")
y = tvm.compute((n + pad_before[0] + pad_after[0],
m + pad_before[1] + pad_after[1],
env.BATCH,
env.BLOCK_OUT), lambda *i: x_buf(*i)>>0, "y_buf")
y = tvm.compute((n + pad_before[0] + pad_after[0],
m + pad_before[1] + pad_after[1],
env.BATCH,
env.BLOCK_OUT), lambda *i: y_buf(*i).astype(env.inp_dtype), "y")
# schedule
s = tvm.create_schedule(y.op)
s[x_buf].set_scope(env.acc_scope)
s[x_buf].pragma(x_buf.op.axis[0], env.dma_copy)
s[y_buf].set_scope(env.acc_scope)
s[y_buf].pragma(y_buf.op.axis[0], env.alu)
s[y].pragma(y.op.axis[0], env.dma_copy)
# build
with vta.build_config():
mod = vta.build(s, [x, y], "ext_dev", env.target_host)
env.BLOCK_OUT), lambda *i: y_buf(*i).astype(env.inp_dtype), "y")
# schedule
s = tvm.create_schedule(y.op)
s[x_buf].set_scope(env.acc_scope)
s[x_buf].pragma(x_buf.op.axis[0], env.dma_copy)
s[y_buf].set_scope(env.acc_scope)
s[y_buf].pragma(y_buf.op.axis[0], env.alu)
s[y].pragma(y.op.axis[0], env.dma_copy)
# build
with vta.build_config():
mod = vta.build(s, [x, y], "ext_dev", env.target_host)

if not remote:
return
temp = util.tempdir()
mod.save(temp.relpath("padded_load.o"))
remote.upload(temp.relpath("padded_load.o"))
f = remote.load_module("padded_load.o")
# verify
ctx = remote.ext_dev(0)
x_np = np.random.randint(-10, 10, size=(
n, m, env.BATCH, env.BLOCK_OUT)).astype(x.dtype)
y_np = np.zeros((n + pad_before[0] + pad_after[0],
m + pad_before[1] + pad_after[1],
env.BATCH,
env.BLOCK_OUT)).astype(y.dtype)
y_np[pad_before[0]:pad_before[0] + n,
pad_before[1]:pad_before[1] + m,
:] = x_np
x_nd = tvm.nd.array(x_np, ctx)
y_nd = tvm.nd.empty(y_np.shape, ctx=ctx, dtype=y_np.dtype)
if not remote:
return
temp = util.tempdir()
mod.save(temp.relpath("padded_load.o"))
remote.upload(temp.relpath("padded_load.o"))
f = remote.load_module("padded_load.o")
# verify
ctx = remote.ext_dev(0)
x_np = np.random.randint(0, 10, size=(
n, m, env.BATCH, env.BLOCK_OUT)).astype(x.dtype)
y_np = np.zeros((n + pad_before[0] + pad_after[0],
m + pad_before[1] + pad_after[1],
env.BATCH,
env.BLOCK_OUT)).astype(y.dtype)
y_np[pad_before[0]:pad_before[0] + n,
pad_before[1]:pad_before[1] + m,
:] = x_np
x_nd = tvm.nd.array(x_np, ctx)
y_nd = tvm.nd.empty(y_np.shape, ctx=ctx, dtype=y_np.dtype)

if env.TARGET in ["sim", "tsim"]:
simulator.clear_stats()
if env.TARGET in ["sim", "tsim"]:
simulator.clear_stats()

f(x_nd, y_nd)
f(x_nd, y_nd)

np.testing.assert_equal(y_np, y_nd.asnumpy())
np.testing.assert_equal(y_np, y_nd.asnumpy())

if env.TARGET in ["sim", "tsim"]:
sim_stats = simulator.stats()
print("Padded load execution statistics:")
for k, v in sim_stats.items():
print("\t{:<16}: {:>16}".format(k, v))
if env.TARGET in ["sim", "tsim"]:
sim_stats = simulator.stats()
print("Padded {} load execution statistics:".format(test_name))
for k, v in sim_stats.items():
print("\t{:<16}: {:>16}".format(k, v))

check_padded_load([2, 0, 0, 0], [0, 0, 0, 0], test_name="Y0")
check_padded_load([0, 2, 0, 0], [0, 0, 0, 0], test_name="Y1")
check_padded_load([0, 0, 0, 0], [2, 0, 0, 0], test_name="X0")
check_padded_load([0, 0, 0, 0], [0, 2, 0, 0], test_name="X1")
check_padded_load([1, 1, 0, 0], [1, 1, 0, 0], test_name="all")

vta.testing.run(_run)

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