def _run(env, remote):
m = 8
n = 10
# compute
a = tvm.placeholder((m, n, env.BATCH, env.BLOCK_OUT),
name="a",
dtype=env.acc_dtype)
a_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT), lambda *i: a(*i),
"a_buf") # DRAM->SRAM
max_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: tvm.max(a_buf(*i), 0),
"res_buf") # relu
min_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: tvm.min(max_buf(*i),
(1 <<
(env.INP_WIDTH - 1)) - 1),
"max_buf") # relu
res = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: min_buf(*i).astype(env.inp_dtype),
"min_buf") # SRAM->DRAM
# schedule
s = tvm.create_schedule(res.op)
s[a_buf].set_scope(env.acc_scope) # SRAM
s[a_buf].pragma(a_buf.op.axis[0], env.dma_copy) # DRAM->SRAM
s[max_buf].set_scope(env.acc_scope) # SRAM
s[min_buf].set_scope(env.acc_scope) # SRAM
s[max_buf].pragma(max_buf.op.axis[0], env.alu) # compute
s[min_buf].pragma(min_buf.op.axis[0], env.alu) # compute
s[res].pragma(res.op.axis[0], env.dma_copy) # SRAM->DRAM
# build
with vta.build_config():
mod = vta.build(s, [a, res], "ext_dev", env.target_host)
if not remote:
return
temp = util.tempdir()
mod.save(temp.relpath("load_act.o"))
remote.upload(temp.relpath("load_act.o"))
f = remote.load_module("load_act.o")
# verify
ctx = remote.ext_dev(0)
a_np = np.random.randint(-256,
256,
size=(m, n, env.BATCH,
env.BLOCK_OUT)).astype(a.dtype)
res_np = np.clip(a_np, 0, (1 <<
(env.INP_WIDTH - 1)) - 1).astype(res.dtype)
a_nd = tvm.nd.array(a_np, ctx)
res_nd = tvm.nd.array(
np.zeros((m, n, env.BATCH, env.BLOCK_OUT)).astype(res.dtype), ctx)
if env.TARGET == "tsim":
simulator.tsim_init("libvta_hw")
f(a_nd, res_nd)
np.testing.assert_equal(res_np, res_nd.asnumpy())
if env.TARGET == "tsim":
print("Relu test took {} clock cycles".format(
simulator.tsim_cycles()))
def _run(env, remote):
m = 2
n = 8
imm_shift = np.random.randint(0, 8)
imm_scale = np.random.randint(1, 5)
# compute
a = tvm.placeholder((m, n, env.BATCH, env.BLOCK_OUT),
name="a",
dtype=env.acc_dtype)
a_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT), lambda *i: a(*i),
"a_buf") # DRAM->SRAM
res_shift = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: a_buf(*i) + imm_shift,
"res_shift") # compute
res_scale = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: res_shift(*i) >> imm_scale,
"res_scale") # compute
res = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: res_scale(*i).astype(env.inp_dtype),
"res") # SRAM->DRAM
# schedule
s = tvm.create_schedule(res.op)
s[a_buf].set_scope(env.acc_scope) # SRAM
s[res_shift].set_scope(env.acc_scope) # SRAM
s[res_scale].set_scope(env.acc_scope) # SRAM
s[a_buf].pragma(a_buf.op.axis[0], env.dma_copy) # DRAM->SRAM
s[res_shift].pragma(res_shift.op.axis[0], env.alu) # compute
s[res_scale].pragma(res_scale.op.axis[0], env.alu) # compute
s[res].pragma(res.op.axis[0], env.dma_copy) # SRAM->DRAM
# build
mod = vta.build(s, [a, res], "ext_dev", env.target_host)
if not remote:
return
temp = util.tempdir()
mod.save(temp.relpath("load_act.o"))
remote.upload(temp.relpath("load_act.o"))
f = remote.load_module("load_act.o")
# verify
ctx = remote.ext_dev(0)
a_np = np.random.randint(-10,
10,
size=(m, n, env.BATCH,
env.BLOCK_OUT)).astype(a.dtype)
res_np = np.right_shift((a_np + imm_shift), imm_scale)
res_np = res_np.astype(res.dtype)
a_nd = tvm.nd.array(a_np, ctx)
res_nd = tvm.nd.array(
np.zeros((m, n, env.BATCH, env.BLOCK_OUT)).astype(res.dtype), ctx)
if env.TARGET == "tsim":
simulator.tsim_init("libvta_hw")
f(a_nd, res_nd)
np.testing.assert_equal(res_np, res_nd.asnumpy())
if env.TARGET == "tsim":
print("Shift/scale test took {} clock cycles".format(
simulator.tsim_cycles()))
def _run(env, remote):
# declare
n = 21
m = 20
pad_before = [0, 1, 0, 0]
pad_after = [1, 3, 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],
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)
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(1, 2, 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 == "tsim":
simulator.tsim_init("libvta_hw")
f(x_nd, y_nd)
np.testing.assert_equal(y_np, y_nd.asnumpy())
if env.TARGET == "tsim":
print("Padded load test took {} clock cycles".format(
simulator.tsim_cycles()))
def _run(env, remote):
n = 6
x = tvm.placeholder((n, n, env.BATCH, env.BLOCK_OUT),
name="x",
dtype=env.acc_dtype)
x_buf = tvm.compute((n, n, env.BATCH, env.BLOCK_OUT), lambda *i: x(*i),
"x_buf")
# insert no-op that won't be optimized away
y_buf = tvm.compute((n, n, env.BATCH, env.BLOCK_OUT),
lambda *i: x_buf(*i) >> 0, "y_buf")
y = tvm.compute((n, n, 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)
# verification
with vta.build_config():
m = vta.build(s, [x, y], "ext_dev", env.target_host)
if not remote:
return
temp = util.tempdir()
m.save(temp.relpath("load_act.o"))
remote.upload(temp.relpath("load_act.o"))
f = remote.load_module("load_act.o")
# verify
ctx = remote.ext_dev(0)
x_np = np.random.randint(1, 10, size=(n, n, env.BATCH,
env.BLOCK_OUT)).astype(x.dtype)
y_np = x_np.astype(y.dtype)
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 == "tsim":
simulator.tsim_init("libvta_hw")
f(x_nd, y_nd)
np.testing.assert_equal(y_np, y_nd.asnumpy())
if env.TARGET == "tsim":
print("Load/store test took {} clock cycles".format(
simulator.tsim_cycles()))
def verify(s, name=None):
mod = vta.build(s, [x, w, y], "ext_dev", env.target_host)
temp = util.tempdir()
mod.save(temp.relpath("gemm.o"))
remote.upload(temp.relpath("gemm.o"))
f = remote.load_module("gemm.o")
# verify
ctx = remote.ext_dev(0)
x_np = np.random.randint(-128,
128,
size=(o, n, env.BATCH,
env.BLOCK_IN)).astype(x.dtype)
w_np = np.random.randint(-128,
128,
size=(m, n, env.BLOCK_OUT,
env.BLOCK_IN)).astype(w.dtype)
y_np = np.zeros((o, m, env.BATCH, env.BLOCK_OUT)).astype(y.dtype)
x_nd = tvm.nd.array(x_np, ctx)
w_nd = tvm.nd.array(w_np, ctx)
y_nd = tvm.nd.array(y_np, ctx)
y_np = y_np.astype(env.acc_dtype)
for b in range(o):
for i in range(m):
for j in range(n):
y_np[b, i, :] += np.dot(
x_np[b, j, :].astype(env.acc_dtype),
w_np[i, j].T.astype(env.acc_dtype))
y_np = np.right_shift(y_np, 8)
y_np = np.clip(y_np, 0, (1 <<
(env.INP_WIDTH - 1)) - 1).astype(y.dtype)
if env.TARGET == "tsim":
simulator.tsim_init("libvta_hw")
if env.TARGET == "sim":
simulator.clear_stats()
f(x_nd, w_nd, y_nd)
print(simulator.stats())
else:
f(x_nd, w_nd, y_nd)
np.testing.assert_equal(y_np, y_nd.asnumpy())
if env.TARGET == "tsim":
print("GEMM schedule:{} test took {} clock cycles".format(
name, simulator.tsim_cycles()))
def check_alu(tvm_op, np_op=None, use_imm=False):
"""Test ALU"""
m = 8
n = 8
imm = np.random.randint(1, 5)
# compute
a = tvm.placeholder((m, n, env.BATCH, env.BLOCK_OUT),
name="a",
dtype=env.acc_dtype)
a_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: a(*i), "a_buf") #DRAM->SRAM
if use_imm:
res_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: tvm_op(a_buf(*i), imm),
"res_buf") #compute
else:
b = tvm.placeholder((m, n, env.BATCH, env.BLOCK_OUT),
name="b",
dtype=env.acc_dtype)
b_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: b(*i), "b_buf") #DRAM->SRAM
res_buf = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: tvm_op(a_buf(*i), b_buf(*i)),
"res_buf") #compute5B
res = tvm.compute((m, n, env.BATCH, env.BLOCK_OUT),
lambda *i: res_buf(*i).astype(env.inp_dtype),
"res") #SRAM->DRAM
# schedule
s = tvm.create_schedule(res.op)
s[a_buf].set_scope(env.acc_scope) # SRAM
s[a_buf].pragma(a_buf.op.axis[0], env.dma_copy) # DRAM->SRAM
s[res_buf].set_scope(env.acc_scope) # SRAM
s[res_buf].pragma(res_buf.op.axis[0], env.alu) # compute
s[res].pragma(res.op.axis[0], env.dma_copy) # SRAM->DRAM
if not use_imm:
s[b_buf].set_scope(env.acc_scope) # SRAM
s[b_buf].pragma(b_buf.op.axis[0], env.dma_copy) # DRAM->SRAM
if not remote:
return
# build
with vta.build_config():
if use_imm:
mod = vta.build(s, [a, res], "ext_dev", env.target_host)
else:
mod = vta.build(s, [a, b, res], "ext_dev", env.target_host)
temp = util.tempdir()
mod.save(temp.relpath("load_act.o"))
remote.upload(temp.relpath("load_act.o"))
f = remote.load_module("load_act.o")
# verify
ctx = remote.ext_dev(0)
a_np = np.random.randint(-16,
16,
size=(m, n, env.BATCH,
env.BLOCK_OUT)).astype(a.dtype)
if use_imm:
res_np = np_op(a_np, imm) if np_op else tvm_op(a_np, imm)
else:
b_np = np.random.randint(-16,
16,
size=(m, n, env.BATCH,
env.BLOCK_OUT)).astype(b.dtype)
res_np = np_op(a_np, b_np) if np_op else tvm_op(a_np, b_np)
res_np = res_np.astype(res.dtype)
a_nd = tvm.nd.array(a_np, ctx)
res_nd = tvm.nd.array(
np.zeros((m, n, env.BATCH, env.BLOCK_OUT)).astype(res.dtype),
ctx)
if env.TARGET == "tsim":
simulator.tsim_init("libvta_hw")
if use_imm:
f(a_nd, res_nd)
else:
b_nd = tvm.nd.array(b_np, ctx)
f(a_nd, b_nd, res_nd)
np.testing.assert_equal(res_np, res_nd.asnumpy())