def test_simplify_if_then_else():
ck = CanonicalChecker()
x = tvm.var("x")
y = tvm.var("y")
# simplification that takes condition into account.
res = tvm.if_then_else((x * 4 + y) >= 466036,
tvm.if_then_else(24512 <= ((((x*4) + y) - 466036) % 24528),
(((((x*4) + y) - 466036) % 24528) -24512) % 16,
x), y)
expected = tvm.if_then_else(
tvm.expr.LE(466036, (x * 4 + y)),
tvm.if_then_else(tvm.expr.LE(24512, ((((x*4) + y) - 4) % 24528)),
(((x*4) + y) - 4) % 16,
x), y)
ck.verify(res, expected)
# can only simplify if condition
res = tvm.expr.Select(tvm.all(x >= -1, y >= 0), (x + y + 100) % 3, (x + 100) % 3)
expected = tvm.expr.Select(tvm.all(x >= -1, y >= 0), (x + y + 1) % 3, (x + 100) % 3)
ck.verify(res, ck.analyzer.canonical_simplify(expected))
res = tvm.expr.Select(x >= 10,
tvm.if_then_else(x / 3 > 2, x, 0), 0)
expected = tvm.expr.Select(x >= 10, x, 0)
ck.verify(res, ck.analyzer.canonical_simplify(expected))
res = tvm.expr.Select(x >= 10,
tvm.if_then_else(x / 3 < 2, x, 0), 0)
ck.verify(res, 0)
def get_valid_counts_scan(data, partial_in, partial):
"""Low level IR to do scan.
Parameters
----------
data: Buffer
3D Buffer with shape [batch_size, num_anchors, elem_length], output of nms.
idx_in : Buffer
2D Buffer of valid data indices with shape [batch_size, num_anchors].
idx : Buffer
2D Buffer of valid data indices with shape [batch_size, num_anchors].
partial : Buffer
2D Buffer of valid data indices with shape [batch_size, new_range].
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch_size = data.shape[0]
num_anchors = data.shape[1]
ib = tvm.ir_builder.create()
partial_in = ib.buffer_ptr(partial_in)
partial = ib.buffer_ptr(partial)
max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
elem_per_thread = num_anchors // max_threads + 1
nthread_tx = max_threads
nthread_bx = batch_size
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
var = tvm.make.node("FloatImm", dtype="float32", value=2)
new_range = num_anchors // elem_per_thread + 1
iteration = log(cast(new_range, "float32")) // math.log(2)
# Scan: Kogge-Stone adder
with ib.if_scope(tvm.all(bx < batch_size, tx < tvm.min(new_range, num_anchors))):
with ib.for_range(0, iteration) as k:
with ib.if_scope(k == 0):
with ib.if_scope(tvm.all(tx > 0, tx < tvm.min(new_range, num_anchors))):
partial[bx * new_range + tx] = \
partial_in[bx * new_range + tx] + partial_in[bx * new_range + tx - 1]
with ib.else_scope():
partial[bx * new_range] = partial_in[bx * new_range]
with ib.else_scope():
with ib.if_scope(tvm.all(tx >= cast(power(var, k), "int32"), \
tx < tvm.min(new_range, num_anchors))):
partial[bx * new_range + tx] += \
partial[bx * new_range + tx - cast(power(var, k), "int32")]
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
return ib.get()
def test_basic():
a = tvm.var("a")
b = tvm.var("b")
c = tvm.var("c")
m = tvm.arith.DetectClipBound(tvm.all(a * 1 < b * 6,
a - 1 > 0), [a])
assert tvm.ir_pass.Simplify(m[1] - (b * 6 - 1)).value == 0
assert m[0].value == 2
m = tvm.arith.DetectClipBound(tvm.all(a * 1 < b * 6,
a - 1 > 0), [a, b])
assert len(m) == 0
m = tvm.arith.DetectClipBound(tvm.all(a + 10 * c <= 20,
b - 1 > 0), [a, b])
assert tvm.ir_pass.Simplify(m[1] - (20 - 10 * c)).value == 0
assert tvm.ir_pass.Simplify(m[2] - 2).value == 0
def test_copy_pad_split():
m = 4 * 3
A = tvm.placeholder((m, ), name="A")
Apad = tvm.compute((m + 2,), lambda i:
tvm.select(tvm.all(i >= 1, i <= m),
A[i - 1], 0.0), "Apad")
B = tvm.compute((m,), lambda i: Apad[i] + Apad[i + 1] + Apad[i + 2])
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=4)
s[Apad].compute_at(s[B], xo)
s[Apad].pragma(s[Apad].op.axis[0], "memcpy")
bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
Bb = tvm.decl_buffer(B.shape, B.dtype, name='B')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B: Bb}, 64)
stmt = tvm.ir_pass.Simplify(stmt)
stmt = tvm.ir_pass.CanonicalSimplify(stmt)
def cb(src, dst, pad_before, pad_after, pad_value):
assert(dst.elem_offset.value == 0)
assert_expr_equal(src.elem_offset, tvm.max(xo * 4, 1) - 1)
rpad_before = tvm.max(1 - xo * 4, 0)
rpad_after = tvm.max(xo * 4 - 7, 0)
assert_expr_equal(pad_before[0], rpad_before)
assert_expr_equal(pad_after[0], rpad_after)
assert_expr_equal(src.shape[0], 6 - rpad_before - rpad_after)
return tvm.make.Evaluate(0)
stmt = tvm.ir_pass.InjectCopyIntrin(stmt, "memcpy", cb)
def nms_ir(sorted_bbox_buf, out_buf, nms_threshold):
"""Non-maximum supression.
Parameters
----------
sorted_bbox_buf : tvm.schedule.Buffer
3-D with shape [batch, num_bbox, 5]. The last dimension is in format of
[w_start, h_start, w_end, h_end, score].
out_buf : tvm.schedule.Buffer
2-D with shape [batch, num_bbox]. Boolean mask of whether a bounding box should be removed.
nms_threshold : float
Non-maximum suppression threshold.
Returns
-------
stmt : Stmt
The result IR statement.
"""
def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
"""Calculate overlap of two boxes.
"""
w = tvm.max(0.0, tvm.min(out_tensor[box_a_idx + 2], out_tensor[box_b_idx + 2])
- tvm.max(out_tensor[box_a_idx], out_tensor[box_b_idx]) + 1.0)
h = tvm.max(0.0, tvm.min(out_tensor[box_a_idx + 3], out_tensor[box_b_idx + 3])
- tvm.max(out_tensor[box_a_idx + 1], out_tensor[box_b_idx + 1]) + 1.0)
i = w * h
u = (out_tensor[box_a_idx + 2] - out_tensor[box_a_idx] + 1.0) * \
(out_tensor[box_a_idx + 3] - out_tensor[box_a_idx + 1] + 1.0) + \
(out_tensor[box_b_idx + 2] - out_tensor[box_b_idx] + 1.0) * \
(out_tensor[box_b_idx + 3] - out_tensor[box_b_idx + 1] + 1.0) - i
return i / u
batch, num_bbox = get_const_tuple(out_buf.shape)
max_threads = int(math.sqrt(tvm.target.current_target(allow_none=False).max_num_threads))
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
ib = tvm.ir_builder.create()
p_data = ib.buffer_ptr(sorted_bbox_buf)
p_out = ib.buffer_ptr(out_buf)
nthread_tx = max_threads
nthread_bx = num_bbox // max_threads + 1
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
i = bx * max_threads + tx
with ib.for_range(0, batch, for_type="unroll", name="n") as b:
base_idx = b * num_bbox
with ib.if_scope(i < num_bbox):
p_out[base_idx + i] = False
with ib.for_range(0, num_bbox - 1) as l:
with ib.if_scope(tvm.all(i < num_bbox, i > l, p_out[base_idx + l] == False)):
iou = calculate_overlap(p_data, (base_idx + l) * 5, (base_idx + i) * 5)
with ib.if_scope(iou > nms_threshold):
p_out[base_idx + i] = True
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
return ib.get()
def select_array(i, j):
now = tvm.const(0.0, dtype)
for ii in range(row):
for jj in range(col):
now = tvm.expr.Select(tvm.all(i % row == ii, j % col == jj),
tvm.const(matrix[ii][jj], dtype),
now)
return now
def get_valid_counts_upsweep(data, idx_in, idx, partial):
"""Low level IR of first step of scan: unsweep.
Parameters
----------
data: Buffer
3D Buffer with shape [batch_size, num_anchors, elem_length], output of nms.
idx_in : Buffer
2D Buffer of valid data indices with shape [batch_size, num_anchors].
idx : Buffer
2D Buffer of valid data indices with shape [batch_size, num_anchors].
partial : Buffer
2D Buffer of valid data indices with shape [batch_size, new_range].
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch_size = data.shape[0]
num_anchors = data.shape[1]
ib = tvm.ir_builder.create()
data = ib.buffer_ptr(data)
idx_in = ib.buffer_ptr(idx_in)
idx = ib.buffer_ptr(idx)
partial = ib.buffer_ptr(partial)
max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
elem_per_thread = num_anchors // max_threads + 1
nthread_tx = max_threads
nthread_bx = batch_size
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
new_range = num_anchors // elem_per_thread + 1
# Scan: Upsweep:
with ib.if_scope(tvm.all(bx < batch_size, tx < new_range)):
with ib.for_range(0, elem_per_thread) as i:
with ib.if_scope(bx * num_anchors + \
tx * elem_per_thread + i < batch_size * num_anchors):
with ib.if_scope(i == 0):
partial[bx * new_range + tx] = idx_in[bx * num_anchors + tx * elem_per_thread]
idx[bx * num_anchors + tx * elem_per_thread] = \
idx_in[bx * num_anchors + tx * elem_per_thread]
with ib.else_scope():
partial[bx * new_range + tx] += \
idx_in[bx * num_anchors + tx * elem_per_thread + i]
idx[bx * num_anchors + tx * elem_per_thread + i] = \
idx[bx * num_anchors + tx * elem_per_thread + i - 1] + \
idx_in[bx * num_anchors + tx * elem_per_thread + i]
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
return ib.get()
def prepare_output_ir(sorted_bbox_buf, remove_mask_buf, out_buf):
"""Copy output after applying nms to continuous memory.
Parameters
----------
sorted_bbox_buf : tvm.schedule.Buffer
3-D with shape [batch, num_bbox, 5]. The last dimension is in format of
[w_start, h_start, w_end, h_end, score].
remove_mask_buf : tvm.schedule.Buffer
2-D with shape [batch, num_bbox]. Boolean mask of whether a bounding box should be removed.
out_buf : tvm.schedule.Buffer
2-D with shape [batch * rpn_post_nms_top_n, 5]. The last dimension is in format of
[batch_index, w_start, h_start, w_end, h_end].
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch, num_bbox, _ = get_const_tuple(sorted_bbox_buf.shape)
rpn_post_nms_top_n = get_const_int(out_buf.shape[0]) // batch
nthread_tx = batch
tx = tvm.thread_axis("threadIdx.x")
ib = tvm.ir_builder.create()
ib.scope_attr(tx, "thread_extent", nthread_tx)
i = ib.allocate('int32', (1,), 'i', scope='local')
i[0] = 0
p_sorted_bbox = ib.buffer_ptr(sorted_bbox_buf)
p_remove = ib.buffer_ptr(remove_mask_buf)
p_out = ib.buffer_ptr(out_buf)
b = tx
nkeep = ib.allocate('int32', (1,), 'nkeep', scope='local')
nkeep[0] = 0 # number of bbox after nms
with ib.for_range(0, num_bbox) as j:
with ib.if_scope(p_remove[b * num_bbox + j] == False):
nkeep[0] += 1
with ib.if_scope(nkeep[0] > 0):
with ib.for_range(0, tvm.ceil(
tvm.const(rpn_post_nms_top_n, 'float32') / nkeep[0]).astype('int32')):
with ib.for_range(0, num_bbox) as j:
offset_j = (b * num_bbox + j) * 5
offset_i = (b * rpn_post_nms_top_n + i[0]) * 5
with ib.if_scope(tvm.all(i[0] < rpn_post_nms_top_n,
p_remove[(b*num_bbox+j)] == False)):
p_out[offset_i] = tvm.expr.Cast('float32', b)
with ib.for_range(0, 4, for_type='unroll') as k:
p_out[offset_i + k + 1] = p_sorted_bbox[offset_j + k]
i[0] = i[0] + 1
body = ib.get()
return body
def test_schedule_bound_condition(): A = tvm.placeholder((64,), name='A', dtype="float32") Apad = tvm.compute((66,), lambda i: tvm.select(tvm.all(i>0, i < 65), A[i-1], tvm.const(0.)), name='Apad') Apad2 = tvm.compute((66,), lambda i: Apad[i]*2, name='Apad2') s = tvm.create_schedule(Apad2.op) AL1 = s.cache_read(A,"local",[Apad]) s = s.normalize() bounds = tvm.schedule.InferBound(s) stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.ir_pass.Simplify(stmt) assert (isinstance(stmt.body.body.first.body.body.then_case, tvm.stmt.IfThenElse))
def test_all():
x = tvm.var('x')
y = tvm.var('y')
z = tvm.var('z')
try:
t = x and x
assert False
except ValueError:
pass
try:
tvm.all()
assert False
except ValueError:
pass
assert str(tvm.all(x < y)) == '(%s < %s)' % (x.name, y.name)
assert str(tvm.all(x < y, x > z)) == '((%s < %s) && (%s > %s))' % (
x.name, y.name, x.name, z.name)
assert str(tvm.all(x < y, y > z + 1, x < z * 2)) == \
'(((%s < %s) && (%s > (%s + 1))) && (%s < (%s*2)))' % (
x.name, y.name, y.name, z.name, x.name, z.name)
def _dilate(*indices):
not_zero = []
index_tuple = []
for i in range(n):
if not equal_const_int(strides[i], 1):
index_tuple.append(indices[i] // strides[i])
not_zero.append((indices[i] % strides[i]).equal(0))
else:
index_tuple.append(indices[i])
if not_zero:
not_zero = tvm.all(*not_zero)
return tvm.if_then_else(not_zero, data(*index_tuple), tvm.const(0.0, data.dtype))
return data(*index_tuple)
def argsort_ir(data_buf, out_index_buf):
"""Batched odd-even transposition sort.
Parameters
----------
data_buf : tvm.schedule.Buffer
2-D with shape [batch, num_bbox]
out_index_buf : tvm.schedule.Buffer
2-D with shape [batch, num_bbox]. Indices of data in sorted order.
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch, num_bbox = get_const_tuple(data_buf.shape)
max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
ib = tvm.ir_builder.create()
p_data = ib.buffer_ptr(data_buf)
index_out = ib.buffer_ptr(out_index_buf)
nthread_tx = max_threads
nthread_bx = (num_bbox + 1) // 2 // max_threads + 1
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("vthread")
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "virtual_thread", nthread_bx)
tid = bx * nthread_tx + tx
temp_data = ib.allocate("float32", (1,), name="temp_data", scope="local")
temp_index = ib.allocate("int32", (1,), name="temp_index", scope="local")
with ib.for_range(0, batch, for_type="unroll") as b:
start = b * num_bbox
for i in range(2):
bbox_id = tid * 2 + i
with ib.if_scope(bbox_id < num_bbox):
index_out[start + bbox_id] = bbox_id
with ib.for_range(0, num_bbox) as k:
offset = start + 2 * tid + (k % 2)
with ib.if_scope(
tvm.all(offset + 1 < num_bbox, p_data[offset] < p_data[offset + 1])):
temp_data[0] = p_data[offset]
p_data[offset] = p_data[offset + 1]
p_data[offset + 1] = temp_data[0]
temp_index[0] = index_out[offset]
index_out[offset] = index_out[offset + 1]
index_out[offset + 1] = temp_index[0]
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
return ib.get()
def _decl_im2col(data, kernel, stride, padding, layout='NCHW', out_dtype='float32'):
"""declare the Im2Col method for conv2d"""
_, CI, IH, IW = [x.value for x in data.shape]
CO, _, KH, KW = [x.value for x in kernel.shape]
HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
if isinstance(stride, (tuple, list)):
HSTR, WSTR = stride
else:
HSTR, WSTR = stride, stride
N = 1
OH = (IH + 2*HPAD - KH) // HSTR + 1
OW = (IW + 2*WPAD - KW) // WSTR + 1
DO_PAD = (HPAD != 0 and WPAD != 0)
if DO_PAD:
data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
else:
data_pad = data
ALIGN = 16
def upround(x, align):
return (x + align - 1) // align * align
# A [CO, CI * KH * KW]
reduce_len = upround(CI * KH * KW, ALIGN)
A = tvm.compute((upround(CO, ALIGN), reduce_len), lambda i, j:
kernel[i][j // KW // KH][j // KW % KH][j % KW], name='A')
# B [CI * KH * KW, N * OH * OW]
B = tvm.compute((reduce_len, upround(N * OH * OW, ALIGN)), lambda i, j:\
tvm.select(tvm.all(i < CI * KH * KW, j < N * OH * OW),
data_pad[j // (OH*OW)][i // (KH*KW)][j // OW % OH*HSTR + i // KW % KH]
[j % OW*WSTR + i % KW],
tvm.const(0, data_pad.dtype)), name='B')
gemm_n, gemm_l, gemm_m = A.shape[0], reduce_len, B.shape[1]
# C [CO, N * OH * OW]
k = tvm.reduce_axis((0, gemm_l), name='k')
C = tvm.compute((gemm_n, gemm_m), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')
# output
# the last term C[gemm_n-1, gemm_m-1] is for enabling the alignment,
# otherwise the alignment above will be eliminated by bound inference
output = tvm.compute((N, CO, OH, OW), lambda n, co, h, w:\
C[co][n * OW * OW + h * OW + w] + tvm.const(0, C.dtype) * C[gemm_n-1, gemm_m-1],
name='output', tag='im2col_conv_output')
return output
def _dilate(*indices):
not_zero = []
index_tuple = []
for i in range(n):
if not equal_const_int(strides[i], 1):
index_tuple.append(indices[i] // strides[i])
not_zero.append((indices[i] % strides[i]).equal(0))
else:
index_tuple.append(indices[i])
if not_zero:
not_zero = tvm.all(*not_zero)
return tvm.if_then_else(not_zero, data(*index_tuple),
tvm.const(0.0, data.dtype))
return data(*index_tuple)
def compute_temp(k, p, eps, nu):
temp_expr = {}
for j in range(4):
t0 = M[0][j][k][p] + M[1][j][k][p]
t1 = M[1][j][k][p] - M[2][j][k][p]
temp_expr[(0, j)] = t0 + M[2][j][k][p]
temp_expr[(1, j)] = t1 - M[3][j][k][p]
now = tvm.const(0.0, "float32")
for ii in range(2):
for jj in range(4):
now = tvm.select(tvm.all(eps == ii, nu == jj),
temp_expr[(ii, jj)], now)
return now
def _pad(*indices):
not_zero = []
index_tuple = []
for i in range(n):
if equal_const_int(pad_before[i], 0) and equal_const_int(pad_after[i], 0):
index_tuple.append(indices[i])
else:
index_tuple.append(indices[i] - pad_before[i])
not_zero.append(indices[i] >= pad_before[i])
not_zero.append(indices[i] < data.shape[i] + pad_before[i])
if not_zero:
not_zero = tvm.all(*not_zero)
return tvm.if_then_else(not_zero, data(*index_tuple), pad_value)
return data(*index_tuple)
def _pad(*indices):
not_zero = []
index_tuple = []
for i in range(n):
if equal_const_int(pad_before[i], 0) and equal_const_int(pad_after[i], 0):
index_tuple.append(indices[i])
else:
index_tuple.append(indices[i] - pad_before[i])
not_zero.append(indices[i] >= pad_before[i])
not_zero.append(indices[i] < data.shape[i] + pad_before[i])
if not_zero:
not_zero = tvm.all(*not_zero)
return tvm.if_then_else(not_zero, data(*index_tuple), pad_value)
return data(*index_tuple)
def test_cmp_load_store():
n = 32
A = tvm.placeholder((n, ), name='A')
B = tvm.placeholder((n, ), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) > B(*i), name='C')
D = tvm.compute(C.shape, lambda *i: tvm.all(C(*i), A(*i) > 1), name="D")
def check_llvm():
if not tvm.module.enabled("llvm"):
return
s = tvm.create_schedule(D.op)
xo, xi = s[C].split(C.op.axis[0], factor=4)
xo1, xo2 = s[C].split(xo, factor=13)
s[C].parallel(xo2)
# BUILD and invoke the kernel.
f = tvm.build(s, [A, B, D], "llvm")
ctx = tvm.cpu(0)
a_np = np.random.uniform(size=n).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=D.dtype), ctx)
f(a, b, d)
np.testing.assert_equal(
d.asnumpy(),
np.logical_and(a.asnumpy() > b.asnumpy(),
a.asnumpy() > 1))
def check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
s = tvm.create_schedule(D.op)
for stage in [C, D]:
xo, xi = s[stage].split(stage.op.axis[0], factor=4)
s[stage].bind(xo, tvm.thread_axis("blockIdx.x"))
s[stage].bind(xi, tvm.thread_axis("threadIdx.x"))
f = tvm.build(s, [A, B, D], device)
a_np = np.random.uniform(size=n).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=D.dtype), ctx)
f(a, b, d)
np.testing.assert_equal(
d.asnumpy(),
np.logical_and(a.asnumpy() > b.asnumpy(),
a.asnumpy() > 1))
check_llvm()
for device in ["vulkan", "opencl", "cuda", "rocm", "metal"]:
check_device(device)
def test_simplify_if_then_else():
ck = CanonicalChecker()
x = tvm.var("x")
y = tvm.var("y")
tdiv = tvm.truncdiv
tmod = tvm.truncmod
# simplification that takes condition into account.
res = tvm.if_then_else(
(x * 4 + y) >= 466036,
tvm.if_then_else(24512 <= tmod(((x * 4) + y) - 466036, 24528),
tmod(tmod(((x * 4) + y) - 466036, 24528) - 24512, 16),
x), y)
res2 = tvm.if_then_else(
(x * 4) >= 466036 - y,
tvm.if_then_else(24512 <= tmod(((x * 4) + y) - 466036, 24528),
tmod(tmod(((x * 4) + y) - 466036, 24528) - 24512, 16),
x), y)
expected = tvm.if_then_else(
tvm.expr.LE(466036, (x * 4 + y)),
tvm.if_then_else(tvm.expr.LE(24512, tmod(((x * 4) + y) - 4, 24528)),
tmod(((x * 4) + y) - 4, 16), x), y)
ck.verify(res, expected)
ck.verify(res2, expected)
# can only simplify if condition
res = tvm.expr.Select(tvm.all(x >= -1, y >= 0), tmod(x + y + 100, 3),
tmod(x + 100, 3))
expected = tvm.expr.Select(tvm.all(x >= -1, y >= 0), tmod(x + y + 1, 3),
tmod(x + 100, 3))
ck.verify(res, ck.analyzer.canonical_simplify(expected))
res = tvm.expr.Select(x >= 10, tvm.if_then_else(tdiv(x, 3) > 2, x, 0), 0)
expected = tvm.expr.Select(x >= 10, x, 0)
ck.verify(res, ck.analyzer.canonical_simplify(expected))
res = tvm.expr.Select(x >= 10, tvm.if_then_else(tdiv(x, 3) < 2, x, 0), 0)
ck.verify(res, 0)
def dropout2d_compute (input, data, out_dtype=None):
if out_dtype is None:
out_dtype = input.dtype
batch, species = input.shape
output_data = lambda on, os: tvm.max(
tvm.expr.Select(
tvm.all(data[on,os] > 0.5),
input[on, os].astype(out_dtype),
0.0),
#(input[on, os].astype(out_dtype), relay.const(0.0)),
axis=[])
return tvm.compute((batch, species), output_data, tag="dropout2d")
def _dilate(*indices):
not_zero = []
index_tuple = []
idxdiv = tvm.indexdiv
idxmod = tvm.indexmod
for i in range(n):
if not util.equal_const_int(strides[i], 1):
index_tuple.append(idxdiv(indices[i], strides[i]))
not_zero.append(idxmod(indices[i], strides[i]).equal(0))
else:
index_tuple.append(indices[i])
if not_zero:
not_zero = tvm.all(*not_zero)
return tvm.if_then_else(not_zero, data(*index_tuple), tvm.const(0.0, data.dtype))
return data(*index_tuple)
def lrn_sqr_nchw(data, size, axis):
out_dtype = data.dtype
radius = size // 2
batch, in_channel, in_height, in_width = data.shape
ls = tvm.reduce_axis((0, size), name='ls')
sqr_out = lambda on, oc, oh, ow: tvm.sum(
tvm.expr.Select(
tvm.all(oc >= radius, oc < (in_channel+radius)),
data[on, oc-radius+ls, oh, ow].astype(out_dtype) * data[on, oc-radius+ls, oh, ow].astype(out_dtype),
0.0) ,
axis=[ls])
return tvm.compute((batch, in_channel, in_height, in_width), sqr_out, tag="lrn_sqrt_op")
def test_schedule_bound_condition():
A = tvm.placeholder((64, ), name='A', dtype="float32")
Apad = tvm.compute(
(66, ),
lambda i: tvm.select(tvm.all(i > 0, i < 65), A[i - 1], tvm.const(0.)),
name='Apad')
Apad2 = tvm.compute((66, ), lambda i: Apad[i] * 2, name='Apad2')
s = tvm.create_schedule(Apad2.op)
AL1 = s.cache_read(A, "local", [Apad])
s = s.normalize()
bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds)
stmt = tvm.ir_pass.Simplify(stmt)
assert (isinstance(stmt.body.body.first.body.body.then_case,
tvm.stmt.IfThenElse))
def test_const_fold3():
def check_throws(f):
try:
f()
except tvm.TVMError:
pass
else:
raise AssertionError("Should have raised an exception but didn't.")
# Test that using ints with logic operations is forbidden
x = tvm.var("x")
for val in [0, 1]:
for func in [tvm.all, tvm.any]:
check_throws(lambda: func(tvm.const(val, 'uint1'), x))
check_throws(lambda: func(x, tvm.const(val, 'uint1')))
# Test const folding when both arguments are const
for tvm_func, py_func in [(tvm.all, lambda a, b: a and b), (tvm.any, lambda a, b: a or b)]:
for v1 in [0, 1]:
for v2 in [0, 1]:
assert tvm.ir_pass.Equal(tvm_func(tvm.const(v1, 'uint1'), tvm.const(v2, 'uint1')),
tvm.const(py_func(v1, v2), 'uint1'))
x = tvm.var("x", 'uint1')
true = tvm.const(1, 'uint1')
false = tvm.const(0, 'uint1')
assert tvm.all(x, true).same_as(x)
assert tvm.all(true, x).same_as(x)
assert tvm.any(x, false).same_as(x)
assert tvm.any(false, x).same_as(x)
assert tvm.all(x, false).same_as(false)
assert tvm.all(false, x).same_as(false)
assert tvm.any(x, true).same_as(true)
assert tvm.any(true, x).same_as(true)
def relu4d_compute(input, out_dtype=None):
if out_dtype is None:
out_dtype = input.dtype
batch, in_channel, in_height, in_width = input.shape
output_data = lambda on, oc, oh, ow: tvm.max(
tvm.expr.Select(tvm.all(input[on, oc, oh, ow] > 0), input[
on, oc, oh, ow].astype(out_dtype), 0.0),
#(input[on, oc, oh, ow].astype(out_dtype), relay.const(0.0)),
axis=[])
return tvm.compute((batch, in_channel, in_height, in_width),
output_data,
tag="relu4D")
def test_const_fold3():
def check_throws(f):
try:
f()
except tvm.TVMError:
pass
else:
raise AssertionError("Should have raised an exception but didn't.")
# Test that using ints with logic operations is forbidden
x = tvm.var("x")
for val in [0, 1]:
for func in [tvm.all, tvm.any]:
check_throws(lambda: func(tvm.const(val, 'uint1'), x))
check_throws(lambda: func(x, tvm.const(val, 'uint1')))
# Test const folding when both arguments are const
for tvm_func, py_func in [(tvm.all, lambda a, b: a and b), (tvm.any, lambda a, b: a or b)]:
for v1 in [0, 1]:
for v2 in [0, 1]:
assert tvm.ir_pass.Equal(tvm_func(tvm.const(v1, 'uint1'), tvm.const(v2, 'uint1')),
tvm.const(py_func(v1, v2), 'uint1'))
x = tvm.var("x", 'uint1')
true = tvm.const(1, 'uint1')
false = tvm.const(0, 'uint1')
assert tvm.all(x, true).same_as(x)
assert tvm.all(true, x).same_as(x)
assert tvm.any(x, false).same_as(x)
assert tvm.any(false, x).same_as(x)
assert tvm.all(x, false).same_as(false)
assert tvm.all(false, x).same_as(false)
assert tvm.any(x, true).same_as(true)
assert tvm.any(true, x).same_as(true)
def test_cmp_load_store():
n = 32
A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) > B(*i), name='C')
D = tvm.compute(C.shape, lambda *i: tvm.all(C(*i), A(*i) > 1), name="D")
def check_llvm():
if not tvm.module.enabled("llvm"):
return
s = tvm.create_schedule(D.op)
xo, xi = s[C].split(C.op.axis[0], factor=4)
xo1, xo2 = s[C].split(xo, factor=13)
s[C].parallel(xo2)
# BUILD and invoke the kernel.
f = tvm.build(s, [A, B, D], "llvm")
ctx = tvm.cpu(0)
a_np = np.random.uniform(size=n).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=D.dtype), ctx)
f(a, b, d)
np.testing.assert_equal(
d.asnumpy(), np.logical_and(a.asnumpy()> b.asnumpy(), a.asnumpy() > 1))
def check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
return
s = tvm.create_schedule(D.op)
for stage in [C, D]:
xo, xi = s[stage].split(stage.op.axis[0], factor=4)
s[stage].bind(xo, tvm.thread_axis("blockIdx.x"))
s[stage].bind(xi, tvm.thread_axis("threadIdx.x"))
f = tvm.build(s, [A, B, D], device)
a_np = np.random.uniform(size=n).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=D.dtype), ctx)
f(a, b, d)
np.testing.assert_equal(
d.asnumpy(), np.logical_and(a.asnumpy()> b.asnumpy(), a.asnumpy() > 1))
check_llvm()
for device in ["vulkan", "opencl", "cuda", "rocm", "metal"]:
check_device(device)
def dilate_kernel(
*indices
): # This function is the same as topi.nn.dilate, but inlined
not_zero = []
index_tuple = []
for i in range(len(dilate_args)):
if not topi.util.equal_const_int(dilate_args[i], 1):
index_tuple.append(indices[i] // dilate_args[i])
not_zero.append((indices[i] % dilate_args[i]).equal(0))
else:
index_tuple.append(indices[i])
if not_zero:
not_zero = tvm.all(*not_zero)
return tvm.select(not_zero, kernel(*index_tuple),
tvm.const(0.0, data.dtype))
return kernel(*index_tuple)
def gaussian_blur2d(M, N, k, dtype="float32"):
A = tvm.placeholder((M, N), dtype=dtype, name="A")
pad = k // 2
number = k * k
Apad = tvm.compute((M + 2 * pad, N + 2 * pad),
lambda i, j: tvm.if_then_else(
tvm.all(i >= pad, i < M + pad, j >= pad, j < N + pad
), A[i - pad, j - pad], 0.0),
name="Apad")
rx = tvm.reduce_axis((0, k), name="rx")
ry = tvm.reduce_axis((0, k), name="ry")
B = tvm.compute(
(M, N),
lambda i, j: tvm.sum(Apad[i + rx, j + ry] / number, axis=[rx, ry]),
name="B")
return B.op, [A, B]
def poolingb(Image, Index, POutput):
"""
reverse 2*2 max pooling revised
Parameters
----------
Image : tvm.tensor.Tensor
4-D with shape [batch_size, image_height, image_width, in_channels]
Index : tvm.tensor.Tensor, specify where Output[i,j,k,l] is from, this follows the convention of
Numpy and PyTorch. You will need this tensor to compute the gradient.
------------------------------------------
For example, if Image is of shape [1, 4, 4, 1] (batch 1 and channel 1), then the slice
Image[0, :, :, 0] is
[[0.7243, 0.3236, 0.0124, 0.4314],
[0.4104, 0.3997, 0.4534, 0.1791],
[0.0973, 0.2673, 0.6907, 0.9207],
[0.9268, 0.6590, 0.0312, 0.2364]]
and Index is of shape [1, 2, 2, 1] and the slice Index[0, :, :, 0] is
[[ 0, 6],
[12, 11]]
because 0 = 0 * 4 + 0 (0, 0)
6 = 1 * 4 + 2 (1, 2)
12= 3 * 4 + 0 (3, 0)
11= 2 * 4 + 3 (2, 3)
--------------------------------------------
4-D with shape [batch_size, out_height, out_width, in_channels]
POutput:tvm.tensor.Tensor, gradient of Output
4-D with shape [batch_size, out_height, out_width, in_channels]
Returns
-------
PImage: tvm.tensor.Tensor, gradient of Image
4-D with shape (Image.shape)
"""
_, _, W, _ = Image.shape
PImage = tvm.compute(
Image.shape, lambda n, i, j, c: tvm.if_then_else(
tvm.all(i == Index[n, i // 2, j // 2, c] // W, j == Index[
n, i // 2, j // 2, c] % W), POutput[n, i // 2, j // 2, c], 0.0)
)
return PImage
def max_pool2d_nchw(input, pool_size, stride, padding, out_dtype=None):
if out_dtype is None:
out_dtype = input.dtype
assert isinstance(pool_size, int) or len(pool_size) == 2
assert isinstance(stride, int) or len(stride) == 2
assert isinstance(padding, int) or len(padding) == 2
if isinstance(stride, int):
stride_h = stride_w = stride
else:
stride_h, stride_w = stride
if isinstance(padding, int):
pad_h = pad_w = padding
else:
pad_h, pad_w = padding
if isinstance(pool_size, int):
kernel_h = kernel_w = pool_size
else:
kernel_h, kernel_w = pool_size
batch, in_channel, in_height, in_width = input.shape
out_channel = in_channel
# In Caffe, when the pooling operator is not divisible, ceil is adopted,
# while the convolution operator is floor
#out_height = math.ceil((in_height+2*pad_h-kernel_h)/stride_h+1)
#out_width = math.ceil((in_width+2*pad_w-kernel_w)/stride_w+1)
out_height = simplify((in_height + 2 * pad_h - kernel_h) // stride_h + 1)
out_width = simplify((in_width + 2 * pad_w - kernel_w) // stride_w + 1)
kh = tvm.reduce_axis((0, kernel_h), name='kh')
kw = tvm.reduce_axis((0, kernel_w), name='kw')
output_data = lambda on, oc, oh, ow: tvm.max(tvm.expr.Select(
tvm.all((oh * stride_h + kh) >= pad_h, (oh * stride_h + kh) <
(in_height + pad_h), (ow * stride_w + kw >= pad_w),
(ow * stride_w + kw < in_width + pad_w)),
input[on, oc, oh * stride_h + kh - pad_h, ow * stride_w + kw - pad_w
].astype(out_dtype), 0.0),
axis=[kh, kw])
return tvm.compute((batch, out_channel, out_height, out_width),
output_data,
tag="max_pool2d_nchw")
def compute_output(n, k, h, w):
b = n * nH * nW + (h // m) * nW + w // m
eps = h % m
nu = w % m
output_expr = {}
for i in range(2):
t0 = temp[k][b][i][0] + temp[k][b][i][1]
t1 = temp[k][b][i][1] - temp[k][b][i][2]
output_expr[(i, 0)] = t0 + temp[k][b][i][2]
output_expr[(i, 1)] = t1 - temp[k][b][i][3]
now = tvm.const(0.0, "float32")
for ii in range(2):
for jj in range(2):
now = tvm.select(tvm.all(eps == ii, nu == jj),
output_expr[(ii, jj)], now)
return now
def make_conv2d_unoptimized(shapeX,
shapeF,
tgt,
tgt_host,
func_name,
dtype="float32"):
in_size, in_size, in_channel, batch = shapeX
kernel, kernel, in_channel, out_channel = shapeF
pad = 1
stride = 1
A = tvm.placeholder((in_size, in_size, in_channel, batch), name='A')
W = tvm.placeholder((kernel, kernel, in_channel, out_channel), name='W')
out_size = (in_size - kernel + 2 * pad) // stride + 1
# Pad input
Apad = tvm.compute(
(in_size + 2 * pad, in_size + 2 * pad, in_channel, batch),
lambda yy, xx, cc, nn: tvm.select(
tvm.all(yy >= pad, yy - pad < in_size, xx >= pad, xx - pad <
in_size), A[yy - pad, xx - pad, cc, nn], tvm.const(0.)),
name='Apad')
# Create reduction variables
rc = tvm.reduce_axis((0, in_channel), name='rc')
ry = tvm.reduce_axis((0, kernel), name='ry')
rx = tvm.reduce_axis((0, kernel), name='rx')
# Compute the convolution
B = tvm.compute(
(out_size, out_size, out_channel, batch),
lambda yy, xx, ff, nn: tvm.sum(Apad[yy * stride + ry, xx * stride + rx,
rc, nn] * W[ry, rx, rc, ff],
axis=[ry, rx, rc]),
name='B')
s = tvm.create_schedule(B.op)
s[Apad].bind(Apad.op.axis[0], tvm.thread_axis("blockIdx.x"))
s[Apad].bind(Apad.op.axis[1], tvm.thread_axis("threadIdx.x"))
s[B].bind(B.op.axis[0], tvm.thread_axis("blockIdx.x"))
s[B].bind(B.op.axis[1], tvm.thread_axis("threadIdx.x"))
f = tvm.build(s, [A, W, B], tgt, target_host=tgt_host, name=func_name)
return _export_module(f, func_name, remote)
def conv2db(Image, Filter, POutput):
"""
convolution with NHWC layout backward
Parameters
----------
Image : tvm.tensor.Tensor
4-D with shape [batch_size, image_height, image_width, in_channels]
Filter: tvm.tensor.Tensor
4-D with shape [out_channels, in_channels, kernel_height, kernel_width]
POutput:tvm.tensor.Tensor, gradient of Output
4-D with shape [batch_size, out_height, out_width, out_channels]
Returns
-------
PImage :tvm.tensor.Tensor, gradient of Image
4-D with shape (Image.shape)
PFilter:tvm.tensor.Tensor, gradient of Filter
4-D with shape (Filter.shape)
"""
N, H, W, C = Image.shape
K, _, Hk, Wk = Filter.shape
rx = tvm.reduce_axis((0, H - (Hk - 1)), name='rx')
ry = tvm.reduce_axis((0, W - (Wk - 1)), name='ry')
rn = tvm.reduce_axis((0, N), name='rn')
PFilter = tvm.compute(
Filter.shape, lambda o, c, h, w: tvm.sum(Image[rn, h + rx, w + ry, c] *
POutput[rn, rx, ry, o],
axis=[rn, rx, ry]))
rx_k = tvm.reduce_axis((0, Hk), name='rx_k')
ry_k = tvm.reduce_axis((0, Wk), name='ry_k')
ro = tvm.reduce_axis((0, K), name='ro')
PImage = tvm.compute(
Image.shape, lambda n, h, w, c: tvm.
sum(Filter[ro, c, Hk - rx_k - 1, Wk - ry_k - 1] * tvm.if_then_else(
tvm.all(h + rx_k >= Hk - 1, h + rx_k < H, w + ry_k >= Wk - 1, w +
ry_k < W), POutput[n, h + rx_k - (Hk - 1), w + ry_k -
(Wk - 1), ro], 0.0),
axis=[rx_k, ry_k, ro]))
return (PImage, PFilter)
def argsort_ir(data_buf, out_index_buf):
"""Batched odd-even transposition sort.
Parameters
----------
data_buf : tvm.schedule.Buffer
2-D with shape [batch, num_bbox]
out_index_buf : tvm.schedule.Buffer
2-D with shape [batch, num_bbox]. Indices of data in sorted order.
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch, num_bbox = get_const_tuple(data_buf.shape)
ib = tvm.ir_builder.create()
p_data = ib.buffer_ptr(data_buf)
index_out = ib.buffer_ptr(out_index_buf)
temp_data = ib.allocate("float32", (1, ), name="temp_data", scope="local")
temp_index = ib.allocate("int32", (1, ), name="temp_index", scope="local")
idxm = tvm.indexmod
with ib.for_range(0, batch, for_type="unroll") as b:
start = b * num_bbox
for i in range(2):
with ib.for_range(0, (num_bbox + 1) // 2) as tid:
bbox_id = tid * 2 + i
with ib.if_scope(bbox_id < num_bbox):
index_out[start + bbox_id] = bbox_id
with ib.for_range(0, num_bbox) as k:
with ib.for_range(0, (num_bbox + 1) // 2) as tid:
offset = start + 2 * tid + idxm(k, 2)
with ib.if_scope(
tvm.all(offset + 1 < num_bbox,
p_data[offset] < p_data[offset + 1])):
temp_data[0] = p_data[offset]
p_data[offset] = p_data[offset + 1]
p_data[offset + 1] = temp_data[0]
temp_index[0] = index_out[offset]
index_out[offset] = index_out[offset + 1]
index_out[offset + 1] = temp_index[0]
return ib.get()
def _sample(i, c, ph, pw):
roi = rois[i]
batch_index = roi[0].astype('int32')
roi_start_w = roi[1] * spatial_scale
roi_start_h = roi[2] * spatial_scale
roi_end_w = roi[3] * spatial_scale
roi_end_h = roi[4] * spatial_scale
roi_h = roi_end_h - roi_start_h
roi_w = roi_end_w - roi_start_w
roi_h = roi_h
roi_w = roi_w
bin_h = roi_h / pooled_size_h
bin_w = roi_w / pooled_size_w
hstart = ph * bin_h
wstart = pw * bin_w
hend = (ph + 1) * bin_h
wend = (pw + 1) * bin_w
hstart = tvm.min(tvm.max(hstart + roi_start_h, 0), height - 1)
wstart = tvm.min(tvm.max(wstart + roi_start_w, 0), width - 1)
hend = tvm.min(tvm.max(hend + roi_start_h, 0), height - 1)
wend = tvm.min(tvm.max(wend + roi_start_w, 0), width - 1)
non_empty = tvm.all(hstart < hend, wstart < wend)
def min_value(dtype):
return tvm.expr.Select(non_empty, tvm.min_value(dtype),
tvm.const(0.0, dtype))
stride_h = (hend - hstart) / 3.0
stride_w = (wend - wstart) / 3.0
hstart += stride_h
wstart += stride_w
stride_h = tvm.max(0.01, stride_h)
stride_w = tvm.max(0.01, stride_w)
_max = tvm.comm_reducer(lambda x, y: tvm.make._OpMax(x, y),
min_value,
name='max')
rh = tvm.reduce_axis((0, tvm.expr.Select(non_empty, 2, 0)), 'rh')
rw = tvm.reduce_axis((0, tvm.expr.Select(non_empty, 2, 0)), 'rw')
return _max(_bilinear(batch_index, c, hstart + rh * stride_h,
wstart + rw * stride_w),
axis=[rh, rw])
def conv2d_batch(B, N, M, K, L, stride=1, padding=0, dtype="float32"):
A = tvm.placeholder((B, N, M), dtype=dtype, name="A")
W = tvm.placeholder((K, L), dtype=dtype, name="W")
N_out = max(0, (N + padding * 2 - K) // stride) + 1
M_out = max(0, (M + padding * 2 - L) // stride) + 1
Apad = tvm.compute(
(B, N + 2 * padding, M + 2 * padding),
lambda b, i, j: tvm.if_then_else(
tvm.all(i >= padding, j >= padding, i < N + padding, j < M +
padding), A[b, i - padding, j - padding], 0.0),
name="Apad")
rx, ry = tvm.reduce_axis((0, K), name="rx"), tvm.reduce_axis((0, L),
name="ry")
Output = tvm.compute((B, N_out, M_out),
lambda b, i, j: tvm.sum(Apad[b, i * stride + rx, j *
stride + ry] * W[rx, ry],
axis=[rx, ry]),
name="Output")
return Output.op, [A, W, Output]
def lrn_nchw(data, size, axis, alpha, beta, bias):
#default : size = 5, axis=1, alpha=0.0001, beta=0.75, bias=1
#sqrt_out = lrn_sqrt()
#pow_out = lrn_pow()
#div_out = lrn_div()
#return div_out
out_dtype = data.dtype
radius = size // 2
batch, in_channel, in_height, in_width = data.shape
ls = tvm.reduce_axis((0, size), name='ls')
# pad and sqrt op:
output_data1 = lambda on, oc, oh, ow: tvm.sum(tvm.expr.Select(
tvm.all(oc >= radius, oc < (in_channel + radius)), data[
on, oc - radius + ls, oh, ow].astype(out_dtype) * data[
on, oc - radius + ls, oh, ow].astype(out_dtype), 0.0),
axis=[ls])
sqr_out = tvm.compute((batch, in_channel, in_height, in_width),
output_data1,
tag="lrn_sqrt_op")
#return sqr_out
# pow op:
output_data2 = lambda on, oc, oh, ow: tvm.power(
(1 + (alpha / size * sqr_out[on, oc, oh, ow].astype(out_dtype))), beta)
pow_out = tvm.compute((batch, in_channel, in_height, in_width),
output_data2,
tag="lrn_pow_op")
#return pow_op
# div op:
output_data3 = lambda on, oc, oh, ow: tvm.expr.Div(
data[on, oc, oh, ow].astype(out_dtype), pow_out[on, oc, oh, ow].astype(
out_dtype))
div_out = tvm.compute((batch, in_channel, in_height, in_width),
output_data3,
tag="lrn_div_op")
return div_out
def compute_X_dot_A(k, b, eps, nu, kk, bb):
temp_expr = {}
for i in range(m):
m1_add_m2 = A_T_dot_M[k][b][i][1][kk][bb] + A_T_dot_M[k][b][i][2][
kk][bb]
m1_sub_m2 = A_T_dot_M[k][b][i][1][kk][bb] - A_T_dot_M[k][b][i][2][
kk][bb]
m3_add_m4 = A_T_dot_M[k][b][i][3][kk][bb] + A_T_dot_M[k][b][i][4][
kk][bb]
m3_sub_m4 = A_T_dot_M[k][b][i][3][kk][bb] - A_T_dot_M[k][b][i][4][
kk][bb]
m5_add_m6 = A_T_dot_M[k][b][i][5][kk][bb] + A_T_dot_M[k][b][i][6][
kk][bb]
m5_sub_m6 = A_T_dot_M[k][b][i][5][kk][bb] - A_T_dot_M[k][b][i][6][
kk][bb]
s0 = A_T_dot_M[k][b][i][0][kk][bb] + m1_add_m2
s5 = A_T_dot_M[k][b][i][7][kk][bb] + m1_sub_m2
s1 = m1_sub_m2 + m5_sub_m6 * 16
s4 = m1_add_m2 + m3_add_m4 * 16
s2 = m1_add_m2 + 8 * m5_add_m6
s3 = m1_sub_m2 + 8 * m3_sub_m4
s0 = s0 + m5_add_m6 * 32
s5 = s5 + m3_sub_m4 * 32
s1 = s1 + m3_sub_m4 * 2
s4 = s4 + m5_add_m6 * 2
s0 = s0 + m3_add_m4
s5 = s5 + m5_sub_m6
s2 = s2 + m3_add_m4 * 4
s3 = s3 + m5_sub_m6 * 4
temp_expr[(i, 0)] = s0
temp_expr[(i, 1)] = s1
temp_expr[(i, 2)] = s2
temp_expr[(i, 3)] = s3
temp_expr[(i, 4)] = s4
temp_expr[(i, 5)] = s5
now = tvm.const(0.0, "float32")
for ii in range(m):
for jj in range(m):
now = tvm.select(tvm.all(eps == ii, nu == jj),
temp_expr[(ii, jj)], now)
return now
def compute_X_dot_B(b, eps, nu, c, bb):
temp_expr = {}
for i in range(alpha):
wd0 = B_T_dot_X[b][c][i][0][bb] - B_T_dot_X[b][c][i][6][bb]
d4_sub_d2 = B_T_dot_X[b][c][i][4][bb] - B_T_dot_X[b][c][i][2][bb]
wd7 = B_T_dot_X[b][c][i][7][bb] - B_T_dot_X[b][c][i][1][bb]
d3_sub_d5 = B_T_dot_X[b][c][i][3][bb] - B_T_dot_X[b][c][i][5][bb]
wd1 = B_T_dot_X[b][c][i][2][bb] + B_T_dot_X[b][c][i][6][bb]
wd2 = B_T_dot_X[b][c][i][1][bb] + B_T_dot_X[b][c][i][5][bb]
wd4 = B_T_dot_X[b][c][i][5][bb] + B_T_dot_X[b][c][i][1][bb] * 0.25
wd5 = B_T_dot_X[b][c][i][6][bb] - B_T_dot_X[b][c][i][4][bb] * 5
wd3 = B_T_dot_X[b][c][i][6][bb] + B_T_dot_X[b][c][i][2][bb] * 0.25
wd6 = B_T_dot_X[b][c][i][1][bb] + B_T_dot_X[b][c][i][5][bb] * 0.25
wd0 = wd0 + d4_sub_d2 * 5.25
wd7 = wd7 + d3_sub_d5 * 5.25
wd1 = wd1 - B_T_dot_X[b][c][i][4][bb] * 4.25
wd2 = wd2 - B_T_dot_X[b][c][i][3][bb] * 4.25
wd3 = wd3 - B_T_dot_X[b][c][i][4][bb] * 1.25
wd5 = wd5 + B_T_dot_X[b][c][i][2][bb] * 4
wd4 = wd4 - B_T_dot_X[b][c][i][3][bb] * 1.25
wd6 = wd6 - B_T_dot_X[b][c][i][3][bb] * 1.25
temp_expr[(i, 0)] = wd0
temp_expr[(i, 1)] = wd1 + wd2
temp_expr[(i, 2)] = wd1 - wd2
temp_expr[(i, 3)] = wd3 + wd4 * 2
temp_expr[(i, 4)] = wd3 - wd4 * 2
temp_expr[(i, 5)] = wd5 + wd6 * 2
temp_expr[(i, 6)] = wd5 - wd6 * 2
temp_expr[(i, 7)] = wd7
now = tvm.const(0.0, "float32")
for ii in range(alpha):
for jj in range(alpha):
now = tvm.select(tvm.all(eps == ii, nu == jj),
temp_expr[(ii, jj)], now)
return now
def test_lower_floormod():
data = get_ref_data()
for dtype in ["int32", "int64", "int16"]:
x = tvm.var("x", dtype=dtype)
y = tvm.var("y", dtype=dtype)
zero = tvm.const(0, dtype)
# no constraints
res = lower_intrin(tvm.floormod(x, y))
check_value(res, x, y, data, lambda a, b: a % b)
# rhs >= 0
res = lower_intrin(tvm.expr.Select(y >= 0, tvm.floormod(x, y), zero))
check_value(res, x, y, data, lambda a, b: a % b if b > 0 else 0)
# lhs >= 0
res = lower_intrin(
tvm.expr.Select(tvm.all(y >= 0, x >= 0), tvm.floormod(x, y), zero))
check_value(res, x, y, data, lambda a, b: a % b
if b > 0 and a >= 0 else 0)
# const power of two
res = lower_intrin(tvm.floormod(x, tvm.const(8, dtype=dtype)))
check_value(res, x, y, [(a, b) for a, b in data if b == 8],
lambda a, b: a % b)
def conv2d_channel(N, M, C, K, L, O, stride=1, padding=0, dtype="float32"):
A = tvm.placeholder((N, M, C), dtype=dtype, name="A")
W = tvm.placeholder((K, L, C, O), dtype=dtype, name="W")
N_out = max(0, (N + padding * 2 - K) // stride) + 1
M_out = max(0, (M + padding * 2 - L) // stride) + 1
Apad = tvm.compute(
(N + 2 * padding, M + 2 * padding, C),
lambda i, j, k: tvm.if_then_else(
tvm.all(i >= padding, j >= padding, i < N + padding, j < M +
padding), A[i - padding, j - padding, k], 0.0),
name="Apad")
rx, ry = tvm.reduce_axis((0, K), name="rx"), tvm.reduce_axis((0, L),
name="ry")
rc = tvm.reduce_axis((0, C), name="rc")
Output = tvm.compute(
(N_out, M_out, O),
lambda i, j, k: tvm.sum(Apad[i * stride + rx, j * stride + ry, rc] * W[
rx, ry, rc, k],
axis=[rx, ry, rc]),
name="Output")
return Output.op, [A, W, Output]
def compute_B_T_dot_X(b, c, eps, nu, bb):
temp_expr = {}
for j in range(alpha):
wd0 = input_tile[b][c][0][j][bb] - input_tile[b][c][6][j][bb]
d4_sub_d2 = input_tile[b][c][4][j][bb] - input_tile[b][c][2][j][bb]
wd7 = input_tile[b][c][7][j][bb] - input_tile[b][c][1][j][bb]
d3_sub_d5 = input_tile[b][c][3][j][bb] - input_tile[b][c][5][j][bb]
wd1 = input_tile[b][c][2][j][bb] + input_tile[b][c][6][j][bb]
wd2 = input_tile[b][c][1][j][bb] + input_tile[b][c][5][j][bb]
wd4 = input_tile[b][c][5][j][bb] + input_tile[b][c][1][j][bb] * 0.25
wd5 = input_tile[b][c][6][j][bb] - input_tile[b][c][4][j][bb] * 5
wd3 = input_tile[b][c][6][j][bb] + input_tile[b][c][2][j][bb] * 0.25
wd6 = input_tile[b][c][1][j][bb] + input_tile[b][c][5][j][bb] * 0.25
wd0 = wd0 + d4_sub_d2 * 5.25
wd7 = wd7 + d3_sub_d5 * 5.25
wd1 = wd1 - input_tile[b][c][4][j][bb] * 4.25
wd2 = wd2 - input_tile[b][c][3][j][bb] * 4.25
wd3 = wd3 - input_tile[b][c][4][j][bb] * 1.25
wd5 = wd5 + input_tile[b][c][2][j][bb] * 4
wd4 = wd4 - input_tile[b][c][3][j][bb] * 1.25
wd6 = wd6 - input_tile[b][c][3][j][bb] * 1.25
temp_expr[(0, j)] = wd0
temp_expr[(1, j)] = wd1 + wd2
temp_expr[(2, j)] = wd1 - wd2
temp_expr[(3, j)] = wd3 + wd4 * 2
temp_expr[(4, j)] = wd3 - wd4 * 2
temp_expr[(5, j)] = wd5 + wd6 * 2
temp_expr[(6, j)] = wd5 - wd6 * 2
temp_expr[(7, j)] = wd7
now = tvm.const(0.0, "float32")
for ii in range(alpha):
for jj in range(alpha):
now = tvm.select(tvm.all(eps == ii, nu == jj),
temp_expr[(ii, jj)], now)
return now
def _pool(i, c, ph, pw):
roi = rois[i]
batch_index = roi[0].astype('int32')
roi_start_w, roi_start_h, roi_end_w, roi_end_h = roi[1], roi[2], roi[
3], roi[4]
roi_start_h = tvm.round(roi_start_h * spatial_scale).astype('int32')
roi_start_w = tvm.round(roi_start_w * spatial_scale).astype('int32')
roi_end_h = tvm.round(roi_end_h * spatial_scale).astype('int32')
roi_end_w = tvm.round(roi_end_w * spatial_scale).astype('int32')
# force malformed ROIs to be 1x1
roi_h = tvm.max(roi_end_h - roi_start_h + 1, tvm.const(1, 'int32'))
roi_w = tvm.max(roi_end_w - roi_start_w + 1, tvm.const(1, 'int32'))
bin_h = roi_h.astype(dtype) / pooled_size_h
bin_w = roi_w.astype(dtype) / pooled_size_w
# use epsilon to prevent floating point precision loss in floor/ceil
epsilon = tvm.const(0.00001, dtype)
hstart = tvm.floor(ph * bin_h + epsilon).astype('int32')
wstart = tvm.floor(pw * bin_w + epsilon).astype('int32')
hend = tvm.ceil((ph + 1) * bin_h - epsilon).astype('int32')
wend = tvm.ceil((pw + 1) * bin_w - epsilon).astype('int32')
hstart = tvm.min(tvm.max(hstart + roi_start_h, 0), height)
wstart = tvm.min(tvm.max(wstart + roi_start_w, 0), width)
hend = tvm.min(tvm.max(hend + roi_start_h, 0), height)
wend = tvm.min(tvm.max(wend + roi_start_w, 0), width)
non_empty = tvm.all(hstart < hend, wstart < wend)
min_value = lambda dtype: tvm.if_then_else(
non_empty, tvm.min_value(dtype), tvm.const(0.0, dtype))
# pylint: disable=unnecessary-lambda
_max = tvm.comm_reducer(lambda x, y: tvm.make._OpMax(x, y),
min_value,
name='max')
rh = tvm.reduce_axis((0, hend - hstart), 'rh')
rw = tvm.reduce_axis((0, wend - wstart), 'rw')
return _max(data[batch_index, c, hstart + rh, wstart + rw],
axis=[rh, rw])
def conv3d(N, M, P, K, L, Q, stride=1, padding=0, dtype="float32"):
A = tvm.placeholder((N, M, P), dtype=dtype, name="A")
W = tvm.placeholder((K, L, Q), dtype=dtype, name="W")
N_out = max(0, (N + padding * 2 - K) // stride) + 1
M_out = max(0, (M + padding * 2 - L) // stride) + 1
P_out = max(0, (P + padding * 2 - Q) // stride) + 1
Apad = tvm.compute(
(N + 2 * padding, M + 2 * padding, P + 2 * padding),
lambda i, j, k: tvm.if_then_else(
tvm.all(i >= padding, j >= padding, k >= padding, i < N + padding,
j < M + padding, k < P + padding), A[
i - padding, j - padding, k - padding], 0.0),
name="Apad")
rx, ry, rz = tvm.reduce_axis((0, K), name="rx"), tvm.reduce_axis(
(0, L), name="ry"), tvm.reduce_axis((0, Q), name="rz")
Output = tvm.compute(
(N_out, M_out, P_out),
lambda i, j, k: tvm.sum(Apad[i * stride + rx, j * stride + ry, k *
stride + rz] * W[rx, ry, rz],
axis=[rx, ry, rz]),
name="Output")
return Output.op, [A, W, Output]
def zero_pad2d(inputs, padding=0):
"""Zero padding for 2d tensor
Args:
-----------------------------
inputs : tvm.tensor.Tensor
shape [batch, channel, height, width]
padding: (optional:0) int or tuple
expected: (h_pad_up, h_pad_down, w_pad_up, w_pad_down)
-----------------------------
Returns:
-----------------------------
tvm.tensor.Tensor
shape [batch, channel, padded_height, padded_width]
-----------------------------
"""
padding = (padding, padding, padding, padding) if isinstance(
padding, (int, tvm.expr.IntImm)) else padding
assert_print(isinstance(padding, tuple),
"type(padding)={}".format(type(padding)))
if len(padding) == 2:
padding = (padding[0], padding[0], padding[1], padding[1])
assert_print(len(padding) == 4)
padding_zero = 0.0 if "float" in inputs.dtype else 0
batch_size, in_channel, height, width = inputs.shape
return tvm.compute(
(batch_size, in_channel, height +
padding[0] + padding[1], width + padding[2] + padding[3]),
lambda b, c, h, w: tvm.if_then_else(
tvm.all(h >= padding[0], h < height + padding[0],
w >= padding[2], w < width + padding[2]),
inputs[b, c, h - padding[0], w - padding[2]],
padding_zero
)
)
def test_copy_pad():
m = tvm.var('m')
l = tvm.var('l')
A = tvm.placeholder((m, l), name='A')
B = tvm.compute((m + 2, l), lambda i, j:
tvm.select(tvm.all(i >= 1, i < m + 1),
A[i - 1, j], 1.0), name='B')
s = tvm.create_schedule(B.op)
s[B].pragma(B.op.axis[0], "memcpy")
bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
Bb = tvm.decl_buffer(B.shape, B.dtype, name='B')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B: Bb}, 64)
def cb(src, dst, pad_before, pad_after, pad_value):
assert tvm.ir_pass.Simplify(src.elem_offset).value == 0
assert pad_before[0].value == 1
assert pad_before[1].value == 0
assert pad_after[0].value == 1
assert pad_after[1].value == 0
assert pad_value.value == 1.0
return tvm.make.Evaluate(0)
stmt = tvm.ir_pass.InjectCopyIntrin(stmt, "memcpy", cb)
def test_cmp_simplify():
ck = RewriteChecker()
x, y, z = tvm.var("x"), tvm.var("y"), tvm.var("z")
# const int bound
ck.verify((x % 2 + 10).equal(0), tvm.const(0, "bool"))
ck.verify(tvm.expr.NE(x % 2 + 10, 0), tvm.const(1, "bool"))
ck.verify(x % 2 + 10 > 1, tvm.const(1, "bool"))
ck.verify(x % 2 + 10 <= 1, tvm.const(0, "bool"))
ck.verify(x * 3 + 10 == 0, tvm.const(0, "bool"))
ck.verify(x * 3 + 10 != 0, tvm.const(1, "bool"))
# canonicalization
ck.verify((x - 10).equal(0), x.equal(10))
ck.verify((10 - x).equal(0), x.equal(10))
ck.verify((x * y).equal(0), tvm.expr.Or(x.equal(0), y.equal(0)))
# cmp bound
ck.verify(x + y < x + z, y < z)
ck.verify(x + y < z + x, y < z)
ck.verify(y + x < x + z, y < z)
ck.verify(y + x < z + x, y < z)
ck.verify(y - x < z - x, y < z)
ck.verify(x - y < x - z, z < y)
ck.verify(x < z + x, tvm.expr.LT(0, z))
ck.verify(x < x + z, tvm.expr.LT(0, z))
ck.verify(100 < x + 1, tvm.expr.LT(99, x))
ck.verify(1 < 100 - x, tvm.expr.LT(x, 99))
ck.verify(x * 3 < y * 3, x < y)
ck.verify(x * (-3) < y * (-3), y < x)
ck.verify(x * 3 >= y * 3, y <= x)
ck.verify(x * 4 >= 2, tvm.expr.LE(1, x))
ck.verify(x * 2 >= 50, tvm.expr.LE(25, x))
ck.verify(x / 2 < 3, x < 6)
ck.verify(x * 4 <= 2, x <= 0)
ck.verify(3 < x / 2, tvm.expr.LT(7, x))
ck.verify(x / 4 * 4 < x, tvm.expr.LT(0, x % 4))
ck.verify(x / 4 * 4 >= x, tvm.expr.LE(x % 4, 0))
ck.verify(x / 4 * 4 < x + y, tvm.expr.LT(0, x % 4 + y))
ck.verify(x / 4 * 4 < x - y, tvm.expr.LT(y, x % 4))
ck.verify((x + 2) / 4 * 4 >= x, tvm.expr.LE((x + 2) % 4, 2))
ck.verify((x + 2) / 4 * 4 >= x + y, tvm.expr.LE((x + 2) % 4 + y, 2))
ck.verify((x + 2) / 4 * 4 >= x - y, tvm.expr.LE((x + 2) % 4 + (-2), y))
ck.verify(tvm.min(x, 11) < 10, x < 10)
ck.verify(tvm.min(x, 8) < 10, tvm.const(1, "bool"))
ck.verify(tvm.max(8, x) > 10, tvm.expr.LT(10, x))
ck.verify(x + 1 < tvm.max(8, x), x < 7)
ck.analyzer.update(x, tvm.arith.ConstIntBound(0, 10), override=True)
ck.analyzer.update(y, tvm.arith.ConstIntBound(-10, 0), override=True)
ck.analyzer.update(z, tvm.arith.ConstIntBound(-5, 5), override=True)
ck.verify(x < 11, tvm.const(1, "bool"))
ck.verify(x <= 10, tvm.const(1, "bool"))
ck.verify(z <= 5, tvm.const(1, "bool"))
ck.verify(x + y <= 10, tvm.const(1, "bool"))
ck.verify(x + y >= -10, tvm.const(1, "bool"))
ck.verify(z - 5 <= y + 10, tvm.const(1, "bool"))
ck.verify(tvm.all(x > -1, z <= x + 5), tvm.const(1, "bool"))
ck.verify(x*y <= 0, tvm.const(1, "bool"))
ck.verify((x + 1)*(y - 1) < 0, tvm.const(1, "bool"))
ck.verify(y*y >= 0, tvm.const(1, "bool"))
def transform_loc_pre(cls_prob, valid_count, temp_valid_count, temp_cls_id, temp_score, threshold):
"""Low level IR routing for transform location data preparation.
Parameters
----------
cls_prob : Buffer
Buffer of class probabilities.
valid_count : Buffer
Buffer of number of valid output boxes.
temp_valid_count : Buffer
Output intermediate result buffer
temp_cls_id : Buffer
Output intermediate result buffer
temp_score : Buffer
Output buffer
threshold : float
Threshold to be a positive prediction.
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch_size = cls_prob.shape[0]
num_classes = cls_prob.shape[1]
num_anchors = cls_prob.shape[2]
ib = tvm.ir_builder.create()
cls_prob = ib.buffer_ptr(cls_prob)
cls_id = ib.buffer_ptr(temp_cls_id)
valid_count = ib.buffer_ptr(valid_count)
temp_valid_count = ib.buffer_ptr(temp_valid_count)
score = ib.buffer_ptr(temp_score)
threshold = tvm.make.node("FloatImm", dtype="float32", value=threshold)
max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
nthread_tx = max_threads
nthread_bx = (batch_size * num_anchors) // max_threads + 1
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
tid = bx * max_threads + tx
with ib.if_scope(tid < batch_size * num_anchors):
i = tid / num_anchors
j = tid % num_anchors
valid_count[i] = 0
score[tid] = -1.0
cls_id[tid] = 0
with ib.for_range(0, num_classes - 1) as k:
temp = cls_prob[i * num_classes * num_anchors + (k + 1) * num_anchors + j]
cls_id[tid] = if_then_else(temp > score[tid], k + 1, cls_id[tid])
score[tid] = tvm.max(temp, score[tid])
with ib.if_scope(tvm.all(cls_id[tid] > 0, score[tid] < threshold)):
cls_id[tid] = 0
with ib.if_scope(cls_id[tid] > 0):
temp_valid_count[tid] = 1
with ib.else_scope():
temp_valid_count[tid] = 0
with ib.if_scope(tid < batch_size):
with ib.for_range(0, num_anchors) as k:
with ib.if_scope(k > 0):
temp_valid_count[tid * num_anchors + k] += \
temp_valid_count[tid * num_anchors + k - 1]
valid_count[i] = temp_valid_count[tid * num_anchors + num_anchors - 1]
return ib.get()
def sort_oet_ir(data, index, new_data, new_index, loc, out_index, axis_mul_before, \
axis_mul_after, axis, is_descend):
"""Low level IR routing subfunction 3/4 for Odd-Even-Transposition sorting.
Parameters
----------
data: Buffer
Buffer of output boxes with class and score.
index : Buffer
Buffer of number of valid output boxes.
new_data : Buffer
Buffer of flattened segmented data.
new_index : Buffer
Buffer of flattened segmented indices.
loc : Buffer
Buffer of start locations of each sorting segment.
out_index : Buffer
Output buffer of output box indexes sorted by score in a flattened segmented format.
axis_mul_before : int
The multiplication result of axis dimensions before axis.
axis_mul_after : int
The multiplication result of axis dimensions after axis.
axis : int
The axis used for sorting.
is_descend : bool
If the sorted data is in descending order.
Returns
-------
stmt : Stmt
The result IR statement.
"""
max_threads = int(
tvm.target.current_target(allow_none=False).max_num_threads)
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
ib = tvm.ir_builder.create()
dshape = loc.shape
fshape = data.shape[axis] * dshape[0]
temp_data = ib.allocate(
"float32", dshape, name="temp_data", scope="local")
p_data = ib.buffer_ptr(data)
p_index = ib.buffer_ptr(index)
data_new = ib.buffer_ptr(new_data)
index_new = ib.buffer_ptr(new_index)
index_out = ib.buffer_ptr(out_index)
sizes = ib.buffer_ptr(loc)
nthread_tx = max_threads
nthread_bx = fshape // max_threads + 1
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
tid = bx * max_threads + tx
with ib.if_scope(axis_mul_before * axis_mul_after > 1):
with ib.if_scope(tid < axis_mul_before * axis_mul_after):
with ib.if_scope(tid == 0):
start = 0
with ib.else_scope():
start = sizes[tid-1]
# OddEvenTransposeSort
with ib.for_range(0, p_index[tid], name="k") as k:
with ib.for_range(0, p_index[tid] - 1, name="i") as i:
with ib.if_scope(i % 2 == k % 2):
with ib.if_scope(((data_new[i+start] < data_new[i+start+1]) == is_descend)):
temp_data[tid] = data_new[i+start]
data_new[i+start] = data_new[i+start+1]
data_new[i+start+1] = temp_data[tid]
index_out[tid] = index_new[i+start]
index_new[i+start] = index_new[i+start+1]
index_new[i+start+1] = index_out[tid]
with ib.if_scope(tid < 1):
with ib.for_range(0, sizes[dshape[0] - 1], name="i") as i:
index_out[i] = index_new[i]
with ib.else_scope():
with ib.for_range(0, fshape, name="k", for_type="unroll") as k:
with ib.if_scope(tvm.all(k % 2 == tid % 2, tid < fshape)):
with ib.if_scope(k % 2 == 0):
with ib.if_scope(tvm.all(tid + 1 < fshape, (p_data[tid] < p_data[tid+1]) \
== is_descend)):
data_new[tid] = p_data[tid+1]
index_out[tid] = index_new[tid+1]
with ib.else_scope():
data_new[tid] = p_data[tid]
index_out[tid] = index_new[tid]
with ib.else_scope():
with ib.if_scope(tvm.all(tid + 1 < fshape, (data_new[tid] < data_new[tid+1]) \
== is_descend)):
p_data[tid] = data_new[tid+1]
index_new[tid] = index_out[tid+1]
with ib.else_scope():
p_data[tid] = data_new[tid]
index_new[tid] = index_out[tid]
with ib.if_scope(tvm.all(k % 2 != tid % 2, tid < fshape)):
with ib.if_scope(k % 2 == 0):
with ib.if_scope(tvm.all(tid > 0, (p_data[tid-1] < p_data[tid]) == is_descend)):
data_new[tid] = p_data[tid-1]
index_out[tid] = index_new[tid-1]
with ib.else_scope():
data_new[tid] = p_data[tid]
index_out[tid] = index_new[tid]
with ib.else_scope():
with ib.if_scope(tvm.all(tid > 0, (data_new[tid-1] < data_new[tid]) \
== is_descend)):
p_data[tid] = data_new[tid-1]
index_new[tid] = index_out[tid-1]
with ib.else_scope():
p_data[tid] = data_new[tid]
index_new[tid] = index_out[tid]
with ib.if_scope(fshape % 2 == 1):
with ib.if_scope(tid < 1):
with ib.for_range(0, fshape, name="k") as k:
index_out[tid] = index_new[tid]
body = ib.get()
return body
def sort_ir(data, output, axis, is_ascend):
"""Low level IR to do nms sorting on the GPU, same usage as tvm.contrib.sort.argsort on the CPU.
Parameters
----------
data: Buffer
Buffer of input data.
output : Buffer
Output buffer of indicies of sorted tensor with same shape as data.
axis : Int
Axis long which to sort the input tensor.
is_ascend : Boolean
Whether to sort in ascending or descending order.
Returns
-------
stmt : Stmt
The result IR statement.
"""
size = 1
axis_mul_before = 1
axis_mul_after = 1
shape = data.shape
if axis < 0:
axis = len(shape) + axis
for i, value in enumerate(shape, 0):
size *= value
if i < axis:
axis_mul_before *= value
elif i > axis:
axis_mul_after *= value
max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
ib = tvm.ir_builder.create()
data = ib.buffer_ptr(data)
output = ib.buffer_ptr(output)
nthread_tx = max_threads
nthread_bx = size // max_threads + 1
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("vthread")
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "virtual_thread", nthread_bx)
tid = bx * nthread_tx + tx
temp_data = ib.allocate("float32", (1,), name="temp_data", scope="local")
temp_index = ib.allocate("float32", (1,), name="temp_index", scope="local")
is_ascend = tvm.make.node("IntImm", dtype="int32", value=is_ascend)
with ib.for_range(0, axis_mul_before) as i:
with ib.for_range(0, axis_mul_after) as j:
current_sort_num = shape[axis]
base_idx = i * shape[axis] * axis_mul_after + j
with ib.if_scope(tid < shape[axis]):
output[base_idx + tid * axis_mul_after] = tid.astype("float32")
# OddEvenTransposeSort
with ib.for_range(0, current_sort_num) as k:
with ib.if_scope(tid < (current_sort_num + 1) // 2):
offset = base_idx + (2 * tid + (k % 2)) * axis_mul_after
with ib.if_scope(tvm.all(is_ascend == 1, \
2 * tid + (k % 2) + 1 < current_sort_num, \
data[offset] > data[offset + axis_mul_after])):
temp_data[0] = data[offset]
data[offset] = data[offset + axis_mul_after]
data[offset + axis_mul_after] = temp_data[0]
temp_index[0] = output[offset]
output[offset] = output[offset + axis_mul_after]
output[offset + axis_mul_after] = temp_index[0]
with ib.if_scope(tvm.all(is_ascend == 0, \
2 * tid + (k % 2) + 1 < current_sort_num, \
data[offset] < data[offset + axis_mul_after])):
temp_data[0] = data[offset]
data[offset] = data[offset + axis_mul_after]
data[offset + axis_mul_after] = temp_data[0]
temp_index[0] = output[offset]
output[offset] = output[offset + axis_mul_after]
output[offset + axis_mul_after] = temp_index[0]
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
return ib.get()
def nms_ir(data, sort_result, valid_count, out, nms_threshold, force_suppress, nms_topk):
"""Low level IR routing for transform location in multibox_detection operator.
Parameters
----------
data: Buffer
Buffer of output boxes with class and score.
sort_result : Buffer
Buffer of output box indexes sorted by score.
valid_count : Buffer
Buffer of number of valid output boxes.
out : Buffer
Output buffer.
nms_threshold : float
Non-maximum suppression threshold.
force_suppress : boolean
Whether to suppress all detections regardless of class_id.
nms_topk : int
Keep maximum top k detections before nms, -1 for no limit.
Returns
-------
stmt : Stmt
The result IR statement.
"""
def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
"""Calculate overlap of two boxes.
"""
w = tvm.make.Max(0.0, tvm.make.Min(out_tensor[box_a_idx + 2], out_tensor[box_b_idx + 2])
- tvm.make.Max(out_tensor[box_a_idx], out_tensor[box_b_idx]))
h = tvm.make.Max(0.0, tvm.make.Min(out_tensor[box_a_idx + 3], out_tensor[box_b_idx + 3])
- tvm.make.Max(out_tensor[box_a_idx + 1], out_tensor[box_b_idx + 1]))
i = w * h
u = (out_tensor[box_a_idx + 2] - out_tensor[box_a_idx]) * \
(out_tensor[box_a_idx + 3] - out_tensor[box_a_idx + 1]) + \
(out_tensor[box_b_idx + 2] - out_tensor[box_b_idx]) * \
(out_tensor[box_b_idx + 3] - out_tensor[box_b_idx + 1]) - i
return tvm.select(u <= 0.0, 0.0, i / u)
max_threads = int(math.sqrt(
tvm.target.current_target(allow_none=False).max_num_threads))
tx = tvm.thread_axis("threadIdx.x")
ty = tvm.thread_axis("threadIdx.y")
bx = tvm.thread_axis("blockIdx.x")
by = tvm.thread_axis("blockIdx.y")
ib = tvm.ir_builder.create()
p_data = ib.buffer_ptr(data)
p_sort_result = ib.buffer_ptr(sort_result)
p_valid_count = ib.buffer_ptr(valid_count)
p_out = ib.buffer_ptr(out)
batch_size = out.shape[0]
num_anchors = out.shape[1]
nthread_tx = max_threads
nthread_bx = num_anchors // max_threads + 1
nthread_ty = max_threads
nthread_by = 6 // max_threads + 1
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(ty, "thread_extent", nthread_ty)
ib.scope_attr(bx, "thread_extent", nthread_bx)
ib.scope_attr(by, "thread_extent", nthread_by)
i = bx * max_threads + tx
j = by * max_threads + ty
nms_threshold_node = tvm.make.node(
"FloatImm", dtype="float32", value=nms_threshold)
nms_topk_node = tvm.make.node("IntImm", dtype="int32", value=nms_topk)
force_suppress_node = tvm.make.node(
"IntImm", dtype="int32", value=1 if force_suppress else 0)
with ib.for_range(0, batch_size, for_type="unroll", name="n") as n:
with ib.if_scope(
tvm.all(nms_threshold_node > 0, nms_threshold_node < 1,
p_valid_count[0] > 0)):
# Reorder output
nkeep = tvm.select(
tvm.all(nms_topk_node > 0, nms_topk < p_valid_count[n]),
nms_topk, p_valid_count[n])
with ib.if_scope(i < nkeep):
with ib.if_scope(j < 6):
p_out[(n * num_anchors * 6
+ i * 6 + j)] = p_data[(n * num_anchors * 6
+ p_sort_result[n * num_anchors + i] * 6 + j)]
with ib.if_scope(tvm.all(nms_topk_node > 0, nms_topk < p_valid_count[n])):
with ib.if_scope(i < p_valid_count[n] - nkeep):
with ib.if_scope(j < 6):
p_out[(n * num_anchors * 6
+ (i + nkeep) * 6 + j)] = p_data[(n * num_anchors * 6
+ (i + nkeep) * 6 + j)]
# Apply nms
with ib.if_scope(i < p_valid_count[n]):
offset_i = i * 6
with ib.if_scope(p_out[n * num_anchors * 6 + offset_i] >= 0):
with ib.if_scope(j < p_valid_count[n]):
offset_j = j * 6
with ib.if_scope(tvm.all(j > i, p_out[n * num_anchors * 6
+ offset_j] >= 0)):
with ib.if_scope(tvm.any(force_suppress_node > 0,
p_out[n * num_anchors * 6 + offset_i] ==
p_out[n * num_anchors * 6 + offset_j])):
# When force_suppress == True or class_id equals
iou = calculate_overlap(
p_out, n * num_anchors * 6 + offset_i + 2,
n * num_anchors * 6 + offset_j + 2)
with ib.if_scope(iou >= nms_threshold):
p_out[
n * num_anchors * 6 + offset_j] = -1.0
with ib.else_scope():
with ib.if_scope(i < p_valid_count[n]):
with ib.if_scope(j < 6):
p_out[(n * num_anchors * 6
+ i * 6 + j)] = p_data[n * num_anchors * 6 + i * 6 + j]
# Set invalid entry to be -1
with ib.if_scope(i < num_anchors - p_valid_count[n]):
with ib.if_scope(j < 6):
p_out[n * num_anchors * 6 + (i +
p_valid_count[n]) * 6 + j] = -1.0
body = ib.get()
return body
def transform_loc_pre(cls_prob, valid_count, temp_flag, temp_id, temp_score_out, threshold):
"""Low level IR routing for transform location data preparation.
Parameters
----------
cls_prob : Buffer
Buffer of class probabilities.
valid_count : Buffer
Buffer of number of valid output boxes.
temp_flag : Buffer
Output intermediate result buffer
temp_id : Buffer
Output intermediate result buffer
temp_score_out : Buffer
Output buffer
threshold : float
Threshold to be a positive prediction.
Returns
-------
stmt : Stmt
The result IR statement.
"""
batch_size = cls_prob.shape[0]
num_classes = cls_prob.shape[1]
num_anchors = cls_prob.shape[2]
max_threads = int(
tvm.target.current_target(allow_none=False).max_num_threads)
ib = tvm.ir_builder.create()
score = ib.buffer_ptr(temp_score_out)
cls_id = ib.buffer_ptr(temp_id)
flag = ib.buffer_ptr(temp_flag)
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
nthread_tx = max_threads
nthread_bx = (batch_size * num_anchors * num_classes) // max_threads + 1
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
tid = bx * max_threads + tx
p_cls_prob = ib.buffer_ptr(cls_prob)
p_valid_count = ib.buffer_ptr(valid_count)
with ib.if_scope(tid < batch_size * num_anchors):
n = tid / num_anchors # number of batches
i = tid % num_anchors # number of anchors
score[i] = -1.0
cls_id[i] = 0
p_valid_count[n] = 0
with ib.for_range(0, num_classes-1, name="k") as k:
temp = p_cls_prob[n * num_anchors * num_classes + (k + 1) * num_anchors + i]
with ib.if_scope(temp > score[i]):
cls_id[i] = k + 1
score[i] = temp
with ib.if_scope(tvm.all(cls_id[i] > 0, score[i] < threshold)):
cls_id[i] = 0
with ib.if_scope(cls_id[i] > 0):
flag[i] = 1
with ib.else_scope():
flag[i] = 0
with ib.if_scope(tid < batch_size):
with ib.for_range(0, num_anchors, name="k") as k:
with ib.if_scope(k > 0):
flag[tid * num_anchors +
k] += flag[tid * num_anchors + k - 1]
p_valid_count[n] = flag[tid * num_anchors + num_anchors - 1]
body = ib.get()
return body
def transform_loc_ir(cls_prob, loc_pred, anchor, valid_count, out, clip, threshold, variances):
"""Low level IR routing for transform location in multibox_detection operator.
Parameters
----------
cls_prob : Buffer
Buffer of class probabilities.
loc_pred : Buffer
Buffer of location regression predictions.
anchor : Buffer
Buffer of prior anchor boxes.
valid_count : Buffer
Buffer of number of valid output boxes.
out : Buffer
Output buffer.
clip : boolean
Whether to clip out-of-boundary boxes.
threshold : float
Threshold to be a positive prediction.
variances : tuple of float
Variances to be decoded from box regression output.
Returns
-------
stmt : Stmt
The result IR statement.
"""
def transform_loc(loc, loc_base_idx, anchor, anchor_base_idx, clip, vx, vy, vw, vh):
"""Transform prior anchor box to output box through location predictions.
"""
al = anchor[anchor_base_idx]
at = anchor[anchor_base_idx + 1]
ar = anchor[anchor_base_idx + 2]
ab = anchor[anchor_base_idx + 3]
aw = ar - al
ah = ab - at
ax = (al + ar) / 2.0
ay = (at + ab) / 2.0
px = loc[loc_base_idx]
py = loc[loc_base_idx + 1]
pw = loc[loc_base_idx + 2]
ph = loc[loc_base_idx + 3]
ox = px * vx * aw + ax
oy = py * vy * ah + ay
ow = tvm.exp(pw * vw) * aw / 2.0
oh = tvm.exp(ph * vh) * ah / 2.0
return tvm.select(clip, tvm.max(0, tvm.min(1, ox - ow)), ox - ow), \
tvm.select(clip, tvm.max(0, tvm.min(1, oy - oh)), oy - oh), \
tvm.select(clip, tvm.max(0, tvm.min(1, ox + ow)), ox + ow), \
tvm.select(clip, tvm.max(0, tvm.min(1, oy + oh)), oy + oh)
batch_size = cls_prob.shape[0]
num_classes = cls_prob.shape[1]
num_anchors = cls_prob.shape[2]
ib = tvm.ir_builder.create()
p_cls_prob = ib.buffer_ptr(cls_prob)
p_loc_pred = ib.buffer_ptr(loc_pred)
p_anchor = ib.buffer_ptr(anchor)
p_valid_count = ib.buffer_ptr(valid_count)
p_out = ib.buffer_ptr(out)
with ib.for_range(0, batch_size, for_type="parallel", name="n") as n:
p_valid_count[n] = 0
with ib.for_range(0, num_anchors, name="i") as i:
# Find the predicted class id and probability
score = ib.allocate('float32', (1,), name="score", scope="local")
cls_id = ib.allocate('int32', (1,), name="id", scope="local")
score[0] = -1.0
cls_id[0] = 0
with ib.for_range(0, num_classes, name="j") as j:
with ib.if_scope(j > 0):
temp = p_cls_prob[n * num_anchors * num_classes + j * num_anchors + i]
cls_id[0] = tvm.select(temp > score[0], j, cls_id[0])
score[0] = tvm.max(temp, score[0])
with ib.if_scope(tvm.all(cls_id[0] > 0, score[0] < threshold)):
cls_id[0] = 0
# [id, prob, xmin, ymin, xmax, ymax]
# Remove background, restore original id
with ib.if_scope(cls_id[0] > 0):
out_base_idx = n * num_anchors * 6 + p_valid_count[n] * 6
p_out[out_base_idx] = cls_id[0] - 1.0
p_out[out_base_idx + 1] = score[0]
offset = i * 4
p_out[out_base_idx + 2], p_out[out_base_idx + 3], p_out[out_base_idx + 4], \
p_out[out_base_idx + 5] = transform_loc(p_loc_pred, n * num_anchors * 4 + offset,
p_anchor, offset, clip, variances[0],
variances[1], variances[2], variances[3])
p_valid_count[n] += 1
return ib.get()
def nms_ir(data, sort_result, valid_count, out, nms_threshold, force_suppress, nms_topk):
"""Low level IR routing for transform location in multibox_detection operator.
Parameters
----------
data: Buffer
Buffer of output boxes with class and score.
sort_result : Buffer
Buffer of output box indexes sorted by score.
valid_count : Buffer
Buffer of number of valid output boxes.
out : Buffer
Output buffer.
nms_threshold : float
Non-maximum suppression threshold.
force_suppress : boolean
Whether to suppress all detections regardless of class_id.
nms_topk : int
Keep maximum top k detections before nms, -1 for no limit.
Returns
-------
stmt : Stmt
The result IR statement.
"""
def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
"""Calculate overlap of two boxes.
"""
w = tvm.make.Max(0.0, tvm.make.Min(out_tensor[box_a_idx + 2], out_tensor[box_b_idx + 2])
- tvm.make.Max(out_tensor[box_a_idx], out_tensor[box_b_idx]))
h = tvm.make.Max(0.0, tvm.make.Min(out_tensor[box_a_idx + 3], out_tensor[box_b_idx + 3])
- tvm.make.Max(out_tensor[box_a_idx + 1], out_tensor[box_b_idx + 1]))
i = w * h
u = (out_tensor[box_a_idx + 2] - out_tensor[box_a_idx]) * \
(out_tensor[box_a_idx + 3] - out_tensor[box_a_idx + 1]) + \
(out_tensor[box_b_idx + 2] - out_tensor[box_b_idx]) * \
(out_tensor[box_b_idx + 3] - out_tensor[box_b_idx + 1]) - i
return tvm.select(u <= 0.0, 0.0, i / u)
ib = tvm.ir_builder.create()
p_data = ib.buffer_ptr(data)
p_sort_result = ib.buffer_ptr(sort_result)
p_valid_count = ib.buffer_ptr(valid_count)
p_out = ib.buffer_ptr(out)
batch_size = out.shape[0]
num_anchors = out.shape[1]
nms_threshold_node = tvm.make.node("FloatImm", dtype="float32", value=nms_threshold)
nms_topk_node = tvm.make.node("IntImm", dtype="int32", value=nms_topk)
force_suppress_node = tvm.make.node("IntImm", dtype="int32", value=1 if force_suppress else 0)
with ib.for_range(0, batch_size, for_type="parallel", name="n") as n:
with ib.if_scope(tvm.all(nms_threshold_node > 0, nms_threshold_node < 1,
p_valid_count[0] > 0)):
# Reorder output
nkeep = tvm.select(tvm.all(nms_topk_node > 0, nms_topk < p_valid_count[n]),
nms_topk, p_valid_count[n])
with ib.for_range(0, nkeep, name="l") as l:
with ib.for_range(0, 6, name="m") as m:
p_out[(n * num_anchors * 6
+ l * 6 + m)] = p_data[(n * num_anchors * 6
+ p_sort_result[n * num_anchors + l] * 6 + m)]
with ib.if_scope(tvm.all(nms_topk_node > 0, nms_topk < p_valid_count[n])):
with ib.for_range(0, p_valid_count[n] - nkeep, name="l") as l:
with ib.for_range(0, 6, name="m") as m:
p_out[(n * num_anchors * 6
+ (l + nkeep) * 6 + m)] = p_data[(n * num_anchors * 6
+ (l + nkeep) * 6 + m)]
# Apply nms
with ib.for_range(0, p_valid_count[n], name="l") as l:
offset_l = l * 6
with ib.if_scope(p_out[n * num_anchors * 6 + offset_l] >= 0):
with ib.for_range(0, p_valid_count[n], name="m") as m:
offset_m = m * 6
with ib.if_scope(tvm.all(m > l, p_out[n * num_anchors * 6
+ offset_m] >= 0)):
with ib.if_scope(tvm.any(force_suppress_node > 0,
p_out[n * num_anchors * 6 + offset_l] ==
p_out[n * num_anchors * 6 + offset_m])):
# When force_suppress == True or class_id equals
iou = calculate_overlap(p_out, n * num_anchors * 6 + offset_l + 2,
n * num_anchors * 6 + offset_m + 2)
with ib.if_scope(iou >= nms_threshold):
p_out[n * num_anchors * 6 + offset_m] = -1.0
with ib.else_scope():
with ib.for_range(0, p_valid_count[n], name="l") as l:
with ib.for_range(0, 6, name="m") as m:
p_out[(n * num_anchors * 6
+ l * 6 + m)] = p_data[n * num_anchors * 6 + l * 6 + m]
# Set invalid entry to be -1
with ib.for_range(0, num_anchors - p_valid_count[n], name="l") as l:
with ib.for_range(0, 6, name="m") as m:
p_out[n * num_anchors * 6 + (l + p_valid_count[n]) * 6 + m] = -1.0
return ib.get()
def nms_ir(data, sorted_index, valid_count, out, box_indices,
max_output_size, iou_threshold, force_suppress,
top_k, coord_start, id_index):
"""Low level IR routing for transform location in multibox_detection operator.
Parameters
----------
data : Buffer
Buffer of output boxes with class and score.
sort_index : Buffer
Buffer of output box indexes sorted by score.
valid_count : Buffer
Buffer of number of valid output boxes.
out : Buffer
Output buffer.
max_output_size : int
Max number of output valid boxes for each instance.
By default all valid boxes are returned.
iou_threshold : float
Overlapping(IoU) threshold to suppress object with smaller score.
force_suppress : boolean
Whether to suppress all detections regardless of class_id.
top_k : int
Keep maximum top k detections before nms, -1 for no limit.
coord_start : int
Start index of the consecutive 4 coordinates.
id_index : int
index of the class categories, -1 to disable.
Returns
-------
stmt : Stmt
The result IR statement.
"""
def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
"""Calculate overlap of two boxes.
"""
w = tvm.max(0.0, tvm.min(out_tensor[box_a_idx + 2], out_tensor[box_b_idx + 2])
- tvm.max(out_tensor[box_a_idx], out_tensor[box_b_idx]))
h = tvm.max(0.0, tvm.min(out_tensor[box_a_idx + 3], out_tensor[box_b_idx + 3])
- tvm.max(out_tensor[box_a_idx + 1], out_tensor[box_b_idx + 1]))
i = w * h
u = (out_tensor[box_a_idx + 2] - out_tensor[box_a_idx]) * \
(out_tensor[box_a_idx + 3] - out_tensor[box_a_idx + 1]) + \
(out_tensor[box_b_idx + 2] - out_tensor[box_b_idx]) * \
(out_tensor[box_b_idx + 3] - out_tensor[box_b_idx + 1]) - i
return tvm.expr.Select(u <= 0.0, 0.0, i / u)
batch_size = data.shape[0]
num_anchors = data.shape[1]
box_data_length = data.shape[2]
ib = tvm.ir_builder.create()
data = ib.buffer_ptr(data)
sorted_index = ib.buffer_ptr(sorted_index)
valid_count = ib.buffer_ptr(valid_count)
out = ib.buffer_ptr(out)
box_indices = ib.buffer_ptr(box_indices)
num_valid_boxes = ib.allocate("int32", (1,), name="num_valid_boxes", scope="local")
max_threads = int(math.sqrt(
tvm.target.current_target(allow_none=False).max_num_threads))
nthread_tx = max_threads
nthread_bx = num_anchors // max_threads + 1
tx = tvm.thread_axis("threadIdx.x")
bx = tvm.thread_axis("blockIdx.x")
ib.scope_attr(tx, "thread_extent", nthread_tx)
ib.scope_attr(bx, "thread_extent", nthread_bx)
k = bx * max_threads + tx
iou_threshold = tvm.make.node("FloatImm", dtype="float32", value=iou_threshold)
top_k = tvm.make.node("IntImm", dtype="int32", value=top_k)
coord_start = tvm.make.node("IntImm", dtype="int32", value=coord_start)
id_index = tvm.make.node("IntImm", dtype="int32", value=id_index)
force_suppress = tvm.make.node("IntImm", dtype="int32", value=1 if force_suppress else 0)
with ib.for_range(0, batch_size, for_type="unroll") as i:
base_idx = i * num_anchors * box_data_length
with ib.if_scope(tvm.all(iou_threshold > 0, valid_count[i] > 0)):
# Reorder output
nkeep = if_then_else( \
tvm.all(top_k > 0, top_k < valid_count[i]),
top_k, valid_count[i])
with ib.for_range(0, nkeep) as j:
with ib.if_scope(k < box_data_length):
out[(base_idx + j * box_data_length + k)] = \
data[(base_idx + sorted_index[i * num_anchors + j] \
* box_data_length + k)]
box_indices[i * num_anchors + j] = sorted_index[i * num_anchors + j]
with ib.if_scope(tvm.all(top_k > 0, top_k < valid_count[i])):
with ib.for_range(0, valid_count[i] - nkeep) as j:
with ib.if_scope(k < box_data_length):
out[(base_idx + (j + nkeep) * box_data_length + k)] = -1.0
box_indices[i * num_anchors + (j + nkeep)] = -1
# Apply nms
with ib.for_range(0, valid_count[i]) as j:
offset_j = j * box_data_length
with ib.if_scope(out[base_idx + offset_j] >= 0):
with ib.if_scope(k < valid_count[i]):
offset_k = k * box_data_length
with ib.if_scope(tvm.all(k > j, out[base_idx + offset_k] >= 0, \
tvm.any(force_suppress > 0, id_index < 0, \
out[base_idx + offset_j] == \
out[base_idx + offset_k]))):
iou = calculate_overlap(out, base_idx + offset_k + coord_start,
base_idx + offset_j + coord_start)
with ib.if_scope(iou >= iou_threshold):
out[base_idx + offset_k] = -1.0
box_indices[i * num_anchors + k] = -1
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
with ib.else_scope():
with ib.for_range(0, valid_count[i]) as j:
offset_j = j * box_data_length
with ib.if_scope(k < box_data_length):
out[(base_idx + offset_j + k)] = data[base_idx + offset_j + k]
box_indices[i * num_anchors + j] = j
# Set invalid entry to be -1
with ib.for_range(0, num_anchors - valid_count[i]) as j:
with ib.if_scope(k < box_data_length):
out[base_idx + (j + valid_count[i]) * box_data_length + k] = -1.0
box_indices[i * num_anchors + j + valid_count[i]] = -1
# Only return max_output_size number of valid boxes
num_valid_boxes[0] = 0
with ib.if_scope(max_output_size > 0):
with ib.for_range(0, valid_count[i]) as j:
offset_j = j * box_data_length
with ib.if_scope(out[base_idx + offset_j] >= 0):
with ib.if_scope(num_valid_boxes[0] == max_output_size):
with ib.if_scope(k < box_data_length):
out[base_idx + offset_j + k] = -1.0
box_indices[i * num_anchors + j] = -1
with ib.else_scope():
num_valid_boxes[0] += 1
ib.emit(tvm.make.Call(None, 'tvm_storage_sync',
tvm.convert(['shared']),
tvm.expr.Call.Intrinsic, None, 0))
return ib.get()
in_channel = 256
out_channel = 512
in_size = 14
kernel = 3
pad = 1
stride = 1
# Algorithm
A = tvm.placeholder((in_size, in_size, in_channel, batch), name='A')
W = tvm.placeholder((kernel, kernel, in_channel, out_channel), name='W')
out_size = (in_size - kernel + 2*pad) // stride + 1
# Pad input
Apad = tvm.compute(
(in_size + 2*pad, in_size + 2*pad, in_channel, batch),
lambda yy, xx, cc, nn: tvm.select(
tvm.all(yy >= pad, yy - pad < in_size,
xx >= pad, xx - pad < in_size),
A[yy - pad, xx - pad, cc, nn], tvm.const(0.)),
name='Apad')
# Create reduction variables
rc = tvm.reduce_axis((0, in_channel), name='rc')
ry = tvm.reduce_axis((0, kernel), name='ry')
rx = tvm.reduce_axis((0, kernel), name='rx')
# Compute the convolution
B = tvm.compute(
(out_size, out_size, out_channel, batch),
lambda yy, xx, ff, nn: tvm.sum(
Apad[yy * stride + ry, xx * stride + rx, rc, nn] * W[ry, rx, rc, ff],
axis=[ry, rx, rc]),
name='B')
