def batch_matmul_cblas(cfg, x, y):
"""Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
data in batch.
Parameters
----------
cfg : ConfigSpace
Autotvm tuning space config file
x : tvm.Tensor
3-D with shape [batch, M, K]
y : tvm.Tensor
3-D with shape [batch, N, K]
Returns
-------
output : tvm.Tensor
3-D with shape [batch, M, N]
"""
assert len(x.shape) == 3 and len(
y.shape) == 3, "only support 3-dim batch_matmul"
XB, M, XK = get_const_tuple(x.shape)
YB, N, YK = get_const_tuple(y.shape)
assert XB == YB, "batch dimension doesn't match"
assert XK == YK, "shapes of x and y is inconsistant"
cfg.add_flop(XB * M * N * XK * 2)
return cblas.batch_matmul(x, y, False, True)
def verify_batch_matmul(batch, m, l, n, transa=False, transb=False, iterative=False, dtype=tvm.float32):
ashape = (batch, l, n) if transa else (batch, n, l)
bshape = (batch, m, l) if transb else (batch, l, m)
A = tvm.placeholder(ashape, name='A', dtype=dtype)
B = tvm.placeholder(bshape, name='B', dtype=dtype)
C = cblas.batch_matmul(A, B, transa, transb)
D = tvm.compute(C.shape, lambda k, i, j: C[k, i,j], name="D")
s = tvm.create_schedule(D.op)
def get_numpy(a, b, transa, transb):
if transa:
a = a.transpose(0, 2, 1)
if not transb:
b = b.transpose(0, 2, 1)
return topi.testing.batch_matmul(a, b)
def verify(target="llvm"):
if not tvm.module.enabled(target):
print("skip because %s is not enabled..." % target)
return
if not tvm.get_global_func("tvm.contrib.cblas.matmul", True):
print("skip because extern function is not available")
return
ctx = tvm.cpu(0)
f = tvm.build(s, [A, B, D], target)
a = tvm.nd.array(np.random.uniform(size=ashape).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=bshape).astype(B.dtype), ctx)
d = tvm.nd.array(np.zeros((batch, n, m), dtype=D.dtype), ctx)
f(a, b, d)
tvm.testing.assert_allclose(
d.asnumpy(), get_numpy(a.asnumpy(), b.asnumpy(), transa, transb), rtol=1e-5)
verify()
def verify_batch_matmul(batch,
m,
l,
n,
lib,
transa=False,
transb=False,
iterative=False,
dtype="float32"):
ashape = (batch, l, n) if transa else (batch, n, l)
bshape = (batch, m, l) if transb else (batch, l, m)
A = te.placeholder(ashape, name="A", dtype=dtype)
B = te.placeholder(bshape, name="B", dtype=dtype)
C = cblas.batch_matmul(A, B, transa, transb)
D = te.compute(C.shape, lambda k, i, j: C[k, i, j], name="D")
s = te.create_schedule(D.op)
def get_numpy(a, b, transa, transb):
if transa:
a = a.transpose(0, 2, 1)
if not transb:
b = b.transpose(0, 2, 1)
return tvm.topi.testing.batch_matmul(a, b)
def compile(f, name="test_batch_matmul", ext=".so"):
path = name + ext
f.export_library(path)
mod = tvm.runtime.load_module(path)
f = mod[name]
return f
def verify(target="llvm"):
if not tvm.testing.device_enabled(target):
print("skip because %s is not enabled..." % target)
return
if not tvm.get_global_func(lib.__name__ + ".matmul", True):
print("skip because extern function is not available")
return
dev = tvm.cpu(0)
name = "test_batch_matmul"
f = tvm.build(s, [A, B, D], target, name=name)
if target == "c":
f = compile(f, name)
a = tvm.nd.array(np.random.uniform(size=ashape).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=bshape).astype(B.dtype), dev)
d = tvm.nd.array(np.zeros((batch, n, m), dtype=D.dtype), dev)
f(a, b, d)
tvm.testing.assert_allclose(d.asnumpy(),
get_numpy(a.asnumpy(), b.asnumpy(), transa,
transb),
rtol=1e-5)
verify("llvm")
verify("c")
def batch_matmul_x86(x, y):
"""Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
data in batch.
Parameters
----------
x : tvm.Tensor
3-D with shape [batch, M, K]
y : tvm.Tensor
3-D with shape [batch, N, K]
Returns
-------
output : tvm.Tensor
3-D with shape [batch, M, N]
"""
target = tvm.target.current_target()
if "cblas" in target.libs:
return cblas.batch_matmul(x, y, False, True)
return batch_matmul_default(x, y)
def _declaration_batch_matmul_nopack(cfg, x, y):
"""Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
data in batch.
Parameters
----------
cfg : ConfigSpace
Autotvm tuning space config file
x : tvm.Tensor
3-D with shape [batch, M, K]
y : tvm.Tensor
3-D with shape [batch, N, K]
Returns
-------
output : tvm.Tensor
3-D with shape [batch, M, N]
"""
target = tvm.target.current_target()
if "cblas" in target.libs:
return cblas.batch_matmul(x, y, False, True)
assert len(x.shape) == 3 and len(
y.shape) == 3, "only support 3-dim batch_matmul"
XB, M, XK = get_const_tuple(x.shape)
YB, N, YK = get_const_tuple(y.shape)
assert XB == YB, "batch dimension doesn't match"
assert XK == YK, "shapes of x and y is inconsistant"
B = XB
K = XK
if cfg.is_fallback:
_default_batch_matmul_nopack_config(cfg, M, N, K)
k = tvm.reduce_axis((0, K), name='k')
C = tvm.compute(
(B, M, N),
lambda b, i, j: tvm.sum(x[b, i, k] * y[b, j, k], axis=k),
tag='batch_matmul')
return C