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Merged
cyber-pioneer merged 2 commits into from
Apr 16, 2024
Merged

decomp unbind tile #63541

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feb60e7
bind tile
cyber-pioneer Apr 15, 2024
cc7e51c
fix test case
cyber-pioneer Apr 16, 2024
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2 changes: 0 additions & 2 deletions paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,6 @@
"squeeze",
"stack",
"unsqueeze",
"tile",
]

# come into effect in generated file op_decomp.cc
Expand Down Expand Up @@ -84,7 +83,6 @@
"squeeze",
"stack",
"unsqueeze",
"tile",
]


Expand Down
86 changes: 0 additions & 86 deletions paddle/fluid/primitive/composite/composite.h
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Original file line number Diff line number Diff line change
Expand Up @@ -990,92 +990,6 @@ std::tuple<Tensor, Tensor, Tensor> group_norm_decomp(
return std::make_tuple(out, mean_out, var_out);
}

template <typename T>
Tensor tile_decomp(const Tensor& x, const IntArray& repeat_times) {
// x.shape = [3,4] repeat_time=(a,b,c)
// shape1 = [1,3,4]
// shape2 = [1,1,1,3,1,4]
// shape3 = [a,1,b,3,c,4]
// shape4 = shape1 -> [a, b*3, c*4]
// t1 = x.reshape(shape1)
// t2 = t1.reshape(shape2)
// t3 = t2.expand(shape3)
// res = t3.reshape(t3)
std::vector<int64_t> repeat_times_ = repeat_times.GetData();
std::vector<int64_t> shape1 = x.shape();
auto diff = int64_t(repeat_times_.size()) - int64_t(shape1.size());
Tensor t1;
if (has_dynamic_shape(shape1)) {
size_t repeat_time_length = repeat_times_.size();
std::vector<int64_t> unsqueeze_idx2;
if (diff > 0) {
std::vector<int64_t> unsqueeze_idx1(diff);
std::iota(unsqueeze_idx1.begin(), unsqueeze_idx1.end(), 0);
t1 = unsqueeze<T>(x, unsqueeze_idx1);
} else {
t1 = x;
}
auto length2 = t1.dims().size();
for (size_t i = 0; i < repeat_times_.size(); i++) {
unsqueeze_idx2.push_back(length2 - repeat_times_.size() + i * 2);
}

Tensor t2 = unsqueeze<T>(t1, unsqueeze_idx2);
std::vector<int64_t> ref_shape(t2.dims().size(), 1);
for (size_t i = 0; i < unsqueeze_idx2.size(); i++) {
ref_shape[unsqueeze_idx2[i]] = repeat_times_[i];
}
Tensor ref_t = full<T>(ref_shape, 1.0, t2.dtype());
Tensor t3 = t2 * ref_t;
Tensor origin_shape_t = shape<T>(t1);
std::vector<Tensor> res_s;
for (int64_t i = int64_t(length2) - 1; i >= 0; i--) {
auto relative_idx =
int64_t(repeat_time_length) - 1 - int64_t(length2 - i - 1);

if (relative_idx >= 0) {
res_s.insert(
res_s.begin(),
get_slice<T>(origin_shape_t, i) * repeat_times_[relative_idx]);
} else {
res_s.insert(res_s.begin(), get_slice<T>(origin_shape_t, i));
}
}
Tensor s4 = concat<T>(res_s, 0);
return backend::reshape_with_tensor<T>(t3, s4);

} else {
if (diff > 0) {
for (int64_t i = 0; i < diff; i++) {
shape1.insert(shape1.begin(), 1);
}
}

auto length = int64_t(shape1.size());
std::vector<int64_t> shape2 = shape1;
std::vector<int64_t> shape3 = shape1;
std::vector<int64_t> final_shape = shape1;
auto r_length = repeat_times_.size();
for (size_t j = 0; j < repeat_times_.size(); j++) {
int64_t i = int64_t(j);

shape2.insert(shape2.begin() + (length - 1 - i), 1);
shape3.insert(shape3.begin() + (length - 1 - i),
repeat_times_[r_length - i - 1]);

final_shape[length - i - 1] =
final_shape[length - i - 1] * repeat_times_[r_length - i - 1];
}

t1 = reshape<T>(x, shape1);

auto t2 = reshape<T>(t1, shape2);
auto t3 = t2.expand(shape3);
auto res = reshape<T>(t3, final_shape);
return res;
}
}

template <typename T>
Tensor square_decomp(const Tensor& x) {
auto org_dtype = x.dtype();
Expand Down
36 changes: 0 additions & 36 deletions test/prim/pir_prim/test_prim_sub_graph_dynamic_shape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,16 +70,6 @@ def stack_net(x):
return paddle.stack([x, y], axis=0)


def tile_net1(x):
y = paddle.tile(x, repeat_times=[2, 5])
return y


def tile_net2(x):
y = paddle.tile(x, repeat_times=[3, 2, 5])
return y


def index_sample_net(x, index):
return paddle.index_sample(x, index)

Expand Down Expand Up @@ -251,32 +241,6 @@ def setUp(self):
self.tol = 1e-6


class TestPrimTile(TestPrimBase):
def setUp(self):
np.random.seed(2023)
self.dtype = "float32"
self.x_shape = [1, 300, 4096]
self.init_x_shape = [None, None, 4096]
self.x = np.random.random(self.x_shape).astype(self.dtype)
self.net = tile_net1
self.necessary_ops = "pd_op.tile"
self.enable_cinn = False
self.tol = 1e-6


class TestPrimTile2(TestPrimBase):
def setUp(self):
np.random.seed(2023)
self.dtype = "float32"
self.x_shape = [300, 4096]
self.init_x_shape = [None, 4096]
self.x = np.random.random(self.x_shape).astype(self.dtype)
self.net = tile_net2
self.necessary_ops = "pd_op.tile"
self.enable_cinn = False
self.tol = 1e-6


class TestPrimTwo(unittest.TestCase):
def setUp(self):
np.random.seed(2023)
Expand Down