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Merge pull request PaddlePaddle#5 from Caozhou1995/new_api_1
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update shard annotation
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@Caozhou1995
Caozhou1995 committed Sep 13, 2022
2 parents 3fa80fe + 1665367 commit 91ce66b
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Showing 22 changed files with 365 additions and 952 deletions.
8 changes: 2 additions & 6 deletions python/paddle/fluid/tests/unittests/auto_parallel/auto_parallel_relaunch_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,11 +103,7 @@ def mlp_pretrain_forward(train_program, start_program):
shape=[batch_size, sequence_len, 1],
dtype='float32')

auto.shard_tensor(input,
dist_attr={
"process_mesh": _global_process_mesh,
"dims_mappig": [-1, -1, -1]
})
auto.shard_tensor(input, _global_process_mesh, [None, None, None])

mlp = MLPLayer(hidden_size=hidden_size,
intermediate_size=4 * hidden_size,
Expand All @@ -127,7 +123,7 @@ def mlp_pretrain_forward(train_program, start_program):

def train():
global _global_process_mesh
_global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
_global_process_mesh = auto.ProcessMesh(mesh=[0, 1], dim_names=["x"])

dist_strategy = fleet.DistributedStrategy()
dist_strategy.amp = False
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31 changes: 8 additions & 23 deletions python/paddle/fluid/tests/unittests/auto_parallel/test_base_cost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,9 +45,10 @@

paddle.enable_static()
_global_parallel_strategy = "dp_mp_pp"
_global_process_mesh = auto.ProcessMesh([[[0, 1], [4, 5]], [[2, 3], [6, 7]]])
PP_MESH_0 = auto.ProcessMesh([[0, 1], [4, 5]])
PP_MESH_1 = auto.ProcessMesh([[2, 3], [6, 7]])
_global_process_mesh = auto.ProcessMesh([[[0, 1], [4, 5]], [[2, 3], [6, 7]]],
dim_names=["x", "y", "z"])
PP_MESH_0 = auto.ProcessMesh([[0, 1], [4, 5]], dim_names=["x", "y"])
PP_MESH_1 = auto.ProcessMesh([[2, 3], [6, 7]], dim_names=["x", "y"])


class MLPLayer(nn.Layer):
Expand All @@ -74,16 +75,8 @@ def __init__(self,
self.norm = nn.LayerNorm(d_model, epsilon=1e-5)

def forward(self, input):
auto.shard_tensor(self.linear0.weight,
dist_attr={
"process_mesh": PP_MESH_0,
"dims_mapping": [-1, 1]
})
auto.shard_tensor(self.linear1.weight,
dist_attr={
"process_mesh": PP_MESH_1,
"dims_mapping": [1, -1]
})
auto.shard_tensor(self.linear0.weight, PP_MESH_0, [None, "y"])
auto.shard_tensor(self.linear1.weight, PP_MESH_1, ["y", None])

out = self.norm(input)
out = self.linear0(out)
Expand Down Expand Up @@ -111,16 +104,8 @@ def mlp_forward(train_program, start_program):
embedding = paddle.nn.Embedding(10, hidden_size, sparse=True)
embedding_out = embedding(fill_constant_out)

auto.shard_tensor(input,
dist_attr={
"process_mesh": PP_MESH_0,
"dims_mapping": [0, -1]
})
auto.shard_tensor(label,
dist_attr={
"process_mesh": PP_MESH_1,
"dims_mapping": [0, -1]
})
auto.shard_tensor(input, PP_MESH_0, ["x", None])
auto.shard_tensor(label, PP_MESH_1, ["x", None])

mlp = MLPLayer(hidden_size=hidden_size,
intermediate_size=4 * hidden_size,
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29 changes: 8 additions & 21 deletions python/paddle/fluid/tests/unittests/auto_parallel/test_dist_context.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,10 @@
batch_size = 4
hidden_size = 1024
sequence_len = 512
_g_process_mesh = [[0, 1], [2, 3]]
_g_process_mesh = [
auto.ProcessMesh([0, 1], dim_names=["x"]),
auto.ProcessMesh([2, 3], dim_names=["x"])
]


def get_random_inputs_and_labels(input_shape, label_shape):
Expand Down Expand Up @@ -82,18 +85,10 @@ def __init__(self,

def forward(self, input):
out = self.norm(input)
auto.shard_tensor(self.linear0.weight,
dist_attr={
"process_mesh": _g_process_mesh[0],
"dims_mapping": [-1, 0]
})
auto.shard_tensor(self.linear0.weight, _g_process_mesh[0], [None, "x"])
out = self.linear0(out)
out = F.gelu(out, approximate=True)
auto.shard_tensor(self.linear1.weight,
dist_attr={
"process_mesh": _g_process_mesh[1],
"dims_mapping": [0, -1]
})
auto.shard_tensor(self.linear1.weight, _g_process_mesh[1], ["x", None])
out = self.linear1(out)

return out
Expand Down Expand Up @@ -123,16 +118,8 @@ def get_program():
dataloader.set_batch_generator(batch_generator_creator(),
places=paddle.static.cuda_places())
# data dist_attr
auto.shard_tensor(input,
dist_attr={
"process_mesh": _g_process_mesh[0],
"dims_mapping": [0, -1, -1]
})
auto.shard_tensor(label,
dist_attr={
"process_mesh": _g_process_mesh[0],
"dims_mapping": [0, -1, -1]
})
auto.shard_tensor(input, _g_process_mesh[0], ["x", None, None])
auto.shard_tensor(label, _g_process_mesh[0], ["x", None, None])

mlp_start = MLPLayer(hidden_size=hidden_size,
intermediate_size=4 * hidden_size,
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18 changes: 6 additions & 12 deletions python/paddle/fluid/tests/unittests/auto_parallel/test_dist_embedding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,19 +42,13 @@ def make_program_lookup_table_v1_mp_dp():
is_sparse=False)
loss = paddle.fluid.layers.reduce_mean(emb_out)

auto.shard_tensor(src_ids,
dist_attr={
"process_mesh": auto.ProcessMesh([[0, 1], [2,
3]]),
"dims_mapping": [0, -1, -1]
})
auto.shard_tensor(
src_ids, auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"]),
["x", None, None])
emb_weight = block.vars["emb_weight"]
auto.shard_tensor(emb_weight,
dist_attr={
"process_mesh": auto.ProcessMesh([[0, 1], [2,
3]]),
"dims_mapping": [1, -1]
})
auto.shard_tensor(
emb_weight, auto.ProcessMesh([[0, 1], [2, 3]],
dim_names=["x", "y"]), ["y", None])

return main_program, start_program, loss

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58 changes: 17 additions & 41 deletions python/paddle/fluid/tests/unittests/auto_parallel/test_dist_matmul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,82 +22,58 @@

paddle.enable_static()

mesh = [[0, 1], [2, 3]]
mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])


def init_x_row(trans_x):
if trans_x:
x = paddle.static.data(name='x', shape=[10, 6, 8], dtype='float32')
auto.shard_tensor(x,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [0, 1, -1]
})
auto.shard_tensor(x, mesh, ["x", "y", None])

return x
else:
x = paddle.static.data(name='x', shape=[10, 8, 6], dtype='float32')
auto.shard_tensor(x,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [0, -1, 1]
})
auto.shard_tensor(x, mesh, ["x", None, "y"])

return x


def init_x_col(trans_x):
if trans_x:
x = paddle.static.data(name='x', shape=[6, 8], dtype='float32')
auto.shard_tensor(x,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [-1, 0]
})
auto.shard_tensor(x, mesh, [None, "x"])

return x
else:
x = paddle.static.data(name='x', shape=[8, 6], dtype='float32')
auto.shard_tensor(x,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [0, -1]
})
auto.shard_tensor(x, mesh, ["x", None])

return x


def init_y_row(trans_y):
if trans_y:
y = paddle.static.data(name='y', shape=[4, 6], dtype='float32')
auto.shard_tensor(y,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [-1, 1]
})
auto.shard_tensor(y, mesh, [None, "y"])

return y
else:
y = paddle.static.data(name='y', shape=[6, 4], dtype='float32')
auto.shard_tensor(y,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [1, -1]
})
auto.shard_tensor(y, mesh, ["y", None])

return y


def init_y_col(trans_y):
if trans_y:
y = paddle.static.data(name='y', shape=[4, 6], dtype='float32')
auto.shard_tensor(y,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [1, -1]
})
auto.shard_tensor(y, mesh, ["y", None])

return y
else:
y = paddle.static.data(name='y', shape=[6, 4], dtype='float32')
auto.shard_tensor(y,
dist_attr={
"process_mesh": mesh,
"dims_mapping": [-1, 1]
})
auto.shard_tensor(y, mesh, [None, "y"])

return y


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