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reduce runtime of unit-tests in windows-trt #60731

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Jan 12, 2024
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reduce runtime of unit-tests in windows-trt #60731

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a50aeb9
modify trt test to deal with Timeout
lizexu123 Jan 10, 2024
1417aa2
windows
lizexu123 Jan 11, 2024
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28 changes: 3 additions & 25 deletions test/ir/inference/test_trt_convert_einsum.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,17 +36,12 @@ def sample_program_configs(self):
def generate_input1(dims, batch):
if dims == 1:
return np.ones(shape=[batch]).astype(np.float32)
elif dims == 2:
return np.ones(shape=[batch, 3]).astype(np.float32)
elif dims == 3:
return np.ones((batch, 2, 3)).astype(np.float32)

def generate_equation1(dims):
if dims == 1:
return ["i->"]
elif dims == 2:
# "ij->"
return ["ij->ji", "ij->i", "ij->j"]
elif dims == 3:
# "ijk->","ijk->j","ijk->k"
# error: The current implementation of Einsum doesn't support mask dimensions on multiple contracting/free dimensions
Expand All @@ -60,7 +55,7 @@ def generate_equation1(dims):
]

# Single operand: transpose, sum
for dims in [1, 2, 3]:
for dims in [1, 3]:
for batch in [2]:
equation_list = generate_equation1(dims)
for equation in equation_list:
Expand Down Expand Up @@ -108,16 +103,7 @@ def generate_dynamic_shape(attrs):
self.dynamic_shape.opt_input_shape = {
"operands_data0": [2],
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"operands_data0": [1, 3],
}
self.dynamic_shape.max_input_shape = {
"operands_data0": [4, 3],
}
self.dynamic_shape.opt_input_shape = {
"operands_data0": [2, 3],
}

elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"operands_data0": [1, 2, 3],
Expand Down Expand Up @@ -219,18 +205,12 @@ def generate_equation_matrix_vector(dims, vec_shape):
# Doubule operands vector
for dims in [1]:
self.dims = dims
for vec_shape in [[2], [3]]:
for vec_shape in [[2]]:
for batch in [2]:
equation_list = generate_equation_matrix_vector(
dims, vec_shape
)
for equation in equation_list:
if (
dims == 1
and vec_shape != [2]
and equation != "i,j->ij"
) or ((dims == 2 or dims == 3) and vec_shape != [3]):
continue
self.equation = equation
self.dims = dims
dics = [{"equation": equation}, {}]
Expand Down Expand Up @@ -379,8 +359,6 @@ def generate_input_matrix(input_shape):
[[3, 4, 5], [4, 5], "ijk,jk->i"],
[[3, 4, 5], [2, 5], "ijk,lk->ijl"],
[[2, 4, 5, 3], [3, 4, 5], "ijkl,lmn->ijkmn"],
[[3, 4, 5], [4, 5], "ijk,jk->ik"],
[[3, 4, 5], [4, 5], "ijk,jk->ij"],
[[4, 5], [4, 2, 5], "ik,ijk->j"],
[[4, 2, 5], [4, 5], "ijk,ik->jk"],
[[2, 4, 5, 3], [3, 2, 4], "ijkl,lmn->kmn"],
Expand Down
4 changes: 2 additions & 2 deletions test/ir/inference/test_trt_convert_grid_sampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,8 +46,8 @@ def generate_input2():
return np.random.random([1, 3, 3, 2, 3]).astype(np.float32)

mode = ["bilinear", "nearest"]
padding_mode = ["zeros", "reflection", "border"]
align_corners = [True, False]
padding_mode = ["zeros", "reflection"]
align_corners = [True]
descs = []
for m in mode:
for p in padding_mode:
Expand Down
4 changes: 2 additions & 2 deletions test/ir/inference/test_trt_convert_squeeze2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,8 +34,8 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_configs(self):
for dims in [2, 3, 4]:
for batch in [3, 4]:
for dims in [4]:
for batch in [4]:
for axes in [[2], [2, 3], [-1]]:
for attr_axis in [True, False]:
self.batch = batch
Expand Down
80 changes: 7 additions & 73 deletions test/ir/inference/test_trt_convert_stack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,42 +44,24 @@ def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
return np.random.random([batch, 3, 24, 24]).astype(np.float32)
elif self.dims == 3:
return np.random.random([batch, 3, 24]).astype(np.float32)
elif self.dims == 2:
return np.random.random([batch, 24]).astype(np.float32)
elif self.dims == 1:
return np.random.random([24]).astype(np.float32)
elif self.dims == 0:
else:
return np.random.random([]).astype(np.float32)

def generate_input2(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
return np.random.random([batch, 3, 24, 24]).astype(np.float32)
elif self.dims == 3:
return np.random.random([batch, 3, 24]).astype(np.float32)
elif self.dims == 2:
return np.random.random([batch, 24]).astype(np.float32)
elif self.dims == 1:
return np.random.random([24]).astype(np.float32)
elif self.dims == 0:
else:
return np.random.random([]).astype(np.float32)

def generate_input3(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
return np.random.random([batch, 3, 24, 24]).astype(np.float32)
elif self.dims == 3:
return np.random.random([batch, 3, 24]).astype(np.float32)
elif self.dims == 2:
return np.random.random([batch, 24]).astype(np.float32)
elif self.dims == 1:
return np.random.random([24]).astype(np.float32)
elif self.dims == 0:
else:
return np.random.random([]).astype(np.float32)

for dims in [0, 1, 2, 3, 4]:
for batch in [1, 4]:
for axis in [-2, -1, 0, 1, 2, 3]:
for dims in [0, 4]:
for batch in [1]:
for axis in [-1, 0, 1]:
self.dims = dims
dics = [{"axis": axis}, {}]
ops_config = [
Expand Down Expand Up @@ -136,55 +118,7 @@ def generate_dynamic_shape(attrs):
"stack_input2": [1, 3, 24, 24],
"stack_input3": [1, 3, 24, 24],
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"stack_input1": [1, 3, 24],
"stack_input2": [1, 3, 24],
"stack_input3": [1, 3, 24],
}
self.dynamic_shape.max_input_shape = {
"stack_input1": [4, 3, 48],
"stack_input2": [4, 3, 48],
"stack_input3": [4, 3, 48],
}
self.dynamic_shape.opt_input_shape = {
"stack_input1": [1, 3, 24],
"stack_input2": [1, 3, 24],
"stack_input3": [1, 3, 24],
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"stack_input1": [1, 24],
"stack_input2": [1, 24],
"stack_input3": [1, 24],
}
self.dynamic_shape.max_input_shape = {
"stack_input1": [4, 48],
"stack_input2": [4, 48],
"stack_input3": [4, 48],
}
self.dynamic_shape.opt_input_shape = {
"stack_input1": [1, 24],
"stack_input2": [1, 24],
"stack_input3": [1, 24],
}
elif self.dims == 1:
self.dynamic_shape.min_input_shape = {
"stack_input1": [24],
"stack_input2": [24],
"stack_input3": [24],
}
self.dynamic_shape.max_input_shape = {
"stack_input1": [48],
"stack_input2": [48],
"stack_input3": [48],
}
self.dynamic_shape.opt_input_shape = {
"stack_input1": [24],
"stack_input2": [24],
"stack_input3": [24],
}
elif self.dims == 0:
else:
self.dynamic_shape.min_input_shape = {
"stack_input1": [],
"stack_input2": [],
Expand Down
117 changes: 38 additions & 79 deletions test/ir/inference/test_trt_convert_top_k_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,81 +25,50 @@

class TrtConvertTopKV2Test(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
if len(inputs['input_data'].shape) <= attrs[0]['axis']:
return False
axis = attrs[0]['axis']
axis = axis if axis >= 0 else axis + len(inputs['input_data'].shape)
if inputs['input_data'].shape[axis] <= attrs[0]['k']:
return False
return True

def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824

def generate_input1(dims, batch, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.random.random([3]).astype(np.float32)
elif dims == 2:
return np.random.random([3, 32]).astype(np.float32)
elif dims == 3:
return np.random.random([3, 32, 32]).astype(np.float32)
else:
return np.random.random([batch, 32, 32, 32]).astype(np.float32)

for dims in [1, 2, 3, 4]:
for batch in [1, 4]:
for k in [1, 3]:
for axis in [-1, 1, 0, 2, 3]:
for largest in [True, False]:
for sort in [True, False]:
self.dims = dims
self.sort = sort
self.axis = axis
dics = [
{
"k": k,
"axis": axis,
"largest": largest,
"sorted": sort,
}
]
ops_config = [
{
"op_type": "top_k_v2",
"op_inputs": {"X": ["input_data"]},
"op_outputs": {
"Out": ["output_data"],
"Indices": ["indices_data"],
},
"op_attrs": dics[0],
"outputs_dtype": {
"indices_data": np.int32
},
}
]
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(
generate_input1,
dims,
batch,
dics,
)
)
},
outputs=["output_data", "indices_data"],
return np.random.random([32]).astype(np.float32)
if dims == 4:
return np.random.random([batch, 3, 32, 32]).astype(np.float32)

for dims in [1, 4]:
for batch in [1]:
for k in [1]:
self.dims = dims
dics = [{"k": k}]
ops_config = [
{
"op_type": "top_k_v2",
"op_inputs": {"X": ["input_data"]},
"op_outputs": {
"Out": ["output_data"],
"Indices": ["indices_data"],
},
"op_attrs": dics[0],
"outputs_dtype": {"indices_data": np.int32},
}
]
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(
generate_input1, dims, batch, dics
)
)
},
outputs=["output_data", "indices_data"],
)

yield program_config
yield program_config

def sample_predictor_configs(
self, program_config
Expand All @@ -109,23 +78,15 @@ def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [64]}
self.dynamic_shape.opt_input_shape = {"input_data": [32]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 1]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 10]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 1, 1]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 10, 10]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 16, 16]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 32, 32, 32]
"input_data": [4, 3, 32, 32]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [4, 3, 32, 32]
"input_data": [1, 3, 32, 32]
}

def clear_dynamic_shape():
Expand All @@ -134,9 +95,7 @@ def clear_dynamic_shape():
self.dynamic_shape.opt_input_shape = {}

def generate_trt_nodes_num(attrs, dynamic_shape):
if not dynamic_shape and (self.dims == 1 or self.axis == 0):
return 0, 4
if not self.sort:
if not dynamic_shape and self.dims == 1:
return 0, 4
return 1, 3

Expand Down
6 changes: 3 additions & 3 deletions test/ir/inference/test_trt_convert_unsqueeze2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,9 +28,9 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_configs(self):
for dims in [2, 3, 4]:
for batch in [3, 4]:
for axes in [[-2, 3], [1], [2], [2, 3]]:
for dims in [2, 4]:
for batch in [4]:
for axes in [[-2, 3], [1]]:
self.batch = batch
self.dims = dims
self.axes = axes
Expand Down