def test_raise_exception_wordsize(self):
"""Raise an exception if an input value is not multiple of word size."""
packer = Packer(2, 32)
test_input = np.zeros([83], dtype=np.float32)
test_input[0:-1:2] = 1
test_input[0:-1:4] = 4
with self.assertRaises(ValueError):
packer.run(test_input)
def test_raise_exception_bitwidth(self):
"""Raise an exception if an input value is larger than bitwidth."""
packer = Packer(2, 32)
test_input = np.zeros([64], dtype=np.float32)
test_input[0:-1:2] = 1
test_input[0:-1:4] = 4
with self.assertRaises(ValueError):
packer.run(test_input)
def test_bw1_dividable_by_wordsize(self):
"""Test for when the input tensor size is able to divide by wordsize (1 bit version)."""
packer = Packer(1, 32)
test_input = np.zeros([32], dtype=np.float32)
test_input[0:6] = [0, 1, 0, 1, 0, 1]
test_output = packer.run(test_input)
self.assertEqual(test_output[0], 42)
def test_bw1_not_dividable_by_wordsize(self):
"""Test for when the input tensor size is not able to divide by wordsize (1 bit version)."""
packer = Packer(1, 37)
test_input = np.zeros([37], dtype=np.float32)
test_input[0::2] = 1
test_output = packer.run(test_input)
expected_output = [1431655765]
np.testing.assert_array_equal(test_output[0], expected_output)
def test_bw2_dividable_by_wordsize(self):
"""Test for when the input tensor size is able to divide by wordsize (2 bit version)."""
packer = Packer(2, 32)
test_input = np.zeros([32], dtype=np.float32)
test_input[0:6] = [0, 3, 0, 3, 0, 3]
test_output = packer.run(test_input)
expected_output = [42, 42]
np.testing.assert_array_equal(test_output[0], expected_output)
def pass_lookup(graph: Graph) -> None:
"""Lookup.
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
quantization_types = [
'BinaryMeanScalingQuantizer', 'LinearMidTreadHalfQuantizer',
'BinaryChannelWiseMeanScalingQuantizer'
]
to_be_removed = []
exec_list = [
n for n in sort_graph(graph) if n.op_type in quantization_types
]
placeholder = [n for n in sort_graph(graph) if n.op_type in 'Input']
for m in exec_list:
quantizer = m
p1 = quantizer.input_nodes[0]
if p1.op_type != 'Reshape':
continue
p2 = p1.input_nodes[0]
if p2.op_type != 'Reshape':
continue
p3 = p2.input_nodes[0]
if p3.op_type != 'Gather':
continue
p4 = p3.input_nodes[0]
if p4.op_type != 'Gather':
continue
gather_params = p4.input_nodes[0]
if gather_params.rank != 2 or gather_params.shape[0] != 256:
continue
params = gather_params.data
data = {'data': params}
qtz_data = quantizer.run(**data)['data']
word_size = 32
lu_bitwidth = quantizer.nbit
packer = Packer(lu_bitwidth, word_size)
lsb = np.zeros((256, ), np.uint32)
msb = np.zeros((256, ), np.uint32)
idx = 0
for p in qtz_data:
data = packer.run(p.astype(np.float32), p.shape).flatten()
lsb[idx] = data[0]
msb[idx] = data[1]
idx += 1
pe_lsb = Constant('pe_lsb_new',
QUANTIZED_PACKED_KERNEL(),
lsb,
dimension_format='TC',
packed=True,
actual_shape=[256, word_size])
pe_msb = Constant('pe_msb_new',
QUANTIZED_PACKED_KERNEL(),
msb,
dimension_format='TC',
packed=True,
actual_shape=[256, word_size])
n, h, w, c = quantizer.shape
shape = [1, h, w, 2, word_size]
pe = Lookup('Lookup',
shape,
QUANTIZED_PACKED(), {
'input': placeholder[0],
'lsb': pe_lsb,
'msb': pe_msb
},
dimension_format='ChHWBCl')
get_nodes_in_branch(quantizer, placeholder[0], to_be_removed)
reserved_placeholder_ops = [
out_op for out_op in placeholder[0].output_op_list
if out_op not in to_be_removed
]
placeholder[0].remove_output('output')
placeholder[0].add_outputs({'output': reserved_placeholder_ops})
pe.add_outputs(quantizer.output_ops)
output_op = quantizer.output_op_list[0]
target_input_name = 'X'
for input_name in output_op._input_names:
if quantizer.equals(output_op._input_ops[input_name]):
target_input_name = input_name
break
output_op.add_input(target_input_name, pe)
graph.add_op(pe_lsb)
graph.add_op(pe_msb)
graph.add_op(pe)
for op in to_be_removed:
graph.remove_op(op)
def pass_pack_weights(graph: Graph) -> None:
"""Given a Quantized convolution node C, it will pack the weights of C into 32 bit words.
If the node Q that apply quantization to the weights of C quantizes, for example, into 1 bit values
then one 32 bit word will contain 32 weights.
Args:
graph (Graph): The input graph. It will be modified in-place.
"""
exec_list = [n for n in sort_graph(graph) if n.op_type == 'Conv']
quantization_types = [
'BinaryMeanScalingQuantizer', 'LinearMidTreadHalfQuantizer',
'BinaryChannelWiseMeanScalingQuantizer'
]
word_size = 32
weight_bitwidth = 1
packer = Packer(weight_bitwidth, word_size)
to_be_removed = []
b = 32
for m in exec_list:
conv_node = m
# check if this is a quantized convolution
if not conv_node.quantizer or not conv_node.a_quantizer:
continue
# Check if we support this kind of quantizer
weight_quantizer = conv_node.quantizer
if weight_quantizer.op_type not in quantization_types:
continue
# Quantize the weights
weight_quantizer.run_forward()
def pad_to_multiple_of_b(tensor, axis, b):
shape = list(tensor.shape)
pad = (((shape[axis] + b - 1) // b) * b) - shape[axis]
shape[axis] = pad
return np.zeros(shape) if pad else None
padded_data = np.copy(weight_quantizer.data)
for axis in [0, 3]:
pad_tensor = pad_to_multiple_of_b(padded_data, axis, b)
if pad_tensor is not None:
padded_data = np.append(padded_data, pad_tensor, axis=axis)
tca_output = np.copy(padded_data)
oc, kh, kw, kd = padded_data.shape[:]
padded_data = padded_data.flatten()
tca_output = tca_output.flatten()
out_index = 0
for g in range(oc // b):
for p in range(kd // b):
for h in range(kh):
for w in range(kw):
for o in range(b):
for d in range(b):
idx = g * (kw * kh * kd * b) + p * b + h * (
kw * kd) + w * kd + o * (kw * kh * kd) + d
tca_output[out_index] = padded_data[idx]
out_index += 1
kn2row_output = np.zeros(oc * kh * kw * kd)
out_index = 0
for h in range(kh):
for w in range(kw):
for o in range(oc):
for i in range(kd):
idx = o * kh * kw * kd + h * kw * kd + w * kd + i
kn2row_output[out_index] = padded_data[idx]
out_index += 1
op_data = weight_quantizer.binarizer(padded_data)
data = packer.run(op_data.astype(np.float32),
weight_quantizer.dimension)
tca_binarized_data = weight_quantizer.binarizer(tca_output)
tca_packed_data = packer.run(tca_binarized_data.astype(np.float32),
weight_quantizer.dimension)
kn2row_binarized_data = weight_quantizer.binarizer(kn2row_output)
kn2row_data = packer.run(kn2row_binarized_data.astype(np.float32),
weight_quantizer.dimension)
shape = [oc, kh, kw, kd]
tca_shape = [oc // b, kd // b, kh, kw, b, b]
kn2row_shape = [kh, kw, oc, kd]
# Create the new constant with the quantized weights
quantized_constant = Constant(
weight_quantizer.name + '_new',
PackedUint32(),
data=np.vectorize(lambda k: (~k) & ((0x1 << 32) - 1))(data),
dimension_format="OHWI",
transposed_dimension_format="OhIhHWOlIl",
packed=True,
actual_shape=shape,
transposed_shape=tca_shape,
transposed_data=[(~k) & ((0x1 << 32) - 1)
for k in tca_packed_data.flatten()],
kn2row_data=[k for k in kn2row_data.flatten()],
kn2row_shape=kn2row_shape,
kn2row_dimension_format="HWOI")
# get nodes to be removed after being disconnected
get_nodes_in_branch(weight_quantizer, None, to_be_removed)
# Add the constant to the graph and connect the new constant
graph.add_op(quantized_constant)
quantized_constant.add_outputs(weight_quantizer.output_ops)
for output_name, consumer_list in weight_quantizer.output_ops.items():
for consumer_node in consumer_list:
for input_name, input_node in consumer_node.input_ops.items():
if input_node == weight_quantizer:
consumer_node.add_input(input_name, quantized_constant)
break
for op in to_be_removed:
graph.remove_op(op)