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import collections
from itertools import product
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
avoid_vpmaddubsw_overflow_fc,
check_quantized_results_close,
)
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPBatchMatMulOpTest(hu.HypothesisTestCase):
# correctness test with no quantization error in inputs
@given(
m=st.integers(0, 32),
n=st.integers(4, 32),
k=st.integers(4, 32),
batch_size=st.integers(0, 4),
**hu.gcs_cpu_only
)
@settings(deadline=10000)
def test_dnnlowp_batch_matmul_int(self, m, n, k, batch_size, gc, dc):
# A and B have scale 1, so exactly represented after quantization
A_min = -77
A_max = A_min + 255
A = np.round(np.random.rand(batch_size, m, k) * 255 + A_min)
A = A.astype(np.float32)
# input channels 0 and 1 are all A_min to avoid overflow from vpmaddubsw
# when multiplied with B_min and B_max
if batch_size > 0 and m > 0:
A[0, :, 0] = A_min
A[0, 0, 1] = A_max
B_min = -100
B_max = B_min + 255
B = np.round(np.random.rand(batch_size, n, k) * 255 + B_min)
B = B.astype(np.float32)
if batch_size > 0:
B[0, 0, 0] = B_min
B[0, 1, 0] = B_max
for i in range(batch_size):
avoid_vpmaddubsw_overflow_fc(
m, k, n, A[i,], A_min, A_max, B[i,], B_min, B_max
)
for trans_a, trans_b in product([0, 1], [0, 1]):
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("BatchMatMul", ""),
("BatchMatMul", "DNNLOWP"),
("BatchMatMul", "DNNLOWP_16"),
("Int8BatchMatMul", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if "DNNLOWP" in engine:
quantize_A = core.CreateOperator(
"Quantize", ["A"], ["A_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_A])
quantize_B = core.CreateOperator(
"Quantize", ["B"], ["B_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_B])
batch_matmul = core.CreateOperator(
op_type,
[
"A_q" if "DNNLOWP" in engine else "A",
"B_q" if "DNNLOWP" in engine else "B",
],
["Y_q" if "DNNLOWP" in engine else "Y"],
trans_a=trans_a,
trans_b=trans_b,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([batch_matmul])
if "DNNLOWP" in engine:
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("A").feed(
np.transpose(A, (0, 2, 1)) if trans_a else A, device_option=gc
)
self.ws.create_blob("B").feed(
B if trans_b else np.transpose(B, (0, 2, 1)), device_option=gc
)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
check_quantized_results_close(outputs)
# correctness test with no quantization error in inputs
@given(
m=st.integers(0, 32),
n=st.integers(4, 32),
k=st.integers(4, 32),
C_1=st.integers(0, 3), # number of batch dims
C_2=st.integers(0, 3),
A_quantized=st.booleans(),
B_quantized=st.booleans(),
out_quantized=st.booleans(),
**hu.gcs_cpu_only
)
@settings(deadline=2000)
def test_dnnlowp_batch_matmul_int_constant_B(
self, m, n, k, C_1, C_2, A_quantized, B_quantized, out_quantized, gc, dc
):
batch_dims = tuple(np.random.randint(3, size=max(C_1, C_2)))
batch_dims_A = batch_dims[-C_1:]
batch_dims_B = batch_dims[-C_2:]
A = np.zeros(batch_dims_A + (m, k)).astype(np.float32)
B = np.zeros(batch_dims_B + (n, k)).astype(np.float32)
if np.prod(batch_dims) > 0:
for index in np.ndindex(batch_dims_A):
# When both input and output are float, each input of the batch has
# scale 1 but with different offset, so input-wise quantization
# shouldn't have any input quantization error
# A_min = -77 if (A_quantized or out_quantized) else -77 + i
A_min = -77
A_max = A_min + 255
A[index] = np.round(np.random.rand(m, k) * 255 + A_min)
# input channels 0 and 1 are all A_min to avoid overflow from vpmaddubsw
# when multiplied with B_min and B_max
A[index][:, 0] = A_min
if m != 0:
A[index][0, 1] = A_max
i = 0
for index in np.ndindex(batch_dims_B):
# When weight is quantized in a lazy manner, each input of the batch has
# scale 1 but with different offset, so input-wise quantization
# shouldn't have any input quantization error when weight is quantized
# in a lazy manner.
B_min = -100 if B_quantized else -100 + i
# B_min = -100
B_max = B_min + 255
B[index] = np.round(np.random.rand(n, k) * 255 + B_min)
B[index][0, 0] = B_min
B[index][1, 0] = B_max
if C_1 > C_2:
# A has more dims
for outer_index in np.ndindex(batch_dims_A[: C_1 - C_2]):
avoid_vpmaddubsw_overflow_fc(
m,
k,
n,
A[outer_index] if C_2 == 0 else A[outer_index + index],
A_min,
A_max,
B[index],
B_min,
B_max,
)
else:
avoid_vpmaddubsw_overflow_fc(
m, k, n, A[index[-C_1:]], A_min, A_max, B[index], B_min, B_max
)
i += 1
for trans_a, trans_b in product([0, 1], [0, 1]):
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("BatchMatMul", ""),
("BatchMatMul", "DNNLOWP"),
("Int8BatchMatMul", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize_A = "DNNLOWP" in engine and A_quantized
do_quantize_B = "DNNLOWP" in engine and B_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
if do_quantize_A:
quantize_A = core.CreateOperator(
"Quantize", ["A"], ["A_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_A])
if do_quantize_B:
int8_given_tensor_fill, B_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
B if trans_b else B.swapaxes(-1, -2), "B_q"
)
net.Proto().op.extend([int8_given_tensor_fill])
batch_matmul = core.CreateOperator(
op_type,
["A_q" if do_quantize_A else "A", "B_q" if do_quantize_B else "B"],
["Y_q" if do_dequantize else "Y"],
trans_a=trans_a,
trans_b=trans_b,
broadcast=True,
constant_B=True,
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
if do_quantize_B:
# When quantized weight is provided, we can't rescale the
# output dynamically by looking at the range of output of each
# batch, so here we provide the range of output observed from
# fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(
batch_matmul, outputs[0][0]
)
net.Proto().op.extend([batch_matmul])
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("A").feed(
A.swapaxes(-1, -2) if trans_a else A, device_option=gc
)
self.ws.create_blob("B").feed(
B if trans_b else B.swapaxes(-1, -2), device_option=gc
)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
if np.prod(batch_dims) > 0:
check_quantized_results_close(outputs)
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