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/usr/local/lib64/python3.6/site-packages/caffe2/quantization/server/

/usr/local/lib64/python3.6/site-packages/caffe2/quantization/server

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
batch_matmul_dnnlowp_op_test.py	10152	0644	edit dl rm
batch_permutation_dnnlowp_op_test.py	1449	0644	edit dl rm
channel_shuffle_dnnlowp_op_test.py	3890	0644	edit dl rm
compute_equalization_scale_test.py	3154	0644	edit dl rm
concat_dnnlowp_op_test.py	3265	0644	edit dl rm
conv_depthwise_dnnlowp_op_test.py	10931	0644	edit dl rm
conv_dnnlowp_acc16_op_test.py	14202	0644	edit dl rm
conv_dnnlowp_op_test.py	17919	0644	edit dl rm
conv_groupwise_dnnlowp_acc16_op_test.py	11664	0644	edit dl rm
conv_groupwise_dnnlowp_op_test.py	9537	0644	edit dl rm
dequantize_dnnlowp_op_test.py	1743	0644	edit dl rm
dnnlowp_test_utils.py	14544	0644	edit dl rm
elementwise_add_dnnlowp_op_test.py	6662	0644	edit dl rm
elementwise_linear_dnnlowp_op_test.py	3101	0644	edit dl rm
elementwise_mul_dnnlowp_op_test.py	6315	0644	edit dl rm
elementwise_sum_dnnlowp_op_test.py	9682	0644	edit dl rm
fully_connected_dnnlowp_acc16_op_test.py	8324	0644	edit dl rm
fully_connected_dnnlowp_op_test.py	10463	0644	edit dl rm
fully_connected_fp16_test.py	2519	0644	edit dl rm
fully_connected_rowwise_dnnlowp_op_test.py	5434	0644	edit dl rm
gather_dnnlowp_op_test.py	2790	0644	edit dl rm
group_norm_dnnlowp_op_test.py	4566	0644	edit dl rm
int8_gen_quant_params_min_max_test.py	3491	0644	edit dl rm
int8_gen_quant_params_test.py	3550	0644	edit dl rm
int8_quant_scheme_blob_fill_test.py	1838	0644	edit dl rm
lstm_unit_dnnlowp_op_test.py	3975	0644	edit dl rm
observer_test.py	1011	0644	edit dl rm
pool_dnnlowp_op_test.py	6125	0644	edit dl rm
quantize_dnnlowp_op_test.py	2668	0644	edit dl rm
relu_dnnlowp_op_test.py	2418	0644	edit dl rm
resize_nearest_3d_dnnlowp_op_test.py	2289	0644	edit dl rm
resize_nearest_dnnlowp_op_test.py	1994	0644	edit dl rm
sigmoid_dnnlowp_op_test.py	2203	0644	edit dl rm
spatial_batch_norm_dnnlowp_op_test.py	4094	0644	edit dl rm
tanh_dnnlowp_op_test.py	2187	0644	edit dl rm
utils.py	16059	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/quantization/server/lstm_unit_dnnlowp_op_test.py (3975B)

import collections

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 hypothesis import given, settings

dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])

class DNNLowPLSTMUnitOpTest(hu.HypothesisTestCase):
    @given(
        N=st.integers(0, 64),
        D=st.integers(4, 64),
        forget_bias=st.integers(0, 4),
        **hu.gcs_cpu_only
    )
    @settings(max_examples=10, deadline=None)
    def test_dnnlowp_lstm_unit(self, N, D, forget_bias, gc, dc):

# X has scale 1, so exactly represented after quantization
        H_in = np.clip(np.random.randn(1, N, D), -1, 1).astype(np.float32)
        C_in = np.clip(np.random.randn(1, N, D), -1, 1).astype(np.float32)
        G = np.clip(np.random.randn(1, N, 4 * D), -1, 1).astype(np.float32)
        seq_lengths = np.round(np.random.rand(N)).astype(np.int32)
        # seq_lengths.fill(0)
        t = np.array([5]).astype(np.int32)

Output = collections.namedtuple("Output", ["H_out", "C_out", "engine"])
        outputs = []

engine_list = ["", "DNNLOWP"]
        for engine in engine_list:
            net = core.Net("test_net")

if engine == "DNNLOWP":
                quantize_H_in = core.CreateOperator(
                    "Quantize", ["H_in"], ["H_in_q"], engine=engine, device_option=gc
                )
                quantize_C_in = core.CreateOperator(
                    "Quantize", ["C_in"], ["C_in_q"], engine=engine, device_option=gc
                )
                quantize_G = core.CreateOperator(
                    "Quantize", ["G"], ["G_q"], engine=engine, device_option=gc
                )
                net.Proto().op.extend([quantize_H_in, quantize_C_in, quantize_G])

lstm = core.CreateOperator(
                "LSTMUnit",
                [
                    "H_in_q" if engine == "DNNLOWP" else "H_in",
                    "C_in_q" if engine == "DNNLOWP" else "C_in",
                    "G_q" if engine == "DNNLOWP" else "G",
                    "seq_lengths",
                    "t",
                ],
                [
                    "H_out_q" if engine == "DNNLOWP" else "H_out",
                    "C_out_q" if engine == "DNNLOWP" else "C_out",
                ],
                engine=engine,
                device_option=gc,
                axis=0,
            )
            net.Proto().op.extend([lstm])

if engine == "DNNLOWP":
                dequantize_H_out = core.CreateOperator(
                    "Dequantize",
                    ["H_out_q"],
                    ["H_out"],
                    engine=engine,
                    device_option=gc,
                )
                dequantize_C_out = core.CreateOperator(
                    "Dequantize",
                    ["C_out_q"],
                    ["C_out"],
                    engine=engine,
                    device_option=gc,
                )
                net.Proto().op.extend([dequantize_H_out, dequantize_C_out])

self.ws.create_blob("H_in").feed(H_in, device_option=gc)
            self.ws.create_blob("C_in").feed(C_in, device_option=gc)
            self.ws.create_blob("G").feed(G, device_option=gc)
            self.ws.create_blob("seq_lengths").feed(seq_lengths, device_option=gc)
            self.ws.create_blob("t").feed(t, device_option=gc)
            self.ws.run(net)
            outputs.append(
                Output(
                    H_out=self.ws.blobs["H_out"].fetch(),
                    C_out=self.ws.blobs["C_out"].fetch(),
                    engine=engine,
                )
            )

for o in outputs:
            np.testing.assert_allclose(o.C_out, outputs[0].C_out, atol=0.1, rtol=0.2)
            np.testing.assert_allclose(o.H_out, outputs[0].H_out, atol=0.1, rtol=0.2)

import collections 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 hypothesis import given, settings dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops") workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"]) class DNNLowPLSTMUnitOpTest(hu.HypothesisTestCase): @given( N=st.integers(0, 64), D=st.integers(4, 64), forget_bias=st.integers(0, 4), **hu.gcs_cpu_only ) @settings(max_examples=10, deadline=None) def test_dnnlowp_lstm_unit(self, N, D, forget_bias, gc, dc): # X has scale 1, so exactly represented after quantization H_in = np.clip(np.random.randn(1, N, D), -1, 1).astype(np.float32) C_in = np.clip(np.random.randn(1, N, D), -1, 1).astype(np.float32) G = np.clip(np.random.randn(1, N, 4 * D), -1, 1).astype(np.float32) seq_lengths = np.round(np.random.rand(N)).astype(np.int32) # seq_lengths.fill(0) t = np.array([5]).astype(np.int32) Output = collections.namedtuple("Output", ["H_out", "C_out", "engine"]) outputs = [] engine_list = ["", "DNNLOWP"] for engine in engine_list: net = core.Net("test_net") if engine == "DNNLOWP": quantize_H_in = core.CreateOperator( "Quantize", ["H_in"], ["H_in_q"], engine=engine, device_option=gc ) quantize_C_in = core.CreateOperator( "Quantize", ["C_in"], ["C_in_q"], engine=engine, device_option=gc ) quantize_G = core.CreateOperator( "Quantize", ["G"], ["G_q"], engine=engine, device_option=gc ) net.Proto().op.extend([quantize_H_in, quantize_C_in, quantize_G]) lstm = core.CreateOperator( "LSTMUnit", [ "H_in_q" if engine == "DNNLOWP" else "H_in", "C_in_q" if engine == "DNNLOWP" else "C_in", "G_q" if engine == "DNNLOWP" else "G", "seq_lengths", "t", ], [ "H_out_q" if engine == "DNNLOWP" else "H_out", "C_out_q" if engine == "DNNLOWP" else "C_out", ], engine=engine, device_option=gc, axis=0, ) net.Proto().op.extend([lstm]) if engine == "DNNLOWP": dequantize_H_out = core.CreateOperator( "Dequantize", ["H_out_q"], ["H_out"], engine=engine, device_option=gc, ) dequantize_C_out = core.CreateOperator( "Dequantize", ["C_out_q"], ["C_out"], engine=engine, device_option=gc, ) net.Proto().op.extend([dequantize_H_out, dequantize_C_out]) self.ws.create_blob("H_in").feed(H_in, device_option=gc) self.ws.create_blob("C_in").feed(C_in, device_option=gc) self.ws.create_blob("G").feed(G, device_option=gc) self.ws.create_blob("seq_lengths").feed(seq_lengths, device_option=gc) self.ws.create_blob("t").feed(t, device_option=gc) self.ws.run(net) outputs.append( Output( H_out=self.ws.blobs["H_out"].fetch(), C_out=self.ws.blobs["C_out"].fetch(), engine=engine, ) ) for o in outputs: np.testing.assert_allclose(o.C_out, outputs[0].C_out, atol=0.1, rtol=0.2) np.testing.assert_allclose(o.H_out, outputs[0].H_out, atol=0.1, rtol=0.2)