reminisce closed pull request #11937: Fix quantized graphpass bug
URL: https://github.com/apache/incubator-mxnet/pull/11937
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diff --git a/src/operator/quantization/quantize_graph_pass.cc
b/src/operator/quantization/quantize_graph_pass.cc
index 5376a0ee9f1..10834868d2b 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -221,6 +221,9 @@ Graph QuantizeGraph(Graph &&src) {
new_node->inputs.emplace_back(NodeEntry{dequantize_node, 0, 0});
mirror_map[e.node.get()] = std::move(dequantize_node);
+ } else if (mirror_node->op() != nullptr
+ && mirror_node->op()->name == "_contrib_quantize") {
+ new_node->inputs.emplace_back(NodeEntry{mirror_node->inputs[0].node,
e.index, e.version});
} else {
new_node->inputs.emplace_back(NodeEntry{mirror_node, e.index,
e.version});
}
diff --git a/tests/python/quantization/test_quantization.py
b/tests/python/quantization/test_quantization.py
index 359bbee569f..bfae58e49d0 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -396,6 +396,17 @@ def get_fp32_sym():
out_grad=False, preserve_shape=False,
use_ignore=False, name='softmax')
return sym
+def get_fp32_residual():
+ data = mx.sym.Variable('data')
+ conv = mx.sym.Convolution(data=data, num_filter=4, kernel=(1,1), pad=(0,0),
+ no_bias=True, name='conv')
+ bn = mx.sym.BatchNorm(data=conv, fix_gamma=False, eps=2e-5, momentum=0.9,
name='bn')
+ act = mx.sym.Activation(data=bn + data, act_type='relu', name='relu')
+ pool = mx.sym.Pooling(act, kernel=(4, 4), pool_type='avg', name='pool')
+ fc = mx.sym.FullyConnected(pool, num_hidden=10, flatten=True, name='fc')
+ sym = mx.sym.SoftmaxOutput(fc, grad_scale=1, ignore_label=-1,
multi_output=False,
+ out_grad=False, preserve_shape=False,
use_ignore=False, name='softmax')
+ return sym
@with_seed()
def test_quantize_model():
@@ -463,6 +474,101 @@ def check_qsym_qdtype(qsym, qdtype):
for qdtype in ['int8', 'uint8']:
check_quantize_model(qdtype)
+@with_seed()
+def test_quantize_residual_unit():
+ def check_quantize_model(qdtype):
+ if is_test_for_native_cpu():
+ print('skipped testing quantized_residual_unit for native cpu
since it is not supported yet')
+ return
+ elif qdtype == 'int8' and is_test_for_mkldnn():
+ print('skipped testing quantized_residual_unit for mkldnn cpu int8
since it is not supported yet')
+ return
+ elif qdtype == 'uint8' and is_test_for_gpu():
+ print('skipped testing quantized_residual_unit for gpu uint8 since
it is not supported yet')
+ return
+
+ def check_params(params, qparams, qsym=None):
+ if qsym is None:
+ assert len(params) == len(qparams)
+ for k, v in params.items():
+ assert k in qparams
+ assert same(v.asnumpy(), qparams[k].asnumpy())
+ else:
+ qparams_ground_truth = mx.contrib.quant._quantize_params(qsym,
params)
+ assert len(qparams) == len(qparams_ground_truth)
+ for k, v in qparams_ground_truth.items():
+ assert k in qparams
+ assert same(v.asnumpy(), qparams[k].asnumpy())
+
+ def check_qsym_calibrated(qsym):
+ attrs = qsym.attr_dict()
+ for k, v in attrs.items():
+ if k.find('requantize_') != -1:
+ assert 'min_calib_range' in v
+ assert 'max_calib_range' in v
+
+ def check_qsym_qdtype(qsym, qdtype):
+ attrs = qsym.attr_dict()
+ for k, v in attrs.items():
+ if k.find('_quantize') != -1:
+ assert 'out_type' in v
+ assert v['out_type'] == qdtype
+
+ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape,
label_shape):
+ mod = mx.mod.Module(symbol=qsym, context=mx.current_context())
+ mod.bind(for_training=False,
+ data_shapes=[('data', data_shape)],
+ label_shapes=[('softmax_label', label_shape)])
+ mod.set_params(qarg_params, qaux_params)
+ data = [mx.random.uniform(-1.0, 1.0, shape=shape) for _, shape in
mod.data_shapes]
+ batch = mx.io.DataBatch(data, [])
+ mod.forward(batch, is_train=False)
+ for output in mod.get_outputs():
+ output.wait_to_read()
+
+
+ sym = get_fp32_residual()
+ mod = Module(symbol=sym)
+ batch_size = 4
+ data_shape = (batch_size, 4, 10, 10)
+ label_shape = (batch_size, 10)
+ mod.bind(data_shapes=[('data', data_shape)],
label_shapes=[('softmax_label', label_shape)])
+ mod.init_params()
+ arg_params, aux_params = mod.get_params()
+ excluded_sym_names = []
+ if mx.current_context() == mx.cpu():
+ excluded_sym_names += ['fc']
+ qsym, qarg_params, qaux_params =
mx.contrib.quant.quantize_model(sym=sym,
+
arg_params=arg_params,
+
aux_params=aux_params,
+
excluded_sym_names=excluded_sym_names,
+
ctx=mx.current_context(),
+
quantized_dtype=qdtype,
+
calib_mode='none')
+ check_params(arg_params, qarg_params, qsym)
+ check_params(aux_params, qaux_params)
+ check_qsym_forward(qsym, qarg_params, qaux_params, data_shape,
label_shape)
+
+ calib_data = mx.nd.random.uniform(shape=data_shape)
+ calib_data = NDArrayIter(data=calib_data)
+ calib_data = DummyIter(calib_data)
+ qsym, qarg_params, qaux_params =
mx.contrib.quant.quantize_model(sym=sym,
+
arg_params=arg_params,
+
aux_params=aux_params,
+
excluded_sym_names=excluded_sym_names,
+
ctx=mx.current_context(),
+
quantized_dtype=qdtype,
+
calib_mode='naive',
+
calib_data=calib_data,
+
num_calib_examples=20)
+ check_params(arg_params, qarg_params, qsym)
+ check_params(aux_params, qaux_params)
+ check_qsym_calibrated(qsym)
+ check_qsym_qdtype(qsym, qdtype)
+ check_qsym_forward(qsym, qarg_params, qaux_params, data_shape,
label_shape)
+
+ for qdtype in ['int8', 'uint8']:
+ check_quantize_model(qdtype)
@with_seed()
def test_quantize_sym_with_calib():
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