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The following commit(s) were added to refs/heads/master by this push:
new 7983ef1 fix quantize graph pass (#14605)
7983ef1 is described below
commit 7983ef1cd4fd8e4895c158f35f57e200d71817c4
Author: Xinyu Chen <xinyu1.c...@intel.com>
AuthorDate: Wed Apr 3 19:58:22 2019 -0700
fix quantize graph pass (#14605)
---
src/operator/quantization/quantize_graph_pass.cc | 12 ++-
tests/python/mkl/test_subgraph.py | 53 ++++++++++++-
tests/python/quantization/test_quantization.py | 95 ++++++++++++++++++++++++
3 files changed, 154 insertions(+), 6 deletions(-)
diff --git a/src/operator/quantization/quantize_graph_pass.cc
b/src/operator/quantization/quantize_graph_pass.cc
index af53397..5bd9e8a 100644
--- a/src/operator/quantization/quantize_graph_pass.cc
+++ b/src/operator/quantization/quantize_graph_pass.cc
@@ -265,7 +265,7 @@ Graph QuantizeGraph(Graph &&src) {
(mirror_node->op() != Op::Get("_contrib_dequantize"))) {
// here we calculate the output number (exclude min/max, in order to
// calculate min/max index from mirror node) based on assumption that
- // there is only 1min and 1max output from mirror node (which is
+ // there is only 1 min and 1 max output from mirror node (which is
// currently true)
size_t num_outputs = mirror_node->num_outputs() - 2;
uint32_t min_index = num_outputs + 2 * e.index;
@@ -297,9 +297,13 @@ Graph QuantizeGraph(Graph &&src) {
// Only insert dequantize for those Ops supports quantize and not
excluded.
NodePtr mirror_node = mirror_map.at(e.node.get());
NodeEntry mirror_entry = NodeEntry{mirror_node, e.index, e.version};
- size_t num_inputs = e.node->num_inputs();
- uint32_t min_index = num_inputs + 2 * e.index;
- uint32_t max_index = num_inputs + 2 * e.index + 1;
+ // here we calculate the output number (exclude min/max, in order to
+ // calculate min/max index from mirror node) based on assumption that
+ // there is only 1 min and 1 max output from mirror node (which is
+ // currently true)
+ size_t num_outputs = e.node->num_outputs();
+ uint32_t min_index = num_outputs + 2 * e.index;
+ uint32_t max_index = num_outputs + 2 * e.index + 1;
NodePtr dequantize_node = CreateNode("_contrib_dequantize",
e.node->attrs.name + "_dequantize");
diff --git a/tests/python/mkl/test_subgraph.py
b/tests/python/mkl/test_subgraph.py
index c8cf79e..761eb47 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -85,7 +85,7 @@ def check_qsym_scale_align(qsym):
def check_qsym_forward(qsym, qarg_params, qaux_params, batch, data_shape,
label_shape):
- mod = mx.mod.Module(symbol=qsym, context=mx.current_context())
+ mod = Module(symbol=qsym, context=mx.current_context())
mod.bind(for_training=False,
data_shapes=[('data', data_shape)],
label_shapes=[('softmax_label', label_shape)])
@@ -96,7 +96,7 @@ def check_qsym_forward(qsym, qarg_params, qaux_params, batch,
data_shape, label_
return mod.get_outputs()
def check_qsym_dummy_forward(qsym, batch, data_shape, label_shape):
- mod = mx.mod.Module(symbol=qsym, context=mx.current_context())
+ mod = Module(symbol=qsym, context=mx.current_context())
mod.bind(for_training=False,
data_shapes=[('data', data_shape)],
label_shapes=[('softmax_label', label_shape)])
@@ -185,6 +185,55 @@ def check_quantize(sym, data_shape, out_type, name='conv',
assert_almost_equal(ref_out[i].asnumpy(), quantized_out[i].asnumpy(),
atol = 1)
@with_seed()
+def check_quantize_whole_model_with_forward():
+ def check_qsym_forward(qsym, qarg_params, qaux_params, data_shape):
+ mod = Module(symbol=qsym, label_names=None, context=mx.current_context())
+ mod.bind(for_training=False,
+ data_shapes=[('data', data_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()
+
+ def check_quantize_whole_model(out_type):
+ batch_size = 4
+ data_shape = (batch_size, 4, 10, 10)
+ data = mx.sym.Variable('data')
+ conv0 = mx.sym.Convolution(data, kernel=(1, 1), num_filter=16,
name='conv0')
+ sym = mx.sym.Convolution(conv0, kernel=(1, 1), num_filter=16, name='conv1')
+ sym_sg = sym.get_backend_symbol('MKLDNN')
+ mod = Module(symbol=sym, label_names=[])
+ mod.bind(for_training=False,
+ data_shapes=[('data', data_shape)])
+
+ mod.init_params(mx.init.Normal(0.5))
+ arg_params, aux_params = mod.get_params()
+
+ excluded_sym_names = []
+
+ calib_data = mx.nd.random.uniform(shape=data_shape)
+ calib_data = NDArrayIter(data=calib_data)
+ calib_data = DummyIter(calib_data)
+ calib_layer = lambda name: name.endswith('_output')
+ qsym, qarg_params, qaux_params =
mx.contrib.quant.quantize_model(sym=sym_sg,
+
arg_params=arg_params,
+
aux_params=aux_params,
+
ctx=mx.current_context(),
+
excluded_sym_names=excluded_sym_names,
+
quantized_dtype=out_type,
+
calib_mode='naive',
+
calib_data=calib_data,
+
calib_layer=calib_layer,
+
num_calib_examples=5)
+ qsym = qsym.get_backend_symbol('MKLDNN_POST_QUANTIZE')
+ check_qsym_forward(qsym, qarg_params, qaux_params, data_shape)
+
+ for qdtype in ['uint8', 'int8', 'auto']:
+ check_quantize_whole_model(qdtype)
+
+@with_seed()
def check_fusion(sym, data_shape, attrs_op, name='conv',
check_quantization=True):
op_name = config[name][OP_NAME]
sg_pass_name = config[name][SG_PASS_NAME]
diff --git a/tests/python/quantization/test_quantization.py
b/tests/python/quantization/test_quantization.py
index eedc867..757df81 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -678,6 +678,101 @@ def test_quantize_model_with_forward():
check_quantize_model(qdtype)
@with_seed()
+def test_quantize_conv_with_forward():
+ def check_quantize_model(qdtype):
+ if is_test_for_native_cpu():
+ print('skipped testing test_quantize_model_with_forward for native
cpu since it is not supported yet')
+ return
+ elif qdtype == 'int8' and is_test_for_mkldnn():
+ print('skipped testing test_quantize_model_with_forward for mkldnn
cpu int8 since it is not supported yet')
+ return
+ elif qdtype == 'uint8' and is_test_for_gpu():
+ print('skipped testing test_quantize_model_with_forward 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, th_dict = {})
+ 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):
+ mod = mx.mod.Module(symbol=qsym, label_names=None,
context=mx.current_context())
+ mod.bind(for_training=False,
+ data_shapes=[('data', data_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()
+
+ batch_size = 4
+ dshape = (batch_size, 4, 10, 10)
+ data = mx.sym.Variable('data')
+ sym = mx.sym.Convolution(data, kernel=(1, 1), num_filter=16,
name='conv0')
+
+ mod = Module(symbol=sym, label_names=None)
+ mod.bind(data_shapes=[('data', dshape)])
+
+ mod.init_params()
+ arg_params, aux_params = mod.get_params()
+ excluded_sym_names = []
+
+ 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, dshape)
+
+ calib_data = mx.nd.random.uniform(shape=dshape)
+ calib_data = NDArrayIter(data=calib_data, batch_size=batch_size)
+ 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, dshape)
+
+ for qdtype in ['uint8', 'int8']:
+ check_quantize_model(qdtype)
+
+@with_seed()
def test_quantize_sym_with_calib():
sym = get_fp32_sym()
offline_params = [name for name in sym.list_arguments()
