[Mxnet-1397] Support symbolic api for requantize and dequantize (apac…

…he#14749)

* Adding support for symbolic API for requantize and dequantize

* Adding name to contributors list

* Removing redundant code

* Addressing indentation and using current_context() instead of cpu()

* merge from master

* merge from master

CONTRIBUTORS.md

@@ -236,6 +236,7 @@ List of Contributors
 * [Zhennan Qin](https://github.com/ZhennanQin)
 * [Zhiyuan Huang](https://github.com/huangzhiyuan)
 * [Zak Jost](https://github.com/zjost)
+* [Shoubhik Bhattacharya](https://github.com/shoubhik)
 * [Zach Kimberg](https://github.com/zachgk)
 
 Label Bot

src/operator/quantization/dequantize.cc

@@ -84,6 +84,10 @@ by keep zero centered for the quantized value:
 .set_attr_parser(ParamParser<DequantizeParam>)
 .set_num_inputs(3)
 .set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "min_range", "max_range"};
+  })
 .set_attr<mxnet::FInferShape>("FInferShape", DequantizeShape)
 .set_attr<nnvm::FInferType>("FInferType", DequantizeType)
 .set_attr<FInferStorageType>("FInferStorageType", DequantizeStorageType)

src/operator/quantization/requantize.cc

@@ -61,6 +61,10 @@ inference accuracy.
 .set_attr_parser(ParamParser<RequantizeParam>)
 .set_num_inputs(3)
 .set_num_outputs(3)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"data", "min_range", "max_range"};
+  })
 .set_attr<mxnet::FInferShape>("FInferShape", QuantizeShape)
 .set_attr<nnvm::FInferType>("FInferType", RequantizeType)
 .set_attr<FInferStorageType>("FInferStorageType", RequantizeStorageType)

tests/python/quantization/test_quantization.py

@@ -63,19 +63,45 @@ def test_quantize_float32_to_int8():
 
 @with_seed()
 def test_dequantize_int8_to_float32():
+
+    def get_test_data(real_range, qdata_np):
+        qdata = mx.nd.array(qdata_np, dtype=np.int8)
+        min_range = mx.nd.array([-real_range], dtype=np.float32)
+        max_range = mx.nd.array([real_range], dtype=np.float32)
+        return qdata, min_range, max_range
+
+    def baseline_dequantization(qdata, real_range, qdata_np):
+        quantized_range = 127.0
+        scale = real_range / quantized_range
+        data_np = qdata_np * scale
+        return data_np
+
+    def test_nd_array_dequantization(qdata, min_range, max_range, expected_result):
+        data = mx.nd.contrib.dequantize(qdata, min_range, max_range, out_type='float32')
+        assert data.dtype == np.float32
+        assert_almost_equal(data.asnumpy(), expected_result)
+
+    def test_symbolic_api_dequantization(qdata, min_range, max_range, expected_result):
+        sym_data = mx.sym.Variable('data')
+        sym_min_range = mx.sym.Variable('min_range')
+        sym_max_range = mx.sym.Variable('max_range')
+        dequant = mx.sym.contrib.dequantize(sym_data, sym_min_range, 
+                                            sym_max_range, out_type='float32')
+        out = dequant.bind(ctx=mx.current_context(), 
+                           args={'data':qdata, 'min_range':min_range, 'max_range':max_range})
+        data = out.forward()[0]
+        assert data.dtype == np.float32
+        assert_almost_equal(data.asnumpy(), expected_result)
+
+    real_range = 402.3347
     shape = rand_shape_nd(4)
     qdata_np = np.random.uniform(low=-127, high=127, size=shape).astype(dtype=np.int8)
-    qdata = mx.nd.array(qdata_np, dtype=np.int8)
-    real_range = 402.3347
-    min_range = mx.nd.array([-real_range], dtype=np.float32)
-    max_range = mx.nd.array([real_range], dtype=np.float32)
-    data = mx.nd.contrib.dequantize(qdata, min_range, max_range, out_type='float32')
-    quantized_range = 127.0
-    scale = real_range / quantized_range
-    assert data.dtype == np.float32
-    data_np = qdata_np * scale
-    assert_almost_equal(data.asnumpy(), data_np)
-
+    qdata, min_range, max_range = get_test_data(real_range, qdata_np)
+    expected_result = baseline_dequantization(qdata, real_range, qdata_np)
+    # test nd array implementation.
+    test_nd_array_dequantization(qdata, min_range, max_range, expected_result)
+    # test symbolic api implementaion.
+    test_symbolic_api_dequantization(qdata, min_range, max_range, expected_result)
 
 @with_seed()
 def test_requantize_int32_to_int8():
@@ -124,7 +150,41 @@ def check_requantize(shape, min_calib_range=None, max_calib_range=None):
         assert_almost_equal(qdata_int8.asnumpy(), qdata_int8_np, atol = 1)
         assert_almost_equal(min_output.asnumpy(), np.array([min_output_np]))
         assert_almost_equal(max_output.asnumpy(), np.array([max_output_np]))
+
+    def check_requantize_with_symbol(shape, min_calib_range=None, max_calib_range=None):
+        qdata = mx.nd.random.uniform(low=-1000.0, high=1000.0, shape=shape).astype('int32')
+        min_range = mx.nd.array([-1010.0])
+        max_range = mx.nd.array([1020.0])
+        sym_data = mx.sym.Variable('data')
+        sym_min_range = mx.sym.Variable('min_range')
+        sym_max_range = mx.sym.Variable('max_range')
+        if min_calib_range is None or max_calib_range is None:
+            requant = mx.sym.contrib.requantize(sym_data, sym_min_range, sym_max_range)
+            out = requant.bind(ctx=mx.current_context(), 
+                               args={'data':qdata, 'min_range':min_range, 
+                               'max_range':max_range}) 
+            qdata_int8, min_output, max_output = out.forward()
+        else:
+            requant = mx.sym.contrib.requantize(sym_data, sym_min_range, sym_max_range, 
+                                                min_calib_range, max_calib_range)
+            out = requant.bind(ctx=mx.current_context(), args={'data':qdata, 'min_range':min_range, 
+                                                   'max_range':max_range})  
+            qdata_int8, min_output, max_output = out.forward()                                   
+
+        qdata_int8_np, min_output_np, max_output_np = requantize_baseline(qdata.asnumpy(), min_range.asscalar(),
+                                                                          max_range.asscalar(),
+                                                                          min_calib_range=min_calib_range,
+                                                                          max_calib_range=max_calib_range)
+        assert_almost_equal(qdata_int8.asnumpy(), qdata_int8_np)
+        assert_almost_equal(min_output.asnumpy(), np.array([min_output_np]))
+        assert_almost_equal(max_output.asnumpy(), np.array([max_output_np]))
 
+    # test with symbol API.
+    check_requantize_with_symbol((3, 4, 10, 10))
+    check_requantize_with_symbol((32, 3, 23, 23))
+    check_requantize_with_symbol((3, 4, 10, 10), min_calib_range=-1050.0, max_calib_range=1040.0)
+    check_requantize_with_symbol((32, 3, 23, 23), min_calib_range=-134.349, max_calib_range=523.43)
+    # Test with nd array API
     check_requantize((3, 4, 10, 10))
     check_requantize((32, 3, 23, 23))
     check_requantize((3, 4, 10, 10), min_calib_range=-1050.0, max_calib_range=1040.0)