MXNet pre-quantized BERT

include/tvm/relay/qnn/attrs.h

@@ -75,6 +75,19 @@ struct QuantizeAttrs : public tvm::AttrsNode<QuantizeAttrs> {
   }
 };
 
+/*! \brief Attribute for dequantize operator */
+struct DequantizeAttrs : public tvm::AttrsNode<DequantizeAttrs> {
+  int axis;
+
+  TVM_DECLARE_ATTRS(DequantizeAttrs, "relay.attrs.DequantizeAttrs") {
+    TVM_ATTR_FIELD(axis)
+        .describe(
+            "The channel axis for channel wise dequantization. Default value is -1,"
+            "which corresponds to the last axis.")
+        .set_default(-1);
+  }
+};
+
 }  // namespace qnn
 }  // namespace relay
 }  // namespace tvm

python/tvm/relay/frontend/mxnet.py

@@ -1944,18 +1944,27 @@ def _qnn_batch_norm(inputs, attrs):
 
 def _qnn_fully_connected(inputs, attrs, subgraphs, params):
 
-    def _get_input_scale_zp(_data, _inputs, _has_bias):
+    def _get_input_scale_zp(_data_dtype, _inputs, _has_bias):
         data_min_idx, data_max_idx = (3, 4) if _has_bias else (2, 3)
         data_min, data_max = _inputs[data_min_idx], _inputs[data_max_idx]
-        data_dtype = _infer_type(_data).checked_type.dtype
         _data_scale = get_mkldnn_uint8_scale(data_min, data_max) \
-            if data_dtype == 'uint8' \
+            if _data_dtype == 'uint8' \
             else get_mkldnn_int8_scale(data_min, data_max)
         _data_zp = 0
         return _data_scale, _data_zp
 
-    def _get_kernel_scale_zp(_kernel, _inputs, _has_bias):
+    def _get_kernel_scale_zp_tensor_quantized(_kernel, _inputs, _has_bias):
         kernel_dtype = _infer_type(_kernel).checked_type.dtype
+
+        if kernel_dtype != "int8":
+            raise tvm.error.OpNotImplemented(\
+                "Tensor wise quantized expects weights in int8 data type")
+
+        if isinstance(_kernel, tvm.relay.Call) and _kernel.op.name == "qnn.quantize":
+            _kernel_scale = _kernel.args[1].data.asnumpy()
+            _kernel_zp = _kernel.args[2].data.asnumpy()
+            return _kernel_scale, _kernel_zp
+
         kernel_min_idx, kernel_max_idx = (5, 6) if _has_bias else (4, 5)
         kernel_min_name = _get_name(_inputs[kernel_min_idx])
         kernel_min = params[kernel_min_name].asnumpy()[0]
@@ -1967,7 +1976,34 @@ def _get_kernel_scale_zp(_kernel, _inputs, _has_bias):
         _kernel_zp = 0
         return _kernel_scale, _kernel_zp
 
+    def _get_kernel_scale_zp_channel_quantized(_kernel, _bias, _data_scale):
+        kernel_dtype = _infer_type(_kernel).checked_type.dtype
+        if kernel_dtype != "float32":
+            raise tvm.error.OpNotImplemented(\
+                "Channel wise quantized expects weights in float32 data type")
+
+        # Get the FP32 values, calculate min/max and then channel quantize them
+        np_kernel = _infer_value(_kernel, params).asnumpy()
+        kernel_channel_min = np.amin(np_kernel, axis=(1, ))
+        kernel_channel_max = np.amax(np_kernel, axis=(1, ))
+
+        np_bias = None
+        if _bias is not None:
+            np_bias = _infer_value(_bias, params).asnumpy()
+        return quantize_conv_weights_bias_channel_mkldnn_from_var(_kernel,
+                                                                  np_bias,
+                                                                  kernel_channel_min,
+                                                                  kernel_channel_max,
+                                                                  _data_scale)
+
     def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale):
+        _bias = _inputs[2]
+        if isinstance(_bias, tvm.relay.Call) and _bias.op.name == "qnn.quantize":
+            _bias_scale = _bias.args[1].data.asnumpy()
+            _bias_requantize_scale = _bias_scale/(_data_scale * _kernel_scale)
+            _bias_requantize_scale = _expr.const(_bias_requantize_scale, dtype="float32")
+            return _bias_requantize_scale
+
         bias_min_name = _get_name(_inputs[7])
         bias_min = params[bias_min_name].asnumpy()[0]
         bias_max_name = _get_name(_inputs[8])
@@ -1987,39 +2023,95 @@ def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale):
         return res
     else:
         has_bias = not subgraph_dense_attrs.get_bool("no_bias", False)
-        # input
-        data = inputs[0]
-        data_scale, data_zp = _get_input_scale_zp(data, inputs, has_bias)
-        # kernel
-        kernel = inputs[1]
-        kernel_scale, kernel_zp = _get_kernel_scale_zp(kernel, inputs, has_bias)
         units = subgraph_dense_attrs.get_int("num_hidden")
+        is_flatten = subgraph_dense_attrs.get_bool("flatten", True)
+        enable_float_output = attrs.get_bool('enable_float_output', False)
+        is_channel_quantized = attrs.get_bool('channel_wise_quantize', False)
+
+        ########################
+        # Get data, kernel, bias
+        ########################
+        data, kernel = inputs[0], inputs[1]
+        bias = None
+        if has_bias:
+            bias = inputs[2]
+
+        ##############################
+        # Handle for shape of data > 2
+        ##############################
+        if is_flatten:
+            data = _op.nn.batch_flatten(data)
         data_shape = _infer_type(data).checked_type.shape
         if len(data_shape) > 2:
-            data = _op.nn.batch_flatten(data)
+            data = _op.reverse_reshape(data, [-1, 0])
+
+        ###############################
+        # Get data scale and zero point
+        ###############################
+        data_dtype = _infer_type(data).checked_type.dtype
+        data_scale, data_zp = _get_input_scale_zp(data_dtype, inputs, has_bias)
+
+        #################################
+        # Get weight scale and zero point
+        #################################
+        if is_channel_quantized:
+            kernel, kernel_scale, kernel_zp = _get_kernel_scale_zp_channel_quantized(kernel,
+                                                                                     bias,
+                                                                                     data_scale)
+        else:
+            kernel_scale, kernel_zp = _get_kernel_scale_zp_tensor_quantized(kernel, inputs,
+                                                                            has_bias)
+
+        ################
+        # Call QNN dense
+        ################
         res = relay.qnn.op.dense(data,
                                  kernel,
                                  input_zero_point=relay.const(data_zp, 'int32'),
                                  kernel_zero_point=relay.const(kernel_zp, 'int32'),
                                  input_scale=relay.const(data_scale, 'float32'),
                                  kernel_scale=relay.const(kernel_scale, 'float32'),
                                  units=units)
+
+        #################
+        # Handle bias add
+        #################
         if has_bias:
-            bias_data = inputs[2]
-            bias_requantize_scale = \
-                _get_bias_requantize_scale(inputs, data_scale, kernel_scale)
-            multiplied_bias = \
-                _op.multiply(_op.cast(bias_data, 'float32'), bias_requantize_scale)
-            rounded_bias = _op.round(multiplied_bias)
-            clipped_bias = _op.clip(rounded_bias,
-                                    a_min=tvm.tir.op.min_value('int32').value,
-                                    a_max=tvm.tir.op.max_value('int32').value)
-            requantized_bias = _op.cast(clipped_bias, 'int32')
-            res = _op.nn.bias_add(res, requantized_bias, axis=-1)
-        enable_float_output = attrs.get_bool('enable_float_output', False)
-        out_dtype = 'uint8' if attrs.get_bool('with_relu', False) else 'int8'
-        input_scale = np.float32(data_scale * kernel_scale)
-        if not enable_float_output:
+            if is_channel_quantized:
+                bias_scale = data_scale * kernel_scale
+                int32_bias = quantize_conv_bias_mkldnn_from_var(bias, bias_scale)
+                res = _op.nn.bias_add(res, int32_bias, axis=-1)
+            else:
+                bias_data = inputs[2]
+                bias_requantize_scale = \
+                    _get_bias_requantize_scale(inputs, data_scale, kernel_scale)
+                multiplied_bias = \
+                    _op.multiply(_op.cast(bias_data, 'float32'), bias_requantize_scale)
+                rounded_bias = _op.round(multiplied_bias)
+                clipped_bias = _op.clip(rounded_bias,
+                                        a_min=tvm.tir.op.min_value('int32').value,
+                                        a_max=tvm.tir.op.max_value('int32').value)
+                requantized_bias = _op.cast(clipped_bias, 'int32')
+                res = _op.nn.bias_add(res, requantized_bias, axis=-1)
+
+        ##############################################
+        # Dequantize if float32 output else Requantize
+        ##############################################
+        if enable_float_output:
+            output_scale = np.float32(data_scale * kernel_scale)
+            res = relay.qnn.op.dequantize(res,
+                                          relay.const(output_scale),
+                                          input_zero_point=relay.const(0, 'int32'),
+                                          axis=1)
+            if with_relu:
+                res = _op.nn.relu(res)
+        else:
+
+            if is_channel_quantized:
+                raise tvm.error.OpNotImplemented(\
+                    "Channel wise quantized dense with non float output is not supported yet")
+            out_dtype = 'uint8' if attrs.get_bool('with_relu', False) else 'int8'
+            input_scale = np.float32(data_scale * kernel_scale)
             min_output_range = attrs.get_float('min_calib_range')
             max_output_range = attrs.get_float('max_calib_range')
             output_scale = get_mkldnn_requantize_scale_outDtype(min_output_range,
@@ -2034,17 +2126,20 @@ def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale):
                 out_dtype=out_dtype)
             if with_relu:
                 res = _op.nn.relu(res)
-            return res, min_output_range, max_output_range
-        else:
-            output_scale = np.float32(data_scale * kernel_scale)
-            res = relay.qnn.op.dequantize(res,
-                                          relay.const(output_scale, 'float32'),
-                                          input_zero_point=relay.const(0, 'int32'))
-            if with_relu:
-                res = _op.nn.relu(res)
-            return res
 
 
+        ##############################
+        # Handle for shape of data > 2
+        ##############################
+        if len(data_shape) > 2:
+            new_shape = data_shape[:-1]
+            new_shape.append(units)
+            res = _op.reshape(res, new_shape)
+
+        if enable_float_output:
+            return res
+        return res, min_output_range, max_output_range
+
 def _mx_broadcast_to(inputs, attrs):
     data = inputs[0]
     tgt_shape = attrs.get_int_tuple("shape", [])

python/tvm/relay/frontend/nnvm_common.py

@@ -17,10 +17,13 @@
 # pylint: disable=invalid-name, import-self, len-as-condition
 """Utility functions common to NNVM and MxNet conversion."""
 import warnings
+from ... import error
+from ...tir.op import min_value
 from .. import expr as _expr
 from .. import op as _op
 from .common import get_relay_op
 from .common import infer_type as _infer_type
+from .common import infer_shape as _infer_shape
 
 def _warn_not_used(attr, op='nnvm'):
     err = "{} is ignored in {}.".format(attr, op)
@@ -57,9 +60,53 @@ def _impl(inputs, attrs):
 def _softmax_op(new_op):
     """softmax/log_softmax"""
     def _impl(inputs, attrs, _dtype='float32'):
-        # TODO(@icemelon9): currently ignore the 2nd input to softmax for mxnet 1.6
-        # assert len(inputs) == 1
         axis = attrs.get_int("axis", -1)
+        use_length = attrs.get_bool("use_length", False)
+        if use_length:
+            # The second arg is valid_length. We can use sequence mask to mask the input before
+            # computing softmax
+            assert len(inputs) == 2
+
+            data = inputs[0]
+            length = inputs[1]
+            data_shape = _infer_shape(data)
+            length_shape = _infer_shape(length)
+
+            if axis < 0:
+                axis = len(data_shape) + axis
+
+            data_ndims = len(data_shape)
+            length_ndims = len(length_shape)
+
+            # Sequence_mask supports axis = 0 and 1 and requires data to be in specific format.
+            if axis == data_ndims - 1 and data_ndims > 2 and length_ndims == 2:
+                new_batch_size = 1
+                for dim in range(length_ndims):
+                    assert data_shape[dim] == length_shape[dim]
+                    new_batch_size *= data_shape[dim]
+
+                # Reshape the data and length to satisfy sequence mask
+                data = _op.reshape(data, newshape=(new_batch_size, -1))
+                length = _op.reshape(length, newshape=(new_batch_size))
+
+                # Input data is now 2D, we can set the axis = 1
+                axis = 1
+            elif data_ndims > 2:
+                raise error.OpNotImplemented(\
+                        "Operator softmax with use_length=True is supported only for axis -1")
+
+            res = _op.sequence_mask(data=data,
+                                    valid_length=length,
+                                    mask_value=float(min_value("float").value),
+                                    axis=axis)
+
+            # Apply softmax
+            res = new_op(res, axis=axis)
+
+            # Reshape back to input data shape
+            if len(data_shape) > 2:
+                return _op.reshape(res, newshape=data_shape)
+            return res
         return new_op(inputs[0], axis=axis)
     return _impl
 

python/tvm/relay/qnn/op/qnn.py

@@ -121,7 +121,8 @@ def quantize(data,
 
 def dequantize(data,
                input_scale,
-               input_zero_point):
+               input_zero_point,
+               axis=-1):
     r""" Dequantize op
     This operator takes quantized int8 and unit8 as input and produces
     dequantized float32 as output. The output shape is the same as input shape. The input
@@ -135,6 +136,8 @@ def dequantize(data,
         The input zero_point.
     input_scale : tvm.relay.Expr
         The input scale.
+    axis : int
+        The channel axis for quantization. Default value is -1 which corresponds to the last axis.
     Returns
     -------
     result : tvm.relay.Expr
@@ -143,7 +146,8 @@ def dequantize(data,
 
     return _make.dequantize(data,
                             input_scale,
-                            input_zero_point)
+                            input_zero_point,
+                            axis)
 
 
 def concatenate(data,