[AutoParallel] Add Quant Pass (#44877)

* add quant pass

paddle/fluid/framework/distributed_strategy.proto

@@ -184,6 +184,14 @@ message TensorParallelConfig {
   optional int32 tensor_init_seed = 2 [ default = -1 ];
 }
 
+message QatConfig {
+  optional bool channel_wise_abs_max = 1 [default = true];
+  optional int32 weight_bits = 2 [default = 8];
+  optional int32 activation_bits = 3 [default = 8];
+  repeated string not_quant_pattern = 4;
+  optional string algo = 5;
+}
+
 enum TableType {
   PS_SPARSE_TABLE = 0;
   PS_DENSE_TABLE = 1;
@@ -327,6 +335,7 @@ message DistributedStrategy {
   optional bool heter_ccl_mode = 38 [ default = false ];
   optional bool is_fl_ps_mode = 39 [ default = false ];
   optional bool with_coordinator = 40 [ default = false ];
+  optional bool qat = 41 [ default = false ];
 
   optional RecomputeConfig recompute_configs = 101;
   optional AMPConfig amp_configs = 102;
@@ -344,6 +353,7 @@ message DistributedStrategy {
   optional TrainerDescConfig trainer_desc_configs = 114;
   repeated TableParameter downpour_table_param = 115;
   optional FsClientParameter fs_client_param = 116;
+  optional QatConfig qat_configs = 117;
 
   optional BuildStrategy build_strategy = 201;
   optional ExecutionStrategy execution_strategy = 202;

python/paddle/distributed/auto_parallel/completion.py

@@ -940,6 +940,12 @@ class Completer:
                             core.op_proto_and_checker_maker.OpRole.Forward):
                 appended_grad_times += 1
 
+            if int(op.attr('op_role')) == int(
+                    int(core.op_proto_and_checker_maker.OpRole.Backward)
+                    | int(core.op_proto_and_checker_maker.OpRole.Loss)):
+                assert op.type == "fill_constant"
+                break
+
             # complete the annotation of grad op (xxx_grad op or sum op)
             # xxx_grad op will have a corresponding forward op in grad_op_id_to_op_id
             grad_op = ops[idx]

python/paddle/distributed/auto_parallel/operators/common.py

@@ -245,6 +245,8 @@ def is_parameter_related(varname, block):
         varname = varname[:varname.index(".subprog_")]
     if ".cast_fp" in varname:
         varname = varname[:varname.index(".cast_fp")]
+    if ".quantized" in varname:
+        varname = varname[:varname.index(".quantized")]
     assert block.has_var(varname)
     var = block.var(varname)
     return var.is_parameter

python/paddle/distributed/auto_parallel/parallelizer_v2.py

@@ -66,9 +66,9 @@ class Parallelizer:
                                                    serial_loss)
             # Apply pre optimization passes
             time0 = time.time()
-            self._apply_pre_optimization(serial_main_program,
-                                         serial_startup_program, serial_loss,
-                                         serial_optimizer, params_grads)
+            serial_main_program, serial_startup_program, params_grads = self._apply_pre_optimization(
+                serial_main_program, serial_startup_program, serial_loss,
+                serial_optimizer, params_grads)
             self._logger.info(
                 "within parallel apply_pre_optimization time: {}, mode {}".
                 format(time.time() - time0, self._mode))
@@ -162,6 +162,22 @@ class Parallelizer:
                                 optimizer, params_grads):
         if self._strategy is None:
             return
+
+        # apply quantization pass
+        # The pass can be applied when mode must be 'train'
+        if self._mode == 'train' and self._strategy.qat:
+            config = copy.deepcopy(self._strategy.qat_configs)
+            config["dist_context"] = self._dist_context
+            config["params_grads"] = params_grads
+            auto_parallel_quantization_pass = new_pass(
+                "auto_parallel_quantization", config)
+            auto_parallel_quantization_pass.apply([main_program],
+                                                  [startup_program],
+                                                  self._pass_context)
+            main_program = self._pass_context.get_attr("main_program")
+            startup_program = self._pass_context.get_attr("startup_program")
+            params_grads = self._pass_context.get_attr("params_grads")
+
         # apply amp pass
         # FIXME we disenable amp for eval since it has a little bug with
         # eval program and which will be fixed in future
@@ -195,6 +211,8 @@ class Parallelizer:
                                                [startup_program],
                                                self._pass_context)
 
+        return main_program, startup_program, params_grads
+
     def _apply_post_optimization(self, main_program, startup_program, rank,
                                  params_grads):
         if self._strategy is None:

python/paddle/distributed/auto_parallel/reshard.py

@@ -685,7 +685,8 @@ class Remover:
                 block._remove_op(idx)
 
     @staticmethod
-    def remove_no_need_vars(auto_parallel_main_prog, dist_params_grads):
+    def remove_no_need_vars(auto_parallel_main_prog, dist_params_grads,
+                            feed_var_names):
         """Remove no need vars in the main program"""
         for block_idx, block in enumerate(auto_parallel_main_prog.blocks):
             remove_vars = set()
@@ -731,7 +732,7 @@ class Remover:
                     idx += 1
 
             for var in remove_vars:
-                if block.vars[var].is_data:
+                if var in feed_var_names:
                     continue
                 block._remove_var(var)
 
@@ -743,7 +744,12 @@ class Remover:
                                    rank_id)
         Resharder.change_while_op_input_and_output(auto_parallel_main_prog,
                                                    dist_context)
-        Remover.remove_no_need_vars(auto_parallel_main_prog, dist_params_grads)
+        # 'feed_var_names' cannot be removed from auto_parallel_main_prog
+        feed_var_names = []
+        for var in sum(list(dist_context.serial_feed_vars.values()), []):
+            feed_var_names.append(var.name)
+        Remover.remove_no_need_vars(auto_parallel_main_prog, dist_params_grads,
+                                    feed_var_names)
 
     @staticmethod
     def remove_no_need_in_startup(auto_parallel_main_prog,

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -1991,6 +1991,60 @@ class DistributedStrategy(object):
         else:
             print("WARNING: auto-search should have value of bool type")
 
+    @property
+    def qat(self):
+        """
+        Indicating whether we are using quantization training
+        Default Value: False
+        """
+        return self.strategy.qat
+
+    @qat.setter
+    def qat(self, flag):
+        if isinstance(flag, bool):
+            self.strategy.qat = flag
+        else:
+            print("WARNING: qat should have value of bool type")
+
+    @property
+    def qat_configs(self):
+        """
+        Set quantization training configurations. In general, qat has serveral configurable
+        settings that can be configured through a dict.
+
+        **Notes**:
+            channel_wise_abs_max(bool): Whether to use `per_channel` quantization training. Default is True.
+
+            weight_bits(int): quantization bit number for weight. Default is 8.
+
+            activation_bits(int): quantization bit number for activation. Default is 8.
+
+            not_quant_pattern(list[str]): When the skip pattern is detected in an op's name scope, 
+                the corresponding op will not be quantized.
+
+            algo(str): Other quantization training algorithm.
+
+        Exampless:
+
+          .. code-block:: python
+
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.qat = True
+            strategy.qat_configs = {
+                "channel_wise_abs_max": True,
+                "weight_bits": 8,
+                "activation_bits: 8,
+                "not_quant_pattern": ['skip_quant']}
+
+        """
+        return get_msg_dict(self.strategy.qat_configs)
+
+    @qat_configs.setter
+    def qat_configs(self, configs):
+        check_configs_key(self.strategy.qat_configs, configs, "qat_configs")
+        assign_configs_value(self.strategy.qat_configs, configs)
+
     @property
     def heter_ccl_mode(self):
         """

python/paddle/distributed/passes/__init__.py

@@ -19,6 +19,7 @@ from .auto_parallel_sharding import *
 from .auto_parallel_amp import *
 from .auto_parallel_fp16 import *
 from .auto_parallel_recompute import *
+from .auto_parallel_quantization import *
 from .auto_parallel_data_parallel_optimization import *
 from .cpp_pass import *
 import os

python/paddle/distributed/passes/auto_parallel_quantization.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+
+from paddle.fluid import core, framework
+from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.fluid.contrib.slim.quantization import utils
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPassV2
+from paddle.fluid.contrib.slim.quantization import AddQuantDequantPassV2
+from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass
+from paddle.distributed.auto_parallel.dist_attribute import OperatorDistributedAttribute, TensorDistributedAttribute
+
+from .pass_base import PassBase, register_pass
+
+TRANSFORM_PASS_OP_TYPES = utils._weight_supported_quantizable_op_type
+QUANT_DEQUANT_PASS_OP_TYPES = utils._act_supported_quantizable_op_type
+
+
+def _node_id(node):
+    return (node.node.graph_id(), node.node.id())
+
+
+@register_pass("auto_parallel_quantization")
+class QuantizationPass(PassBase):
+
+    def __init__(self):
+        super(QuantizationPass, self).__init__()
+        self.set_attr("dist_context", None)
+        self.set_attr("params_grads", None)
+
+    def _check_self(self):
+        if self.get_attr("dist_context") is None:
+            return False
+        if self.get_attr("params_grads") is None:
+            return False
+        return True
+
+    def _check_conflict(self, other_pass):
+        return True
+
+    def _apply_single_impl(self, main_program, startup_program, context):
+
+        dist_context = self.get_attr("dist_context")
+        params_grads = self.get_attr("params_grads")
+
+        # TODO: scope and place will be removed,
+        # cause params should be initialized by engine module.
+        scope = paddle.static.global_scope()
+        place = paddle.fluid.CUDAPlace(ParallelEnv().dev_id)
+
+        # 1. Program convert to Graph, and this pass is only for train mode
+        main_graph = framework.IrGraph(core.Graph(main_program.desc),
+                                       for_test=False)
+
+        # 2. Prepare inputs
+        transform_pass_ops = []
+        quant_dequant_ops = []
+        quantize_op_types = [
+            'conv2d', 'depthwise_conv2d', 'mul', 'matmul', 'matmul_v2'
+        ]
+        for op_type in quantize_op_types:
+            if op_type in TRANSFORM_PASS_OP_TYPES:
+                transform_pass_ops.append(op_type)
+            elif op_type in QUANT_DEQUANT_PASS_OP_TYPES:
+                quant_dequant_ops.append(op_type)
+
+        weight_quantize_type = "channel_wise_abs_max" if self.get_attr(
+            'channel_wise_abs_max') else "abs_max"
+
+        # 3. Add quant op for ops which have parameters
+        transform_pass = QuantizationTransformPassV2(
+            scope=scope,
+            place=place,
+            weight_bits=self.get_attr('weight_bits'),
+            activation_bits=self.get_attr('activation_bits'),
+            skip_pattern=self.get_attr('not_quant_pattern'),
+            activation_quantize_type="moving_average_abs_max",
+            quantizable_op_type=transform_pass_ops,
+            weight_quantize_type=weight_quantize_type,
+            weight_quantize_func=None,
+            act_quantize_func=None,
+            weight_preprocess_func=None,
+            act_preprocess_func=None,
+            optimizer_func=None,
+            executor=None)
+        transform_pass.apply(main_graph)
+
+        # 4. Add quant op for ops which don't have parameter
+        quant_dequant_pass = AddQuantDequantPassV2(
+            scope=scope,
+            place=place,
+            quant_bits=self.get_attr('activation_bits'),
+            skip_pattern=self.get_attr('not_quant_pattern'),
+            quantizable_op_type=quant_dequant_ops)
+        quant_dequant_pass.apply(main_graph)
+
+        # 5. Gather quantitative information for the output
+        out_scale_training_pass = OutScaleForTrainingPass(scope=scope,
+                                                          place=place)
+        out_scale_training_pass.apply(main_graph)
+
+        # 6. Convert Graph back to Program
+        quant_program = main_graph.to_program()
+
+        # 7. get new prams_grads from quant_program
+        new_params_grads = []
+        for param, grad in params_grads:
+            if param.name not in quant_program.global_block().vars:
+                continue
+
+            new_param = quant_program.global_block().vars[param.name]
+            new_grad = quant_program.global_block().vars[grad.name]
+            new_params_grads.append((new_param, new_grad))
+
+        # 8. complete distributed attribution
+        # NOTE: hack implement, upgrading soon
+        for ib, block in enumerate(quant_program.blocks):
+            # recover origin ops' dist_attr and set quant ops' dist_attr
+            qat_offset = 0
+            for ip, quant_op in enumerate(block.ops):
+                quant_op_dist_attr = OperatorDistributedAttribute()
+
+                if "quantize" in quant_op.type or \
+                    quant_op.type == "moving_average_abs_max_scale":
+
+                    input_name = quant_op.desc.input('X')[0]
+                    if "quantize" in input_name:
+                        input_name = input_name[:input_name.index(".quantized")]
+
+                    if quant_op.type == "moving_average_abs_max_scale":
+                        consume_op = main_program.blocks[ib].vars[input_name].op
+                    else:
+                        consume_op = main_program.blocks[ib].ops[ip -
+                                                                 qat_offset]
+                    consume_op_dist_attr = dist_context.get_dist_op_for_program(
+                        consume_op).dist_attr
+                    ref_process_mesh = consume_op_dist_attr.process_mesh
+
+                    if input_name in consume_op_dist_attr.outputs_dist_attrs:
+                        consume_input_dist_attr = consume_op_dist_attr.outputs_dist_attrs[
+                            input_name]
+                    else:
+                        consume_input_dist_attr = consume_op_dist_attr.inputs_dist_attrs[
+                            input_name]
+
+                    quant_op_dist_attr.impl_idx = 0
+                    quant_op_dist_attr.impl_type = "default"
+                    quant_op_dist_attr.process_mesh = ref_process_mesh
+                    quant_op_dist_attr.set_input_dist_attr(
+                        quant_op.desc.input('X')[0], consume_input_dist_attr)
+
+                    for slot_name in quant_op.desc.input_names():
+                        if slot_name == "X":
+                            continue
+                        for in_name in quant_op.desc.input(slot_name):
+                            input_var = block.vars[in_name]
+                            tensor_dist_attr = TensorDistributedAttribute()
+                            tensor_dist_attr.process_mesh = ref_process_mesh
+                            tensor_dist_attr.dims_mapping = [-1]
+                            dist_context.set_tensor_dist_attr_for_program(
+                                input_var, tensor_dist_attr)
+                            quant_op_dist_attr.set_input_dist_attr(
+                                in_name, tensor_dist_attr)
+
+                    for slot_name in quant_op.desc.output_names():
+                        output_name = quant_op.desc.output(slot_name)[0]
+                        output_var = block.vars[output_name]
+                        if slot_name == "Y":
+                            dist_context.set_tensor_dist_attr_for_program(
+                                output_var, consume_input_dist_attr)
+                            quant_op_dist_attr.set_output_dist_attr(
+                                output_name, consume_input_dist_attr)
+                        else:
+                            tensor_dist_attr = TensorDistributedAttribute()
+                            tensor_dist_attr.process_mesh = ref_process_mesh
+                            tensor_dist_attr.dims_mapping = [-1]
+                            dist_context.set_tensor_dist_attr_for_program(
+                                output_var, tensor_dist_attr)
+                            quant_op_dist_attr.set_output_dist_attr(
+                                output_name, tensor_dist_attr)
+
+                    quant_op._set_attr("op_device", "")
+                    qat_offset += 1
+
+                else:
+
+                    origin_op = main_program.blocks[ib].ops[ip - qat_offset]
+                    quant_op.desc.set_original_id(origin_op.desc.original_id())
+                    dist_origin_op = dist_context.get_dist_op_for_program(
+                        origin_op)
+                    assert dist_origin_op is not None, "origin op must have dist attr."
+
+                    origin_op_dist_attr = dist_origin_op.dist_attr
+                    quant_op_dist_attr.impl_idx = origin_op_dist_attr.impl_idx
+                    quant_op_dist_attr.impl_type = origin_op_dist_attr.impl_type
+                    quant_op_dist_attr.process_mesh = origin_op_dist_attr.process_mesh
+                    for idx, input_name in enumerate(quant_op.input_arg_names):
+                        origin_input_name = origin_op.input_arg_names[idx]
+                        origin_input_dist_attr = origin_op_dist_attr.inputs_dist_attrs[
+                            origin_input_name]
+                        quant_op_dist_attr.set_input_dist_attr(
+                            input_name, origin_input_dist_attr)
+
+                        if input_name not in main_program.blocks[ib].vars:
+                            origin_input_var = main_program.blocks[ib].vars[
+                                origin_input_name]
+                            origin_in_tensor_dist_attr = dist_context.get_dist_tensor_for_program(
+                                origin_input_var).dist_attr
+                            quant_input_var = block.vars[input_name]
+                            dist_context.set_tensor_dist_attr_for_program(
+                                quant_input_var, origin_in_tensor_dist_attr)
+
+                    for idx, output_name in enumerate(
+                            quant_op.output_arg_names):
+                        origin_output_name = origin_op.output_arg_names[idx]
+                        origin_output_dist_attr = origin_op_dist_attr.outputs_dist_attrs[
+                            origin_output_name]
+                        quant_op_dist_attr.set_output_dist_attr(
+                            output_name, origin_output_dist_attr)
+
+                        if output_name not in main_program.blocks[ib].vars:
+                            origin_output_var = main_program.blocks[ib].vars[
+                                origin_output_name]
+                            origin_out_tensor_dist_attr = dist_context.get_dist_tensor_for_program(
+                                origin_output_var).dist_attr
+                            quant_output_var = block.vars[output_name]
+                            dist_context.set_tensor_dist_attr_for_program(
+                                quant_output_var, origin_out_tensor_dist_attr)
+
+                dist_context.set_op_dist_attr_for_program(
+                    quant_op, quant_op_dist_attr)
+
+            # recover vars' dist_attr
+            for name, dst_var in block.vars.items():
+                if name in main_program.blocks[ib].vars:
+                    src_var = main_program.blocks[ib].vars[name]
+                    dist_tensor = dist_context.get_dist_tensor_for_program(
+                        src_var)
+                    if not dist_tensor:
+                        continue
+                    dist_context.set_tensor_dist_attr_for_program(
+                        dst_var, dist_tensor.dist_attr)
+
+        context.set_attr("main_program", quant_program)
+        context.set_attr("startup_program", startup_program)
+        context.set_attr("params_grads", new_params_grads)

python/paddle/distributed/passes/auto_parallel_recompute.py

@@ -236,14 +236,14 @@ class RecomputePass(PassBase):
     def _check_conflict(self, other_pass):
         return True
 
-    def _apply_single_impl(self, main_programs, startup_programs, context):
+    def _apply_single_impl(self, main_program, startup_program, context):
         checkpoints = self.get_attr("checkpoints")
         loss = self.get_attr("loss")
         no_grad_set = self.get_attr("no_grad_set")
         self._dist_context = self.get_attr("dist_context")
 
-        main_block = main_programs.global_block()
-        no_grad_set_name = _get_stop_gradients(main_programs, no_grad_set)
+        main_block = main_program.global_block()
+        no_grad_set_name = _get_stop_gradients(main_program, no_grad_set)
         # get op_path which is related to loss
         op_path = _find_op_path_(main_block, [loss], [], no_grad_set_name)
 
@@ -373,7 +373,7 @@ class RecomputePass(PassBase):
                     ckpt_ops_dict[fwd_op_id][0] = False
                     main_block._sync_with_cpp()
 
-        main_programs._sync_with_cpp()
+        main_program._sync_with_cpp()
 
     def reset_op_dist_attr(self, op, var_name_dict):
         op_dist_attr = self._dist_context.get_op_dist_attr_for_program(op)

python/paddle/fluid/contrib/slim/quantization/__init__.py

@@ -25,7 +25,8 @@ from .post_training_quantization import *
 from . import imperative
 from .imperative import *
 
-__all__ = quantization_pass.__all__
+__all__ = []
+__all__ += quantization_pass.__all__
 __all__ += quant_int8_mkldnn_pass.__all__
 __all__ += quant2_int8_mkldnn_pass.__all__
 __all__ += post_training_quantization.__all__

python/paddle/fluid/contrib/slim/quantization/imperative/qat.py

@@ -42,6 +42,17 @@ _logger = get_logger(__name__,
                      fmt='%(asctime)s-%(levelname)s: %(message)s')
 
 
+def lazy_import_fleet(layer_name_map, fake_quant_input_layers):
+    from paddle.distributed import fleet
+    layer_name_map[
+        'ColumnParallelLinear'] = fleet.meta_parallel.parallel_layers.mp_layers.ColumnParallelLinear
+    layer_name_map[
+        'RowParallelLinear'] = fleet.meta_parallel.parallel_layers.mp_layers.RowParallelLinear
+    fake_quant_input_layers.append(fleet.meta_parallel.RowParallelLinear)
+    fake_quant_input_layers.append(fleet.meta_parallel.ColumnParallelLinear)
+    return layer_name_map, fake_quant_input_layers
+
+
 class ImperativeQuantAware(object):
     """
     Applying quantization aware training (QAT) to the dgraph model.
@@ -300,13 +311,15 @@ class ImperativeQuantizeInputs(object):
         Please refer to the args of ImperativeQuantAware.
         """
         super(ImperativeQuantizeInputs, self).__init__()
+        self.layer_name_map, self.fake_quant_input_layers = lazy_import_fleet(
+            utils.layer_name_map, utils.fake_quant_input_layers)
 
         self._quantizable_layer_type = tuple(
-            utils.layer_name_map[layer] if layer in
-            utils.layer_name_map else layer for layer in quantizable_layer_type)
+            self.layer_name_map[layer] if layer in
+            self.layer_name_map else layer for layer in quantizable_layer_type)
         for layer in self._quantizable_layer_type:
             assert not isinstance(layer, str) \
-                and layer in utils.fake_quant_input_layers, \
+                and layer in self.fake_quant_input_layers, \
                 "%s is unspported to be quantized." % layer
 
         quantize_type = {
@@ -383,7 +396,7 @@ class ImperativeQuantizeInputs(object):
     def _get_input_quantized_layer(self, layer):
         quant_layer_name = None
 
-        for key, value in utils.layer_name_map.items():
+        for key, value in self.layer_name_map.items():
             if isinstance(layer, value):
                 quant_layer_name = 'Quantized' + key
                 break

python/paddle/fluid/contrib/slim/quantization/imperative/utils.py

@@ -16,63 +16,38 @@ import math
 import numpy as np
 
 import paddle
-from paddle.distributed import fleet
 import paddle.nn.quant.quant_layers as quant_layers
 
 from ..utils import _get_op_input_var_names, _get_op_output_var_names, _get_output_name_index, _get_input_name_index
 
 layer_name_map = {
-    'Conv2DTranspose':
-    paddle.nn.Conv2DTranspose,
-    'Conv2D':
-    paddle.nn.Conv2D,
-    'Linear':
-    paddle.nn.Linear,
-    'AdaptiveAvgPool2D':
-    paddle.nn.AdaptiveAvgPool2D,
-    'AdaptiveMaxPool2D':
-    paddle.nn.AdaptiveMaxPool2D,
-    'AvgPool2D':
-    paddle.nn.AvgPool2D,
-    'MaxPool2D':
-    paddle.nn.MaxPool2D,
-    'Hardswish':
-    paddle.nn.Hardswish,
-    'LeakyReLU':
-    paddle.nn.LeakyReLU,
-    'PReLU':
-    paddle.nn.PReLU,
-    'ReLU':
-    paddle.nn.ReLU,
-    'ReLU6':
-    paddle.nn.ReLU6,
-    'Sigmoid':
-    paddle.nn.Sigmoid,
-    'Softmax':
-    paddle.nn.Softmax,
-    'Swish':
-    paddle.nn.Swish,
-    'Tanh':
-    paddle.nn.Tanh,
-    'Hardswish':
-    paddle.nn.Hardswish,
-    'BatchNorm':
-    paddle.nn.BatchNorm,
-    'GroupNorm':
-    paddle.nn.GroupNorm,
-    'LayerNorm':
-    paddle.nn.LayerNorm,
-    'ColumnParallelLinear':
-    fleet.meta_parallel.parallel_layers.mp_layers.ColumnParallelLinear,
-    'RowParallelLinear':
-    fleet.meta_parallel.parallel_layers.mp_layers.RowParallelLinear
+    'Conv2DTranspose': paddle.nn.Conv2DTranspose,
+    'Conv2D': paddle.nn.Conv2D,
+    'Linear': paddle.nn.Linear,
+    'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
+    'AdaptiveMaxPool2D': paddle.nn.AdaptiveMaxPool2D,
+    'AvgPool2D': paddle.nn.AvgPool2D,
+    'MaxPool2D': paddle.nn.MaxPool2D,
+    'Hardswish': paddle.nn.Hardswish,
+    'LeakyReLU': paddle.nn.LeakyReLU,
+    'PReLU': paddle.nn.PReLU,
+    'ReLU': paddle.nn.ReLU,
+    'ReLU6': paddle.nn.ReLU6,
+    'Sigmoid': paddle.nn.Sigmoid,
+    'Softmax': paddle.nn.Softmax,
+    'Swish': paddle.nn.Swish,
+    'Tanh': paddle.nn.Tanh,
+    'Hardswish': paddle.nn.Hardswish,
+    'BatchNorm': paddle.nn.BatchNorm,
+    'GroupNorm': paddle.nn.GroupNorm,
+    'LayerNorm': paddle.nn.LayerNorm,
 }
 
 # Apply fake quant for the inputs of these layers
 fake_quant_input_layers = [
-    paddle.nn.Conv2D, paddle.nn.Linear, paddle.nn.Conv2DTranspose,
-    fleet.meta_parallel.RowParallelLinear,
-    fleet.meta_parallel.ColumnParallelLinear
+    paddle.nn.Conv2D,
+    paddle.nn.Linear,
+    paddle.nn.Conv2DTranspose,
 ]
 
 # Apply fake quant for the output of these layers

python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt

@@ -65,4 +65,5 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
   py_test_modules(test_process_mesh_v2 MODULES test_process_mesh_v2)
   py_test_modules(test_dist_attr_v2 MODULES test_dist_attr_v2)
   py_test_modules(test_lr_grad_clip MODULES test_lr_grad_clip)
+  py_test_modules(test_quantization MODULES test_quantization)
 endif()

python/paddle/fluid/tests/unittests/auto_parallel/test_quantization.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import sys
+import numpy as np
+import paddle
+
+import paddle.distributed.fleet as fleet
+import paddle.distributed.auto_parallel as auto
+
+from paddle.distributed.auto_parallel.engine import Engine
+from paddle.distributed.auto_parallel.utils import print_program_with_dist_attr
+
+sys.path.append("..")
+import auto_parallel_gpt_model as modeling
+from auto_parallel_gpt_model import GPTModel, GPTForPretraining, GPTPretrainingCriterion
+
+paddle.enable_static()
+
+
+class FakeDataset:
+
+    def __init__(self, num_samples, sequence_len, vocab_size):
+        self.num_samples = num_samples
+        self.sequence_len = sequence_len
+        self.vocab_size = vocab_size
+
+    def __getitem__(self, idx):
+        tokens = np.random.randint(self.vocab_size, size=self.sequence_len)
+        position_ids = np.arange(self.sequence_len)
+        attention_mask = np.tril(np.ones(self.sequence_len)).reshape(
+            (1, self.sequence_len, self.sequence_len)).astype(np.float32)
+        labels = np.random.randint(self.vocab_size, size=self.sequence_len)
+        loss_mask = np.ones(self.sequence_len).astype(np.float32)
+        return tokens, position_ids, attention_mask, labels, loss_mask
+
+    def __len__(self):
+        return self.num_samples
+
+
+def apply_pass():
+    dist_strategy = fleet.DistributedStrategy()
+    dist_strategy.semi_auto = True
+    dist_strategy.qat = True
+    dist_strategy.qat_configs = {
+        'channel_wise_abs_max': True,
+        'weight_bits': 8,
+        'activation_bits': 8,
+        'not_quant_pattern': ['skip_quant'],
+    }
+    return dist_strategy
+
+
+def create_data_holder(batch_size, sequence_len):
+    tokens = paddle.static.InputSpec(name="tokens",
+                                     shape=[batch_size, sequence_len],
+                                     dtype='int64')
+    position_ids = paddle.static.InputSpec(name="position_ids",
+                                           shape=[batch_size, sequence_len],
+                                           dtype='int64')
+    attention_mask = paddle.static.InputSpec(
+        name="attention_mask",
+        shape=[batch_size, 1, sequence_len, sequence_len],
+        dtype='float32')
+    labels = paddle.static.InputSpec(name="labels",
+                                     shape=[batch_size, sequence_len],
+                                     dtype='int64')
+    loss_mask = paddle.static.InputSpec(name="loss_mask",
+                                        shape=[batch_size, sequence_len],
+                                        dtype='float32')
+    return [tokens, position_ids, attention_mask], [labels, loss_mask]
+
+
+def get_gpt_model():
+    modeling.init_global()
+    modeling._global_parallel_strategy = "serial"
+    modeling._global_process_mesh = auto.ProcessMesh(mesh=[0])
+
+    gpt = GPTModel(vocab_size=1000,
+                   hidden_size=64,
+                   num_hidden_layers=2,
+                   num_attention_heads=8,
+                   intermediate_size=256,
+                   hidden_act="gelu",
+                   hidden_dropout_prob=0.0,
+                   attention_probs_dropout_prob=0.0,
+                   max_position_embeddings=1024,
+                   type_vocab_size=1,
+                   initializer_range=0.02,
+                   pad_token_id=0,
+                   eos_token_id=7,
+                   bos_token_id=0,
+                   eol_token_id=3)
+    model = GPTForPretraining(gpt,
+                              vocab_size=1000,
+                              hidden_size=64,
+                              initializer_range=0.02)
+    criterion = GPTPretrainingCriterion()
+    return model, criterion
+
+
+class TestQuantizationPass(unittest.TestCase):
+
+    def test_qat_pass(self):
+
+        batch_size = 8
+        batch_num = 10
+        sequence_len = 512
+        vocab_size = 1000
+
+        strategy = apply_pass()
+        model, loss = get_gpt_model()
+        opt = paddle.optimizer.AdamW(learning_rate=0.00001)
+        inputs_spec, labels_spec = create_data_holder(batch_size=batch_size,
+                                                      sequence_len=sequence_len)
+
+        engine = Engine(model, inputs_spec, labels_spec, strategy=strategy)
+        engine.prepare(optimizer=opt, loss=loss)
+
+        dataset = FakeDataset(batch_size * batch_num, sequence_len, vocab_size)
+        engine.fit(train_data=dataset, batch_size=batch_size)
+
+        self.check_program(engine.main_program)
+
+    def check_program(self, program):
+
+        quantizable_op_and_inputs = {'matmul_v2': ['X', 'Y']}
+        quantizable_grad_op_inputs = {'matmul_v2_grad': ['X', 'Y']}
+
+        quantized_ops = set()
+        for block in program.blocks:
+            for op in block.ops:
+                is_quntized = False
+                if op.type in quantizable_op_and_inputs:
+                    for arg_name in op.input_arg_names:
+                        if ".quantized" in arg_name:
+                            is_quntized = True
+
+                if not is_quntized:
+                    continue
+
+                # check forward
+                if op.type in quantizable_op_and_inputs:
+                    for arg_name in op.input_arg_names:
+                        assert arg_name.endswith('.quantized.dequantized')
+                        quantized_ops.add(arg_name)
+
+            for op in block.ops:
+                is_quntized = False
+                if op.type in quantizable_grad_op_inputs:
+                    for pname in quantizable_grad_op_inputs[op.type]:
+                        arg_name = op.input(pname)[0]
+                        if ".quantized" in arg_name:
+                            is_quntized = True
+
+                if not is_quntized:
+                    continue
+
+                # check backward
+                if op.type in quantizable_grad_op_inputs:
+                    for pname in quantizable_grad_op_inputs[op.type]:
+                        arg_name = op.input(pname)[0]
+                        assert arg_name.endswith('.quantized.dequantized')
+                        assert arg_name in quantized_ops
+
+
+if __name__ == "__main__":
+    unittest.main()