[Distributed] [CustomDevices] Adapt SP on lora && polish MC2 APIs (#8303

)

* [Distributed] adapt sequence parallel on LoRA (#8235)

* [Distributed] [CustomDevices] adapt lora sp && polish MC2 APIs

paddlenlp/peft/lora/lora_layers.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import math
-import os
 from typing import List, Optional
 
 import paddle
@@ -25,13 +24,25 @@
     RowParallelLinear,
 )
 
-from .lora_quick_layers import quick_lora
+try:
+    from paddle.distributed.fleet.utils.sequence_parallel_utils import (
+        AllGatherOp,
+        ColumnSequenceParallelLinear,
+        ReduceScatterOp,
+        RowSequenceParallelLinear,
+        mark_as_sequence_parallel_parameter,
+    )
+except:
+    pass
+
+from paddlenlp.transformers.mc2_parallel_linear import (
+    MC2ColumnParallelCoreLinear,
+    MC2ColumnSeqParallelCoreLinear,
+    MC2RowParallelCoreLinear,
+    MC2RowSeqParallelCoreLinear,
+)
 
-if "npu" in paddle.device.get_all_custom_device_type():
-    from .mc2_lora_npu import MC2LoRaColumnParallelLinear, MC2LoRaRowParallelLinear
-else:
-    MC2LoRaRowParallelLinear = None
-    MC2LoRaColumnParallelLinear = None
+from .lora_quick_layers import quick_lora
 
 
 class LoRALinear(nn.Linear):
@@ -266,16 +277,16 @@ def forward(self, x: paddle.Tensor):
             )
         else:
             # x @ W : [bz, in_f / ws] ===> [bz, out_f]
-            if "npu" in paddle.device.get_all_custom_device_type() and int(os.getenv("MC2", "0")):
-                output = MC2LoRaRowParallelLinear.apply(input_mp, self.weight, self.model_parallel_group)
-            else:
+            if MC2RowParallelCoreLinear is None:
                 result_mp = F.linear(x=input_mp, weight=self.weight, name=self.name)
                 output = mp_ops._mp_allreduce(
                     result_mp,
                     group=self.model_parallel_group,
                     use_calc_stream=True,
                     use_model_parallel=True,
                 )
+            else:
+                output = MC2RowParallelCoreLinear.apply(input_mp, self.weight, self.model_parallel_group)
 
             if not self.merged:
                 # x @ A: [bz, in_f/ ws] ===> [bz, r]
@@ -298,6 +309,120 @@ def extra_repr(self):
         return f"in_features={self.weight.shape[0]}, out_features={self.weight.shape[1]}, rank={self.r}{name}"
 
 
+class RowSequenceParallelLoRALinear(RowSequenceParallelLinear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        rslora: bool = False,
+        lora_plus_scale: float = 1.0,
+        merge_weights: bool = True,
+        use_quick_lora: bool = False,
+        **kwargs
+    ):
+        RowSequenceParallelLinear.__init__(self, in_features, out_features, **kwargs)
+        if not isinstance(r, int) or r <= 0:
+            raise ValueError("Lora rank r should be a positive integer")
+        self.r = r
+        self.lora_alpha = lora_alpha
+        # Optional dropout
+        if lora_dropout > 0.0:
+            self.lora_dropout = nn.Dropout(p=lora_dropout)
+        else:
+            self.lora_dropout = lambda x: x
+        # Mark the weight as unmerged
+        self.merged = False
+        self.merge_weights = merge_weights
+
+        # compatible
+        self.name = self._name
+
+        # Actual trainable parameters
+        self.lora_A = self.create_parameter(
+            shape=[self.input_size_per_partition, r],
+            dtype=self._dtype,
+            is_bias=False,
+            attr=paddle.ParamAttr(
+                initializer=nn.initializer.KaimingUniform(negative_slope=math.sqrt(5), nonlinearity="leaky_relu")
+            ),
+        )
+        self.lora_B = self.create_parameter(
+            shape=[r, self.out_features],
+            dtype=self._dtype,
+            is_bias=False,
+            attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=0.0),
+                learning_rate=lora_plus_scale,
+            ),
+        )
+
+        self.lora_A.is_distributed = True
+        self.lora_A.split_axis = 0
+        self.lora_B.is_distributed = False
+        mark_as_sequence_parallel_parameter(self.lora_B)
+        if not rslora:
+            self.scaling = self.lora_alpha / self.r
+        else:
+            self.scaling = self.lora_alpha / math.sqrt(self.r)
+
+        # Freezing the pre-trained weight matrix
+        self.weight.stop_gradient = True
+        self._use_quick_lora = use_quick_lora and lora_dropout == 0.0
+
+    @property
+    def use_quick_lora(self):
+        # TODO(@gexiao): support qlora
+        return False  # self._use_quick_lora and self.training and not self.merged
+
+    def train(self):
+        super().train()
+        if self.merge_weights and self.merged:
+            # Make sure that the weights are not merged
+            new_weight = self.weight - self.lora_A @ self.lora_B * self.scaling
+            self.weight.set_value(new_weight)
+            self.merged = False
+
+    def eval(self):
+        super().eval()
+        if self.merge_weights and not self.merged:
+            # Merge the weights and mark it
+            new_weight = self.weight + self.lora_A @ self.lora_B * self.scaling
+            self.weight.set_value(new_weight)
+            self.merged = True
+
+    def forward(self, x: paddle.Tensor):
+        if not self.input_is_parallel:
+            input_mp = mp_ops._c_split(x, group=self.model_parallel_group)
+        else:
+            input_mp = x
+
+        if MC2RowSeqParallelCoreLinear is None:
+            output_parallel = self.linear(input_mp, self.weight, name=self._name)
+            output_ = ReduceScatterOp.apply(output_parallel)
+            result_mp = output_ + self.bias if self.bias is not None else output_
+        else:
+            output_ = MC2RowSeqParallelCoreLinear.apply(input_mp, self.weight, self.model_parallel_group)
+            result_mp = output_ + self.bias if self.bias is not None else output_
+
+        if not self.merged:
+            input_mp = self.lora_dropout(input_mp)
+            if MC2RowSeqParallelCoreLinear is None:
+                input_mp = input_mp @ self.lora_A
+                input_mp = ReduceScatterOp.apply(input_mp)
+            else:
+                input_mp = MC2RowSeqParallelCoreLinear.apply(input_mp, self.lora_A, self.model_parallel_group)
+            delta_mp = (input_mp @ self.lora_B) * self.scaling
+            result_mp += delta_mp
+        return result_mp
+
+    def extra_repr(self):
+        name = f", name={self.name}" if self.name else ""
+        return f"in_features={self.weight.shape[0]}, out_features={self.weight.shape[1]}, rank={self.r}{name}"
+
+
 class ColumnParallelLoRALinear(ColumnParallelLinear):
     def __init__(
         self,
@@ -400,21 +525,21 @@ def forward(self, input: paddle.Tensor):
                 world_size=self.world_size,
             )
         else:
-            if "npu" in paddle.device.get_all_custom_device_type() and int(os.getenv("MC2", "0")):
-                res_mp = MC2LoRaColumnParallelLinear.apply(input, self.weight, self.model_parallel_group)
-                result_mp = res_mp + self.bias
-            else:
+            if MC2ColumnParallelCoreLinear is None:
                 input_mp = mp_ops._c_identity(input, group=self.model_parallel_group)
                 result_mp = F.linear(x=input_mp, weight=self.weight, bias=self.bias, name=self.name)
+            else:
+                res_mp = MC2ColumnParallelCoreLinear.apply(input, self.weight, self.model_parallel_group)
+                result_mp = res_mp + self.bias
 
             if not self.merged:
                 input_a = self.lora_dropout(input) @ self.lora_A
-                if "npu" in paddle.device.get_all_custom_device_type() and int(os.getenv("MC2", "0")):
-                    tmp = MC2LoRaColumnParallelLinear.apply(input_a, self.lora_B, self.model_parallel_group)
-                    delta_mp = tmp * self.scaling
-                else:
+                if MC2ColumnParallelCoreLinear is None:
                     input_a_mp = mp_ops._c_identity(input_a, group=self.model_parallel_group)
                     delta_mp = (input_a_mp @ self.lora_B) * self.scaling
+                else:
+                    tmp = MC2ColumnParallelCoreLinear.apply(input_a, self.lora_B, self.model_parallel_group)
+                    delta_mp = tmp * self.scaling
                 result_mp += delta_mp
 
         if self.gather_output and self.is_mp:
@@ -428,6 +553,123 @@ def extra_repr(self):
         return f"in_features={self.weight.shape[0]}, out_features={self.weight.shape[1]}, rank={self.r}{name}"
 
 
+class ColumnSequenceParallelLoRALinear(ColumnSequenceParallelLinear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        rslora: bool = False,
+        lora_plus_scale: float = 1.0,
+        merge_weights: bool = True,
+        lora_A_weight_attr: Optional[paddle.ParamAttr] = None,
+        use_quick_lora: bool = False,
+        **kwargs
+    ):
+        ColumnSequenceParallelLinear.__init__(self, in_features, out_features, **kwargs)
+        if not isinstance(r, int) or r <= 0:
+            raise ValueError("Lora rank r should be a positive integer")
+        self.r = r
+        self.lora_alpha = lora_alpha
+        # Optional dropout
+        if lora_dropout > 0.0:
+            self.lora_dropout = nn.Dropout(p=lora_dropout)
+        else:
+            self.lora_dropout = lambda x: x
+        # Mark the weight as unmerged
+        self.merged = False
+        self.merge_weights = merge_weights
+
+        # compatible
+        self.name = self._name
+
+        # Actual trainable parameters
+        self.lora_A = self.create_parameter(
+            shape=[in_features, r],
+            dtype=self._dtype,
+            is_bias=False,
+            attr=lora_A_weight_attr,
+        )
+        self.lora_A.is_distributed = False
+        mark_as_sequence_parallel_parameter(self.lora_A)
+
+        self.lora_B = self.create_parameter(
+            shape=[r, self.output_size_per_partition],
+            dtype=self._dtype,
+            is_bias=False,
+            attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=0.0),
+                learning_rate=lora_plus_scale,
+            ),
+        )
+
+        self.lora_B.is_distributed = True
+        self.lora_B.split_axis = 1
+        if not rslora:
+            self.scaling = self.lora_alpha / self.r
+        else:
+            self.scaling = self.lora_alpha / math.sqrt(self.r)
+
+        # Freezing the pre-trained weight matrix
+        self.weight.stop_gradient = True
+        self._use_quick_lora = use_quick_lora and lora_dropout == 0.0
+
+    @property
+    def use_quick_lora(self):
+        # TODO(@gexiao): support qlora
+        return False  # self._use_quick_lora and self.training and not self.merged
+
+    def train(self):
+        super().train()
+        if self.merge_weights and self.merged:
+            # Make sure that the weights are not merged
+            new_weight = self.weight - self.lora_A @ self.lora_B * self.scaling
+            self.weight.set_value(new_weight)
+            self.merged = False
+
+    def eval(self):
+        super().eval()
+        if self.merge_weights and not self.merged:
+            # Merge the weights and mark it
+            new_weight = self.weight + self.lora_A @ self.lora_B * self.scaling
+            self.weight.set_value(new_weight)
+            self.merged = True
+
+    def forward(self, x: paddle.Tensor):
+        if MC2ColumnSeqParallelCoreLinear is None:
+            if self.is_mp:
+                input_parallel = AllGatherOp.apply(x)
+            else:
+                input_parallel = x
+            result_mp = self.linear(input_parallel, self.weight, self.bias, name=self._name)
+        else:
+            result_mp = MC2ColumnSeqParallelCoreLinear.apply(x, self.weight, self.model_parallel_group)
+            if self.bias is not None:
+                result_mp += self.bias
+
+        if not self.merged:
+            input_a = self.lora_dropout(x) @ self.lora_A
+            if MC2ColumnSeqParallelCoreLinear is None:
+                input_a = AllGatherOp.apply(input_a)
+                delta_mp = (input_a @ self.lora_B) * self.scaling
+            else:
+                input_a = MC2ColumnSeqParallelCoreLinear.apply(input_a, self.lora_B, self.model_parallel_group)
+                delta_mp = input_a * self.scaling
+            result_mp += delta_mp
+
+        if self.gather_output and self.is_mp:
+            result = mp_ops._c_concat(result_mp, group=self.model_parallel_group)
+        else:
+            result = result_mp
+        return result
+
+    def extra_repr(self):
+        name = f", name={self.name}" if self.name else ""
+        return f"in_features={self.weight.shape[0]}, out_features={self.weight.shape[1]}, rank={self.r}{name}"
+
+
 class LoRAMergedLinear(nn.Linear):
     # LoRA implemented in a dense layer  with merged linear weights for q, k, v
     def __init__(