[autoparallel] adapt solver with self attention (#2037)

* [autoparallel] adapt solver with self attention

* polish code

colossalai/auto_parallel/tensor_shard/constants.py

@@ -26,7 +26,14 @@ ELEMENTWISE_METHOD_OP = [
     # TODO: contiguous maybe need some extra processes.
     torch.Tensor.contiguous
 ]
-RESHAPE_FUNC_OP = [torch.flatten, torch.reshape]
+RESHAPE_FUNC_OP = [
+    torch.flatten,
+    torch.reshape,
+    torch.transpose,
+    torch.split,
+    torch.permute,
+    operator.getitem,
+]
 RESHAPE_METHOD_OP = [
     torch.Tensor.view,
     torch.Tensor.unsqueeze,

colossalai/auto_parallel/tensor_shard/sharding_strategy.py

@@ -9,7 +9,14 @@ from torch.fx.node import Node
 from colossalai.tensor.shape_consistency import CommSpec
 from colossalai.tensor.sharding_spec import ShardingSpec
 
-from .constants import BCAST_FUNC_OP, ELEMENTWISE_FUNC_OP, ELEMENTWISE_MODULE_OP, RESHAPE_FUNC_OP
+from .constants import (
+    BCAST_FUNC_OP,
+    ELEMENTWISE_FUNC_OP,
+    ELEMENTWISE_METHOD_OP,
+    ELEMENTWISE_MODULE_OP,
+    RESHAPE_FUNC_OP,
+    RESHAPE_METHOD_OP,
+)
 
 __all__ = ['OperationDataType', 'OperationData', 'TrainCycleItem', 'MemoryCost', 'ShardingStrategy', 'StrategiesVector']
 
@@ -249,8 +256,15 @@ class StrategiesVector(list):
             # we could merge bcast op if the rhs is a scalar, because it will fall back to the element-wise case.
             if self.node.target in BCAST_FUNC_OP and len(self.predecessor_nodes) == 1:
                 merge_label = True
-            # we could merge reshape op, because the output sharding spec of reshape op is always fully replicated.
+            # we could merge reshape op, because their computation costs are negligible.
             if self.node.target in RESHAPE_FUNC_OP:
                 merge_label = True
 
+        if self.node.op == 'call_method':
+            # we could merge reshape op, because their computation costs are negligible.
+            method = getattr(self.node.args[0]._meta_data.__class__, self.node.target)
+            if method in RESHAPE_METHOD_OP:
+                merge_label = True
+            if method in ELEMENTWISE_METHOD_OP:
+                merge_label = True
         return merge_label

colossalai/auto_parallel/tensor_shard/solver/cost_graph.py

@@ -63,14 +63,40 @@ class CostGraph:
                             edge_cost[(j, i)] = resharding_cost_item.total
                 self.edge_costs[node_pair] = edge_cost
             # add parents and children attribute to node
-            parent_nodes = [node for node in strategies_vector.predecessor_nodes]
-            children_nodes = [node for node in strategies_vector.successor_nodes]
+            # parent_nodes = [node for node in strategies_vector.predecessor_nodes]
+            # children_nodes = [node for node in strategies_vector.successor_nodes]
+            parent_nodes = []
+            children_nodes = []
+
+            def _check_tensor_in_node(data):
+                """
+                This method is used to check whether the data has a tensor inside or not.
+                """
+                has_tensor_flag = False
+                if isinstance(data, torch.Tensor):
+                    return True
+                elif isinstance(data, (tuple, list)):
+                    for d in data:
+                        has_tensor_flag = has_tensor_flag or _check_tensor_in_node(d)
+                return has_tensor_flag
+
+            for node in strategies_vector.predecessor_nodes:
+                if _check_tensor_in_node(node._meta_data):
+                    parent_nodes.append(node)
+            for node in strategies_vector.successor_nodes:
+                if _check_tensor_in_node(node._meta_data):
+                    children_nodes.append(node)
+
             setattr(dst_node, 'parents', parent_nodes)
             setattr(dst_node, 'children', children_nodes)
 
             if self.simplify and strategies_vector.check_merge():
                 for followed_node in strategies_vector.predecessor_nodes:
-                    self.merge_pair.append((followed_node, dst_node))
+                    # we only merge node pairs which src node has a tensor element inside.
+                    # This is necessay because the node without a tensor element inside will not
+                    # be assigned any strategy.
+                    if _check_tensor_in_node(followed_node._meta_data):
+                        self.merge_pair.append((followed_node, dst_node))
 
     def get_edge_cost(self, src_node, dst_node):
         return self.edge_costs[(src_node, dst_node)]

colossalai/auto_parallel/tensor_shard/solver/solver.py

@@ -154,12 +154,16 @@ class Solver:
                 if self.forward_only:
                     origin_communication_cost = communication_cost_item.fwd
                     compute_cost = compute_cost_item.fwd
+                    # extract MemoryCost item from the memory TrainCycleItem
                     memory_cost = memory_cost_item.fwd
                 else:
                     origin_communication_cost = communication_cost_item.total
                     compute_cost = compute_cost_item.total
+                    # extract MemoryCost item from the memory TrainCycleItem
                     memory_cost = memory_cost_item.total
 
+                # extract the memory cost in float from MemoryCost item and sum them up
+                memory_cost = memory_cost.parameter + memory_cost.activation + memory_cost.buffer
                 compute_costs.append(compute_cost)
                 # node in extra_node_costs means it has some extra communication
                 # cost from node merging, so we need to add those extra communication
@@ -366,6 +370,8 @@ class Solver:
             for liveness_stage in liveness_set:
                 mem = 0
                 for live_variable in liveness_stage.unique_live_vars:
+                    if live_variable.node not in self.node_index_dict:
+                        continue
                     node_index = self.node_index_dict[live_variable.node]
                     mem += lpSum(s[node_index][j] * m[node_index][j] for j in range(len(s[node_index])))
                 prob += mem <= memory_budget

colossalai/auto_parallel/tensor_shard/utils/reshape.py

@@ -53,17 +53,38 @@ def detect_reshape_mapping(origin_shape: torch.Size, tgt_shape: torch.Size) -> D
     while origin_index != len(origin_shape) or tgt_index != len(tgt_shape):
         if original_dimension_size == tgt_dimension_size:
             reshape_mapping_dict[tuple(origin_dims)] = tuple(tgt_dims)
-            origin_index += 1
-            tgt_index += 1
+            # if the origin_dims has no element, it means the original tensor has been fully matched.
+            # Therefore, we do not have to increase the origin_index for that case.
+            if len(origin_dims) > 0:
+                origin_index += 1
+            # if the tgt_dims has no element, it means the original tensor has been fully matched.
+            # Therefore, we do not have to increase the tgt_index for that case.
+            if len(tgt_dims) > 0:
+                tgt_index += 1
             # the last step of loop should always end with condition
             # so we need to manually skip the preparation for next step
             # in the last step.
-            if origin_index == len(origin_shape):
+            if origin_index == len(origin_shape) and tgt_index == len(tgt_shape):
                 continue
-            original_dimension_size = origin_shape[origin_index]
-            tgt_dimension_size = tgt_shape[tgt_index]
-            origin_dims = [origin_len - origin_index - 1]
-            tgt_dims = [tgt_len - tgt_index - 1]
+
+            # If origin_index equals to origin_len, we just need to set the original_dimension_size
+            # to 1 to match the remaining '1's in the target tensor shape.
+            if origin_index == len(origin_shape):
+                original_dimension_size = 1
+                origin_dims = []
+            else:
+                original_dimension_size = origin_shape[origin_index]
+                origin_dims = [origin_len - origin_index - 1]
+
+            # If tgt_index equals to tgt_len, we just need to set the tgt_dimension_size
+            # to 1 to match the remaining '1's in the original tensor shape.
+            if tgt_index == len(tgt_shape):
+                tgt_dimension_size = 1
+                tgt_dims = []
+            else:
+                tgt_dimension_size = tgt_shape[tgt_index]
+                tgt_dims = [tgt_len - tgt_index - 1]
+
             previous_label = PreviousStatus.RESET
 
         elif original_dimension_size > tgt_dimension_size:
@@ -141,6 +162,9 @@ def check_keep_sharding_status(input_dim_partition_dict: Dict[int, List[int]],
     """
     sharded_dims = list(input_dim_partition_dict.keys())
     for input_dims in reshape_mapping_dict.keys():
+        # if input_dims has no element, we could just skip this iteration.
+        if len(input_dims) == 0:
+            continue
         min_element = min(input_dims)
         for dim in input_dims:
             if dim in sharded_dims and dim is not min_element:

tests/test_auto_parallel/test_tensor_shard/test_solver_self_attention_block.py

+from typing import Optional, Tuple, Union
+
+import torch
+# from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
+import torch.nn as nn
+import transformers
+from torch.fx import GraphModule
+from torchvision.models import resnet50
+from transformers.pytorch_utils import Conv1D
+
+from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP
+from colossalai.auto_parallel.tensor_shard.solver import (
+    CostGraph,
+    GraphAnalyser,
+    Solver,
+    SolverOptions,
+    StrategiesConstructor,
+)
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+
+BATCH_SIZE = 1
+SEQ_LENGTH = 32
+HIDDEN_DIM = 768
+
+
+# The reason Why we don't import GPT2Attention from transformers directly is that:
+# 1. The tracer will not work correctly when we feed meta_args and concrete_args at same time,
+# so we have to build the customized GPT2Attention class and remove the conditional branch manually.
+# 2. The order of split and view op has been changed in the customized GPT2Attention class, the new
+# order is same as megatron-lm gpt model.
+class GPT2Attention(nn.Module):
+
+    def __init__(self, config, is_cross_attention=False, layer_idx=None):
+        super().__init__()
+
+        max_positions = config.max_position_embeddings
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones((max_positions, max_positions),
+                                  dtype=torch.uint8)).view(1, 1, max_positions, max_positions),
+        )
+        self.register_buffer("masked_bias", torch.tensor(-1e4))
+
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        self.split_size = self.embed_dim
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads}).")
+
+        self.scale_attn_weights = config.scale_attn_weights
+        self.is_cross_attention = is_cross_attention
+
+        # Layer-wise attention scaling, reordering, and upcasting
+        self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
+        self.layer_idx = layer_idx
+        self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
+
+        if self.is_cross_attention:
+            self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
+            self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
+        else:
+            self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
+        self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
+
+        self.attn_dropout = nn.Dropout(config.attn_pdrop)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+        self.pruned_heads = set()
+
+    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
+        attn_weights = torch.matmul(query, key.transpose(-1, -2))
+
+        if self.scale_attn_weights:
+            attn_weights = attn_weights / (value.size(-1)**0.5)
+
+        # Layer-wise attention scaling
+        if self.scale_attn_by_inverse_layer_idx:
+            attn_weights = attn_weights / float(self.layer_idx + 1)
+
+        if not self.is_cross_attention:
+            # if only "normal" attention layer implements causal mask
+            query_length, key_length = query.size(-2), key.size(-2)
+            causal_mask = self.bias[:, :, key_length - query_length:key_length, :key_length].to(torch.bool)
+            attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
+        attn_weights = attn_weights.type(value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        attn_output = torch.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    def _split_heads(self, tensor, num_heads, attn_head_size):
+        """
+        Splits hidden_size dim into attn_head_size and num_heads
+        """
+        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
+        tensor = tensor.view(new_shape)
+        return tensor.permute(0, 2, 1, 3)    # (batch, head, seq_length, head_features)
+
+    def _merge_heads(self, tensor, num_heads, attn_head_size):
+        """
+        Merges attn_head_size dim and num_attn_heads dim into hidden_size
+        """
+        tensor = tensor.permute(0, 2, 1, 3).contiguous()
+        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
+        return tensor.view(new_shape)
+
+    def forward(
+        self,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
+        if encoder_hidden_states is not None:
+            if not hasattr(self, "q_attn"):
+                raise ValueError(
+                    "If class is used as cross attention, the weights `q_attn` have to be defined. "
+                    "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`.")
+
+            query = self.q_attn(hidden_states)
+            key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
+            attention_mask = encoder_attention_mask
+        else:
+            # query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
+            qkv = self.c_attn(hidden_states)
+
+        # query = self._split_heads(query, self.num_heads, self.head_dim)
+        # key = self._split_heads(key, self.num_heads, self.head_dim)
+        # value = self._split_heads(value, self.num_heads, self.head_dim)
+        query, key, value = self._split_heads(qkv, self.num_heads, 3 * self.head_dim).split(self.head_dim, dim=3)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            key = torch.cat((past_key, key), dim=-2)
+            value = torch.cat((past_value, value), dim=-2)
+
+        present = (key, value)
+
+        if self.reorder_and_upcast_attn:
+            attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
+        else:
+            attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+
+        attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
+        attn_output = self.c_proj(attn_output)
+        attn_output = self.resid_dropout(attn_output)
+
+        outputs = (attn_output, present)
+        outputs += (attn_weights,)
+
+        return outputs    # a, present, (attentions)
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+def test_self_attention_block():
+    config = transformers.GPT2Config(n_position=64, n_layer=4, n_head=16, n_embd=HIDDEN_DIM)
+    model_cls = GPT2Attention
+    model = model_cls(config=config)
+    # output = model(torch.rand(BATCH_SIZE, SEQ_LENGTH, HIDDEN_DIM),  attention_mask=torch.rand(1, SEQ_LENGTH))
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    input_sample = {
+        'hidden_states': torch.rand(BATCH_SIZE, SEQ_LENGTH, HIDDEN_DIM).to('meta'),
+        'attention_mask': torch.rand(1, SEQ_LENGTH).to('meta'),
+    }
+
+    graph = tracer.trace(root=model, meta_args=input_sample)
+
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    print(gm.graph)
+    gm.recompile()
+    graph_analyser = GraphAnalyser(gm)
+    liveness_list = graph_analyser.liveness_analysis()
+    solver_options = SolverOptions()
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
+    strategies_constructor.build_strategies_and_cost()
+
+    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
+    cost_graph.simplify_graph()
+    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser, memory_budget=-1)
+    ret = solver.call_solver_serialized_args()
+    strategies_list = solver.last_s_val
+    nodes = [strategies_vector.node for strategies_vector in strategies_constructor.leaf_strategies]
+
+    computation_cost = 0
+    communication_cost = 0
+    memory_cost = 0
+    for index, node in enumerate(nodes):
+        print(node.name, node.strategies_vector[strategies_list[index]].name)
+        computation_cost += node.strategies_vector[strategies_list[index]].compute_cost.total
+        communication_cost += node.strategies_vector[strategies_list[index]].communication_cost.total
+        node_memory_cost = node.strategies_vector[strategies_list[index]].memory_cost.total
+        if isinstance(node_memory_cost, tuple):
+            node_memory_cost = node_memory_cost[0]
+        memory_cost += node_memory_cost.activation + node_memory_cost.parameter
+
+    print(f'computation cost is {computation_cost}')
+    print(f'communication cost is {communication_cost}')
+    print(f'memory cost is {memory_cost}')
+
+
+if __name__ == '__main__':
+    test_self_attention_block()