add tutel.examples.helloworld_demo based on custom experts (#227)

README.md

@@ -84,7 +84,7 @@ Tutel MoE: An Optimized Mixture-of-Experts Implementation.
         $ python3 -m tutel.examples.helloworld_ddp --batch_size=16           # Test Tutel-optimized MoE + Pytorch DDP distribution (requires: Pytorch >= 1.8.0)
         $ python3 -m tutel.examples.helloworld_ddp_tutel --batch_size=16     # Test Tutel-optimized MoE + Tutel DDP distribution (ZeRO on optimizors)
         $ python3 -m tutel.examples.helloworld_amp --batch_size=16           # Test Tutel-optimized MoE with AMP data type + manual distribution
-        $ python3 -m tutel.examples.helloworld_deepspeed --batch_size=16     # Test Deepspeed (0.5.6) MoE + manual distribution
+        $ python3 -m tutel.examples.helloworld_demo --batch_size=16          # Test Tutel-optimized MoE + custom defined expert layer
         $ python3 -m tutel.examples.helloworld_from_scratch                  # Test Custom MoE implementation from scratch
         $ python3 -m tutel.examples.moe_mnist                                # Test MoE layer in end-to-end MNIST dataset
         $ python3 -m tutel.examples.moe_cifar10                              # Test MoE layer in end-to-end CIFAR10 dataset

tutel/examples/helloworld_demo.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import os
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
+from torch import nn
+import argparse
+
+from tutel import system
+from tutel import moe as tutel_moe
+from tutel import net
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument('--local_rank', type=int, default=-1)
+parser.add_argument('--batch_size', type=int, default=16)
+parser.add_argument('--num_tokens', type=int, default=512)
+parser.add_argument('--model_dim', type=int, default=2048)
+parser.add_argument('--num_local_experts', type=int, default=2)
+parser.add_argument('--dtype', type=str, default='float32')
+parser.add_argument('--fp32_gate', default=False, action='store_true')
+parser.add_argument('--top', type=int, default=2)
+parser.add_argument('--l_aux_wt', type=float, default=0.0)
+parser.add_argument('--a2a_ffn_overlap_degree', type=int, default=1)
+parser.add_argument('--allreduce_degree', type=int, default=1)
+parser.add_argument('--num_steps', type=int, default=100)
+parser.add_argument('--checkpoint_path', type=str, default='')
+parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu')
+parser.add_argument('--use_2dh', default=False, action='store_true')
+parser.add_argument('--eval', default=False, action='store_true')
+parser.add_argument('--capacity_factor', type=float, default=1.0)  # 0.0 for dMoE (dropless-MoE), negative for no-padded capacity.
+
+args = parser.parse_args()
+
+parallel_env = system.init_data_model_parallel(backend='nccl' if args.device == 'cuda' else 'gloo')
+dist_rank, dist_world_size, dist_print = parallel_env.global_rank, parallel_env.global_size, parallel_env.dist_print
+args.local_rank = parallel_env.local_device.index
+
+batch_size = args.batch_size
+num_tokens = args.num_tokens
+model_dim = args.model_dim
+num_local_experts = args.num_local_experts
+top_value = args.top
+a2a_ffn_overlap_degree = args.a2a_ffn_overlap_degree
+device = parallel_env.local_device
+
+if args.dtype == 'float32':
+    torch.set_default_dtype(torch.float32)
+elif args.dtype == 'float64':
+    torch.set_default_dtype(torch.float64)
+elif args.dtype == 'float16':
+    torch.set_default_dtype(torch.float16)
+elif args.dtype == 'bfloat16':
+    torch.set_default_dtype(torch.bfloat16)
+else:
+    raise Exception('Unrecognized data type specified: %s' % args.dtype)
+
+
+class CustomExpertDemo(torch.nn.Module):
+    def __init__(self, model_dim, local_experts, sharded_count, my_config):
+        super().__init__()
+        self.W = torch.nn.Parameter(torch.empty(local_experts, model_dim, model_dim))
+        self.my_activation = torch.nn.functional.relu if my_config == 'relu' else None
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        pass
+
+    def forward(self, x, ctx):
+        if ctx.sharded_count > 1:
+          raise Exception("`sharded_count > 1` is not implemented within this expert, Model parallel is disabled.")
+        y = torch.matmul(x, self.W)
+        if self.my_activation is not None:
+          y = self.my_activation(y)
+        return y
+
+
+class ExampleModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self._moe_layer = tutel_moe.moe_layer(
+            gate_type = {'type': 'top', 'k': top_value, 'fp32_gate': args.fp32_gate, 'capacity_factor': args.capacity_factor},
+            experts = {'type': 'custom', 'module': CustomExpertDemo, 'count_per_node': num_local_experts, 'my_config': None},
+            model_dim = model_dim,
+            scan_expert_func = lambda name, param: setattr(param, 'skip_allreduce', True),
+            seeds = (1, dist_rank + 1, 1),
+            a2a_ffn_overlap_degree = a2a_ffn_overlap_degree,
+            use_2dh=args.use_2dh,
+        )
+
+        # Summary of different parameter types: gate, local_experts
+        local_count = sum([torch.numel(param) for name, param in self._moe_layer.get_parameter_iterator(param_type='local_experts')])
+        shared_count = sum([torch.numel(param) for name, param in self._moe_layer.get_parameter_iterator(param_type='gate')])
+        dist_print('[Statistics] param count for MoE local_experts = %s, param count for MoE gate = %s.\n' % (local_count, shared_count))
+
+    def forward(self, input):
+        result = self._moe_layer(input)
+        result = F.log_softmax(torch.sum(result, dim=2), dim=1)
+        return result
+
+model = ExampleModel().to(device)
+dist_print(model)
+
+if args.checkpoint_path:
+    checkpoint_path = system.apply_rank_size_from_pattern(args.checkpoint_path, rank=parallel_env.global_rank, size=parallel_env.global_size)
+    if os.path.exists(checkpoint_path):
+        model.load_state_dict(torch.load(checkpoint_path))
+    else:
+        print('Checkpoint not loaded: file `%s` is not found. Will train the model from start.' % checkpoint_path)
+
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-5)
+
+torch.manual_seed(0)
+x = torch.tensor(torch.randn([batch_size, num_tokens, model_dim], dtype=torch.float32, device='cpu').detach().numpy(), dtype=torch.get_default_dtype(), requires_grad=False, device=device)
+y = torch.LongTensor(batch_size).random_(1).to(device)
+
+tuples = (dist_world_size, args.dtype, model_dim, batch_size * num_tokens, num_local_experts, top_value, a2a_ffn_overlap_degree, device)
+dist_print('[Benchmark] world_size = %s, dtype = %s, model_dim = %s, samples = %s, num_local_experts = %s, topK = %s, a2a_ffn_overlap_degree = %s, device = `%s`' % tuples)
+
+average_time, num_steps = 0, args.num_steps
+
+if args.allreduce_degree == -1:
+    params_for_all_reduce = []
+else:
+    params_for_all_reduce = [p for p in model.parameters() if not hasattr(p, 'skip_allreduce') and getattr(p, 'requires_grad', False)]
+
+for i in range(num_steps):
+    t_start = system.record_time()
+
+    if not args.eval:
+        optimizer.zero_grad()
+        output = model(x)
+        loss = F.nll_loss(output, y)
+        if args.l_aux_wt:
+            loss += args.l_aux_wt * model._moe_layer.l_aux
+        loss.backward()
+        if dist_world_size > 1:
+            for p in params_for_all_reduce:
+                p.grad /= dist_world_size
+                p.grad = net.simple_all_reduce(p.grad)
+        optimizer.step()
+    else:
+        with torch.no_grad():
+            output = model(x)
+            loss = F.nll_loss(output, y)
+
+    t_stop = system.record_time()
+
+    num_global_experts = tutel_moe.moe_layer.global_expert_count(num_local_experts, group=system.get_local_session().model_group)
+    mm_ceof, cap_ceof = 1 if args.eval else 3, min(args.top, num_global_experts)
+    dist_print('STEP-%s: loss = %.5f, step_time = %.6f sec.' % (i, float(loss.data), t_stop - t_start))
+
+    if i + 10 >= num_steps:
+        average_time += t_stop - t_start
+
+average_time /= 10
+dist_print('\n[Summary] Average synchronized step_time = %s sec.' % average_time)
+
+if args.checkpoint_path:
+    torch.save(model.state_dict(), checkpoint_path)

tutel/impls/moe_layer.py

@@ -157,7 +157,11 @@ def __init__(
 
         experts_type = experts.pop('type')
         if experts_type == 'custom':
-            self.experts = cast(ModuleList, experts['module'])
+            expert_module = experts.pop('module')
+            experts['model_dim'] = self.model_dim
+            experts['local_experts'] = self.num_local_experts
+            experts['sharded_count'] = self.sharded_count
+            self.experts = cast(ModuleList, expert_module(**experts))
         else:
             assert re.match(r'[a-zA-Z0-9\_]+', experts_type), "Expert type must only include digits, letters and underline characters."
             try: