[shardformer] zero1+pp and the corresponding tests

colossalai/pipeline/schedule/one_f_one_b.py

@@ -128,11 +128,11 @@ def forward_step(self,
             Union[torch.Tensor, dict]: The intermediate output (dict) of the current stage. If it is the last stage, the output is the loss (Tensor).
         """
         micro_batch = self.load_micro_batch()
-
         # for the first stage, input_obj is None
         # for the non-first stage, input_obj is the output of the previous stage and it's must be a dict
         output_obj = model_forward(model, micro_batch, input_obj)
         if self.stage_manager.is_last_stage():
+
             loss = criterion(output_obj, micro_batch) / self.num_microbatches
             if accum_loss is not None:
                 accum_loss.add_(loss.detach())
@@ -158,7 +158,6 @@ def backward_step(self, optimizer: OptimizerWrapper, input_obj: Optional[dict],
 
         # Retain the grad on the input_obj.
         tree_map(retain_grad, input_obj)
-
         # Backward pass.
         if output_obj_grad is None:
             optimizer.backward(output_obj)

colossalai/zero/low_level/low_level_optim.py

@@ -316,7 +316,6 @@ def _add_to_bucket(self, param, group_id):
     def backward(self, loss, retain_graph=False):
         assert not(self._partition_grads and not self.require_grad_sync), \
             "ZeRO2(partition_grads) and gradient accumulation(no_sync) are not compatible"
-
         if self.mixed_precision_mixin is not None:
             loss = self.mixed_precision_mixin.pre_backward(loss)
 
@@ -333,6 +332,13 @@ def backward(self, loss, retain_graph=False):
 
         self.zero_grad()
 
+    def backward_by_grad(self, tensor, grad):
+        # in lower stage which grad is transfered by higher stage
+        # we need to pass the optim state down.
+        if self.mixed_precision_mixin is not None:
+            grad = self.mixed_precision_mixin.pre_backward_by_grad(tensor, grad)
+        torch.autograd.backward(tensor, grad)
+
     def zero_grad(self, set_to_none=True):
         """
         Set parameter gradients to zero. If set_to_none = True, gradient
@@ -358,7 +364,6 @@ def zero_grad(self, set_to_none=True):
 
     def step(self, closure=None):
         assert closure is None, 'closure is not supported by step()'
-
         if not self.require_grad_sync:
             return
 

tests/test_shardformer/test_model/test_shard_bert.py

@@ -107,6 +107,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 def run_bert_test(test_config):
 

tests/test_shardformer/test_model/test_shard_bloom.py

@@ -110,6 +110,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 def run_bloom_test(test_config):
 

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -128,6 +128,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 @clear_cache_before_run()
 def run_gpt2_test(test_config):

tests/test_shardformer/test_model/test_shard_llama.py

@@ -142,6 +142,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 def run_llama_test(test_config):
 

tests/test_shardformer/test_model/test_shard_opt.py

@@ -135,6 +135,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 def run_opt_test(test_config):
 

tests/test_shardformer/test_model/test_shard_t5.py

@@ -118,6 +118,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 2,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 @clear_cache_before_run()
 def run_t5_test(test_config):

tests/test_shardformer/test_model/test_shard_vit.py

@@ -45,7 +45,6 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
 
         if org_model.__class__.__name__ == 'ViTModel':
             check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
-
         check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
 
     # unwrap model
@@ -97,6 +96,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     torch.cuda.empty_cache()
 
 
+#TODO: num_microbatch size = 2 inf loss
 @parameterize('test_config', [{
     'tp_size': 2,
     'pp_size': 2,
@@ -132,6 +132,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'zero_stage': 2,
     'precision': 'fp16',
     'initial_scale': 1
+}, {
+    'tp_size': 1,
+    'pp_size': 2,
+    'num_microbatches': 4,
+    'enable_all_optimization': True,
+    'use_lazy_init': False,
+    'zero_stage': 1,
+    'precision': 'fp16',
+    'initial_scale': 1
 }])
 def run_vit_test(test_config):
 

tests/test_shardformer/test_model/test_shard_whisper.py

@@ -112,37 +112,44 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
 
     torch.cuda.empty_cache()
 
-
+#TODO fix WhisperForConditionalGeneration enable jit fused operato
 # TODO（jianghai) fix fp16
-#TODO fix WhisperForConditionalGeneration enable jit fused operator
-@parameterize('test_config', [{
-    'tp_size': 2,
-    'pp_size': 2,
-    'num_microbatches': 2,
-    'enable_all_optimization': True,
-    'use_lazy_init': True,
-    'precision': 'fp32',
-    'initial_scale': 1,
-}, {
-    'tp_size': 1,
-    'pp_size': 2,
-    'num_microbatches': 4,
-    'use_lazy_init': False,
-    'precision': 'fp32',
-    'initial_scale': 1,
-}, {
-    'tp_size': 4,
-    'pp_size': 1,
-    'enable_all_optimization': True,
-    'use_lazy_init': False,
-    'precision': 'fp32',
-}, {
-    'tp_size': 1,
-    'pp_size': 4,
-    'num_microbatches': 4,
-    'use_lazy_init': False,
-    'precision': 'fp32',
-}])
+@parameterize(
+    'test_config',
+    [
+        {
+            'tp_size': 2,
+            'pp_size': 2,
+            'num_microbatches': 2,
+            'enable_all_optimization': True,
+            'use_lazy_init': True,
+            'precision': 'fp32',
+            'initial_scale': 1,
+        },
+        {
+            'tp_size': 1,
+            'pp_size': 2,
+            'num_microbatches': 4,
+            'use_lazy_init': False,
+            'precision': 'fp32',
+            'initial_scale': 1,
+        },
+        {
+            'tp_size': 4,
+            'pp_size': 1,
+            'enable_all_optimization': True,
+            'use_lazy_init': False,
+            'precision': 'fp32',
+        },
+        {
+            'tp_size': 1,
+            'pp_size': 4,
+            'num_microbatches': 4,
+            'use_lazy_init': False,
+            'precision': 'fp32',
+        },
+    # whisper is not supported fp16 for now.
+    ])
 def run_whisper_test(test_config):
     sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')
     for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():