Merge branch 'master' into loadams/hpu-uts

deepspeed/checkpoint/universal_checkpoint.py

@@ -4,22 +4,26 @@
 # DeepSpeed Team
 
 import os
+import re
 import torch
 import types
 from .constants import (FP32_WEIGHT_KEY, PARAM, VOCAB_TENSOR, CAT_DIM, PARAM_N_SUB_PARAMS)
 
 
 def load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):
     hp_mapping = self._hp_mapping
-    optim_state_keys = hp_mapping.get_optim_state_keys()
-    hp_keys = [FP32_WEIGHT_KEY] + optim_state_keys
-    #print(f'{hp_keys=}')
-    checkpoint_files = {key: os.path.join(folder, f"{key}.pt") for key in hp_keys}
-    for file in checkpoint_files.values():
-        assert os.path.isfile(file), f'{file} is not a valid file'
+    hp_mapping.optim_fragment = {}
+
+    hp_keys = []
+    for file in os.listdir(folder):
+        # We expect files named something like "exp_avg.pt", "exp_avg_sq.pt", "fp32.pt"
+        pattern = r'(.+).pt'
+        match = re.search(pattern, file)
+        if match:
+            hp_keys.append(match.group(1))
 
     for key in hp_keys:
-        ckpt_file = checkpoint_files[key]
+        ckpt_file = os.path.join(folder, f"{key}.pt")
         ckpt_dict = torch.load(ckpt_file)
         full_hp_param = ckpt_dict[PARAM]
 
@@ -62,7 +66,6 @@ def load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):
 
         assert full_param_numel == tp_world_size * tp_slice_numel, \
             f'Loading {ckpt_file} full param numel {full_param_numel} != tensor slice numel {tp_slice_numel} * tp_world_size {tp_world_size}'
-        dst_tensor = hp_mapping.hp_fragment if key == FP32_WEIGHT_KEY else hp_mapping.get_optim_state_fragment(key)
 
         #        print(f"{full_hp_param.shape=} {full_param_numel=} {folder=}")
         #        print(f"{dst_tensor.shape=} {dst_tensor.numel()=}{folder=}")
@@ -84,13 +87,21 @@ def load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):
 
         lp_frag_address = hp_mapping.lp_fragment_address
         tp_hp_fragment = tp_hp_slice.narrow(0, lp_frag_address.start, lp_frag_address.numel)
-        assert dst_tensor.numel() == lp_frag_address.numel, \
-            f'Load checkpoint {key} dst_tensor numel {dst_tensor.numel()} != src numel {lp_frag_address.numel}'
 
         #        print(f"{key} SHAPE: {tp_hp_slice.shape=}")
         #        print(f"{key} SHAPE: {dst_tensor.shape=}")
         #        print(f"{key} SHAPE: {tp_hp_fragment.shape=}")
-        dst_tensor.data.copy_(tp_hp_fragment.data)
+
+        if key == FP32_WEIGHT_KEY:
+            dst_tensor = hp_mapping.get_hp_fragment()
+            assert dst_tensor.numel() == lp_frag_address.numel, \
+                f'Load checkpoint {key} dst numel {dst_tensor.numel()} != src numel {lp_frag_address.numel}'
+            dst_tensor.data.copy_(tp_hp_fragment.data)
+        else:
+            assert tp_hp_fragment.numel() == lp_frag_address.numel, \
+                f'Load checkpoint {key} dst numel {tp_hp_fragment.numel()} != src numel {lp_frag_address.numel}'
+
+            hp_mapping.optim_fragment[key] = tp_hp_fragment.clone().detach()
 
 
 def enable_universal_checkpoint(param_list):

deepspeed/runtime/bf16_optimizer.py

@@ -18,7 +18,7 @@
                                      align_dense_tensors, all_gather_dp_groups, bwc_tensor_model_parallel_rank,
                                      is_model_parallel_parameter, see_memory_usage, graph_process)
 
-from deepspeed.utils import link_hp_params, lazy_init_hp_params_optimizer_state, fragment_address
+from deepspeed.utils import link_hp_params, lazy_init_hp_params_optimizer_state, fragment_address, map_to_flat_opt_states
 from deepspeed.checkpoint import enable_universal_checkpoint
 from deepspeed.checkpoint.constants import (DS_VERSION, PARTITION_COUNT, BASE_OPTIMIZER_STATE,
                                             SINGLE_PARTITION_OF_FP32_GROUPS, CLIP_GRAD, GROUP_PADDINGS,
@@ -457,12 +457,18 @@ def _load_hp_checkpoint_state(self, checkpoint_dir):
         tp_rank = bwc_tensor_model_parallel_rank(mpu=self.mpu)
         tp_world_size = self.mpu.get_slice_parallel_world_size()
 
-        for i, _ in enumerate(self.optimizer.param_groups):
+        for i, param_group in enumerate(self.optimizer.param_groups):
+            # We have an assumption that all params in the same param_group have the same keys
+            opt_keys = set()
+
             for lp in self.bf16_groups[i]:
                 if lp._hp_mapping is not None:
                     #print(f"Loading {self.param_names[lp]} {tp_rank=} {tp_world_size=}")
                     lp.load_hp_checkpoint_state(os.path.join(checkpoint_dir, self.param_names[lp]), tp_rank,
                                                 tp_world_size)
+                    for key in lp._hp_mapping.get_optim_state_keys():
+                        opt_keys.add(key)
+            map_to_flat_opt_states(param_group['params'][0], self.bf16_groups[i], self.optimizer.state, opt_keys)
 
     def accumulate_hp_grads_and_remove_lp(self, lp_param, group_idx, param_idx):
         assert self.immediate_grad_update

deepspeed/runtime/engine.py

@@ -2785,7 +2785,7 @@ def load_checkpoint(self,
         if self.load_universal_checkpoint():
             self.optimizer.update_lp_params()
             if load_zero_checkpoint:
-                self.update_optimizer_step(step=client_states['iteration'] + 1)
+                self.update_optimizer_step(step=client_states['iteration'])
 
         return load_path, client_states
 
@@ -2966,7 +2966,7 @@ def _load_zero_checkpoint(self, load_dir, tag, load_optimizer_states=True):
     def update_optimizer_step(self, step):
 
         def set_step(d):
-            if isinstance(d['step'], torch.Tensor):
+            if 'step' in d and isinstance(d['step'], torch.Tensor):
                 d['step'] = torch.tensor(step, dtype=d['step'].dtype, device=d['step'].device)
             else:
                 d['step'] = step
@@ -2975,10 +2975,9 @@ def set_step(d):
         base_optimizer = optimizer.optimizer
         state = base_optimizer.state
         for group in optimizer.param_groups:
-            if 'step' in group:
-                set_step(group)
+            set_step(group)
             for p in group['params']:
-                if p in state and len(state[p]) > 0 and 'step' in state[p]:
+                if p in state and len(state[p]) > 0:
                     set_step(state[p])
 
     def _get_mp_rank_zero_checkpoint_names(self, load_dir, tag, mp_rank, dp_world_size, bf16_mode):

deepspeed/runtime/zero/stage_1_and_2.py

@@ -28,7 +28,7 @@
 from deepspeed.checkpoint.constants import (DS_VERSION, GROUP_PADDINGS, PARTITION_COUNT, LOSS_SCALER,
                                             SINGLE_PARTITION_OF_FP32_GROUPS, BASE_OPTIMIZER_STATE,
                                             BASE_OPTIMIZER_STATE_STEP, CLIP_GRAD, ZERO_STAGE, PARAM_SLICE_MAPPINGS)
-from deepspeed.utils import link_hp_params, lazy_init_hp_params_optimizer_state
+from deepspeed.utils import link_hp_params, lazy_init_hp_params_optimizer_state, map_to_flat_opt_states
 from deepspeed.checkpoint import enable_universal_checkpoint
 
 from deepspeed.utils import groups
@@ -1360,7 +1360,7 @@ def reduce_ipg_grads(self):
                 self.average_tensor(extra_large_grad_reduc.view(-1))
                 self.extra_large_param_to_reduce = None
             else:
-                self.average_tensor(self.ipg_buffer[self.ipg_index])
+                self.average_tensor(self.ipg_buffer[self.ipg_index].narrow(0, 0, self.elements_in_ipg_bucket))
         else:
             self.buffered_reduce_fallback(None,
                                           self.grads_in_ipg_bucket,
@@ -2310,12 +2310,18 @@ def _load_hp_checkpoint_state(self, checkpoint_dir):
             tp_world_size = self.mpu.get_slice_parallel_world_size() if hasattr(self.mpu, "get_slice_parallel_world_size") \
                 else self.mpu.get_tensor_model_parallel_world_size()
 
-        for i, _ in enumerate(self.optimizer.param_groups):
+        for i, param_group in enumerate(self.optimizer.param_groups):
+            # We have an assumption that all params in the same param_group have the same keys
+            opt_keys = set()
+
             for lp in self.bit16_groups[i]:
                 if lp._hp_mapping is not None:
                     #print(f"Loading {self.param_names[lp]} {tp_rank=} {tp_world_size=}")
                     lp.load_hp_checkpoint_state(os.path.join(checkpoint_dir, self.param_names[lp]), tp_rank,
                                                 tp_world_size)
+                    for key in lp._hp_mapping.get_optim_state_keys():
+                        opt_keys.add(key)
+            map_to_flat_opt_states(param_group['params'][0], self.bit16_groups[i], self.optimizer.state, opt_keys)
 
     def _load_global_state(self, sd):
         self.loss_scaler = sd.get(LOSS_SCALER, self.loss_scaler)

deepspeed/utils/__init__.py

@@ -10,7 +10,7 @@
 from .groups import *
 from .nvtx import instrument_w_nvtx
 # TODO: Move tensor fragment and mixed precision to zero utils
-from .tensor_fragment import tensor_fragment, get_full_hp_param, get_hp_fragment_mapping, fragment_address, get_full_hp_grad
+from .tensor_fragment import tensor_fragment, get_full_hp_param, get_hp_fragment_mapping, fragment_address, get_full_hp_grad, map_to_flat_opt_states
 from .tensor_fragment import safe_get_full_fp32_param, safe_get_full_grad, safe_get_full_optimizer_state
 from .tensor_fragment import set_full_hp_param
 from .tensor_fragment import safe_set_full_fp32_param, safe_set_full_optimizer_state

deepspeed/utils/tensor_fragment.py

@@ -58,6 +58,21 @@ def get_hp_fragment(self, optim_state_key=None):
         return self.get_optim_state_fragment(optim_state_key)
 
 
+def map_to_flat_opt_states(flat_hp_tensor, lp_tensors, optim_state, opt_keys):
+    for key in opt_keys:
+        hp_param = flat_hp_tensor
+        buffer = torch.zeros_like(hp_param)
+
+        for lp in lp_tensors:
+            if lp._hp_mapping is not None:
+                hp_fragment_address = lp._hp_mapping.get_hp_fragment_address()
+                hp_fragment = buffer.narrow(0, hp_fragment_address.start, hp_fragment_address.numel)
+                hp_fragment.data.copy_(lp._hp_mapping.get_hp_fragment(optim_state_key=key).data)
+                lp._hp_mapping.hp_fragment = hp_fragment
+
+        optim_state[hp_param][key] = buffer
+
+
 def get_full_hp_param(self, optim_state_key=None):
     reduce_buffer = torch.zeros_like(self, dtype=torch.float32).flatten()
     if self._hp_mapping is not None: