Merge branch 'master' into add-safetensors-support

.gitignore

@@ -1,31 +1,40 @@
+# Ignore Python compiled files
 *.pyc
-.idea/
-*~
-*.swp
+
+# Ignore IDE-specific files and directories
+.idea/         # JetBrains IDE settings
+.vscode/       # Visual Studio Code settings
+.theia/        # Theia IDE settings
+
+# Ignore temporary and backup files
+*~             # General backup files
+*.swp          # Vim swap files
+
+# Ignore log files
 *.log
+
+# Ignore a specific generated file
 deepspeed/git_version_info_installed.py
+
+# Ignore Python bytecode cache
 __pycache__
 
 # Build + installation data
-build/
-dist/
-*.so
-deepspeed.egg-info/
-build.txt
-
-# Website
-docs/_site/
-docs/build
+build/                  # Build artifacts
+dist/                   # Distribution files
+*.so                    # Compiled shared objects
+deepspeed.egg-info/     # Deepspeed package info
+build.txt               # Build information
+
+# Website generated files
+docs/_site/             # Jekyll generated site
+docs/build              # Generated documentation
 docs/code-docs/source/_build
 docs/code-docs/_build
 docs/code-docs/build
-.sass-cache/
-.jekyll-cache/
+.sass-cache/            # SASS cache
+.jekyll-cache/          # Jekyll cache
 .jekyll-metadata
 
 # Testing data
-tests/unit/saved_checkpoint/
-
-# Dev/IDE data
-.vscode
-.theia
+tests/unit/saved_checkpoint/  # Saved checkpoints for testing

csrc/deepspeed4science/evoformer_attn/gemm/custom_mma_multistage.h

@@ -243,10 +243,18 @@ class CustomMmaMultistage : public CustomMmaBase<Shape_, Policy_, Stages> {
     }
 
     CUTLASS_DEVICE
-    bool set_prologue_done(bool value) { prologue_done_ = value; }
+    bool set_prologue_done(bool value)
+    {
+        prologue_done_ = value;
+        return true;
+    }
 
     CUTLASS_DEVICE
-    bool set_zero_outside_bounds(bool value) { zero_outside_bounds_ = value; }
+    bool set_zero_outside_bounds(bool value)
+    {
+        zero_outside_bounds_ = value;
+        return true;
+    }
 
     template <bool kLoadA = true, bool kLoadB = true>
     CUTLASS_DEVICE static void prologue(typename Base::SharedStorage& shared_storage,

deepspeed/checkpoint/constants.py

@@ -57,7 +57,7 @@
 UNIVERSAL_CHECKPOINT_VERSION_VALUE = 0.2
 
 # Vocabulary padding
-VOCAB_DIVISIBILITY_PADDING_TENSOR = 'vocab_divisibility_padding_tensor'
+VOCAB_TENSOR = 'vocab_tensor'
 PADDED_VOCAB_SIZE = 'padded_vocab_size'
 ORIGINAL_VOCAB_SIZE = 'original_vocab_size'
 

deepspeed/checkpoint/ds_to_universal.py

@@ -26,8 +26,7 @@
     PARAM_SHAPES,
     PARAM,
     CAT_DIM,
-    VOCAB_DIVISIBILITY_PADDING_TENSOR,
-    ORIGINAL_VOCAB_SIZE,
+    VOCAB_TENSOR,
     UNIVERSAL_CHECKPOINT_INFO,
     VOCABULARY_PARAMETER_PATTERNS,
     PIPELINE_REPLICATED_PARAMETER_PATTERNS,
@@ -55,6 +54,10 @@ def parse_arguments():
     parser.add_argument('--keep_temp_folder',
                         action='store_true',
                         help='Preserve temporary folder of intermediate checkpoint slice files. Useful for debugging.')
+    parser.add_argument('--no_strict',
+                        dest='strict',
+                        action='store_false',
+                        help='Do not perform validity checks on converted checkpoint.')
     args = parser.parse_args()
     print(f'args = {args}')
     return args
@@ -149,15 +152,8 @@ def _merge_zero_shards(param_base_path, state, tp_degree, slice_shape):
     return slices
 
 
-def _get_vocab_divisibility_padding_tensor(universal_checkpoint_info, padded_vocab_tensor):
-    original_vocab_size = universal_checkpoint_info.get(ORIGINAL_VOCAB_SIZE)
-    if padded_vocab_tensor.shape[0] > original_vocab_size:
-        return padded_vocab_tensor[-1]
-    else:
-        return torch.zeros(padded_vocab_tensor.shape[1])
-
-
 def merge_tp_slices(ds_checkpoint, dir, slice_dir, tp_degree, name_and_shape):
+
     name, shape = name_and_shape
     slice_base_path = os.path.join(slice_dir, name)
     param_base_path = os.path.join(dir, name)
@@ -167,39 +163,53 @@ def merge_tp_slices(ds_checkpoint, dir, slice_dir, tp_degree, name_and_shape):
     parameters_to_average = universal_checkpoint_info.get(PARAMETER_TO_AVERAGE_PATTERNS, [])
     parameters_with_row_parallelism = universal_checkpoint_info.get(PARAMETER_WITH_ROW_PARALLELISM_PATTERNS, [])
     vocabulary_parameters = universal_checkpoint_info.get(VOCABULARY_PARAMETER_PATTERNS, [])
+    unmatched_patterns = set(replicated_parameters + parameters_to_average + parameters_with_row_parallelism +
+                             vocabulary_parameters)
+
+    def get_matched_pattern(patterns_, name_):
+        matched_ = [pattern_ for pattern_ in patterns_ if re.match(pattern_, name_)]
+        assert len(matched_) <= 1, f'Got more than one matching patterns={matched_} for {name_}'
+        if matched_:
+            pattern_ = matched_[0]
+            unmatched_patterns.discard(pattern_)
+            return pattern_
+        return None
+
     for state in ("fp32", "exp_avg", "exp_avg_sq"):
         slices = _merge_zero_shards(slice_base_path, state, tp_degree, shape)
         final_path = os.path.join(param_base_path, f"{state}.pt")
 
         #print(f"Expected shape: {shape}")
         #print(f"Fragment sizes:", list(frag.shape for frag in slices))
         ckpt_dict = {}
-        if any(re.match(pattern, name) for pattern in replicated_parameters):
+        if get_matched_pattern(replicated_parameters, name):
             if len(slices) > 1:
                 assert all([slices[0].equal(other_slice) for other_slice in slices[1:]])
             param = slices[0]
             # print(f'replicate {name} using first slice')
-        elif any(re.match(pattern, name) for pattern in parameters_to_average):
+        elif get_matched_pattern(parameters_to_average, name):
             param = sum(slices) / len(slices)
             # print(f'merge {name} using average')
         else:
-            cat_dim = 1 if any(re.match(pattern, name) for pattern in parameters_with_row_parallelism) else 0
+            cat_dim = 1 if get_matched_pattern(parameters_with_row_parallelism, name) else 0
             # print(f"merge {name} with CAT DIM: {cat_dim}")
             param = torch.cat(slices, dim=cat_dim)
             ckpt_dict[CAT_DIM] = cat_dim
 
-        if any(re.match(pattern, name) for pattern in vocabulary_parameters):
+        if get_matched_pattern(vocabulary_parameters, name):
             #print(f"Before {param.shape=}")
             # strip padding
-            #param = _strip_vocab_padding(ds_checkpoint, param)
-            ckpt_dict[VOCAB_DIVISIBILITY_PADDING_TENSOR] = _get_vocab_divisibility_padding_tensor(
-                universal_checkpoint_info, param)
+            original_vocab_size = universal_checkpoint_info['original_vocab_size']
+            param = param[:original_vocab_size, :]
+            ckpt_dict[VOCAB_TENSOR] = True
             #print(f"After {param.shape=}")
 
         #print(f"Final shape: {param.shape}")
         ckpt_dict[PARAM] = param
         _save_checkpoint(final_path, ckpt_dict)
 
+    return unmatched_patterns
+
 
 def _get_chunks(l, n):
     for i in range(0, len(l), n):
@@ -208,10 +218,13 @@ def _get_chunks(l, n):
 
 def _do_parallel_work(do_work, work_chunks, num_workers):
     pool = multiprocessing.Pool(num_workers)
+    results = []
     for batch in tqdm.tqdm(work_chunks):
-        pool.map(do_work, batch)
+        res = pool.map(do_work, batch)
+        results.extend(res)
     pool.close()
     pool.join()
+    return results
 
 
 def _extract_zero_shard_files(args, ds_checkpoint, temp_dir):
@@ -232,7 +245,16 @@ def _merge_tp_slice_files(args, ds_checkpoint, slice_shapes, temp_dir):
     #pprint(work_chunks)
     zero_output_folder = os.path.join(args.output_folder, "zero")
     do_work = partial(merge_tp_slices, ds_checkpoint, zero_output_folder, temp_dir, ds_checkpoint.tp_degree)
-    _do_parallel_work(do_work, work_chunks, args.num_merge_workers)
+    unmatched_patterns_lists = _do_parallel_work(do_work, work_chunks, args.num_merge_workers)
+
+    # verify that all patterns were used
+    # if a pattern was not used by any of the workers, then it was not used at all -> assert/alert
+    sets = [set(lst) for lst in unmatched_patterns_lists]
+    unmatched_patterns = list(set.intersection(*sets))
+    if args.strict:
+        assert not unmatched_patterns, f'Unused patterns={unmatched_patterns} while merging tp slices'
+    elif unmatched_patterns:
+        print(f'Warning: Unused patterns={unmatched_patterns} while merging tp slices')
 
 
 def _save_optimizer_state(args, ds_checkpoint):

deepspeed/checkpoint/universal_checkpoint.py

@@ -6,7 +6,7 @@
 import os
 import torch
 import types
-from .constants import (FP32_WEIGHT_KEY, PARAM, VOCAB_DIVISIBILITY_PADDING_TENSOR, CAT_DIM)
+from .constants import (FP32_WEIGHT_KEY, PARAM, VOCAB_TENSOR, CAT_DIM)
 
 
 def load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):
@@ -43,21 +43,16 @@ def load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):
         # the converter to universal currently strips the original padding completely so the saved
         # weight is padding-free and we just need to add new padding depending on the target TP
         # degree
-        vocab_divisibility_padding_tensor = ckpt_dict.get(VOCAB_DIVISIBILITY_PADDING_TENSOR, None)
-        if vocab_divisibility_padding_tensor is not None:
+        is_vocab_tensor = ckpt_dict.get(VOCAB_TENSOR, False)
+        if is_vocab_tensor:
             # In the absence of data passed from the user wrt new padded vocab specific to tp degree
             # we can again derive that data by reverse engineering the target shapes like so:
             padded_target_vocab_size = self.shape[0] * tp_world_size
+            assert padded_target_vocab_size >= full_hp_param.shape[0], \
+                f'Vocab tensor padded size {padded_target_vocab_size} < loaded universal size {full_hp_param.shape[0]}'
             if padded_target_vocab_size > full_hp_param.shape[0]:
-                # Need to expand
                 padding_size = padded_target_vocab_size - full_hp_param.shape[0]
-                # Implement the following concat in efficient way using pad
-                #full_hp_param = torch.cat((full_hp_param, padding_tensor), 0)
                 full_hp_param = torch.nn.functional.pad(full_hp_param, (0, 0, 0, padding_size), "constant", 0)
-                full_hp_param[:-padding_size, :] = vocab_divisibility_padding_tensor
-            else:
-                # Need to shrink or keep the same
-                full_hp_param = full_hp_param[:padded_target_vocab_size, :]
 
         full_param_numel = full_hp_param.numel()
         tp_slice_numel = self.numel()

deepspeed/ops/deepspeed4science/evoformer_attn.py

@@ -30,7 +30,7 @@ def _attention(Q, K, V, bias1, bias2):
     return O, lse
 
 
-def attention_bwd(dO, Q, K, V, O, lse, bias1, bias2):
+def attention_bwd(dO, Q, K, V, O, lse, bias1, bias2, bias1_grad, bias2_grad):
     assert max(Q.shape[-1], V.shape[-1]) <= 64, "Hidden size is too large. Need to change kMax to a larger value"
     dQ = torch.empty_like(Q, dtype=Q.dtype)
     dK = torch.empty_like(K, dtype=K.dtype)
@@ -44,8 +44,14 @@ def attention_bwd(dO, Q, K, V, O, lse, bias1, bias2):
     if kernel_ is None:
         kernel_ = EvoformerAttnBuilder().load()
     delta = torch.empty_like(lse)
-    dB1 = torch.zeros_like(bias1, dtype=torch.float32)
-    dB2 = torch.zeros_like(bias2, dtype=torch.float32)
+    if bias1_grad:
+        dB1 = torch.zeros_like(bias1, dtype=torch.float32)
+    else:
+        dB1 = torch.tensor([], dtype=torch.float32, device=bias1.device)
+    if bias2_grad:
+        dB2 = torch.zeros_like(bias2, dtype=torch.float32)
+    else:
+        dB2 = torch.tensor([], dtype=torch.float32, device=bias2.device)
     kernel_.attention_bwd(dO, Q, K, V, O, lse, delta, bias1, bias2, dQ, dK, dV, dB1, dB2)
     return dQ, dK, dV, dB1.to(dO.dtype), dB2.to(dO.dtype)
 
@@ -69,10 +75,12 @@ def forward(ctx, q, k, v, bias1=None, bias2=None):
     @staticmethod
     def backward(ctx, grad_output):
         q, k, v, o, lse, bias1, bias2 = ctx.saved_tensors
-        dQ, dK, dV, dB1, dB2 = attention_bwd(grad_output, q, k, v, o, lse, bias1, bias2)
-        if bias1.numel() == 0:
+        is_b1_grad = bias1.numel() != 0 and ctx.needs_input_grad[3]
+        is_b2_grad = bias2.numel() != 0 and ctx.needs_input_grad[4]
+        dQ, dK, dV, dB1, dB2 = attention_bwd(grad_output, q, k, v, o, lse, bias1, bias2, is_b1_grad, is_b2_grad)
+        if not is_b1_grad:
             dB1 = None
-        if bias2.numel() == 0:
+        if not is_b2_grad:
             dB2 = None
         return dQ, dK, dV, dB1, dB2
 
@@ -90,13 +98,9 @@ def DS4Sci_EvoformerAttention(Q, K, V, biases):
     bias_2_shape = lambda x: (x.shape[0], 1, x.shape[3], x.shape[2], x.shape[2])
 
     if biases[0] is not None:
-        assert biases[0].shape == bias_1_shape(Q)
-    else:
-        biases[0] = Q.new_zeros(bias_1_shape(Q))
+        assert biases[0].shape == bias_1_shape(Q), "bias1 shape is incorrect"
 
     if biases[1] is not None:
-        assert biases[1].shape == bias_2_shape(Q)
-    else:
-        biases[1] = Q.new_zeros(bias_2_shape(Q))
+        assert biases[1].shape == bias_2_shape(Q), "bias2 shape is incorrect"
 
     return EvoformerFusedAttention.apply(Q, K, V, biases[0], biases[1])

deepspeed/runtime/activation_checkpointing/checkpointing.py

@@ -237,6 +237,14 @@ def model_parallel_cuda_manual_seed(seed):
     _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, model_parallel_seed)
 
 
+def model_parallel_reconfigure_tp_seed(seed):
+    global mpu
+    tp_rank = bwc_tensor_model_parallel_rank(mpu)
+    model_parallel_seed = seed + 2718 + tp_rank
+    with _CUDA_RNG_STATE_TRACKER.fork():
+        get_accelerator().manual_seed(model_parallel_seed)
+
+
 def get_partition_start(item):
     global mp_rank, mp_size, mp_group
     size = item.numel()

deepspeed/runtime/bf16_optimizer.py

@@ -75,7 +75,6 @@ def __init__(self,
         self.fp32_groups_gradient_flat_partition = []
         self.fp32_groups_has_gradients = []
 
-        self.step_count = 0
         self.group_paddings = []
 
         if self.using_real_optimizer:
@@ -252,7 +251,6 @@ def step(self, closure=None):
         self.update_lp_params()
 
         self.clear_hp_grads()
-        self.step_count += 1
 
     def backward(self, loss, update_hp_grads=True, clear_lp_grads=False, **bwd_kwargs):
         """Perform a backward pass and copy the low-precision gradients to the

deepspeed/runtime/engine.py

@@ -2726,6 +2726,8 @@ 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)
 
         return load_path, client_states
 
@@ -2903,6 +2905,24 @@ def _load_zero_checkpoint(self, load_dir, tag, load_optimizer_states=True):
             logger.info(f"loading {len(zero_sd_list)} zero partition checkpoints for rank {self.global_rank}")
         return True
 
+    def update_optimizer_step(self, step):
+
+        def set_step(d):
+            if isinstance(d['step'], torch.Tensor):
+                d['step'] = torch.tensor(step, dtype=d['step'].dtype, device=d['step'].device)
+            else:
+                d['step'] = step
+
+        optimizer = self.optimizer
+        base_optimizer = optimizer.optimizer
+        state = base_optimizer.state
+        for group in optimizer.param_groups:
+            if 'step' in group:
+                set_step(group)
+            for p in group['params']:
+                if p in state and len(state[p]) > 0 and 'step' in state[p]:
+                    set_step(state[p])
+
     def _get_mp_rank_zero_checkpoint_names(self, load_dir, tag, mp_rank, dp_world_size, bf16_mode):
         zero_ckpt_names = []
         for dp_rank in range(dp_world_size):

deepspeed/runtime/fp16/fused_optimizer.py

@@ -107,7 +107,6 @@ def __init__(self,
 
         self.clip_grad = clip_grad
         self.norm_type = 2
-        self.step_count = 0
 
         if required_torch_version(max_version=0.4):
             self.clip_grad_norm = torch.nn.utils.clip_grad_norm
@@ -289,8 +288,6 @@ def step(self, closure=None):
 
         self.timers.log(STEP_TIMERS)
 
-        self.step_count += 1
-
         return self.overflow
 
     def _get_norm_with_moe_layers(self, all_groups_norm):