Enable SNIP on multiple cards using DeepSpeed ZeRO-3 (#1492)

Signed-off-by: yiliu30 <yi4.liu@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

examples/pytorch/nlp/huggingface_models/language-modeling/pruning/magnitude/README.md → examples/pytorch/nlp/huggingface_models/language-modeling/pruning/multi_cards/README.md

@@ -1,7 +1,7 @@
 Step-by-Step
 ============
 
-# single GPU
+# Single GPU
 
 ```
 export CUDA_VISIBLE_DEVICES=0
@@ -15,10 +15,11 @@ bash run.sh \
     --pruning_frequency=1000
 ```
 
-# multi GPU
+# Multi GPU
 
-we use `accelerate` and `deepspeed ZeRO Stage-2` to conduct weight magnitude pruning
+We use `accelerate` and `deepspeed ZeRO` to conduct weight magnitude, snip pruning. Below are two usage examples: 1) magnitude pruning with ZeRO Stage-2, and 2) snip pruning with ZeRO Stage-3.
 
+## Magnitude pruning with ZeRO Stage-2
 ### Accelerate DeepSpeed Plugin
 
 On your machine(s) just run:
@@ -105,3 +106,82 @@ bash run_ds.sh \
     --pruning_pattern=4x1 \
     --pruning_frequency=1000
 ```
+
+
+## SNIP pruning with ZeRO Stage-3
+
+To specify the accelerate use DeepSpeed ZeRO Stage-3. On your machine(s) just run:
+``` shell
+accelerate config
+
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+ deepspeed_config_file: config/zero_stage3_config.json
+ zero3_init_flag: true
+distributed_type: DEEPSPEED
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 2
+use_cpu: false
+```
+with the contents of `config/zero_stage3_config.json` being:
+
+```
+{
+  "train_batch_size": 64,
+  "train_micro_batch_size_per_gpu": 8,
+  "gradient_accumulation_steps": 4,
+  "fp16": {
+    "enabled": true,
+    "min_loss_scale": 1,
+    "opt_level": "O2"
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "contiguous_gradients": true
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "torch_adam": true,
+      "adam_w_mode": true
+    }
+  },
+  "scheduler": {
+    "type": "WarmupDecayLR",
+    "params": {
+      "warmup_min_lr": 0.0,
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto",
+      "total_num_steps": "auto",
+      "warmup_type": "cosine"
+    }
+  }
+}
+```
+
+### Pruning
+> Note: As the ZeRO Stage-3 partitions all three model states(optimizer states, gradients, and parameters), please specify the `pruning_scope` as `local`. Choosing `global` requires gathering all parameters to update the mask, which compromises the benefits of ZeRO Stage-3.
+
+
+```
+# 2 gpu cards example
+export CUDA_VISIBLE_DEVICES=0,1  USE_DEEPSPEED=1
+bash run_ds_z3.sh \
+    --model_name_or_path=facebook/opt-125m \
+    --dataset_name=NeelNanda/pile-10k \
+    --block_size=128 \
+    --output_dir=./test-clm \
+    --pruning_type=snip_momentum \
+    --pruning_scope=local \
+    --pruning_pattern=4x1 \
+    --pruning_frequency=1000
+```

examples/pytorch/nlp/huggingface_models/language-modeling/pruning/multi_cards/config/zero_stage3_config.json

@@ -0,0 +1,34 @@
+{
+  "train_batch_size": 64,
+  "train_micro_batch_size_per_gpu": 8,
+  "gradient_accumulation_steps": 4,
+  "fp16": {
+    "enabled": true,
+    "min_loss_scale": 1,
+    "opt_level": "O2"
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "contiguous_gradients": true
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "torch_adam": true,
+      "adam_w_mode": true
+    }
+  },
+  "scheduler": {
+    "type": "WarmupDecayLR",
+    "params": {
+      "warmup_min_lr": 0.0,
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto",
+      "total_num_steps": "auto",
+      "warmup_type": "cosine"
+    }
+  }
+}

examples/pytorch/nlp/huggingface_models/language-modeling/pruning/magnitude/run_clm_no_trainer_deepspeed.py → examples/pytorch/nlp/huggingface_models/language-modeling/pruning/multi_cards/run_clm_no_trainer_deepspeed.py

@@ -274,6 +274,13 @@ def parse_args():
         help="pruning criteria to use.",
         choices=["magnitude", "snip", "snip_momentum"],
     )
+    parser.add_argument(
+        "--pruning_scope",
+        type=str,
+        default="global",
+        help="determine layers' scores should be gather together to sort.",
+        choices=["local", "global"],
+    )
     parser.add_argument(
         "--warm_epochs",
         type=int,
@@ -688,7 +695,7 @@ def group_texts(examples):
     pruning_configs=[
         {
             "pruning_type": args.pruning_type,
-            "pruning_scope": "global",
+            "pruning_scope": args.pruning_scope,
             "sparsity_decay_type": "exp",
             "excluded_op_names": ["pooler"],
             "pruning_op_types": ["Linear"],
@@ -800,7 +807,8 @@ def group_texts(examples):
 
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
-        unwrapped_model = accelerator.unwrap_model(model)
+        # fetch the ds model from inc model
+        unwrapped_model = accelerator.unwrap_model(model.model)
         unwrapped_model.save_pretrained(
             args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
         )

examples/pytorch/nlp/huggingface_models/language-modeling/pruning/multi_cards/run_ds_z3.sh

@@ -0,0 +1,94 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_pruning
+
+}
+
+# init params
+function init_params {
+  dataset_name="NeelNanda/pile-10k"
+  model_name_or_path="facebook/opt-125m"
+  output_dir="./test-clm"
+  per_device_train_batch_size=8
+  block_size=128
+  gradient_accumulation_steps=4
+  num_train_epochs=3
+  target_sparsity=0.8
+  pruning_type="snip_momentum"
+  pruning_scope="local"
+  pruning_pattern="4x1"
+  pruning_frequency=1000
+  for var in "$@"
+  do
+    case $var in
+      --dataset_name=*)
+          dataset_name=$(echo $var |cut -f2 -d=)
+      ;;
+      --model_name_or_path=*)
+          model_name_or_path=$(echo $var |cut -f2 -d=)
+      ;;
+       --output_dir=*)
+           output_dir=$(echo $var |cut -f2 -d=)
+       ;;
+       --per_device_train_batch_size=*)
+           per_device_train_batch_size=$(echo $var |cut -f2 -d=)
+       ;;
+       --block_size=*)
+           block_size=$(echo $var |cut -f2 -d=)
+       ;;
+       --gradient_accumulation_steps=*)
+           gradient_accumulation_steps=$(echo $var |cut -f2 -d=)
+       ;;
+       --num_train_epochs=*)
+          num_train_epochs=$(echo $var |cut -f2 -d=)
+      ;;
+       --target_sparsity=*)
+           target_sparsity=$(echo $var |cut -f2 -d=)
+       ;;
+       --pruning_type=*)
+           pruning_type=$(echo $var |cut -f2 -d=)
+       ;;
+       --pruning_scope=*)
+           pruning_scope=$(echo $var |cut -f2 -d=)
+       ;;
+       --pruning_pattern=*)
+           pruning_pattern=$(echo $var |cut -f2 -d=)
+       ;;
+       --pruning_frequency=*)
+           pruning_frequency=$(echo $var |cut -f2 -d=)
+       ;;
+      *)
+          echo "Error: No such parameter: ${var}"
+          exit 1
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_pruning {
+  accelerate launch --deepspeed_config_file config/ds_config.json --mixed_precision fp16 \
+      run_clm_no_trainer_deepspeed.py \
+      --dataset_name $dataset_name \
+      --model_name_or_path $model_name_or_path \
+      --block_size $block_size \
+      --per_device_train_batch_size $per_device_train_batch_size \
+      --gradient_accumulation_steps $gradient_accumulation_steps \
+      --output_dir $output_dir \
+      --do_prune \
+      --num_train_epochs $num_train_epochs \
+      --target_sparsity $target_sparsity \
+      --pruning_type $pruning_type \
+      --pruning_scope $pruning_scope \
+      --pruning_pattern $pruning_pattern \
+      --pruning_frequency $pruning_frequency
+
+}
+
+main "$@"
+

neural_compressor/compression/pruner/criteria.py

@@ -16,7 +16,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .utils import torch
+from .utils import safe_get_data, safe_get_grad, safe_get_shape, torch
 
 CRITERIA = {}
 
@@ -96,7 +96,8 @@ def on_step_begin(self):
         """Calculate and store the pruning scores based on a magnitude criterion."""
         with torch.no_grad():
             for key in self.modules.keys():
-                p = self.modules[key].weight.data
+                param = self.modules[key].weight
+                p = safe_get_data(param)
                 if hasattr(self.pattern, "reduce_score"):
                     self.scores[key] = self.pattern.reduce_score(torch.abs(p), key)
                 else:
@@ -161,12 +162,15 @@ def on_before_optimizer_step(self):
         """Calculate and store the pruning scores based on snip criterion."""
         with torch.no_grad():
             for key in self.modules.keys():
-                p = self.modules[key].weight
+                # p = self.modules[key].weight
+                param = self.modules[key].weight
+                data = safe_get_data(param)
+                grad = safe_get_grad(param)
                 # self.scores[key] = torch.abs(p * p.grad)
                 if hasattr(self.pattern, "reduce_score"):
-                    self.scores[key] = self.pattern.reduce_score(torch.abs(p * p.grad), key)
+                    self.scores[key] = self.pattern.reduce_score(torch.abs(data * grad), key)
                 else:
-                    self.scores[key] = torch.abs(p * p.grad)
+                    self.scores[key] = torch.abs(data * grad)
 
 
 @register_criterion("snip_momentum")
@@ -191,15 +195,19 @@ def __init__(self, modules, config, pattern):
         super(SnipMomentumCriterion, self).__init__(modules, config, pattern)
         assert self.config.end_step > 0, "please set end_step > 0 for gradient based criterion"
         for key in modules.keys():
-            p = modules[key].weight
+            param = modules[key].weight
+            # p = modules[key].weight
+            param_shape = safe_get_shape(param)
             dtype = torch.float32
             if self.low_memory_usage:
-                dtype = torch.bfloat16 if p.device.type == "cpu" else torch.float16
+                dtype = torch.bfloat16 if param.device.type == "cpu" else torch.float16
             # self.scores[key] = torch.zeros(p.shape, dtype=dtype).to(p.device)
             if hasattr(self.pattern, "reduce_score"):
-                self.scores[key] = self.pattern.reduce_score(torch.zeros(p.shape, dtype=dtype).to(p.device), key)
+                self.scores[key] = self.pattern.reduce_score(
+                    torch.zeros(param_shape, dtype=dtype).to(param.device), key
+                )
             else:
-                self.scores[key] = torch.zeros(p.shape, dtype=dtype).to(p.device)
+                self.scores[key] = torch.zeros(param_shape, dtype=dtype).to(param.device)
 
         self.alpha = 0.9
         self.beta = 1.0
@@ -209,8 +217,11 @@ def on_before_optimizer_step(self):
         with torch.no_grad():
             for key in self.modules.keys():
                 p = self.modules[key].weight
+                param = self.modules[key].weight
+                data = safe_get_data(param)
+                grad = safe_get_grad(param)
                 self.scores[key] *= self.alpha
-                tmp = torch.abs(p * p.grad)
+                tmp = torch.abs(data * grad)
                 if hasattr(self.pattern, "reduce_score"):
                     tmp = self.pattern.reduce_score(tmp, key, force=True)
                 if self.low_memory_usage:

neural_compressor/compression/pruner/patterns/base.py

@@ -20,7 +20,7 @@
 
 import numpy as np
 
-from ..utils import tf, torch
+from ..utils import safe_get_data, safe_get_grad, safe_get_shape, tf, torch
 
 PATTERNS = {}
 
@@ -75,12 +75,18 @@ def _reshape_2dims_to_orig(data, orig_shape):
         Returns:
             Reshaped data.
         """
-        if len(orig_shape) == 4:
+        if len(orig_shape) == 2:
+            return data
+        elif len(orig_shape) == 4:
             data = data.reshape(orig_shape[0], orig_shape[2], orig_shape[3], orig_shape[1])
             data = data.permute(0, 3, 1, 2)
-        if len(orig_shape) == 3:
+        elif len(orig_shape) == 3:
             data = data.reshape(orig_shape[0], orig_shape[2], orig_shape[1])
             data = data.permute(0, 2, 1)
+        elif len(orig_shape) == 1:
+            data = data.reshape(orig_shape)
+        else:
+            raise NotImplementedError(f"not support {data.shape}")
         return data
 
     # some util functions which can be used.
@@ -601,12 +607,16 @@ def get_pattern_lock_masks(self, modules):
         """
         pattern_lock_masks = {}
         for key in modules.keys():
-            weight = modules[key].weight
-            shape = weight.shape
+            # weight = modules[key].weight
+            # shape = weight.shape
+            param = modules[key].weight
+            data = safe_get_data(param)
+            shape = safe_get_shape(param)
             mask = torch.ones(shape)
-            mask[weight == 0] = 0.0
+            # mask[weight == 0] = 0.0
+            mask[data == 0] = 0.0
             mask = mask.bool()
-            pattern_lock_masks[key] = mask.to(weight.device)
+            pattern_lock_masks[key] = mask.to(param.device)
 
         return pattern_lock_masks