Merge branch 'master' into zero2_param_idx

.github/workflows/cpu-torch-latest.yml

@@ -42,7 +42,7 @@ jobs:
           git clone https://github.com/huggingface/transformers
           cd transformers
           # if needed switch to the last known good SHA until transformers@master is fixed
-          git checkout 6c3f168b3
+          # git checkout 6c3f168b3
           git rev-parse --short HEAD
           pip install .
 

.github/workflows/nv-torch-latest-v100.yml

@@ -38,7 +38,7 @@ jobs:
           git clone https://github.com/huggingface/transformers
           cd transformers
           # if needed switch to the last known good SHA until transformers@master is fixed
-          git checkout 6c3f168b3
+          # git checkout 6c3f168b3
           git rev-parse --short HEAD
           pip install .
 

.github/workflows/nv-torch-nightly-v100.yml

@@ -37,7 +37,7 @@ jobs:
           git clone https://github.com/huggingface/transformers
           cd transformers
           # if needed switch to the last known good SHA until transformers@master is fixed
-          git checkout 6c3f168b3
+          # git checkout 6c3f168b3
           git rev-parse --short HEAD
           pip install .
 

.github/workflows/xpu-compile.yml

@@ -31,10 +31,10 @@ jobs:
       run: |
         apt-get update
         apt-get install clinfo libaio-dev python3-pip -y
-        pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torch/
-        pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/intel-extension-for-pytorch/
-        pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/oneccl-bind-pt/
-        pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torchvision/
+        pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torch/
+        pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/intel-extension-for-pytorch/
+        pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/oneccl-bind-pt/
+        pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torchvision/
         pip install https://github.com/intel/intel-xpu-backend-for-triton/releases/download/v3.0.0b2/triton_xpu-3.0.0b2-cp310-cp310-linux_x86_64.whl
         pip install py-cpuinfo numpy
         pip install .[dev,autotuning]

.github/workflows/xpu-max1100.yml

@@ -47,10 +47,10 @@ jobs:
       run: |
         apt-get update
         apt-get install clinfo libaio-dev python3-pip -y
-        pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torch/
-        pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/intel-extension-for-pytorch/
-        pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/oneccl-bind-pt/
-        pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torchvision/
+        pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torch/
+        pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/intel-extension-for-pytorch/
+        pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/oneccl-bind-pt/
+        pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/torchvision/
         pip install py-cpuinfo numpy
         pip install .[dev,autotuning]
 

COMMITTERS.md

@@ -5,5 +5,7 @@
 | Olatunji Ruwase | [tjruwase](https://github.com/tjruwase)     | Microsoft |
 | Logan Adams     | [loadams](https://github.com/loadams)      | Microsoft |
 | Masahiro Tanaka | [tohtana](https://github.com/tohtana)      | Microsoft |
-| Jeff Rasley     | [jeffra](https://github.com/jeffra)       | SnowFlake |
-| Minjia Zhang    | [minjiazhang](https://github.com/minjiazhang)  | UIUC      |
+| Jeff Rasley     | [jeffra](https://github.com/jeffra)       | SnowFlake  |
+| Minjia Zhang    | [minjiazhang](https://github.com/minjiazhang)  | UIUC  |
+| Ashwin Aji      | [ashwinma](https://github.com/ashwinma)        | AMD   |
+| Sam Foreman     | [saforem2](https://github.com/saforem2)        | Argonne National Laboratory |

accelerator/real_accelerator.py

@@ -125,10 +125,9 @@ def get_accelerator():
         if accelerator_name is None:
             try:
                 import intel_extension_for_pytorch as ipex
+
                 if ipex._C._has_xpu():
                     accelerator_name = "xpu"
-                else:
-                    accelerator_name = "cpu"
             except ImportError as e:
                 pass
         if accelerator_name is None:
@@ -162,23 +161,27 @@ def get_accelerator():
             except ImportError as e:
                 pass
         if accelerator_name is None:
-            # borrow this log from PR#5084
             try:
                 import torch
 
                 # Determine if we are on a GPU or x86 CPU with torch.
-                if torch.cuda.is_available():  #ignore-cuda
+                # "torch.cuda.is_available()" provides a stronger guarantee,     #ignore-cuda
+                # ensuring that we are free from CUDA initialization errors.
+                # While "torch.cuda.device_count() > 0" check ensures that       #ignore-cuda
+                # we won't try to do any CUDA calls when no device is available
+                # For reference: https://github.com/microsoft/DeepSpeed/pull/6810
+                if torch.cuda.device_count() > 0 and torch.cuda.is_available():  #ignore-cuda
                     accelerator_name = "cuda"
-                else:
-                    if accel_logger is not None:
-                        accel_logger.warn(
-                            "Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it."
-                        )
-                    accelerator_name = "cpu"
             except (RuntimeError, ImportError) as e:
                 # TODO need a more decent way to detect which accelerator to use, consider using nvidia-smi command for detection
-                accelerator_name = "cuda"
                 pass
+        if accelerator_name is None:
+            # borrow this log from PR#5084
+            if accel_logger is not None:
+                accel_logger.warn(
+                    "Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.")
+            # cpu added as catch-all when accelerator detection fails
+            accelerator_name = "cpu"
 
         ds_set_method = "auto detect"
 

deepspeed/inference/config.py

@@ -40,6 +40,9 @@ class DeepSpeedTPConfig(DeepSpeedConfigModel):
     tp_size: int = 1
     """ Number of devices to split the model across using tensor parallelism. """
 
+    tp_grain_size: int = 64
+    "Desired MLP/lm_head tp size granularity. DNN library favors tensor size in granularity of power of 2, we pick 64 as a default size."
+
     mpu: object = None
     """
     A model parallelism unit object that implements

deepspeed/module_inject/replace_module.py

@@ -17,7 +17,7 @@
 from .layers import TensorParallelOcShardConv2d, TensorParallelIcShardConv2d
 
 from deepspeed import comm as dist
-from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd, set_num_attention_heads
+from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd, set_num_attention_heads, set_tp_grain_size
 
 from .load_checkpoint import load_model_with_checkpoint
 import time
@@ -303,6 +303,9 @@ def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None):
         if hasattr(model_config, 'num_attention_heads'):
             set_num_attention_heads(getattr(model_config, 'num_attention_heads'))
 
+        # 4.4 set tp_grain_size
+        set_tp_grain_size(config.tensor_parallel.tp_grain_size)
+
         # 5. Set linear policies
         _autotp.update_linear_policies()
 

deepspeed/module_inject/tp_shard.py

@@ -22,6 +22,11 @@ def set_n_embd(num):
     n_embd = num
 
 
+def set_tp_grain_size(num):
+    global tp_grain_size
+    tp_grain_size = num
+
+
 def get_num_kv_heads():
     global num_kv_heads
     if 'num_kv_heads' in globals():
@@ -45,9 +50,9 @@ def get_shard_size(total_size, mp_size, name=None, rank=None):
         my_slices = (num_kv_heads // mp_size) + (1 if rank < (num_kv_heads % mp_size) else 0)
         return total_size * my_slices // num_kv_heads
     else:
-        if total_size >= 64:
-            grain_size = total_size // 64
-            return (grain_size // mp_size + (1 if rank < (grain_size % mp_size) else 0)) * 64
+        if total_size >= tp_grain_size:
+            grain_size = total_size // tp_grain_size
+            return (grain_size // mp_size + (1 if rank < (grain_size % mp_size) else 0)) * tp_grain_size
         else:
             return total_size // mp_size + (1 if rank < (total_size % mp_size) else 0)
 

deepspeed/runtime/engine.py

@@ -3738,6 +3738,11 @@ def offload_states(self,
         assert self.zero_optimization_stage(
         ) == ZeroStageEnum.weights, "Moving buffers across devices is supported only for ZeRO stage 3."
 
+        opt_offload_config = self.zero_offload_optimizer()
+        assert opt_offload_config is None or opt_offload_config.device == OffloadDeviceEnum.none, "Moving states across devices is not supported for offloaded optimizer states."
+        param_offload_config = self.zero_offload_param()
+        assert param_offload_config is None or param_offload_config.device == OffloadDeviceEnum.none, "Moving states across devices is not supported for offloaded parameters."
+
         assert not self.zero_offload_param(), "Moving states across devices is not supported for offloaded parameters."
 
         if device == OffloadDeviceEnum.none:

deepspeed/runtime/fp16/onebit/zoadam.py

@@ -12,9 +12,11 @@
 
 
 class ZeroOneAdam(torch.optim.Optimizer):
-    """Implements the 0/1 Adam algorithm. Currently GPU-only.
+    """
+    Implements the 0/1 Adam algorithm. Currently GPU-only.
     For usage example please see https://www.deepspeed.ai/tutorials/zero-one-adam/
     For technical details please read https://arxiv.org/abs/2202.06009
+
     Arguments:
         params (iterable): iterable of parameters to optimize or dicts defining
             parameter groups.

deepspeed/runtime/lr_schedules.py

@@ -274,7 +274,7 @@ class LRRangeTest(object):
     """Sets the learning rate of each parameter group according to
     learning rate range test (LRRT) policy. The policy increases learning
     rate starting from a base value with a constant frequency, as detailed in
-    the paper `A disciplined approach to neural network hyper-parameters: Part1`_.
+    the paper `A disciplined approach to neural network hyper-parameters: Part 1 <https://arxiv.org/abs/1803.09820>`_
 
     LRRT policy is used for finding maximum LR that trains a model without divergence, and can be used to
     configure the LR boundaries for Cyclic LR schedules.
@@ -379,7 +379,7 @@ class OneCycle(object):
     1CLR policy changes the learning rate after every batch.
     `step` should be called after a batch has been used for training.
 
-    This implementation was adapted from the github repo: `pytorch/pytorch`_
+    This implementation was adapted from the github repo: `PyTorch <https://github.com/pytorch/pytorch>`_.
 
     Args:
         optimizer (Optimizer): Wrapped optimizer.

docs/_data/navigation.yml

@@ -75,6 +75,8 @@ lnav:
         url: /tutorials/data-efficiency/
       - title: 'DeepNVMe'
         url: /tutorials/deepnvme/
+      - title: 'Domino'
+        url: /tutorials/domino/
       - title: 'DS4Sci_EvoformerAttention'
         url: /tutorials/ds4sci_evoformerattention/
       - title: 'Flops Profiler'

docs/_tutorials/domino.md

@@ -0,0 +1,6 @@
+---
+title: "Domino"
+tags: training
+---
+
+Domino achieves near-complete communication hiding behind computation for tensor parallel training. Please find our [Domino-tutorial](https://github.com/microsoft/DeepSpeedExamples/blob/master/training/DeepSpeed-Domino/README.md) in DeepSpeedExample repo.

docs/code-docs/source/monitor.rst

@@ -9,15 +9,15 @@ overview of what DeepSpeed will log automatically.
     :header: "Field", "Description", "Condition"
     :widths: 20, 20, 10
 
-    `Train/Samples/train_loss`,The training loss.,None
-    `Train/Samples/lr`,The learning rate during training.,None
-    `Train/Samples/loss_scale`,The loss scale when training using `fp16`.,`fp16` must be enabled.
-    `Train/Eigenvalues/ModelBlockParam_{i}`,Eigen values per param block.,`eigenvalue` must be enabled.
-    `Train/Samples/elapsed_time_ms_forward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward`,The global duration of the forward pass.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward_inner`,The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_backward_allreduce`,The global duration of the allreduce operation.,`flops_profiler.enabled` or `wall_clock_breakdown`.
-    `Train/Samples/elapsed_time_ms_step`,The optimizer step time,`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/train_loss`,"The training loss.",None
+    `Train/Samples/lr`,"The learning rate during training.",None
+    `Train/Samples/loss_scale`,"The loss scale when training using `fp16`.",`fp16` must be enabled.
+    `Train/Eigenvalues/ModelBlockParam_{i}`,"Eigen values per param block.",`eigenvalue` must be enabled.
+    `Train/Samples/elapsed_time_ms_forward`,"The global duration of the forward pass.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward`,"The global duration of the forward pass.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward_inner`,"The backward time that does not include the gradient reduction time. Only in cases where the gradient reduction is not overlapped, if it is overlapped then the inner time should be about the same as the entire backward time.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_backward_allreduce`,"The global duration of the allreduce operation.",`flops_profiler.enabled` or `wall_clock_breakdown`.
+    `Train/Samples/elapsed_time_ms_step`,"The optimizer step time.",`flops_profiler.enabled` or `wall_clock_breakdown`.
 
 TensorBoard
 -----------

docs/index.md

@@ -7,25 +7,25 @@ title: "Latest News"
 ---
 <b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
 
+* [2024/12] [DeepSpeed Domino: Communication-Free LLM Training Engine](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md)
+
 * [2024/08] [DeepSpeed on Windows](https://github.com/microsoft/DeepSpeed/blob/master/blogs/windows/08-2024/README.md)[[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/windows/08-2024/japanese/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/windows/08-2024/chinese/README.md)]
 
 * [2024/08] [DeepNVMe: Improving DL Applications through I/O Optimizations](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-gds/README.md)[[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-gds/japanese/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-gds/chinese/README.md)]
 * [2024/07] [DeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/README.md)[[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/japanese/README.md)]
 * [2024/03] [DeepSpeed-FP6: The Power of FP6-Centric Serving for Large Language Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README-Chinese.md)]
-* [2024/01] [DeepSpeed-FastGen: Introducting Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19)
 
 <!-- NOTE: we must use html for news items otherwise links will be broken in the 'more news' section -->
 
 <details>
  <summary>More news</summary>
  <ul>
+  <li>[2024/01] <a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19">DeepSpeed-FastGen: Introducting Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.</a></li>
 
   <li>[2023/11] <a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/intel-inference/README.md">Llama 2 Inference on 4th Gen Intel® Xeon® Scalable Processor with DeepSpeed</a> [<a href="https://www.intel.com/content/www/us/en/developer/articles/technical/xllama-2-on-xeon-scalable-processor-with-deepspeed.html">Intel version</a>]</li>
 
   <li>[2023/11] <a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp/README.md">DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow</a></li>
 
-  <li>[2023/11] <a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen">DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference</a> [<a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md">中文</a>] [<a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/japanese/README.md">日本語</a>]</li>
-
 
  </ul>
 </details>