Enable Sequence Parallelism

megatron/arguments.py

@@ -399,7 +399,8 @@ def validate_args(args, defaults={}):
         args.async_tensor_model_parallel_allreduce = False
 
     if not args.use_dataset_only:
-        if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
+        if deepspeed.accelerator.get_accelerator().device_name() == "cuda" \
+            and os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
             if args.sequence_parallel:
                 raise RuntimeError(
                     "Using sequence parallelism requires setting the environment variable "

megatron/core/tensor_parallel/layers.py

@@ -1,3 +1,4 @@
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 # Parts of the code here are adapted from PyTorch
@@ -450,7 +451,8 @@ def linear_with_grad_accumulation_and_async_allreduce(
     ]
 
     if not linear_with_grad_accumulation_and_async_allreduce.warned:
-        if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
+        if get_accelerator().device_name() == "cuda" \
+            and os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
             if sequence_parallel:
                 warnings.warn(
                     "When using sequence parallelism it is recommended to set the "

megatron/model/fused_layer_norm.py

@@ -1,9 +1,11 @@
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 """This code is copied fron NVIDIA apex:
       https://github.com/NVIDIA/apex
    with some changes. """
 
+from deepspeed.accelerator.real_accelerator import get_accelerator
 import numbers
 import torch
 from torch.nn.parameter import Parameter
@@ -13,6 +15,7 @@
 import inspect
 
 from megatron.core.utils import make_viewless_tensor
+from megatron import get_args
 
 try:
     from apex.contrib.layer_norm.layer_norm import FastLayerNormFN
@@ -56,8 +59,15 @@ def __init__(self, normalized_shape, eps=1e-5,
             normalized_shape = (normalized_shape,)
         self.normalized_shape = torch.Size(normalized_shape)
         self.eps = eps
-        self.weight = Parameter(torch.Tensor(*normalized_shape))
-        self.bias = Parameter(torch.Tensor(*normalized_shape))
+        init_device = None
+        if get_accelerator().device_name() == 'hpu':
+            init_device = get_accelerator().current_device_name() 
+        self.weight = Parameter(torch.empty(*normalized_shape,
+                                device=init_device,
+                                dtype=get_args().params_dtype))
+        self.bias = Parameter(torch.empty(*normalized_shape,
+                              device=init_device,
+                              dtype=get_args().params_dtype))
         self.reset_parameters()
         self.no_persist_layer_norm = no_persist_layer_norm
         self.sequence_parallel = sequence_parallel

megatron/model/gpt_model.py

@@ -1,3 +1,4 @@
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 """GPT-2 model."""
@@ -393,9 +394,12 @@ def _to_float16(inputs):
         if args.normalization == 'layernorm':
             self.specs.append(LayerSpec(LayerNorm,
                           args.hidden_size,
-                          eps=args.layernorm_epsilon))
+                          eps=args.layernorm_epsilon,
+                          sequence_parallel=args.sequence_parallel))
         else:
-            self.specs.append(LayerSpec(RMSNorm, args.hidden_size, args.layernorm_epsilon))
+            self.specs.append(LayerSpec(RMSNorm, args.hidden_size,
+                                        args.layernorm_epsilon,
+                                        sequence_parallel=args.sequence_parallel))
 
         def _logits_helper(embedding, lm_output):
             """A wrapper to massage inputs/outputs from pipeline. """

megatron/model/language_model.py

@@ -1,3 +1,4 @@
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 """Transformer based language model."""
@@ -256,8 +257,8 @@ def forward(self, input_ids, position_ids, tokentype_ids=None):
 
         # Dropout.
         if self.sequence_parallel:
-            # already partition sequence, do not need scatter_to_sequence_parallel_region
-            # embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
+            # already partition sequence, do not need scatter_to_sequence_parallel_region ?
+            embeddings = tensor_parallel.scatter_to_sequence_parallel_region(embeddings)
             with tensor_parallel.get_cuda_rng_tracker().fork():
                 embeddings = self.embedding_dropout(embeddings)
         else:

megatron/model/rmsnorm.py

@@ -1,16 +1,29 @@
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
+
+from deepspeed.accelerator import get_accelerator
+from megatron import get_args
+
 import torch
+from torch.nn import init
 from torch.nn.parameter import Parameter
 
 # Taken from facebookresearch/llama
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, eps: float = 1e-6):
         super().__init__()
         self.eps = eps
-        self.weight = Parameter(torch.ones(dim))
+        init_device = None
+        if get_accelerator().device_name() == 'hpu':
+            init_device = get_accelerator().current_device_name() 
+        self.weight = Parameter(torch.empty(dim,
+                                device=init_device,
+                                dtype=get_args().params_dtype))
+        init.ones_(self.weight)
+        setattr(self.weight, 'sequence_parallel', sequence_parallel)
 
     def _norm(self, x):
         return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
 
     def forward(self, x):
         output = self._norm(x.float()).type_as(x)
-        return output * self.weight
+        return output * self.weight

megatron/model/transformer.py

@@ -922,7 +922,8 @@ def __init__(self, config,
                     config.hidden_size,
                     eps=config.layernorm_epsilon)
         else:
-            self.input_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon)
+            self.input_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon,
+                                           sequence_parallel=config.sequence_parallel)
         # Self attention.
         self.self_attention = ParallelAttention(
             config,
@@ -948,7 +949,8 @@ def __init__(self, config,
                     config.hidden_size,
                     eps=config.layernorm_epsilon)
         else:
-            self.post_attention_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon)
+            self.post_attention_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon,
+                                                    sequence_parallel=config.sequence_parallel)
             # Cross attention.
         if self.layer_type in (LayerType.decoder,
                                LayerType.retro_decoder,
@@ -968,7 +970,9 @@ def __init__(self, config,
                     apply_layernorm_1p=args.apply_layernorm_1p,
                     mem_efficient_ln=args.mem_efficient_ln)
             else:
-                self.post_inter_attention_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon)
+                self.post_inter_attention_layernorm = RMSNorm(config.hidden_size,
+                                                              config.layernorm_epsilon,
+                                                              sequence_parallel=config.sequence_parallel)
 
         # MLP
         self.num_experts = num_experts
@@ -1771,7 +1775,8 @@ def build_layer(layer_number, n_e):
                         config.hidden_size,
                         eps=config.layernorm_epsilon)
             else:
-                self.final_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon)
+                self.final_layernorm = RMSNorm(config.hidden_size, config.layernorm_epsilon,
+                                               sequence_parallel=config.sequence_parallel)
 
     def _get_layer(self, layer_number):
         return self.layers[layer_number]