Remove unused parts in Megatron-LM code and add copyright notice

cacheflow/model_executor/parallel_utils/__init__.py

@@ -1,12 +1,10 @@
 import cacheflow.model_executor.parallel_utils.parallel_state
 import cacheflow.model_executor.parallel_utils.tensor_parallel
-import cacheflow.model_executor.parallel_utils.utils
 
 # Alias parallel_state as mpu, its legacy name
 mpu = parallel_state
 
 __all__ = [
     "parallel_state",
     "tensor_parallel",
-    "utils",
 ]

cacheflow/model_executor/parallel_utils/parallel_state.py

@@ -1,12 +1,12 @@
+# Copyright 2023 The CacheFlow team.
+# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 """Model and data parallel groups."""
 
 import torch
 from typing import Optional
 
-from .utils import GlobalMemoryBuffer
-
 # Intra-layer model parallel group that the current rank belongs to.
 _TENSOR_MODEL_PARALLEL_GROUP = None
 # Inter-layer model parallel group that the current rank belongs to.
@@ -44,9 +44,6 @@
 # rank when broadcasting weights from src to all other data parallel ranks
 _DATA_PARALLEL_GLOBAL_RANKS = None
 
-# Memory buffers to avoid dynamic memory allocation
-_GLOBAL_MEMORY_BUFFER = None
-
 _ALL_REDUCE_LAUNCHER: Optional['GraphAllReduce'] = None
 
 def initialize_model_parallel(
@@ -199,13 +196,6 @@ def initialize_model_parallel(
         if rank in ranks:
             _POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
 
-    # Initialize global memory buffer
-    # This isn't really "parallel state" but there isn't another good place to
-    # put this. If we end up with a more generic initialization of megatron-core
-    # we could stick it there
-    _set_global_memory_buffer()
-
-
 def initialize_all_reduce_launcher(
     max_num_tokens: int,
     hidden_size: int,
@@ -495,17 +485,6 @@ def get_data_parallel_rank():
     """Return my rank for the data parallel group."""
     return torch.distributed.get_rank(group=get_data_parallel_group())
 
-def _set_global_memory_buffer():
-    """Initialize global buffer"""
-    global _GLOBAL_MEMORY_BUFFER
-    assert _GLOBAL_MEMORY_BUFFER is None, 'global memory buffer is already initialized'
-    _GLOBAL_MEMORY_BUFFER = GlobalMemoryBuffer()
-
-def get_global_memory_buffer():
-    """Return the global GlobalMemoryBuffer object"""
-    assert _GLOBAL_MEMORY_BUFFER is not None, 'global memory buffer is not initialized'
-    return _GLOBAL_MEMORY_BUFFER
-
 def get_all_reduce_launcher() -> 'GraphAllReduce':
     assert _ALL_REDUCE_LAUNCHER is not None, 'all reduce launcher is not initialized'
     return _ALL_REDUCE_LAUNCHER
@@ -536,8 +515,6 @@ def destroy_model_parallel():
     _MPU_TENSOR_MODEL_PARALLEL_RANK = None
     global _MPU_PIPELINE_MODEL_PARALLEL_RANK
     _MPU_PIPELINE_MODEL_PARALLEL_RANK = None
-    global _GLOBAL_MEMORY_BUFFER
-    _GLOBAL_MEMORY_BUFFER = None
 
 
 class GraphAllReduce:

cacheflow/model_executor/parallel_utils/tensor_parallel/__init__.py

@@ -17,15 +17,12 @@
 )
 
 from .random import (
-    checkpoint,
     get_cuda_rng_tracker,
     model_parallel_cuda_manual_seed,
 )
 
 from .utils import (
     split_tensor_along_last_dim,
-    split_tensor_into_1d_equal_chunks,
-    gather_split_1d_tensor,
 )
 
 __all__ = [
@@ -45,11 +42,8 @@
     "scatter_to_tensor_model_parallel_region",
     "scatter_to_sequence_parallel_region",
     # random.py
-    "checkpoint",
     "get_cuda_rng_tracker",
     "model_parallel_cuda_manual_seed",
     # utils.py
     "split_tensor_along_last_dim",
-    "split_tensor_into_1d_equal_chunks",
-    "gather_split_1d_tensor",
 ]

cacheflow/model_executor/parallel_utils/tensor_parallel/layers.py

@@ -1,3 +1,5 @@
+# Copyright 2023 The CacheFlow team.
+# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/layers.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 # Parts of the code here are adapted from PyTorch

cacheflow/model_executor/parallel_utils/tensor_parallel/mappings.py

@@ -1,3 +1,5 @@
+# Copyright 2023 The CacheFlow team.
+# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/mappings.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 import torch

cacheflow/model_executor/parallel_utils/tensor_parallel/random.py

@@ -1,3 +1,5 @@
+# Copyright 2023 The CacheFlow team.
+# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/random.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 # Parts of the code here are adapted from PyTorch
@@ -8,22 +10,11 @@
 import torch
 from torch import _C
 from torch.cuda import _lazy_call, device as device_ctx_manager
-from torch.utils.checkpoint import detach_variable
 
 from cacheflow.model_executor.parallel_utils.parallel_state import (
-    get_data_parallel_rank,
-    get_tensor_model_parallel_group,
     get_tensor_model_parallel_rank,
-    get_tensor_model_parallel_world_size,
 )
 
-from .utils import (
-    split_tensor_into_1d_equal_chunks,
-    gather_split_1d_tensor,
-)
-
-from cacheflow.model_executor.parallel_utils.utils import safely_set_viewless_tensor_data
-
 # Default name for the model parallel rng tracker.
 _MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng'
 
@@ -171,83 +162,3 @@ def model_parallel_cuda_manual_seed(seed):
     # and model parallel state.
     _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME,
                                 tensor_model_parallel_seed)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    """This function is adapted from torch.utils.checkpoint with
-       two main changes:
-           1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
-           2) the states in the model parallel tracker are also properly
-              tracked/set/reset.
-    """
-    @staticmethod
-    def forward(ctx, run_function, distribute_saved_activations, *args):
-        ctx.run_function = run_function
-        ctx.distribute_saved_activations \
-            = distribute_saved_activations
-
-        # Copy the rng states.
-        ctx.fwd_cpu_rng_state = torch.get_rng_state()
-        ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
-        ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
-
-        with torch.no_grad():
-            outputs = run_function(*args)
-
-        # Divide hidden states across model parallel group and only keep
-        # the chunk corresponding to the current rank.
-        if distribute_saved_activations:
-            ctx.input_0_shape = args[0].data.shape
-            safely_set_viewless_tensor_data(
-                args[0],
-                split_tensor_into_1d_equal_chunks(args[0].data, new_buffer=True))
-
-        # Store everything.
-        ctx.save_for_backward(*args)
-
-        return outputs
-
-    @staticmethod
-    def backward(ctx, *args):
-        if not torch.autograd._is_checkpoint_valid():
-            raise RuntimeError("Checkpointing is not compatible with .grad(), "
-                               "please use .backward() if possible")
-        inputs = ctx.saved_tensors
-        if ctx.distribute_saved_activations:
-            safely_set_viewless_tensor_data(
-                inputs[0],
-                gather_split_1d_tensor(inputs[0].data).view(ctx.input_0_shape))
-
-        # Store the current states.
-        bwd_cpu_rng_state = torch.get_rng_state()
-        bwd_cuda_rng_state = torch.cuda.get_rng_state()
-        bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
-
-        # Set the states to what it used to be before the forward pass.
-        torch.set_rng_state(ctx.fwd_cpu_rng_state)
-        _set_cuda_rng_state(ctx.fwd_cuda_rng_state)
-        get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)
-
-        # Compute the forward pass.
-        detached_inputs = detach_variable(inputs)
-        with torch.enable_grad():
-            outputs = ctx.run_function(*detached_inputs)
-
-        # Set the states back to what it was at the start of this function.
-        torch.set_rng_state(bwd_cpu_rng_state)
-        _set_cuda_rng_state(bwd_cuda_rng_state)
-        get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)
-
-        if isinstance(outputs, torch.Tensor):
-            outputs = (outputs,)
-        torch.autograd.backward(outputs, args)
-        grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp
-                      for inp in detached_inputs)
-        return (None, None) + grads
-
-
-def checkpoint(function, distribute_saved_activations, *args):
-    """Checkpoint a model or part of the model.
-    This has been directly copied from torch.utils.checkpoint."""
-    return CheckpointFunction.apply(function,
-                                    distribute_saved_activations, *args)

cacheflow/model_executor/parallel_utils/tensor_parallel/utils.py

@@ -1,10 +1,23 @@
+# Copyright 2023 The CacheFlow team.
+# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/utils.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 
 import torch
 from typing import List, Sequence
 
-from cacheflow.model_executor.parallel_utils.utils import divide
-from cacheflow.model_executor.parallel_utils import parallel_state
+def ensure_divisibility(numerator, denominator):
+    """Ensure that numerator is divisible by the denominator."""
+    assert numerator % denominator == 0, "{} is not divisible by {}".format(
+        numerator, denominator
+    )
+
+
+def divide(numerator, denominator):
+    """Ensure that numerator is divisible by the denominator and return
+    the division value."""
+    ensure_divisibility(numerator, denominator)
+    return numerator // denominator
+
 
 def split_tensor_along_last_dim(
     tensor: torch.Tensor,
@@ -33,57 +46,6 @@ def split_tensor_along_last_dim(
 
     return tensor_list
 
-def split_tensor_into_1d_equal_chunks(tensor, new_buffer=False):
-    """ Break a tensor into equal 1D chunks across tensor parallel ranks.
-
-        Returns a Tensor or View with this rank's portion of the data.
-
-        Arguments:
-            tensor: The tensor to split
-
-        Keyword Arguments:
-            new_buffer (bool): If True, returns a new Tensor.
-                               If False, returns a view into the existing Tensor.
-                               Default is False
-
-    """
-    partition_size = torch.numel(tensor) // \
-        parallel_state.get_tensor_model_parallel_world_size()
-    start_index = partition_size * parallel_state.get_tensor_model_parallel_rank()
-    end_index = start_index + partition_size
-    if new_buffer:
-        data = torch.empty(partition_size, dtype=tensor.dtype,
-                           device=torch.cuda.current_device(),
-                           requires_grad=False)
-        data.copy_(tensor.view(-1)[start_index:end_index])
-    else:
-        data = tensor.view(-1)[start_index:end_index]
-    return data
-
-
-def gather_split_1d_tensor(tensor):
-    """ Opposite of split_tensor_into_1d_equal_chunks. Gather values from tensor
-        model parallel ranks.
-
-        Returns a new Tensor with the gathered data.
-
-        Arguments:
-            tensor: A Tensor or view of this rank's portion of the data.
-    """
-    numel_gathered = torch.numel(tensor) * \
-        parallel_state.get_tensor_model_parallel_world_size()
-    gathered = torch.empty(numel_gathered, dtype=tensor.dtype,
-                           device=torch.cuda.current_device(),
-                           requires_grad=False)
-    # TODO: This API is experimental in pytorch (as of Feb 2022) and
-    # this might break in future pytorch releases. We chose this API
-    # as opposed to torch.distributed.all_gather for efficiency reasons.
-    # This API calls directly NCCL all-gather versus the former does
-    # internal copies and can potentially cause slow down.
-    torch.distributed._all_gather_base(gathered, tensor,
-                                       group=parallel_state.get_tensor_model_parallel_group())
-    return gathered
-
 
 class VocabUtility:
     """ Split the vocabulary into `world_size` chunks and return the first