From 471e91b0ba7b383d7198d2f9f2f36acef647f811 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Thu, 23 May 2024 13:10:10 +0000
Subject: [PATCH 01/19] first commit for ring attention

---
 src/nanotron/config/parallelism_config.py     |   1 +
 src/nanotron/models/llama.py                  | 213 +++++++++---
 src/nanotron/parallel/context.py              |  48 ++-
 src/nanotron/parallel/tied_parameters.py      |   2 +-
 .../ring_flash_attn/ring_flash_attn.py        | 282 +++++++++++++++
 src/nanotron/ring_flash_attn/utils.py         | 132 +++++++
 .../ring_flash_attn/zigzag_ring_flash_attn.py | 325 ++++++++++++++++++
 src/nanotron/trainer.py                       |   5 +-
 8 files changed, 948 insertions(+), 60 deletions(-)
 create mode 100644 src/nanotron/ring_flash_attn/ring_flash_attn.py
 create mode 100644 src/nanotron/ring_flash_attn/utils.py
 create mode 100644 src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py

diff --git a/src/nanotron/config/parallelism_config.py b/src/nanotron/config/parallelism_config.py
index 5912425b..1fc2cced 100644
--- a/src/nanotron/config/parallelism_config.py
+++ b/src/nanotron/config/parallelism_config.py
@@ -28,6 +28,7 @@ class ParallelismArgs:
     dp: int
     pp: int
     tp: int
+    sp: int
     pp_engine: Optional[PipelineEngine] = None
     tp_mode: Optional[TensorParallelLinearMode] = None
     tp_linear_async_communication: Optional[bool] = None
diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index 32aab9cd..dfe163c3 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 """PyTorch LLaMa model."""
 
-from typing import Dict, Optional, Union, List
+from typing import Dict, Optional, Union
 
 import torch
 from torch import nn
@@ -32,6 +32,7 @@
 from nanotron.parallel.parameters import NanotronParameter
 from nanotron.parallel.pipeline_parallel.block import PipelineBlock, TensorPointer
 from nanotron.parallel.pipeline_parallel.p2p import P2P
+from nanotron.parallel.tensor_parallel.distributed_differentiable_primitives import differentiable_all_reduce_sum
 from nanotron.parallel.tensor_parallel.functional import sharded_cross_entropy
 from nanotron.parallel.tensor_parallel.nn import (
     TensorParallelColumnLinear,
@@ -40,9 +41,13 @@
     TensorParallelRowLinear,
 )
 from nanotron.random import RandomStates
+from nanotron.ring_flash_attn.ring_flash_attn import ring_flash_attn_func
+from nanotron.ring_flash_attn.utils import normal_split, zigzag_split
+from nanotron.ring_flash_attn.zigzag_ring_flash_attn import zigzag_ring_flash_attn_func
 from nanotron.scaling.parametrization import SpectralMupParametrizator, StandardParametrizator
 from nanotron.utils import checkpoint_method
 
+zigzag = True
 logger = logging.get_logger(__name__)
 
 
@@ -172,6 +177,61 @@ def forward(self, hidden_states):  # [seq_length, batch_size, hidden_dim]
         return {"hidden_states": hidden_states}
 
 
+class RingFlashAttention(nn.Module):
+    def __init__(self, config: LlamaConfig, pg: dist.ProcessGroup):
+        super().__init__()
+        # TODO @thomasw21: GPT has a weird `d_kv` config which I'm guessing is essentically a `d_qkv`
+        assert (
+            config.hidden_size % config.num_attention_heads == 0
+        ), f"Hidden size {config.hidden_size} must be divisible by number of attention heads {config.num_attention_heads}."
+        self.d_qk = config.hidden_size // config.num_attention_heads
+        self.d_v = config.hidden_size // config.num_attention_heads
+        self.is_using_mup = config.is_using_mup
+        self.checkpoint_attention = False  # Because flash_attn already does checkpointing
+        self.pg = pg
+
+    @checkpoint_method(attr_name="checkpoint_attention")
+    def forward(
+        self,
+        local_q: torch.Tensor,  # [batch_size, q_length, n_local_q_heads, inner_dim]
+        local_k: torch.Tensor,  # [batch_size, kv_length, n_local_kv_heads, inner_dim]
+        local_v: torch.Tensor,  # [batch_size, kv_length, n_local_kv_heads, inner_dim]
+    ):
+        # NOTE: this scale is for µTransfer,
+        # in SP, we use sqrt(1/d_h)
+        # softmax_scale = 1 / local_q.shape[-1] if self.is_using_mup else None
+        causal = True
+
+        if zigzag:
+            ring_out, _, _ = zigzag_ring_flash_attn_func(
+                local_q,
+                local_k,
+                local_v,
+                dropout_p=0.0,
+                causal=causal,
+                window_size=(-1, -1),
+                alibi_slopes=None,
+                deterministic=False,
+                return_attn_probs=True,
+                group=self.pg,
+            )
+        else:
+            ring_out, _, _ = ring_flash_attn_func(
+                local_q,
+                local_k,
+                local_v,
+                dropout_p=0.0,
+                causal=causal,
+                window_size=(-1, -1),
+                alibi_slopes=None,
+                deterministic=False,
+                return_attn_probs=True,
+                group=self.pg,
+            )
+        assert ring_out.requires_grad
+        return ring_out
+
+
 class CoreAttention(nn.Module):
     def __init__(self, config: LlamaConfig, parallel_config: Optional[ParallelismArgs], layer_idx: int):
         super().__init__()
@@ -260,6 +320,7 @@ def __init__(
         config: LlamaConfig,
         parallel_config: Optional[ParallelismArgs],
         tp_pg: dist.ProcessGroup,
+        sp_pg: dist.ProcessGroup,
         layer_idx: int,
     ):
         from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
@@ -334,12 +395,19 @@ def __init__(
             async_communication=tp_linear_async_communication,
         )
 
-        self.attention = CoreAttention(
-            config,
-            parallel_config=parallel_config,
-            layer_idx=layer_idx,
-        )
-
+        if not sp_pg.size() > 1:
+            self.attention = CoreAttention(
+                config,
+                parallel_config=parallel_config,
+                layer_idx=layer_idx,
+            )
+        # ring attention
+        else:
+            self.attention = RingFlashAttention(
+                config,
+                pg=sp_pg,
+            )
+        self.sp_pg = sp_pg
         self.prefill_kv_len = (
             config.max_position_embeddings
         )  # TODO @nouamane: compute based on free memory, because in rope we can surpass max_position_embeddings
@@ -348,6 +416,7 @@ def forward(
         self,
         hidden_states,  # [seq_length, batch_size, hidden_size]
         sequence_mask,  # [batch_size, seq_length]
+        position_ids: Optional[torch.LongTensor] = None,
     ):
         from flash_attn import bert_padding
         from flash_attn.flash_attn_interface import (
@@ -554,41 +623,46 @@ def forward(
             )
 
         else:  # Training case
-            # Apply rotary embeddings to query/key states
-            # NOTE: The layout is different from models/llama.py which is [batch_size, num_heads, seq_length, d_qk]
-            # Here it is, [batch_size, seq_length, num_heads, d_qk]
-            # [2, batch_size, seq_length, num_heads, d_qk]
-            key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
-            # [batch_size, seq_length, 2, num_heads, d_qk]
-            key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
-            query_states, key_value_states = self.flash_rotary_embedding(query_states, kv=key_value_states)
-            # [batch_size, seq_length, num_heads, d_qk]
-            key_states, value_states = torch.split(key_value_states, 1, dim=2)
-
-            q_sequence_mask = sequence_mask
-            kv_sequence_mask = sequence_mask
+            # apply rotary embedding. Will adapt to flash rotary embedding later
+            if self.sp_pg.size() > 1:
+                query_states = self.rotary_embedding(query_states, position_ids=position_ids)
+                key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+            else:
+                # Apply rotary embeddings to query/key states
+                # NOTE: The layout is different from models/llama.py which is [batch_size, num_heads, seq_length, d_qk]
+                # Here it is, [batch_size, seq_length, num_heads, d_qk]
+                # [2, batch_size, seq_length, num_heads, d_qk]
+                key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
+                # [batch_size, seq_length, 2, num_heads, d_qk]
+                key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+                query_states, key_value_states = self.flash_rotary_embedding(query_states, kv=key_value_states)
+                # [batch_size, seq_length, num_heads, d_qk]
+                key_states, value_states = torch.split(key_value_states, 1, dim=2)
 
             kv_length = key_states.shape[1]
-            # [batch_size, seq_length, num_heads, d_qk]
-            # Shaping for use in `flash-attn` version of flash-attn: `flash_attn_unpadded_func`
-            query_states = query_states.view(
-                batch_size * q_length, self.n_local_q_heads, self.d_qk
-            )  # [batch_size * q_length, self.n_heads, d_qk]
-
-            key_states = key_states.view(
-                batch_size * kv_length, self.n_local_kv_heads, self.d_qk
-            )  # [batch_size * kv_length, self.n_heads, d_qk]
-            value_states = value_states.view(
-                batch_size * kv_length, self.n_local_kv_heads, self.d_v
-            )  # [batch_size * kv_length, self.n_heads, d_v]
-
-            attention_output = self.attention(
-                query_states=query_states,
-                key_states=key_states,
-                value_states=value_states,
-                q_sequence_mask=q_sequence_mask,
-                kv_sequence_mask=kv_sequence_mask,
-            )
+
+            ## ring attention
+            if self.sp_pg.size() > 1:
+                key_states = key_states.view(batch_size, kv_length, self.n_local_kv_heads, self.d_qk)
+                value_states = value_states.view(batch_size, kv_length, self.n_local_kv_heads, self.d_v)
+
+                attention_output = self.attention(
+                    query_states,
+                    key_states,
+                    value_states,
+                )
+                assert attention_output.requires_grad
+            else:
+                query_states = query_states.view(batch_size * q_length, self.n_local_q_heads, self.d_qk)
+                key_states = key_states.view(batch_size * kv_length, self.n_local_kv_heads, self.d_qk)
+                value_states = value_states.view(batch_size * kv_length, self.n_local_kv_heads, self.d_v)
+                attention_output = self.attention(
+                    query_states=query_states,
+                    key_states=key_states,
+                    value_states=value_states,
+                    q_sequence_mask=sequence_mask,
+                    kv_sequence_mask=sequence_mask,
+                )
 
         attention_output = (
             attention_output.contiguous().view(batch_size, q_length, self.n_local_q_heads * self.d_v).transpose(0, 1)
@@ -604,6 +678,7 @@ def __init__(
         config: LlamaConfig,
         parallel_config: Optional[ParallelismArgs],
         tp_pg: dist.ProcessGroup,
+        sp_pg: dist.ProcessGroup,
         layer_idx: int,
     ):
         super().__init__()
@@ -612,6 +687,7 @@ def __init__(
             config=config,
             parallel_config=parallel_config,
             tp_pg=tp_pg,
+            sp_pg=sp_pg,
             layer_idx=layer_idx,
         )
 
@@ -622,11 +698,12 @@ def forward(
         self,
         hidden_states: Union[torch.Tensor, TensorPointer],
         sequence_mask: Union[torch.Tensor, TensorPointer],
+        position_ids: Optional[torch.LongTensor] = None,
     ) -> Dict[str, Union[torch.Tensor, TensorPointer]]:
         residual = hidden_states
         hidden_states = self.input_layernorm(hidden_states)
 
-        output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+        output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask, position_ids=position_ids)
         hidden_states = output["hidden_states"]
         hidden_states = hidden_states + residual
 
@@ -638,6 +715,7 @@ def forward(
         return {
             "hidden_states": hidden_states,
             "sequence_mask": output["sequence_mask"],
+            "position_ids": position_ids,
         }
 
 
@@ -682,6 +760,9 @@ def __init__(
     ):
         super().__init__()
 
+        if parallel_context.sp_pg.size() > 1:
+            log_rank("Using ring attention", logger=logger, level=logging.INFO, rank=0)
+
         # Declare all the nodes
         self.p2p = P2P(parallel_context.pp_pg, device=torch.device("cuda"))
         self.config = config
@@ -713,10 +794,11 @@ def __init__(
                         "config": config,
                         "parallel_config": parallel_config,
                         "tp_pg": parallel_context.tp_pg,
+                        "sp_pg": parallel_context.sp_pg,
                         "layer_idx": layer_idx,
                     },
-                    module_input_keys={"hidden_states", "sequence_mask"},
-                    module_output_keys={"hidden_states", "sequence_mask"},
+                    module_input_keys={"hidden_states", "sequence_mask", "position_ids"},
+                    module_output_keys={"hidden_states", "sequence_mask", "position_ids"},
                 )
                 for layer_idx in range(config.num_hidden_layers)
             ]
@@ -767,13 +849,33 @@ def forward_with_hidden_states(
         input_ids: Union[torch.Tensor, TensorPointer],  # [batch_size, seq_length]
         input_mask: Union[torch.Tensor, TensorPointer],  # [batch_size, seq_length]
     ):
+        if isinstance(input_ids, torch.Tensor):
+            batch_size, seq_length = input_ids.shape
+            position_ids = torch.arange(seq_length, device=input_ids.device).unsqueeze(0).expand(batch_size, -1)
+            # split input if using ring attention
+            if self.parallel_context.sp_pg.size() > 1:
+                world_size = self.parallel_context.sp_pg.size()
+                rank = dist.get_rank(self.parallel_context.sp_pg)
+
+                # zigzag split
+                if zigzag:
+                    input_ids, input_mask, position_ids = zigzag_split(
+                        rank, world_size, input_ids, input_mask, position_ids
+                    )
+                # normal split
+                else:
+                    input_ids, input_mask, position_ids = normal_split(
+                        rank, world_size, input_ids, input_mask, position_ids
+                    )
+        else:
+            position_ids = TensorPointer(input_ids.group_rank)
         # all tensors are optional as most ranks don't need anything from the dataloader.
-
         output = self.token_position_embeddings(input_ids=input_ids, input_mask=input_mask)
 
         hidden_encoder_states = {
             "hidden_states": output["input_embeds"],
             "sequence_mask": input_mask,
+            "position_ids": position_ids,
         }
         for encoder_block in self.decoder:
             hidden_encoder_states = encoder_block(**hidden_encoder_states)
@@ -831,9 +933,10 @@ def masked_mean(loss, label_mask, dtype):
 
 
 class Loss(nn.Module):
-    def __init__(self, tp_pg: dist.ProcessGroup):
+    def __init__(self, tp_pg: dist.ProcessGroup, sp_pg: dist.ProcessGroup):
         super().__init__()
         self.tp_pg = tp_pg
+        self.sp_pg = sp_pg
 
     def forward(
         self,
@@ -844,6 +947,18 @@ def forward(
         # Megatron by defaults cast everything in fp32. `--f16-lm-cross-entropy` is an option you can use to keep current precision.
         # https://github.com/NVIDIA/Megatron-LM/blob/f267e6186eae1d6e2055b412b00e2e545a8e896a/megatron/model/gpt_model.py#L38
 
+        # ring attention: split the label
+        if isinstance(label_ids, torch.Tensor) and self.sp_pg.size() > 1:
+            world_size = self.sp_pg.size()
+            rank = dist.get_rank(self.sp_pg)
+
+            # zigzag split
+            if zigzag:
+                label_ids, label_mask = zigzag_split(rank, world_size, label_ids, label_mask)
+            # normal split
+            else:
+                label_ids, label_mask = normal_split(rank, world_size, label_ids, label_mask)
+
         loss = sharded_cross_entropy(
             sharded_logits, label_ids.transpose(0, 1).contiguous(), group=self.tp_pg, dtype=torch.float
         ).transpose(0, 1)
@@ -851,6 +966,12 @@ def forward(
         loss = masked_mean(loss, label_mask, dtype=torch.float)
         # I think indexing causes a sync we don't actually want
         # loss = loss[label_mask].sum()
+
+        # Calculate the average loss over sp
+        if self.sp_pg.size() > 1:
+            differentiable_all_reduce_sum(loss, self.sp_pg)
+            loss /= torch.tensor(self.sp_pg.size(), dtype=loss.dtype, device=loss.device)
+
         return {"loss": loss}
 
 
@@ -867,7 +988,7 @@ def __init__(
         self.loss = PipelineBlock(
             p2p=self.model.p2p,
             module_builder=Loss,
-            module_kwargs={"tp_pg": parallel_context.tp_pg},
+            module_kwargs={"tp_pg": parallel_context.tp_pg, "sp_pg": parallel_context.sp_pg},
             module_input_keys={
                 "sharded_logits",
                 "label_ids",
diff --git a/src/nanotron/parallel/context.py b/src/nanotron/parallel/context.py
index e04e26f5..1a1b8444 100644
--- a/src/nanotron/parallel/context.py
+++ b/src/nanotron/parallel/context.py
@@ -1,5 +1,5 @@
 import os
-from typing import Literal, Tuple, Annotated
+from typing import Literal, Tuple
 
 import numpy as np
 import torch
@@ -15,11 +15,14 @@ def __init__(
         tensor_parallel_size: int,
         pipeline_parallel_size: int,
         data_parallel_size: int,
+        sequence_parallel_size: int,
         expert_parallel_size: int = 1,
         backend: DistributedBackend = "nccl",
     ):
         """Initialize parallel context."""
-        num_gpus_per_model = tensor_parallel_size * pipeline_parallel_size * expert_parallel_size
+        num_gpus_per_model = (
+            tensor_parallel_size * pipeline_parallel_size * expert_parallel_size * sequence_parallel_size
+        )
         world_size = int(os.environ["WORLD_SIZE"])
 
         assert (
@@ -41,6 +44,7 @@ def __init__(
         self.tensor_parallel_size = tensor_parallel_size
         self.pipeline_parallel_size = pipeline_parallel_size
         self.data_parallel_size = data_parallel_size
+        self.sequence_parallel_size = sequence_parallel_size
         self.expert_parallel_size = expert_parallel_size
 
         self._groups = {}
@@ -68,6 +72,7 @@ def _init_parallel_groups(self):
         world_size = int(os.environ["WORLD_SIZE"])
         ranks = np.arange(0, world_size).reshape(
             (
+                self.sequence_parallel_size,
                 self.expert_parallel_size,
                 self.pipeline_parallel_size,
                 self.data_parallel_size,
@@ -77,24 +82,44 @@ def _init_parallel_groups(self):
         self.world_ranks_to_pg = {}
 
         # Relevant process groups containing the current rank
-        self.tp_pg = self.create_new_group(ranks.transpose((0, 1, 2, 3)).reshape((-1, self.tensor_parallel_size)))
-        self.dp_pg = self.create_new_group(ranks.transpose((3, 0, 1, 2)).reshape((-1, self.data_parallel_size)))
-        self.pp_pg = self.create_new_group(ranks.transpose((2, 3, 0, 1)).reshape((-1, self.pipeline_parallel_size)))
-        self.expert_pg = self.create_new_group(ranks.transpose((1, 2, 3, 0)).reshape((-1, self.expert_parallel_size)))
 
-        # model parallel group = combination of tp and pp and exp for a given dp rank
+        self.tp_pg = self.create_new_group(ranks.transpose((0, 1, 2, 3, 4)).reshape((-1, self.tensor_parallel_size)))
+        self.dp_pg = self.create_new_group(ranks.transpose((4, 0, 1, 2, 3)).reshape((-1, self.data_parallel_size)))
+        self.pp_pg = self.create_new_group(ranks.transpose((3, 4, 0, 1, 2)).reshape((-1, self.pipeline_parallel_size)))
+        self.expert_pg = self.create_new_group(
+            ranks.transpose((2, 3, 4, 0, 1)).reshape((-1, self.expert_parallel_size))
+        )
+        self.sp_pg = self.create_new_group(ranks.transpose((1, 2, 3, 4, 0)).reshape((-1, self.sequence_parallel_size)))
         self.mp_pg = self.create_new_group(
-            [ranks[:, :, dp_rank, :].reshape(-1) for dp_rank in range(self.data_parallel_size)]
+            [ranks[:, :, :, dp_rank, :].reshape(-1) for dp_rank in range(self.data_parallel_size)]
         )
-
         self.tp_and_expert_pg = self.create_new_group(
             [
-                ranks[:, pp_rank, dp_rank, :].reshape(-1)
+                ranks[sp_rank, :, pp_rank, dp_rank, :].reshape(-1)
                 for pp_rank in range(self.pipeline_parallel_size)
                 for dp_rank in range(self.data_parallel_size)
+                for sp_rank in range(self.sequence_parallel_size)
             ]
         )
 
+        # self.tp_pg = self.create_new_group(ranks.transpose((0, 1, 2, 3)).reshape((-1, self.tensor_parallel_size)))
+        # self.dp_pg = self.create_new_group(ranks.transpose((3, 0, 1, 2)).reshape((-1, self.data_parallel_size)))
+        # self.pp_pg = self.create_new_group(ranks.transpose((2, 3, 0, 1)).reshape((-1, self.pipeline_parallel_size)))
+        # self.expert_pg = self.create_new_group(ranks.transpose((1, 2, 3, 0)).reshape((-1, self.expert_parallel_size)))
+
+        # model parallel group = combination of tp and pp and exp for a given dp rank
+        # self.mp_pg = self.create_new_group(
+        #     [ranks[:, :, dp_rank, :].reshape(-1) for dp_rank in range(self.data_parallel_size)]
+        # )
+
+        # self.tp_and_expert_pg = self.create_new_group(
+        #     [
+        #         ranks[:, pp_rank, dp_rank, :].reshape(-1)
+        #         for pp_rank in range(self.pipeline_parallel_size)
+        #         for dp_rank in range(self.data_parallel_size)
+        #     ]
+        # )
+
         self.world_rank_matrix: np.ndarray = ranks
 
     def create_new_group(self, all_groups_ranks: np.ndarray) -> dist.ProcessGroup:
@@ -137,6 +162,7 @@ def destroy(self):
 
     def get_global_rank(
         self,
+        sp_rank: int,
         ep_rank: int,
         pp_rank: int,
         dp_rank: int,
@@ -152,4 +178,4 @@ def get_global_rank(
 
         :return: numpy.int64, The global rank.
         """
-        return self.world_rank_matrix[ep_rank, pp_rank, dp_rank, tp_rank]
\ No newline at end of file
+        return self.world_rank_matrix[sp_rank, ep_rank, pp_rank, dp_rank, tp_rank]
diff --git a/src/nanotron/parallel/tied_parameters.py b/src/nanotron/parallel/tied_parameters.py
index 6aa0cbf5..873238b1 100644
--- a/src/nanotron/parallel/tied_parameters.py
+++ b/src/nanotron/parallel/tied_parameters.py
@@ -50,7 +50,7 @@ def tie_parameters(
     dp_ranks = tuple(
         sorted(
             {
-                parallel_context.get_local_ranks(world_rank=global_rank)[2]
+                parallel_context.get_local_ranks(world_rank=global_rank)[3]
                 for _, global_ranks in ties
                 for global_rank in global_ranks
             }
diff --git a/src/nanotron/ring_flash_attn/ring_flash_attn.py b/src/nanotron/ring_flash_attn/ring_flash_attn.py
new file mode 100644
index 00000000..48b394f8
--- /dev/null
+++ b/src/nanotron/ring_flash_attn/ring_flash_attn.py
@@ -0,0 +1,282 @@
+import torch
+from flash_attn.flash_attn_interface import _flash_attn_backward, _flash_attn_forward
+
+from .utils import RingComm, update_out_and_lse
+
+
+# copy from https://github.com/zhuzilin/ring-flash-attention
+def ring_flash_attn_forward(
+    process_group,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    softmax_scale,
+    dropout_p=0,
+    causal=True,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+):
+    comm = RingComm(process_group)
+
+    out = None
+    lse = None
+
+    next_k, next_v = None, None
+
+    for step in range(comm.world_size):
+        if step + 1 != comm.world_size:
+            next_k: torch.Tensor = comm.send_recv(k)
+            next_v: torch.Tensor = comm.send_recv(v)
+            comm.commit()
+
+        if not causal or step <= comm.rank:
+            block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(
+                q,
+                k,
+                v,
+                dropout_p,
+                softmax_scale,
+                causal=causal and step == 0,
+                window_size=window_size,
+                alibi_slopes=alibi_slopes,
+                return_softmax=True and dropout_p > 0,
+            )
+            out, lse = update_out_and_lse(out, lse, block_out, block_lse)
+
+        if step + 1 != comm.world_size:
+            comm.wait()
+            k = next_k
+            v = next_v
+
+    out = out.to(q.dtype)
+    lse = lse.squeeze(dim=-1).transpose(1, 2)
+    return out, lse
+
+
+def ring_flash_attn_backward(
+    process_group,
+    doubt,
+    q,
+    k,
+    v,
+    out,
+    softmax_lse,
+    softmax_scale,
+    dropout_p=0,
+    causal=True,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+):
+    kv_comm = RingComm(process_group)
+    d_kv_comm = RingComm(process_group)
+    dq, dk, dv = None, None, None
+    next_dk, next_dv = None, None
+
+    block_dq_buffer = torch.empty(q.shape, dtype=q.dtype, device=q.device)
+    block_dk_buffer = torch.empty(k.shape, dtype=k.dtype, device=k.device)
+    block_dv_buffer = torch.empty(v.shape, dtype=v.dtype, device=v.device)
+
+    next_dk, next_dv = None, None
+    next_k, next_v = None, None
+
+    for step in range(kv_comm.world_size):
+        if step + 1 != kv_comm.world_size:
+            next_k = kv_comm.send_recv(k)
+            next_v = kv_comm.send_recv(v)
+            kv_comm.commit()
+        if step <= kv_comm.rank or not causal:
+            bwd_causal = causal and step == 0
+            _flash_attn_backward(
+                doubt,
+                q,
+                k,
+                v,
+                out,
+                softmax_lse,
+                block_dq_buffer,
+                block_dk_buffer,
+                block_dv_buffer,
+                dropout_p,
+                softmax_scale,
+                bwd_causal,
+                window_size,
+                alibi_slopes,
+                deterministic,
+                rng_state=None,
+            )
+
+            if dq is None:
+                dq = block_dq_buffer.to(torch.float32)
+                dk = block_dk_buffer.to(torch.float32)
+                dv = block_dv_buffer.to(torch.float32)
+            else:
+                dq += block_dq_buffer
+                d_kv_comm.wait()
+                dk = block_dk_buffer + next_dk
+                dv = block_dv_buffer + next_dv
+        elif step != 0:
+            d_kv_comm.wait()
+            dk = next_dk
+            dv = next_dv
+
+        if step + 1 != kv_comm.world_size:
+            kv_comm.wait()
+            k = next_k
+            v = next_v
+
+        next_dk = d_kv_comm.send_recv(dk)
+        next_dv = d_kv_comm.send_recv(dv)
+        d_kv_comm.commit()
+
+    d_kv_comm.wait()
+
+    return dq.to(torch.bfloat16), next_dk.to(q.dtype), next_dv.to(q.dtype)
+
+
+class RingFlashAttnFunc(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        q,
+        k,
+        v,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_softmax,
+        group,
+    ):
+        if softmax_scale is None:
+            softmax_scale = q.shape[-1] ** (-0.5)
+
+        assert alibi_slopes is None
+        k = k.contiguous()
+        v = v.contiguous()
+        out, softmax_lse = ring_flash_attn_forward(
+            group,
+            q,
+            k,
+            v,
+            softmax_scale=softmax_scale,
+            dropout_p=dropout_p,
+            causal=causal,
+            window_size=window_size,
+            alibi_slopes=alibi_slopes,
+            deterministic=False,
+        )
+        # this should be out_padded
+        ctx.save_for_backward(q, k, v, out, softmax_lse)
+        ctx.dropout_p = dropout_p
+        ctx.softmax_scale = softmax_scale
+        ctx.causal = causal
+        ctx.window_size = window_size
+        ctx.alibi_slopes = alibi_slopes
+        ctx.deterministic = deterministic
+        ctx.group = group
+        return out if not return_softmax else (out, softmax_lse, None)
+
+    @staticmethod
+    def backward(ctx, doubt, *args):
+        q, k, v, out, softmax_lse = ctx.saved_tensors
+        dq, dk, dv = ring_flash_attn_backward(
+            ctx.group,
+            doubt,
+            q,
+            k,
+            v,
+            out,
+            softmax_lse,
+            softmax_scale=ctx.softmax_scale,
+            dropout_p=ctx.dropout_p,
+            causal=ctx.causal,
+            window_size=ctx.window_size,
+            alibi_slopes=ctx.alibi_slopes,
+            deterministic=ctx.deterministic,
+        )
+        return dq, dk, dv, None, None, None, None, None, None, None, None
+
+
+def ring_flash_attn_qkvpacked_func(
+    qkv,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return RingFlashAttnFunc.apply(
+        qkv[:, :, 0],
+        qkv[:, :, 1],
+        qkv[:, :, 2],
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )
+
+
+def ring_flash_attn_kvpacked_func(
+    q,
+    kv,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return RingFlashAttnFunc.apply(
+        q,
+        kv[:, :, 0],
+        kv[:, :, 1],
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )
+
+
+def ring_flash_attn_func(
+    q,
+    k,
+    v,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return RingFlashAttnFunc.apply(
+        q,
+        k,
+        v,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )
diff --git a/src/nanotron/ring_flash_attn/utils.py b/src/nanotron/ring_flash_attn/utils.py
new file mode 100644
index 00000000..dacda6cf
--- /dev/null
+++ b/src/nanotron/ring_flash_attn/utils.py
@@ -0,0 +1,132 @@
+from typing import Optional, Tuple
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+
+__all__ = ["update_out_and_lse", "RingComm"]
+
+# copy from https://github.com/zhuzilin/ring-flash-attention
+@torch.jit.script
+def _update_out_and_lse(
+    out: torch.Tensor,
+    lse: torch.Tensor,
+    block_out: torch.Tensor,
+    block_lse: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+
+    block_out = block_out.to(torch.float32)
+    block_lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1)
+
+    # new_lse = lse + torch.log(1 + torch.exp(block_lse - lse))
+    # torch.exp(lse - new_lse) * out + torch.exp(block_lse - new_lse) * block_out
+    # For additional context and discussion, please refer to:
+    # https://github.com/zhuzilin/ring-flash-attention/pull/34#issuecomment-2076126795
+    out = out - F.sigmoid(block_lse - lse) * (out - block_out)
+    lse = lse - F.logsigmoid(lse - block_lse)
+
+    return out, lse
+
+
+def update_out_and_lse(
+    out: Optional[torch.Tensor],
+    lse: Optional[torch.Tensor],
+    block_out: torch.Tensor,
+    block_lse: torch.Tensor,
+    slice_=None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    if out is None:
+        if slice_ is not None:
+            raise RuntimeError("first update_out_and_lse should not pass slice_ args")
+        out = block_out.to(torch.float32)
+        lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1)
+    elif slice_ is not None:
+        slice_out, slice_lse = out[slice_], lse[slice_]
+        slice_out, slice_lse = _update_out_and_lse(slice_out, slice_lse, block_out, block_lse)
+        out[slice_], lse[slice_] = slice_out, slice_lse
+    else:
+        out, lse = _update_out_and_lse(out, lse, block_out, block_lse)
+    return out, lse
+
+
+@torch.jit.script
+def flatten_varlen_lse(lse, cu_seqlens):
+    new_lse = []
+    for i in range(len(cu_seqlens) - 1):
+        start, end = cu_seqlens[i], cu_seqlens[i + 1]
+        new_lse.append(lse[i, :, : end - start])
+    return torch.cat(new_lse, dim=1)
+
+
+@torch.jit.script
+def unflatten_varlen_lse(lse, cu_seqlens, max_seqlen: int):
+    num_seq = len(cu_seqlens) - 1
+    num_head = lse.shape[-2]
+    new_lse = torch.empty((num_seq, max_seqlen, num_head, 1), dtype=torch.float32, device=lse.device)
+    for i in range(num_seq):
+        start, end = cu_seqlens[i], cu_seqlens[i + 1]
+        new_lse[i, : end - start] = lse[start:end]
+    return new_lse.squeeze(dim=-1).transpose(1, 2).contiguous()
+
+
+class RingComm:
+    def __init__(self, process_group: dist.ProcessGroup):
+        self._process_group = process_group
+        self._ops = []
+        self.rank = dist.get_rank(self._process_group)
+        self.world_size = dist.get_world_size(self._process_group)
+        self._reqs = None
+
+        self.send_rank = (self.rank + 1) % self.world_size
+        self.recv_rank = (self.rank - 1) % self.world_size
+
+        if process_group is not None:
+            self.send_rank = dist.get_global_rank(self._process_group, self.send_rank)
+            self.recv_rank = dist.get_global_rank(self._process_group, self.recv_rank)
+
+    def send_recv(self, to_send: torch.Tensor, recv_tensor: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if recv_tensor is None:
+            res = torch.empty_like(to_send)
+        else:
+            res = recv_tensor
+
+        send_op = dist.P2POp(dist.isend, to_send, self.send_rank, group=self._process_group)
+        recv_op = dist.P2POp(dist.irecv, res, self.recv_rank, group=self._process_group)
+        self._ops.append(send_op)
+        self._ops.append(recv_op)
+        return res
+
+    def commit(self):
+        if self._reqs is not None:
+            raise RuntimeError("commit called twice")
+        self._reqs = dist.batch_isend_irecv(self._ops)
+
+    def wait(self):
+        if self._reqs is None:
+            raise RuntimeError("wait called before commit")
+        for req in self._reqs:
+            req.wait()
+        self._reqs = None
+        self._ops = []
+
+
+def extract_local(tensor: torch.Tensor, rank: int, world_size: int, dim: int = 1) -> torch.Tensor:
+    value_chunks = tensor.chunk(2 * world_size, dim=dim)
+    local_value = torch.cat([value_chunks[rank], value_chunks[2 * world_size - rank - 1]], dim=dim)
+    return local_value.contiguous()
+
+
+def zigzag_split(rank: int, world_size: int, *args: torch.Tensor, dim=1) -> Tuple[torch.Tensor, ...]:
+    local_values = []
+    for tensor in args:
+        local_value = extract_local(tensor, rank, world_size, dim)
+        local_values.append(local_value)
+    return tuple(local_values)
+
+
+def normal_split(rank: int, world_size: int, *args: torch.Tensor, dim=1) -> Tuple[torch.Tensor, ...]:
+    local_values = []
+    for value in args:
+        local_value = value.chunk(world_size, dim=dim)[rank]
+        local_values.append(local_value)
+    return tuple(local_values)
diff --git a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
new file mode 100644
index 00000000..2ac1e4e3
--- /dev/null
+++ b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
@@ -0,0 +1,325 @@
+import torch
+from flash_attn.flash_attn_interface import _flash_attn_backward, _flash_attn_forward
+
+from .utils import RingComm, update_out_and_lse
+
+
+# copy from https://github.com/zhuzilin/ring-flash-attention
+def zigzag_ring_flash_attn_forward(
+    process_group,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    softmax_scale,
+    dropout_p=0,
+    causal=True,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+):
+    assert causal is True, "zigzag ring is meaningless for causal=False"
+    comm = RingComm(process_group)
+
+    block_seq_len = q.shape[1] // 2
+    q1 = q[:, block_seq_len:]
+
+    out = None
+    lse = None
+    next_k, next_v = None, None
+
+    def forward(q, k, v, causal):
+        block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(
+            q,
+            k,
+            v,
+            dropout_p,
+            softmax_scale,
+            causal=causal,
+            window_size=window_size,
+            alibi_slopes=alibi_slopes,
+            return_softmax=True and dropout_p > 0,
+        )
+        return block_out, block_lse
+
+    for step in range(comm.world_size):
+        if step + 1 != comm.world_size:
+            next_k: torch.Tensor = comm.send_recv(k)
+            next_v: torch.Tensor = comm.send_recv(v)
+            comm.commit()
+
+        if step == 0:
+            block_out, block_lse = forward(q, k, v, causal=True)
+            out, lse = update_out_and_lse(out, lse, block_out, block_lse)
+        elif step <= comm.rank:
+            k0 = k[:, :block_seq_len]
+            v0 = v[:, :block_seq_len]
+            block_out, block_lse = forward(q, k0, v0, causal=False)
+            out, lse = update_out_and_lse(out, lse, block_out, block_lse)
+        else:
+            block_out, block_lse = forward(q1, k, v, causal=False)
+            out, lse = update_out_and_lse(
+                out,
+                lse,
+                block_out,
+                block_lse,
+                slice_=(slice(None), slice(block_seq_len, None)),
+            )
+
+        if step + 1 != comm.world_size:
+            comm.wait()
+            k = next_k
+            v = next_v
+
+    out = out.to(q.dtype)
+    lse = lse.squeeze(dim=-1).transpose(1, 2)
+    return out, lse
+
+
+def zigzag_ring_flash_attn_backward(
+    process_group,
+    doubt,
+    q,
+    k,
+    v,
+    out,
+    softmax_lse,
+    softmax_scale,
+    dropout_p=0,
+    causal=True,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+):
+    assert causal is True, "zigzag ring is meaningless for causal=False"
+    kv_comm = RingComm(process_group)
+    d_kv_comm = RingComm(process_group)
+    dq, dk, dv = None, None, None
+    next_dk, next_dv = None, None
+    next_k, next_v = None, None
+    dk_comm_buffer, dv_comm_buffer = None, None
+
+    dout1 = doubt.chunk(2, dim=1)[1]
+    q1 = q.chunk(2, dim=1)[1]
+    out1 = out.chunk(2, dim=1)[1]
+    softmax_lse1 = softmax_lse.chunk(2, dim=2)[1].contiguous()
+    block_seq_len = q.shape[1] // 2
+
+    # repeatedly allocating buffer may be slow...
+    dq_buffer = torch.empty(q.shape, dtype=q.dtype, device=q.device)
+    dk_buffer = torch.empty(k.shape, dtype=k.dtype, device=k.device)
+    dv_buffer = torch.empty(v.shape, dtype=v.dtype, device=v.device)
+
+    def backward(doubt, q, k, v, out, softmax_lse, causal):
+        seqlen_q = q.shape[1]
+        seqlen_kv = k.shape[1]
+        _flash_attn_backward(
+            doubt,
+            q,
+            k,
+            v,
+            out,
+            softmax_lse,
+            dq_buffer[:, :seqlen_q],
+            dk_buffer[:, :seqlen_kv],
+            dv_buffer[:, :seqlen_kv],
+            dropout_p,
+            softmax_scale,
+            causal,
+            window_size,
+            alibi_slopes,
+            deterministic,
+            rng_state=None,
+        )
+
+    for step in range(kv_comm.world_size):
+        if step + 1 != kv_comm.world_size:
+            next_k = kv_comm.send_recv(k)
+            next_v = kv_comm.send_recv(v)
+            kv_comm.commit()
+
+        if step == 0:
+            backward(doubt, q, k, v, out, softmax_lse, causal=True)
+            dq = dq_buffer.to(torch.float32)
+            dk = dk_buffer.to(torch.float32)
+            dv = dv_buffer.to(torch.float32)
+        else:
+            if step <= kv_comm.rank:
+                k0 = k[:, :block_seq_len]
+                v0 = v[:, :block_seq_len]
+                backward(doubt, q, k0, v0, out, softmax_lse, causal=False)
+                dq += dq_buffer
+            else:
+                backward(dout1, q1, k, v, out1, softmax_lse1, causal=False)
+                # always use the first half in dq_buffer.
+                dq[:, block_seq_len:] += dq_buffer[:, :block_seq_len]
+
+            d_kv_comm.wait()
+            dk_comm_buffer, dv_comm_buffer = dk, dv
+            dk, dv = next_dk, next_dv
+
+            if step <= kv_comm.rank:
+                dk[:, :block_seq_len] += dk_buffer[:, :block_seq_len]
+                dv[:, :block_seq_len] += dv_buffer[:, :block_seq_len]
+            else:
+                dk += dk_buffer
+                dv += dv_buffer
+
+        if step + 1 != kv_comm.world_size:
+            kv_comm.wait()
+            k = next_k
+            v = next_v
+
+        next_dk = d_kv_comm.send_recv(dk, dk_comm_buffer)
+        next_dv = d_kv_comm.send_recv(dv, dv_comm_buffer)
+        d_kv_comm.commit()
+
+    d_kv_comm.wait()
+
+    return dq.to(q.dtype), next_dk.to(q.dtype), next_dv.to(q.dtype)
+
+
+class ZigZagRingFlashAttnFunc(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        q,
+        k,
+        v,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_softmax,
+        group,
+    ):
+        if softmax_scale is None:
+            softmax_scale = q.shape[-1] ** (-0.5)
+
+        assert alibi_slopes is None
+        k = k.contiguous()
+        v = v.contiguous()
+        out, softmax_lse = zigzag_ring_flash_attn_forward(
+            group,
+            q,
+            k,
+            v,
+            softmax_scale=softmax_scale,
+            dropout_p=dropout_p,
+            causal=causal,
+            window_size=window_size,
+            alibi_slopes=alibi_slopes,
+            deterministic=False,
+        )
+        # this should be out_padded
+        ctx.save_for_backward(q, k, v, out, softmax_lse)
+        ctx.dropout_p = dropout_p
+        ctx.softmax_scale = softmax_scale
+        ctx.causal = causal
+        ctx.window_size = window_size
+        ctx.alibi_slopes = alibi_slopes
+        ctx.deterministic = deterministic
+        ctx.group = group
+        return out if not return_softmax else (out, softmax_lse, None)
+
+    @staticmethod
+    def backward(ctx, doubt, *args):
+        q, k, v, out, softmax_lse = ctx.saved_tensors
+        dq, dk, dv = zigzag_ring_flash_attn_backward(
+            ctx.group,
+            doubt,
+            q,
+            k,
+            v,
+            out,
+            softmax_lse,
+            softmax_scale=ctx.softmax_scale,
+            dropout_p=ctx.dropout_p,
+            causal=ctx.causal,
+            window_size=ctx.window_size,
+            alibi_slopes=ctx.alibi_slopes,
+            deterministic=ctx.deterministic,
+        )
+        return dq, dk, dv, None, None, None, None, None, None, None, None
+
+
+def zigzag_ring_flash_attn_qkvpacked_func(
+    qkv,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return ZigZagRingFlashAttnFunc.apply(
+        qkv[:, :, 0],
+        qkv[:, :, 1],
+        qkv[:, :, 2],
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )
+
+
+def zigzag_ring_flash_attn_kvpacked_func(
+    q,
+    kv,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return ZigZagRingFlashAttnFunc.apply(
+        q,
+        kv[:, :, 0],
+        kv[:, :, 1],
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )
+
+
+def zigzag_ring_flash_attn_func(
+    q,
+    k,
+    v,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return ZigZagRingFlashAttnFunc.apply(
+        q,
+        k,
+        v,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )
diff --git a/src/nanotron/trainer.py b/src/nanotron/trainer.py
index 0eda00dc..19b979cc 100644
--- a/src/nanotron/trainer.py
+++ b/src/nanotron/trainer.py
@@ -147,6 +147,7 @@ def __init__(
             pipeline_parallel_size=self.config.parallelism.pp,
             data_parallel_size=self.config.parallelism.dp,
             expert_parallel_size=self.config.parallelism.expert_parallel_size,
+            sequence_parallel_size=self.config.parallelism.sp,
         )
 
         self.pre_init()
@@ -234,7 +235,7 @@ def __init__(
 
         # Setup tensorboard write and log writers on output rank
         self.logger_ranks = self.parallel_context.get_global_rank(
-            ep_rank=0, pp_rank=self.unwrapped_model.output_pp_rank, dp_rank=0, tp_rank=0
+            ep_rank=0, pp_rank=self.unwrapped_model.output_pp_rank, dp_rank=0, tp_rank=0, sp_rank=0
         ).flatten()
         self.loggerwriter = self.setup_log_writers()
 
@@ -440,7 +441,6 @@ def train(
                 # Checkpoint
                 if self.iteration_step % self.config.checkpoints.checkpoint_interval == 0:
                     self.save_checkpoint()
-
         dist.barrier()  # let's wait for everyone before leaving
 
         self.post_training()
@@ -907,6 +907,7 @@ def mark_tied_parameters(
                 target,
                 (
                     parallel_context.get_global_rank(
+                        sp_rank=dist.get_rank(parallel_context.sp_pg),
                         ep_rank=dist.get_rank(parallel_context.expert_pg),
                         pp_rank=get_pp_rank_of(target, module=model),
                         dp_rank=dist.get_rank(parallel_context.dp_pg),

From 111c4fc01f18ef6e5483d56bc21b9166d0ce09df Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Tue, 11 Jun 2024 08:39:58 +0000
Subject: [PATCH 02/19] still testing

---
 run_generate.py                               |  46 ++++++-
 src/nanotron/config/models_config.py          |   4 +
 src/nanotron/helpers.py                       |  12 +-
 src/nanotron/logging.py                       |   2 +-
 src/nanotron/models/llama.py                  | 120 +++++++++++++++---
 src/nanotron/parallel/context.py              |  15 ++-
 .../ring_flash_attn/ring_flash_attn.py        |  67 ++++++++++
 src/nanotron/ring_flash_attn/utils.py         |  54 ++++++++
 .../ring_flash_attn/zigzag_ring_flash_attn.py |  87 +++++++++++++
 src/nanotron/sanity_checks.py                 |   9 +-
 src/nanotron/serialize/utils.py               |   6 +-
 src/nanotron/serialize/weights.py             |   2 +-
 src/nanotron/trainer.py                       | 115 ++++++++++++++++-
 13 files changed, 504 insertions(+), 35 deletions(-)

diff --git a/run_generate.py b/run_generate.py
index afdf32b4..22c61ab5 100644
--- a/run_generate.py
+++ b/run_generate.py
@@ -59,7 +59,8 @@ def get_args():
     parser.add_argument("--dp", type=int, default=0)
     parser.add_argument("--pp", type=int, default=0)
     parser.add_argument("--tp", type=int, default=0)
-    parser.add_argument("--max-new-tokens", type=int, default=128, help="Maximum number of new tokens to generate")
+    parser.add_argument("--sp", type=int, default=0)
+    parser.add_argument("--max-new-tokens", type=int, default=50, help="Maximum number of new tokens to generate")
     return parser.parse_args()
 
 
@@ -76,6 +77,7 @@ def main():
         dp=args.dp or config.parallelism.dp,
         pp=args.pp or config.parallelism.pp,
         tp=args.tp or config.parallelism.tp,
+        sp=args.sp or config.parallelism.sp,
         pp_engine=OneForwardOneBackwardPipelineEngine(),
         tp_mode=TensorParallelLinearMode.ALL_REDUCE,
         tp_linear_async_communication=False,
@@ -86,6 +88,7 @@ def main():
         data_parallel_size=parallel_config.dp,
         pipeline_parallel_size=parallel_config.pp,
         tensor_parallel_size=parallel_config.tp,
+        sequence_parallel_size=parallel_config.sp,
     )
 
     # Set log levels
@@ -163,7 +166,30 @@ def main():
                 tokenizer.add_special_tokens({"pad_token": "[PAD]"})
         tokenizer.padding_side = "left"
         tokenizer.truncation_side = "left"  # TODO @nouamane: do we want this?
+
+        # PREFIX = "This is a very long story book: <book>"
+        # NEEDLE_FORMAT = "\nThe special magic Singapore number is: 353233.\n"
+        # book=f""
+        # QUESTION_STR = "</book>.\n Based on the content of the book, Question: What is the special magic Singapore number? Answer: The special magic Singapore number is:"
+
+        # some different length input file
+        # file_path = '/fsx/haojun/PaulGrahamEssays/13k.txt'
+        file_path = "/fsx/haojun/PaulGrahamEssays/11k.txt"
+        file_path_89 = "/fsx/haojun/PaulGrahamEssays/8900.txt"
+        file_path_81 = "/fsx/haojun/PaulGrahamEssays/8140.txt"
+
+        # Open and read the file
+        with open(file_path_89, "r") as file:
+            file.read()
+        with open(file_path, "r") as file:
+            file.read()
+        with open(file_path_81, "r") as file:
+            file.read()
+
         dummy_inputs = [
+            # long_text_110,
+            # long_text_89,
+            # long_text_81,
             "The future of AI is",
             "Passage: Daniel went back to the garden. Mary travelled to the kitchen. Sandra journeyed to the kitchen. Sandra went to the hallway. John went to the bedroom. Mary went back to the garden. Where is Mary?\nAnswer:",
             "def fib(n)",
@@ -193,19 +219,33 @@ def main():
             assert isinstance(generated_ids, torch.Tensor)
 
             log_rank(
-                f"input: {tokenizer.decode(input_ids, clean_up_tokenization_spaces=False)[:1000]}",
+                f"input: {tokenizer.decode(input_ids[:50], clean_up_tokenization_spaces=False)}",
                 logger=logger,
                 level=logging.INFO,
                 rank=0,
             )
 
             log_rank(
-                f"generation: {tokenizer.decode(generated_ids[len(input_ids) :], clean_up_tokenization_spaces=False)}",
+                f"Number of input tokens: {len(input_ids)}",
                 logger=logger,
                 level=logging.INFO,
                 rank=0,
             )
 
+            log_rank(
+                f"generation: {tokenizer.decode(generated_ids[len(input_ids) :], clean_up_tokenization_spaces=True)}",
+                logger=logger,
+                level=logging.INFO,
+                rank=0,
+            )
+
+            # log_rank(
+            #     f"generation token ids: {generated_ids[len(input_ids) :]}",
+            #     logger=logger,
+            #     level=logging.INFO,
+            #     rank=0,
+            # )
+
             log_rank(
                 "--------------------------------------------------",
                 logger=logger,
diff --git a/src/nanotron/config/models_config.py b/src/nanotron/config/models_config.py
index ba4559cf..2630e1d6 100644
--- a/src/nanotron/config/models_config.py
+++ b/src/nanotron/config/models_config.py
@@ -47,6 +47,10 @@ class LlamaConfig:
     pretraining_tp: int = 1
     rms_norm_eps: float = 1e-6
     rope_scaling: Optional[dict] = None
+    rope_theta: float = 10000.0
+    rope_interleaved: bool = (
+        True  # The default value has been True, but for loading Llama3 checkpoints you have to set it to False
+    )
     tie_word_embeddings: bool = False
     use_cache: bool = True
     vocab_size: int = 32000
diff --git a/src/nanotron/helpers.py b/src/nanotron/helpers.py
index f7bf63e5..1c8bc54e 100644
--- a/src/nanotron/helpers.py
+++ b/src/nanotron/helpers.py
@@ -381,7 +381,8 @@ def grad_optimizer_builder(named_param_groups):
             named_params_or_groups=named_param_groups,
             # TODO @thomasw21: We need a better API for gradient accumulation/zero etc ...
             optimizer_builder=optimizer_builder,
-            dp_pg=parallel_context.dp_pg,
+            # dp_pg=parallel_context.dp_pg,
+            dp_pg=parallel_context.dp_sp_pg,
         )
 
         # SANITY CHECK: assert that optimizer's named_params point to model's params (check only the first one)
@@ -406,7 +407,8 @@ def grad_optimizer_builder(named_param_groups):
 
         assert isinstance(grad_accumulator, FP32GradientAccumulator)
         grad_accumulator.assign_param_offsets(
-            dp_rank=dist.get_rank(parallel_context.dp_pg),
+            # dp_rank=dist.get_rank(parallel_context.dp_pg),
+            dp_rank=dist.get_rank(parallel_context.dp_sp_pg),
             param_name_to_offsets=param_name_to_dp_rank_offsets,
         )
 
@@ -415,7 +417,8 @@ def grad_optimizer_builder(named_param_groups):
         assert isinstance(grad_accumulator, FP32GradientAccumulator)
         model.register_comm_hook(
             state=FP32GradBucketManager(
-                dp_pg=parallel_context.dp_pg,
+                # dp_pg=parallel_context.dp_pg,
+                dp_pg=parallel_context.dp_sp_pg,
                 accumulator=grad_accumulator,
                 param_id_to_name={
                     id(param): param.get_tied_info().get_full_name_from_module_id_to_prefix(
@@ -687,7 +690,7 @@ def is_resume_from_training():
     else:
         next_stage = next((s for s in config.data_stages if s.start_training_step > stage.start_training_step), None)
         total_train_steps = next_stage.start_training_step
-    
+
     if metadata.last_train_step > stage.start_training_step:
         # NOTE: if the last_train_step is larger than the start_training_step of the current stage,
         # it means that the training has already passed this stage
@@ -702,6 +705,7 @@ def get_consumed_train_samples_of_a_data_stage_from_ckp(
     stage: DatasetStageArgs, metadata: TrainingMetadata
 ) -> Optional[int]:
     start_training_step = stage.start_training_step
+    # TODO: if a new dataset is added, we know that it has consumed 0 tokens? Cannot add new dataset for now.
     return next(
         (s.consumed_train_samples for s in metadata.data_stages if s.start_training_step == start_training_step),
         None,
diff --git a/src/nanotron/logging.py b/src/nanotron/logging.py
index 708393b5..e762b10e 100644
--- a/src/nanotron/logging.py
+++ b/src/nanotron/logging.py
@@ -293,7 +293,7 @@ def set_logger_verbosity_format(logging_level: str, parallel_context: ParallelCo
     )
     formatter = Formatter(
         fmt=f"%(asctime)s [%(levelname)s|DP={dist.get_rank(parallel_context.dp_pg)}|PP={dist.get_rank(parallel_context.pp_pg)}|"
-        f"TP={dist.get_rank(parallel_context.tp_pg)}{expert_parallel_log}{'|' + node_name if node_name else ''}]: %(message)s",
+        f"TP={dist.get_rank(parallel_context.tp_pg)}|SP={dist.get_rank(parallel_context.sp_pg)}{expert_parallel_log}{'|' + node_name if node_name else ''}]: %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S",
     )
     log_level = log_levels[logging_level]
diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index dfe163c3..7f0f63b3 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -32,7 +32,6 @@
 from nanotron.parallel.parameters import NanotronParameter
 from nanotron.parallel.pipeline_parallel.block import PipelineBlock, TensorPointer
 from nanotron.parallel.pipeline_parallel.p2p import P2P
-from nanotron.parallel.tensor_parallel.distributed_differentiable_primitives import differentiable_all_reduce_sum
 from nanotron.parallel.tensor_parallel.functional import sharded_cross_entropy
 from nanotron.parallel.tensor_parallel.nn import (
     TensorParallelColumnLinear,
@@ -122,6 +121,73 @@ def forward(
         return x_out.type(dtype)
 
 
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+### llama
+class LlamaRotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, end: int, theta: float = 500000.0):
+        super().__init__()
+        self.dim = dim
+        self.end = end
+        self.theta = theta
+        self.init_rotary_embeddings()
+
+    def init_rotary_embeddings(self):
+        inv_freq = 1.0 / (self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float, device="cuda") / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        x: torch.Tensor,  # [batch_size, seq_length, num_heads, d_qk]
+        position_ids: Optional[torch.LongTensor],  # [batch_size, seq_length]
+    ):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # print("rotary")
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=2):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
 class GLUActivation(nn.Module):
     def __init__(self, act_fn_name: str):
         super().__init__()
@@ -378,13 +444,22 @@ def __init__(
             contiguous_chunks=qkv_contiguous_chunks,
         )
         # TODO(kunhao): We want to have only one version per device and not one version per layer.
-        self.rotary_embedding = RotaryEmbedding(
-            dim=self.d_qk,
-            end=config.max_position_embeddings,
-        )
+        if config.rope_interleaved:
+            self.rotary_embedding = RotaryEmbedding(
+                dim=self.d_qk,
+                end=config.max_position_embeddings,
+            )
+        else:
+            self.rotary_embedding = LlamaRotaryEmbedding(
+                dim=self.d_qk,
+                end=config.max_position_embeddings,
+            )
+        self.rope_interleaved = config.rope_interleaved
 
         # NOTE: Only supported for training (TODO(fmom): position_ids not supported yet)
-        self.flash_rotary_embedding = FlashRotaryEmbedding(dim=self.d_qk, interleaved=True)
+        self.flash_rotary_embedding = FlashRotaryEmbedding(
+            dim=self.d_qk, interleaved=config.rope_interleaved, base=config.rope_theta
+        )
 
         self.o_proj = TensorParallelRowLinear(
             config.num_attention_heads * self.d_qk,
@@ -471,8 +546,19 @@ def forward(
             # Compute rotary embeddings
             # Note: keep track of old rotary embedding end to check if we need to enlarge k_cache and v_cache
             old_rotary_embed_end = self.rotary_embedding.end
-            query_states = self.rotary_embedding(query_states, position_ids=position_ids)
-            key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+
+            # Rotate half rotary_embedding
+            # cos, sin = self.rotary_embedding(value_states, position_ids)
+            # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+            # interleaved
+            if self.rope_interleaved:
+                query_states = self.rotary_embedding(query_states, position_ids=position_ids)
+                key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+            # llama rotary position embedding
+            else:
+                cos, sin = self.rotary_embedding(value_states, position_ids)
+                query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
             if "key" not in store:
                 # First inference iteration (Prefill)
@@ -623,10 +709,19 @@ def forward(
             )
 
         else:  # Training case
+            # # # # test my implementation. will delete later
+            # cos, sin = self.rotary_embedding(value_states, position_ids)
+            # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
             # apply rotary embedding. Will adapt to flash rotary embedding later
             if self.sp_pg.size() > 1:
-                query_states = self.rotary_embedding(query_states, position_ids=position_ids)
-                key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+                if self.rope_interleaved:
+                    query_states = self.rotary_embedding(query_states, position_ids=position_ids)
+                    key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+                # llama rotary position embedding
+                else:
+                    cos, sin = self.rotary_embedding(value_states, position_ids)
+                    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
             else:
                 # Apply rotary embeddings to query/key states
                 # NOTE: The layout is different from models/llama.py which is [batch_size, num_heads, seq_length, d_qk]
@@ -967,11 +1062,6 @@ def forward(
         # I think indexing causes a sync we don't actually want
         # loss = loss[label_mask].sum()
 
-        # Calculate the average loss over sp
-        if self.sp_pg.size() > 1:
-            differentiable_all_reduce_sum(loss, self.sp_pg)
-            loss /= torch.tensor(self.sp_pg.size(), dtype=loss.dtype, device=loss.device)
-
         return {"loss": loss}
 
 
diff --git a/src/nanotron/parallel/context.py b/src/nanotron/parallel/context.py
index 1a1b8444..b070debb 100644
--- a/src/nanotron/parallel/context.py
+++ b/src/nanotron/parallel/context.py
@@ -84,12 +84,24 @@ def _init_parallel_groups(self):
         # Relevant process groups containing the current rank
 
         self.tp_pg = self.create_new_group(ranks.transpose((0, 1, 2, 3, 4)).reshape((-1, self.tensor_parallel_size)))
+        self.sp_pg = self.create_new_group(ranks.transpose((1, 2, 3, 4, 0)).reshape((-1, self.sequence_parallel_size)))
+        # only need sync the gradient. Don't need to load different data. Be careful!
+        # Anything related to dp_pg get changed.
+        # Create a group DP+CP. Sync between this group.
+        # temporary fix
+        # self.dp_pg = self.sp_pg
+        self.dp_sp_pg = self.create_new_group(
+            ranks.transpose((1, 4, 2, 0, 3)).reshape((-1, self.data_parallel_size * self.sequence_parallel_size))
+        )  # the last two dimension should correspond to sp and dp.
+        # self.dp_pg = self.create_new_group(ranks.transpose((1, 4, 2, 0, 3)).reshape((-1, self.data_parallel_size*self.sequence_parallel_size))) #the last two dimension should correspond to sp and dp.
         self.dp_pg = self.create_new_group(ranks.transpose((4, 0, 1, 2, 3)).reshape((-1, self.data_parallel_size)))
         self.pp_pg = self.create_new_group(ranks.transpose((3, 4, 0, 1, 2)).reshape((-1, self.pipeline_parallel_size)))
+        # print("dp_sp_pg: ",ranks.transpose((1, 4, 2, 0, 3)).reshape((-1, self.data_parallel_size*self.sequence_parallel_size)))
+        # print("dp_pg: ", ranks.transpose((4, 0, 1, 2, 3)).reshape((-1, self.data_parallel_size)))
+        # print("sp_pg: ", ranks.transpose((1, 2, 3, 4, 0)).reshape((-1, self.sequence_parallel_size)))
         self.expert_pg = self.create_new_group(
             ranks.transpose((2, 3, 4, 0, 1)).reshape((-1, self.expert_parallel_size))
         )
-        self.sp_pg = self.create_new_group(ranks.transpose((1, 2, 3, 4, 0)).reshape((-1, self.sequence_parallel_size)))
         self.mp_pg = self.create_new_group(
             [ranks[:, :, :, dp_rank, :].reshape(-1) for dp_rank in range(self.data_parallel_size)]
         )
@@ -178,4 +190,5 @@ def get_global_rank(
 
         :return: numpy.int64, The global rank.
         """
+        # return self.world_rank_matrix[sp_rank, ep_rank, pp_rank, 0, tp_rank]
         return self.world_rank_matrix[sp_rank, ep_rank, pp_rank, dp_rank, tp_rank]
diff --git a/src/nanotron/ring_flash_attn/ring_flash_attn.py b/src/nanotron/ring_flash_attn/ring_flash_attn.py
index 48b394f8..1fae3367 100644
--- a/src/nanotron/ring_flash_attn/ring_flash_attn.py
+++ b/src/nanotron/ring_flash_attn/ring_flash_attn.py
@@ -54,6 +54,73 @@ def ring_flash_attn_forward(
     return out, lse
 
 
+# # My one more buffer version
+# def ring_flash_attn_forward(
+#     process_group,
+#     q: torch.Tensor,
+#     k: torch.Tensor,
+#     v: torch.Tensor,
+#     softmax_scale,
+#     dropout_p=0,
+#     causal=True,
+#     window_size=(-1, -1),
+#     alibi_slopes=None,
+#     deterministic=False,
+# ):
+#     comm = RingComm(process_group)
+
+#     out = None
+#     lse = None
+
+#     next_k_even, next_v_even = None, None
+#     next_k_odd, next_v_odd = None, None
+
+#     for step in range(comm.world_size):
+#         if step + 1 != comm.world_size:
+#             if step==0:
+#                 # order is important. should wait for the odd
+#                 next_k_odd = comm.send_recv(k) # for 1st
+#                 next_v_odd = comm.send_recv(v)
+#                 next_k_even = comm.send_recv(k,next_next=True) # for 2nd step
+#                 next_v_even = comm.send_recv(v,next_next=True)
+#             else:
+#                 # for step+2
+#                 if step % 2 == 0:
+#                     next_k_even = comm.send_recv(next_k_odd)
+#                     next_v_even = comm.send_recv(next_v_odd)
+#                 else:
+#                     next_k_odd = comm.send_recv(next_k_even)
+#                     next_v_odd = comm.send_recv(next_v_even)
+#             comm.commit()
+
+#         if not causal or step <= comm.rank:
+#             block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(
+#                 q,
+#                 k,
+#                 v,
+#                 dropout_p,
+#                 softmax_scale,
+#                 causal=causal and step == 0,
+#                 window_size=window_size,
+#                 alibi_slopes=alibi_slopes,
+#                 return_softmax=True and dropout_p > 0,
+#             )
+#             out, lse = update_out_and_lse(out, lse, block_out, block_lse)
+
+#         if step + 1 != comm.world_size:
+#             comm.wait()
+#             if step % 2 == 0:
+#                 k = next_k_odd
+#                 v = next_v_odd
+#             else:
+#                 k = next_k_even
+#                 v = next_v_even
+
+#     out = out.to(q.dtype)
+#     lse = lse.squeeze(dim=-1).transpose(1, 2)
+#     return out, lse
+
+
 def ring_flash_attn_backward(
     process_group,
     doubt,
diff --git a/src/nanotron/ring_flash_attn/utils.py b/src/nanotron/ring_flash_attn/utils.py
index dacda6cf..c3b27873 100644
--- a/src/nanotron/ring_flash_attn/utils.py
+++ b/src/nanotron/ring_flash_attn/utils.py
@@ -110,6 +110,60 @@ def wait(self):
         self._ops = []
 
 
+###### my implementation
+# class RingComm:
+#     def __init__(self, process_group: dist.ProcessGroup):
+#         self._process_group = process_group
+#         self._ops = []
+#         self.rank = dist.get_rank(self._process_group)
+#         self.world_size = dist.get_world_size(self._process_group)
+#         self._reqs = []
+
+#         self.send_rank = (self.rank + 1) % self.world_size
+#         self.recv_rank = (self.rank - 1) % self.world_size
+#         self.send_next_next_rank = (self.rank + 2) % self.world_size
+#         self.recv_next_next_rank = (self.rank - 2) % self.world_size
+
+#         if process_group is not None:
+#             self.send_rank = dist.get_global_rank(self._process_group, self.send_rank)
+#             self.recv_rank = dist.get_global_rank(self._process_group, self.recv_rank)
+#             self.send_next_next_rank = dist.get_global_rank(self._process_group, self.send_next_next_rank)
+#             self.recv_next_next_rank = dist.get_global_rank(self._process_group, self.recv_next_next_rank)
+
+#     def send_recv(self, to_send: torch.Tensor, recv_tensor: Optional[torch.Tensor] = None, next_next=False) -> torch.Tensor:
+#         if recv_tensor is None:
+#             res = torch.empty_like(to_send)
+#         else:
+#             res = recv_tensor
+#         if next_next:
+#             send_op = dist.P2POp(dist.isend, to_send, self.send_next_next_rank, group=self._process_group)
+#             recv_op = dist.P2POp(dist.irecv, res, self.recv_next_next_rank, group=self._process_group)
+#         else:
+#             send_op = dist.P2POp(dist.isend, to_send, self.send_rank, group=self._process_group)
+#             recv_op = dist.P2POp(dist.irecv, res, self.recv_rank, group=self._process_group)
+#         self._ops.append(send_op)
+#         self._ops.append(recv_op)
+#         return res
+
+#     def commit(self):
+#         # if self._reqs is not None:
+#         #     raise RuntimeError("commit called twice")
+#         self._reqs.extend(dist.batch_isend_irecv(self._ops))
+
+#     def wait(self):
+#         if self._reqs is None:
+#             raise RuntimeError("wait called before commit")
+#         # for req in self._reqs:
+#         #     req.wait()
+#         # self._reqs = None
+
+#         # use more buffer. This may help
+#         for req in self._reqs[:2]:  # Only wait for the first two requests
+#             req.wait()
+#         self._reqs = self._reqs[2:]  # Remove the first two requests from the list
+#         self._ops = []
+
+
 def extract_local(tensor: torch.Tensor, rank: int, world_size: int, dim: int = 1) -> torch.Tensor:
     value_chunks = tensor.chunk(2 * world_size, dim=dim)
     local_value = torch.cat([value_chunks[rank], value_chunks[2 * world_size - rank - 1]], dim=dim)
diff --git a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
index 2ac1e4e3..6b621e2d 100644
--- a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
+++ b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
@@ -75,6 +75,93 @@ def forward(q, k, v, causal):
     return out, lse
 
 
+# ### my implementation
+# def zigzag_ring_flash_attn_forward(
+#     process_group,
+#     q: torch.Tensor,
+#     k: torch.Tensor,
+#     v: torch.Tensor,
+#     softmax_scale,
+#     dropout_p=0,
+#     causal=True,
+#     window_size=(-1, -1),
+#     alibi_slopes=None,
+#     deterministic=False,
+# ):
+#     assert causal is True, "zigzag ring is meaningless for causal=False"
+#     comm = RingComm(process_group)
+
+#     block_seq_len = q.shape[1] // 2
+#     q1 = q[:, block_seq_len:]
+
+#     out = None
+#     lse = None
+#     next_k_even, next_v_even = None, None
+#     next_k_odd, next_v_odd = None, None
+
+#     def forward(q, k, v, causal):
+#         block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(
+#             q,
+#             k,
+#             v,
+#             dropout_p,
+#             softmax_scale,
+#             causal=causal,
+#             window_size=window_size,
+#             alibi_slopes=alibi_slopes,
+#             return_softmax=True and dropout_p > 0,
+#         )
+#         return block_out, block_lse
+
+#     for step in range(comm.world_size):
+#         if step==0:
+#                 # order is important. should wait for the odd
+#                 next_k_odd = comm.send_recv(k) # for 1st
+#                 next_v_odd = comm.send_recv(v)
+#                 next_k_even = comm.send_recv(k,next_next=True) # for 2nd step
+#                 next_v_even = comm.send_recv(v,next_next=True)
+#         else:
+#             # for step+2
+#             if step % 2 == 0:
+#                 next_k_even = comm.send_recv(next_k_odd)
+#                 next_v_even = comm.send_recv(next_v_odd)
+#             else:
+#                 next_k_odd = comm.send_recv(next_k_even)
+#                 next_v_odd = comm.send_recv(next_v_even)
+#         comm.commit()
+
+#         if step == 0:
+#             block_out, block_lse = forward(q, k, v, causal=True)
+#             out, lse = update_out_and_lse(out, lse, block_out, block_lse)
+#         elif step <= comm.rank:
+#             k0 = k[:, :block_seq_len]
+#             v0 = v[:, :block_seq_len]
+#             block_out, block_lse = forward(q, k0, v0, causal=False)
+#             out, lse = update_out_and_lse(out, lse, block_out, block_lse)
+#         else:
+#             block_out, block_lse = forward(q1, k, v, causal=False)
+#             out, lse = update_out_and_lse(
+#                 out,
+#                 lse,
+#                 block_out,
+#                 block_lse,
+#                 slice_=(slice(None), slice(block_seq_len, None)),
+#             )
+
+#         if step + 1 != comm.world_size:
+#             comm.wait()
+#             if step % 2 == 0:
+#                 k = next_k_odd
+#                 v = next_v_odd
+#             else:
+#                 k = next_k_even
+#                 v = next_v_even
+
+#     out = out.to(q.dtype)
+#     lse = lse.squeeze(dim=-1).transpose(1, 2)
+#     return out, lse
+
+
 def zigzag_ring_flash_attn_backward(
     process_group,
     doubt,
diff --git a/src/nanotron/sanity_checks.py b/src/nanotron/sanity_checks.py
index b18e5433..dd9b913e 100644
--- a/src/nanotron/sanity_checks.py
+++ b/src/nanotron/sanity_checks.py
@@ -64,7 +64,8 @@ def before_tbi_sanity_checks(
         for name, param in sorted(unwrapped_model.named_parameters(), key=lambda x: x[0]):
             assert_tensor_synced_across_pg(
                 tensor=param,
-                pg=parallel_context.dp_pg,
+                # pg=parallel_context.dp_pg,
+                pg=parallel_context.dp_sp_pg,
                 msg=lambda err: f"{name} are not synchronized across DP {err}",
             )
 
@@ -185,7 +186,8 @@ def before_optim_step_sanity_checks(
             assert grad is not None, f"Grad is None for {name}"
             assert_tensor_synced_across_pg(
                 tensor=grad,
-                pg=parallel_context.dp_pg,
+                # pg=parallel_context.dp_pg,
+                pg=parallel_context.dp_sp_pg,
                 msg=lambda err: f"[Before optimizer step] weights grads for {name} are not synchronized across DP. {err}",
             )
 
@@ -193,7 +195,8 @@ def before_optim_step_sanity_checks(
         for name, param in sorted(unwrapped_model.named_parameters(), key=lambda x: x[0]):
             assert_tensor_synced_across_pg(
                 tensor=param,
-                pg=parallel_context.dp_pg,
+                # pg=parallel_context.dp_pg,
+                pg=parallel_context.dp_sp_pg,
                 msg=lambda err: f"{name} are not synchronized across DP {err}",
             )
 
diff --git a/src/nanotron/serialize/utils.py b/src/nanotron/serialize/utils.py
index ebdec2b6..921b072c 100644
--- a/src/nanotron/serialize/utils.py
+++ b/src/nanotron/serialize/utils.py
@@ -21,9 +21,9 @@ def get_exp_tp_pp_rank_and_size_from(
 ) -> Tuple[Tuple[int, int], Tuple[int, int]]:
     result = parallel_context.get_local_ranks(world_rank=world_rank)
     return (
-        (result[0], parallel_context.expert_pg.size()),
-        (result[3], parallel_context.tp_pg.size()),
-        (result[1], parallel_context.pp_pg.size()),
+        (result[1], parallel_context.expert_pg.size()),
+        (result[4], parallel_context.tp_pg.size()),
+        (result[2], parallel_context.pp_pg.size()),
     )
 
 
diff --git a/src/nanotron/serialize/weights.py b/src/nanotron/serialize/weights.py
index 96d2be4c..621c6f4c 100644
--- a/src/nanotron/serialize/weights.py
+++ b/src/nanotron/serialize/weights.py
@@ -32,7 +32,7 @@ def save_weights(model: nn.Module, parallel_context: ParallelContext, root_folde
 
     # We save only `dist.get_rank(parallel_context.dp_pg) == 0`
     # TODO @thomasw21: Figure how this works with Zero-3
-    if dist.get_rank(parallel_context.dp_pg) != 0:
+    if dist.get_rank(parallel_context.dp_pg) != 0 or dist.get_rank(parallel_context.sp_pg) != 0:
         return
 
     module_id_to_prefix = {id(module): f"{module_name}." for module_name, module in model.named_modules()}
diff --git a/src/nanotron/trainer.py b/src/nanotron/trainer.py
index 19b979cc..df518e9c 100644
--- a/src/nanotron/trainer.py
+++ b/src/nanotron/trainer.py
@@ -441,6 +441,79 @@ def train(
                 # Checkpoint
                 if self.iteration_step % self.config.checkpoints.checkpoint_interval == 0:
                     self.save_checkpoint()
+                #     ################# My test for saving and loading params #################
+                #     ####### load from checkpoint and compare with the model.
+                #     checkpoint_model = build_model(
+                #         model_builder=lambda: CONFIG_TO_MODEL_CLASS[self.model_config.__class__.__name__](
+                #             config=self.model_config,
+                #             parallel_context=self.parallel_context,
+                #             parallel_config=self.config.parallelism,
+                #             random_states=self.random_states,
+                #         ),
+                #         dtype=self.config.model.dtype,
+                #         parallel_context=self.parallel_context,
+                #     )
+
+                #     # Mark some parameters as tied
+                #     # TODO @nouamane: this is only needed for training, can we just mark params as NanotronParameter instead?
+                #     mark_tied_parameters(model=checkpoint_model, parallel_context=self.parallel_context, parallel_config=self.config.parallelism,)
+
+                #     # Sanity check model
+                #     sanity_check(root_module=checkpoint_model)
+
+                #     # Load checkpoint
+                #     checkpoints_path = self.config.checkpoints.checkpoints_path
+                #     checkpoint_path = checkpoints_path / f"{self.iteration_step}"
+                #     log_rank(
+                #         f"Loading checkpoint from {checkpoint_path}:",
+                #         logger=logger,
+                #         level=logging.INFO,
+                #         rank=0,
+                #     )
+                #     load_weights(model=checkpoint_model, parallel_context=self.parallel_context, root_folder=checkpoint_path)
+
+                #     ## compare keys firstly
+                #     model_state_dict = self.model.module.state_dict() if isinstance(self.model, DistributedDataParallel) else  self.model.state_dict()
+                #     checkpoint_state_dict = checkpoint_model.state_dict()
+
+                #     model_keys = set(model_state_dict.keys())
+                #     checkpoint_keys = set(checkpoint_state_dict.keys())
+
+                #     # Find keys that are in model but not in checkpoint
+                #     missing_in_checkpoint = model_keys - checkpoint_keys
+                #     # Find keys that are in checkpoint but not in model
+                #     missing_in_model = checkpoint_keys - model_keys
+
+                #     # # Print out the differences
+                #     # print("Keys in current model but not in checkpoint:", missing_in_checkpoint)
+                #     # print("Keys in checkpoint but not in current model:", missing_in_model)
+                #     # # print all the params
+                #     # print("self.model.state_dict().keys(): ",model_state_dict.keys(), "length: ", len(model_state_dict.keys()))
+                #     # print("checkpoint_model.state_dict().keys(): ",checkpoint_state_dict.keys(), "length: ", len(checkpoint_state_dict.keys()))
+
+                #     assert model_state_dict.keys() == checkpoint_state_dict.keys(), "Models have different structures."
+
+                #     # Compare parameters
+                #     all_params_same = True
+                #     for param_tensor in model_state_dict:
+                #         model_param = model_state_dict[param_tensor]
+                #         checkpoint_param = checkpoint_state_dict[param_tensor]
+                #         if not torch.equal(model_param, checkpoint_param):
+                #             all_params_same = False
+                #             break
+                #             num_different_elements = torch.sum(model_param != checkpoint_param).item()
+                #             total_elements = model_param.numel()
+                #             print(f"Mismatch found in parameter {param_tensor}: {num_different_elements}/{total_elements} elements are different.")
+                #         # else:
+                #         #     print(f"{param_tensor} is OK!")
+                #     if all_params_same:
+                #         print("All parameters are the same between the two models.")
+                #     else:
+                #         print("Parameters differ between the two models.")
+
+                # # stop_here = 1
+
+                # #################
         dist.barrier()  # let's wait for everyone before leaving
 
         self.post_training()
@@ -478,7 +551,8 @@ def training_step(
             # Manually sync across DP if it's not handled by DDP
             sync_gradients_across_dp(
                 module=self.model,
-                dp_pg=self.parallel_context.dp_pg,
+                dp_pg=self.parallel_context.dp_sp_pg,
+                # dp_pg=self.parallel_context.dp_pg,
                 reduce_op=dist.ReduceOp.AVG,
                 # TODO @thomasw21: This is too memory hungry, instead we run all_reduce
                 reduce_scatter=False,  # optimizer.inherit_from(ZeroDistributedOptimizer),
@@ -517,8 +591,11 @@ def training_step(
             loss_avg = torch.stack(
                 [output["loss"] for output in outputs]
             ).sum()  # already divided by n_micro_batches_per_batch
-            # sync loss across DP
-            handle = dist.all_reduce(loss_avg, group=self.parallel_context.dp_pg, async_op=True, op=dist.ReduceOp.AVG)
+            # sync loss across DP/SP
+            # handle = dist.all_reduce(loss_avg, group=self.parallel_context.dp_pg, async_op=True, op=dist.ReduceOp.AVG)
+            handle = dist.all_reduce(
+                loss_avg, group=self.parallel_context.dp_sp_pg, async_op=True, op=dist.ReduceOp.AVG
+            )
         else:
             loss_avg = None
             handle = None
@@ -798,9 +875,39 @@ def _init_model(
             # Check that the model has at least one grad. Necessary for DDP
             check_model_has_grad(model=model, parallel_context=parallel_context)
             # TODO @thomasw21: DDP doesn't support broadcasting complex buffers (and we don't really need that broadcasting anyway)
+
+            # ### debug
+            # tensor_names = [name for name in model.state_dict()]
+            # log_rank(
+            #     f"Params: {tensor_names}, length {len(tensor_names)}",
+            #     logger=logger,
+            #     level=logging.INFO,
+            #     rank=0,
+            # )
+            # log_rank(
+            #     f"Params: {tensor_names}, length {len(tensor_names)}",
+            #     logger=logger,
+            #     level=logging.INFO,
+            #     rank=1,
+            # )
+            # log_rank(
+            #     f"Params: {tensor_names}, length {len(tensor_names)}",
+            #     logger=logger,
+            #     level=logging.INFO,
+            #     rank=2,
+            # )
+            # log_rank(
+            #     f"Params: {tensor_names}, length {len(tensor_names)}",
+            #     logger=logger,
+            #     level=logging.INFO,
+            #     rank=3,
+            # )
+            # ###
+
             model = DistributedDataParallel(
                 model,
-                process_group=parallel_context.dp_pg,
+                # process_group=parallel_context.dp_pg,
+                process_group=parallel_context.dp_sp_pg,
                 broadcast_buffers=False,
                 bucket_cap_mb=config.model.ddp_bucket_cap_mb,
             )

From 3ef191d402d99e31963aebe1db59bdcd538d4580 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Sun, 30 Jun 2024 15:48:24 +0000
Subject: [PATCH 03/19] refactor some code

---
 run_generate.py                               | 45 +----------
 src/nanotron/models/llama.py                  | 33 +++-----
 .../ring_flash_attn/ring_flash_attn.py        | 75 +------------------
 3 files changed, 18 insertions(+), 135 deletions(-)

diff --git a/run_generate.py b/run_generate.py
index 22c61ab5..8c753a53 100644
--- a/run_generate.py
+++ b/run_generate.py
@@ -60,7 +60,7 @@ def get_args():
     parser.add_argument("--pp", type=int, default=0)
     parser.add_argument("--tp", type=int, default=0)
     parser.add_argument("--sp", type=int, default=0)
-    parser.add_argument("--max-new-tokens", type=int, default=50, help="Maximum number of new tokens to generate")
+    parser.add_argument("--max-new-tokens", type=int, default=128, help="Maximum number of new tokens to generate")
     return parser.parse_args()
 
 
@@ -166,30 +166,7 @@ def main():
                 tokenizer.add_special_tokens({"pad_token": "[PAD]"})
         tokenizer.padding_side = "left"
         tokenizer.truncation_side = "left"  # TODO @nouamane: do we want this?
-
-        # PREFIX = "This is a very long story book: <book>"
-        # NEEDLE_FORMAT = "\nThe special magic Singapore number is: 353233.\n"
-        # book=f""
-        # QUESTION_STR = "</book>.\n Based on the content of the book, Question: What is the special magic Singapore number? Answer: The special magic Singapore number is:"
-
-        # some different length input file
-        # file_path = '/fsx/haojun/PaulGrahamEssays/13k.txt'
-        file_path = "/fsx/haojun/PaulGrahamEssays/11k.txt"
-        file_path_89 = "/fsx/haojun/PaulGrahamEssays/8900.txt"
-        file_path_81 = "/fsx/haojun/PaulGrahamEssays/8140.txt"
-
-        # Open and read the file
-        with open(file_path_89, "r") as file:
-            file.read()
-        with open(file_path, "r") as file:
-            file.read()
-        with open(file_path_81, "r") as file:
-            file.read()
-
         dummy_inputs = [
-            # long_text_110,
-            # long_text_89,
-            # long_text_81,
             "The future of AI is",
             "Passage: Daniel went back to the garden. Mary travelled to the kitchen. Sandra journeyed to the kitchen. Sandra went to the hallway. John went to the bedroom. Mary went back to the garden. Where is Mary?\nAnswer:",
             "def fib(n)",
@@ -205,7 +182,7 @@ def main():
             model=model.model,
             parallel_context=parallel_context,
             max_new_tokens=args.max_new_tokens,
-            max_micro_batch_size=2,
+            max_micro_batch_size=1,
             generation_config=GenerationArgs(sampler="greedy", use_cache=True),
             tokenizer_config=TokenizerConfig(max_input_length=None),
             is_bench=os.environ.get("USE_BENCH", "0") == "1",
@@ -219,33 +196,19 @@ def main():
             assert isinstance(generated_ids, torch.Tensor)
 
             log_rank(
-                f"input: {tokenizer.decode(input_ids[:50], clean_up_tokenization_spaces=False)}",
-                logger=logger,
-                level=logging.INFO,
-                rank=0,
-            )
-
-            log_rank(
-                f"Number of input tokens: {len(input_ids)}",
+                f"input: {tokenizer.decode(input_ids, clean_up_tokenization_spaces=False)[:1000]}",
                 logger=logger,
                 level=logging.INFO,
                 rank=0,
             )
 
             log_rank(
-                f"generation: {tokenizer.decode(generated_ids[len(input_ids) :], clean_up_tokenization_spaces=True)}",
+                f"generation: {tokenizer.decode(generated_ids[len(input_ids) :], clean_up_tokenization_spaces=False)}",
                 logger=logger,
                 level=logging.INFO,
                 rank=0,
             )
 
-            # log_rank(
-            #     f"generation token ids: {generated_ids[len(input_ids) :]}",
-            #     logger=logger,
-            #     level=logging.INFO,
-            #     rank=0,
-            # )
-
             log_rank(
                 "--------------------------------------------------",
                 logger=logger,
diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index 7f0f63b3..b825bfb1 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -121,6 +121,7 @@ def forward(
         return x_out.type(dtype)
 
 
+## Copy from transformers. Non interleaved version of RoPE. Will be refactored later
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -128,13 +129,13 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-### llama
 class LlamaRotaryEmbedding(nn.Module):
     def __init__(self, dim: int, end: int, theta: float = 500000.0):
         super().__init__()
         self.dim = dim
         self.end = end
         self.theta = theta
+        log_rank(f"Initialize LlamaRotaryEmbedding: RoPE Theta = {theta}", logger=logger, level=logging.DEBUG, rank=0)
         self.init_rotary_embeddings()
 
     def init_rotary_embeddings(self):
@@ -264,8 +265,6 @@ def forward(
         local_v: torch.Tensor,  # [batch_size, kv_length, n_local_kv_heads, inner_dim]
     ):
         # NOTE: this scale is for µTransfer,
-        # in SP, we use sqrt(1/d_h)
-        # softmax_scale = 1 / local_q.shape[-1] if self.is_using_mup else None
         causal = True
 
         if zigzag:
@@ -294,7 +293,6 @@ def forward(
                 return_attn_probs=True,
                 group=self.pg,
             )
-        assert ring_out.requires_grad
         return ring_out
 
 
@@ -448,11 +446,13 @@ def __init__(
             self.rotary_embedding = RotaryEmbedding(
                 dim=self.d_qk,
                 end=config.max_position_embeddings,
+                theta=config.rope_theta,
             )
         else:
             self.rotary_embedding = LlamaRotaryEmbedding(
                 dim=self.d_qk,
                 end=config.max_position_embeddings,
+                theta=config.rope_theta,
             )
         self.rope_interleaved = config.rope_interleaved
 
@@ -469,7 +469,7 @@ def __init__(
             bias=False,
             async_communication=tp_linear_async_communication,
         )
-
+        # Normal attention when Sequence parallelism group size = 1
         if not sp_pg.size() > 1:
             self.attention = CoreAttention(
                 config,
@@ -547,15 +547,11 @@ def forward(
             # Note: keep track of old rotary embedding end to check if we need to enlarge k_cache and v_cache
             old_rotary_embed_end = self.rotary_embedding.end
 
-            # Rotate half rotary_embedding
-            # cos, sin = self.rotary_embedding(value_states, position_ids)
-            # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-            # interleaved
+            # interleaved version.
             if self.rope_interleaved:
                 query_states = self.rotary_embedding(query_states, position_ids=position_ids)
                 key_states = self.rotary_embedding(key_states, position_ids=position_ids)
-            # llama rotary position embedding
+            # non interleaved version.
             else:
                 cos, sin = self.rotary_embedding(value_states, position_ids)
                 query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
@@ -709,16 +705,13 @@ def forward(
             )
 
         else:  # Training case
-            # # # # test my implementation. will delete later
-            # cos, sin = self.rotary_embedding(value_states, position_ids)
-            # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
             # apply rotary embedding. Will adapt to flash rotary embedding later
+            # interleaved version.
             if self.sp_pg.size() > 1:
                 if self.rope_interleaved:
                     query_states = self.rotary_embedding(query_states, position_ids=position_ids)
                     key_states = self.rotary_embedding(key_states, position_ids=position_ids)
-                # llama rotary position embedding
+                # non interleaved version.
                 else:
                     cos, sin = self.rotary_embedding(value_states, position_ids)
                     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
@@ -735,7 +728,6 @@ def forward(
                 key_states, value_states = torch.split(key_value_states, 1, dim=2)
 
             kv_length = key_states.shape[1]
-
             ## ring attention
             if self.sp_pg.size() > 1:
                 key_states = key_states.view(batch_size, kv_length, self.n_local_kv_heads, self.d_qk)
@@ -746,7 +738,7 @@ def forward(
                     key_states,
                     value_states,
                 )
-                assert attention_output.requires_grad
+            # flash attention
             else:
                 query_states = query_states.view(batch_size * q_length, self.n_local_q_heads, self.d_qk)
                 key_states = key_states.view(batch_size * kv_length, self.n_local_kv_heads, self.d_qk)
@@ -855,9 +847,6 @@ def __init__(
     ):
         super().__init__()
 
-        if parallel_context.sp_pg.size() > 1:
-            log_rank("Using ring attention", logger=logger, level=logging.INFO, rank=0)
-
         # Declare all the nodes
         self.p2p = P2P(parallel_context.pp_pg, device=torch.device("cuda"))
         self.config = config
@@ -1053,7 +1042,6 @@ def forward(
             # normal split
             else:
                 label_ids, label_mask = normal_split(rank, world_size, label_ids, label_mask)
-
         loss = sharded_cross_entropy(
             sharded_logits, label_ids.transpose(0, 1).contiguous(), group=self.tp_pg, dtype=torch.float
         ).transpose(0, 1)
@@ -1061,7 +1049,6 @@ def forward(
         loss = masked_mean(loss, label_mask, dtype=torch.float)
         # I think indexing causes a sync we don't actually want
         # loss = loss[label_mask].sum()
-
         return {"loss": loss}
 
 
diff --git a/src/nanotron/ring_flash_attn/ring_flash_attn.py b/src/nanotron/ring_flash_attn/ring_flash_attn.py
index 1fae3367..ba20b2e1 100644
--- a/src/nanotron/ring_flash_attn/ring_flash_attn.py
+++ b/src/nanotron/ring_flash_attn/ring_flash_attn.py
@@ -54,76 +54,9 @@ def ring_flash_attn_forward(
     return out, lse
 
 
-# # My one more buffer version
-# def ring_flash_attn_forward(
-#     process_group,
-#     q: torch.Tensor,
-#     k: torch.Tensor,
-#     v: torch.Tensor,
-#     softmax_scale,
-#     dropout_p=0,
-#     causal=True,
-#     window_size=(-1, -1),
-#     alibi_slopes=None,
-#     deterministic=False,
-# ):
-#     comm = RingComm(process_group)
-
-#     out = None
-#     lse = None
-
-#     next_k_even, next_v_even = None, None
-#     next_k_odd, next_v_odd = None, None
-
-#     for step in range(comm.world_size):
-#         if step + 1 != comm.world_size:
-#             if step==0:
-#                 # order is important. should wait for the odd
-#                 next_k_odd = comm.send_recv(k) # for 1st
-#                 next_v_odd = comm.send_recv(v)
-#                 next_k_even = comm.send_recv(k,next_next=True) # for 2nd step
-#                 next_v_even = comm.send_recv(v,next_next=True)
-#             else:
-#                 # for step+2
-#                 if step % 2 == 0:
-#                     next_k_even = comm.send_recv(next_k_odd)
-#                     next_v_even = comm.send_recv(next_v_odd)
-#                 else:
-#                     next_k_odd = comm.send_recv(next_k_even)
-#                     next_v_odd = comm.send_recv(next_v_even)
-#             comm.commit()
-
-#         if not causal or step <= comm.rank:
-#             block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(
-#                 q,
-#                 k,
-#                 v,
-#                 dropout_p,
-#                 softmax_scale,
-#                 causal=causal and step == 0,
-#                 window_size=window_size,
-#                 alibi_slopes=alibi_slopes,
-#                 return_softmax=True and dropout_p > 0,
-#             )
-#             out, lse = update_out_and_lse(out, lse, block_out, block_lse)
-
-#         if step + 1 != comm.world_size:
-#             comm.wait()
-#             if step % 2 == 0:
-#                 k = next_k_odd
-#                 v = next_v_odd
-#             else:
-#                 k = next_k_even
-#                 v = next_v_even
-
-#     out = out.to(q.dtype)
-#     lse = lse.squeeze(dim=-1).transpose(1, 2)
-#     return out, lse
-
-
 def ring_flash_attn_backward(
     process_group,
-    doubt,
+    dim_out,
     q,
     k,
     v,
@@ -156,7 +89,7 @@ def ring_flash_attn_backward(
         if step <= kv_comm.rank or not causal:
             bwd_causal = causal and step == 0
             _flash_attn_backward(
-                doubt,
+                dim_out,
                 q,
                 k,
                 v,
@@ -248,11 +181,11 @@ def forward(
         return out if not return_softmax else (out, softmax_lse, None)
 
     @staticmethod
-    def backward(ctx, doubt, *args):
+    def backward(ctx, dim_out, *args):
         q, k, v, out, softmax_lse = ctx.saved_tensors
         dq, dk, dv = ring_flash_attn_backward(
             ctx.group,
-            doubt,
+            dim_out,
             q,
             k,
             v,

From 019d263e8db77c464df2d9758bf1abab13804234 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Sun, 30 Jun 2024 16:40:42 +0000
Subject: [PATCH 04/19] add SP to the tests

---
 src/nanotron/trainer.py       | 73 -----------------------------------
 tests/helpers/utils.py        | 12 +++---
 tests/test_base_model.py      |  6 +--
 tests/test_optimizer.py       |  6 +--
 tests/test_parametrization.py |  6 +--
 tests/test_random_state.py    |  6 +--
 tests/test_tensor_parallel.py | 24 +++++++-----
 tests/test_zero.py            | 12 +++---
 8 files changed, 39 insertions(+), 106 deletions(-)

diff --git a/src/nanotron/trainer.py b/src/nanotron/trainer.py
index df518e9c..fde511f8 100644
--- a/src/nanotron/trainer.py
+++ b/src/nanotron/trainer.py
@@ -441,79 +441,6 @@ def train(
                 # Checkpoint
                 if self.iteration_step % self.config.checkpoints.checkpoint_interval == 0:
                     self.save_checkpoint()
-                #     ################# My test for saving and loading params #################
-                #     ####### load from checkpoint and compare with the model.
-                #     checkpoint_model = build_model(
-                #         model_builder=lambda: CONFIG_TO_MODEL_CLASS[self.model_config.__class__.__name__](
-                #             config=self.model_config,
-                #             parallel_context=self.parallel_context,
-                #             parallel_config=self.config.parallelism,
-                #             random_states=self.random_states,
-                #         ),
-                #         dtype=self.config.model.dtype,
-                #         parallel_context=self.parallel_context,
-                #     )
-
-                #     # Mark some parameters as tied
-                #     # TODO @nouamane: this is only needed for training, can we just mark params as NanotronParameter instead?
-                #     mark_tied_parameters(model=checkpoint_model, parallel_context=self.parallel_context, parallel_config=self.config.parallelism,)
-
-                #     # Sanity check model
-                #     sanity_check(root_module=checkpoint_model)
-
-                #     # Load checkpoint
-                #     checkpoints_path = self.config.checkpoints.checkpoints_path
-                #     checkpoint_path = checkpoints_path / f"{self.iteration_step}"
-                #     log_rank(
-                #         f"Loading checkpoint from {checkpoint_path}:",
-                #         logger=logger,
-                #         level=logging.INFO,
-                #         rank=0,
-                #     )
-                #     load_weights(model=checkpoint_model, parallel_context=self.parallel_context, root_folder=checkpoint_path)
-
-                #     ## compare keys firstly
-                #     model_state_dict = self.model.module.state_dict() if isinstance(self.model, DistributedDataParallel) else  self.model.state_dict()
-                #     checkpoint_state_dict = checkpoint_model.state_dict()
-
-                #     model_keys = set(model_state_dict.keys())
-                #     checkpoint_keys = set(checkpoint_state_dict.keys())
-
-                #     # Find keys that are in model but not in checkpoint
-                #     missing_in_checkpoint = model_keys - checkpoint_keys
-                #     # Find keys that are in checkpoint but not in model
-                #     missing_in_model = checkpoint_keys - model_keys
-
-                #     # # Print out the differences
-                #     # print("Keys in current model but not in checkpoint:", missing_in_checkpoint)
-                #     # print("Keys in checkpoint but not in current model:", missing_in_model)
-                #     # # print all the params
-                #     # print("self.model.state_dict().keys(): ",model_state_dict.keys(), "length: ", len(model_state_dict.keys()))
-                #     # print("checkpoint_model.state_dict().keys(): ",checkpoint_state_dict.keys(), "length: ", len(checkpoint_state_dict.keys()))
-
-                #     assert model_state_dict.keys() == checkpoint_state_dict.keys(), "Models have different structures."
-
-                #     # Compare parameters
-                #     all_params_same = True
-                #     for param_tensor in model_state_dict:
-                #         model_param = model_state_dict[param_tensor]
-                #         checkpoint_param = checkpoint_state_dict[param_tensor]
-                #         if not torch.equal(model_param, checkpoint_param):
-                #             all_params_same = False
-                #             break
-                #             num_different_elements = torch.sum(model_param != checkpoint_param).item()
-                #             total_elements = model_param.numel()
-                #             print(f"Mismatch found in parameter {param_tensor}: {num_different_elements}/{total_elements} elements are different.")
-                #         # else:
-                #         #     print(f"{param_tensor} is OK!")
-                #     if all_params_same:
-                #         print("All parameters are the same between the two models.")
-                #     else:
-                #         print("Parameters differ between the two models.")
-
-                # # stop_here = 1
-
-                # #################
         dist.barrier()  # let's wait for everyone before leaving
 
         self.post_training()
diff --git a/tests/helpers/utils.py b/tests/helpers/utils.py
index d0fb01b5..44813cac 100644
--- a/tests/helpers/utils.py
+++ b/tests/helpers/utils.py
@@ -243,7 +243,7 @@ def _run_until_success(*args, **kwargs):
     return _wrapper
 
 
-def global_wrapper(rank, func, tp, pp, dp, port, kwargs):
+def global_wrapper(rank, func, tp, pp, dp, sp, port, kwargs):
     def setup_dist_env(rank, world_size, port):
         os.environ["WORLD_SIZE"] = str(world_size)
         os.environ["RANK"] = str(rank)
@@ -255,20 +255,22 @@ def setup_dist_env(rank, world_size, port):
 
     world_size = tp * pp * dp
     setup_dist_env(rank, world_size, port)
-    parallel_context = ParallelContext(data_parallel_size=dp, pipeline_parallel_size=pp, tensor_parallel_size=tp)
+    parallel_context = ParallelContext(
+        data_parallel_size=dp, pipeline_parallel_size=pp, tensor_parallel_size=tp, sequence_parallel_size=sp
+    )
     func(parallel_context, **kwargs)
 
 
-def init_distributed(tp: int, dp: int, pp: int):
+def init_distributed(tp: int, dp: int, pp: int, sp: int):
     def _init_distributed(func):
         def wrapper(**kwargs):
             from nanotron.utils import find_free_port
 
-            world_size = tp * pp * dp
+            world_size = tp * pp * dp * sp
             port = find_free_port()
 
             # Note that kwargs needs to be passed as part of args in a way that can be unpacked
-            args = (func, tp, pp, dp, port, kwargs)
+            args = (func, tp, pp, dp, sp, port, kwargs)
             mp.spawn(global_wrapper, args=args, nprocs=world_size)
 
         return wrapper
diff --git a/tests/test_base_model.py b/tests/test_base_model.py
index b4759905..4b49615d 100644
--- a/tests/test_base_model.py
+++ b/tests/test_base_model.py
@@ -9,14 +9,14 @@
 from torch import nn
 
 
-@pytest.mark.parametrize("tp,dp,pp", [(1, 1, 1), (2, 2, 2)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [(1, 1, 1, 1), (2, 2, 2, 1)])
 @pytest.mark.skip
 @rerun_if_address_is_in_use()
-def test_get_named_modules_in_pp_rank(tp: int, dp: int, pp: int):
+def test_get_named_modules_in_pp_rank(tp: int, dp: int, pp: int, sp: int):
     model_args = ModelArgs(init_method=RandomInit(std=1.0), model_config=TINY_LLAMA_CONFIG)
     config = get_llama_training_config(model_args)
 
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_get_named_modules_in_pp_rank)(config=config)
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_get_named_modules_in_pp_rank)(config=config)
 
 
 def _test_get_named_modules_in_pp_rank(
diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py
index 1a28f967..036b0a8f 100644
--- a/tests/test_optimizer.py
+++ b/tests/test_optimizer.py
@@ -10,13 +10,13 @@
 from nanotron.scaling.parametrization import ParametrizationMethod
 
 
-@pytest.mark.parametrize("tp,dp,pp", [(1, 1, 1), (2, 1, 1), (1, 1, 2), (2, 1, 2)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [(1, 1, 1, 1), (2, 1, 1, 1), (1, 1, 2, 1), (2, 1, 2, 1)])
 @pytest.mark.parametrize(
     "parametrization_method", [ParametrizationMethod.STANDARD, ParametrizationMethod.SPECTRAL_MUP]
 )
 @pytest.mark.skip
 @rerun_if_address_is_in_use()
-def test_get_custom_lr(tp: int, dp: int, pp: int, parametrization_method: ParametrizationMethod):
+def test_get_custom_lr(tp: int, dp: int, pp: int, sp: int, parametrization_method: ParametrizationMethod):
     LR = 1e-3
 
     if parametrization_method == ParametrizationMethod.STANDARD:
@@ -24,7 +24,7 @@ def test_get_custom_lr(tp: int, dp: int, pp: int, parametrization_method: Parame
     elif parametrization_method == ParametrizationMethod.SPECTRAL_MUP:
         init_method = SpectralMupInit(use_mup=True)
 
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_get_custom_lr)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_get_custom_lr)(
         lr=LR,
         init_method=init_method,
         parametrization_method=parametrization_method,
diff --git a/tests/test_parametrization.py b/tests/test_parametrization.py
index fe76826a..88aabc2c 100644
--- a/tests/test_parametrization.py
+++ b/tests/test_parametrization.py
@@ -10,17 +10,17 @@
 from nanotron.scaling.parametrization import ParametrizationMethod
 
 
-@pytest.mark.parametrize("tp,dp,pp", [(2, 1, 1)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [(2, 1, 1, 1)])
 @pytest.mark.parametrize("parametrization_method", [ParametrizationMethod.SPECTRAL_MUP])
 @pytest.mark.skip
 @rerun_if_address_is_in_use()
-def test_parametrization(tp: int, dp: int, pp: int, parametrization_method: ParametrizationMethod):
+def test_parametrization(tp: int, dp: int, pp: int, sp: int, parametrization_method: ParametrizationMethod):
     if parametrization_method == ParametrizationMethod.STANDARD:
         init_method = RandomInit(std=1.0)
     elif parametrization_method == ParametrizationMethod.SPECTRAL_MUP:
         init_method = SpectralMupInit(use_mup=True)
 
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_parametrization)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_parametrization)(
         init_method=init_method,
         parametrization_method=parametrization_method,
     )
diff --git a/tests/test_random_state.py b/tests/test_random_state.py
index 7abd0b13..19561410 100644
--- a/tests/test_random_state.py
+++ b/tests/test_random_state.py
@@ -12,11 +12,11 @@
 
 
 @pytest.mark.skipif(available_gpus() < 2, reason="Testing test_random_state_sync requires at least 2 gpus")
-@pytest.mark.parametrize("tp,dp,pp", [(2, 1, 1), (1, 2, 1), (1, 1, 2)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [(2, 1, 1, 1), (1, 2, 1, 1), (1, 1, 2, 1)])
 @rerun_if_address_is_in_use()
-def test_random_state_sync(tp: int, dp: int, pp: int):
+def test_random_state_sync(tp: int, dp: int, pp: int, sp: int):
     # TODO @nouamane: Make a test with 4 gpus (2 in one pg, 2 in other pg)
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_random_state_sync)()
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_random_state_sync)()
 
 
 def _test_random_state_sync(parallel_context: ParallelContext):
diff --git a/tests/test_tensor_parallel.py b/tests/test_tensor_parallel.py
index 127ba2fa..261e6a4f 100644
--- a/tests/test_tensor_parallel.py
+++ b/tests/test_tensor_parallel.py
@@ -15,14 +15,16 @@
 from torch import nn as torch_nn
 
 
-@pytest.mark.parametrize("tp,dp,pp", [pytest.param(i, 1, 1) for i in range(1, min(4, available_gpus()) + 1)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [pytest.param(i, 1, 1, 1) for i in range(3, min(4, available_gpus()) + 1)])
 @pytest.mark.parametrize("tp_mode", list(TensorParallelLinearMode))
 @pytest.mark.parametrize("async_communication", [False, True])
 @rerun_if_address_is_in_use()
-def test_column_linear(tp: int, dp: int, pp: int, tp_mode: TensorParallelLinearMode, async_communication: bool):
+def test_column_linear(
+    tp: int, dp: int, pp: int, sp: int, tp_mode: TensorParallelLinearMode, async_communication: bool
+):
     if tp_mode is TensorParallelLinearMode.ALL_REDUCE and async_communication:
         pytest.skip("ALL_REDUCE mode does not support async communication")
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_column_linear)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_column_linear)(
         tp_mode=tp_mode, async_communication=async_communication
     )
 
@@ -86,7 +88,7 @@ def _test_column_linear(
             random_input = sharded_random_input
     else:
         ValueError(f"Unsupported mode: {tp_mode}")
-    # It's important that `random_input` and `sharded_random_input` are two seperate tensors with seperate storage
+    # It's important that `random_input` and `sharded_random_input` are two separate tensors with separate storage
     sharded_random_input = sharded_random_input.clone()
     random_input.requires_grad = True
     sharded_random_input.requires_grad = True
@@ -147,15 +149,17 @@ def _test_column_linear(
     parallel_context.destroy()
 
 
-@pytest.mark.parametrize("tp,dp,pp", [pytest.param(i, 1, 1) for i in range(1, min(4, available_gpus()) + 1)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [pytest.param(i, 1, 1, 1) for i in range(1, min(4, available_gpus()) + 1)])
 @pytest.mark.parametrize("tp_mode", list(TensorParallelLinearMode))
 @pytest.mark.parametrize("async_communication", [False, True])
 @rerun_if_address_is_in_use()
-def test_row_linear(tp: int, dp: int, pp: int, tp_mode: TensorParallelLinearMode, async_communication: bool):
+def test_row_linear(tp: int, dp: int, pp: int, sp: int, tp_mode: TensorParallelLinearMode, async_communication: bool):
     if tp_mode is TensorParallelLinearMode.ALL_REDUCE and async_communication:
         pytest.skip("ALL_REDUCE mode does not support async communication")
 
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_row_linear)(tp_mode=tp_mode, async_communication=async_communication)
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_row_linear)(
+        tp_mode=tp_mode, async_communication=async_communication
+    )
 
 
 def _test_row_linear(parallel_context: ParallelContext, tp_mode: TensorParallelLinearMode, async_communication: bool):
@@ -264,11 +268,11 @@ def _test_row_linear(parallel_context: ParallelContext, tp_mode: TensorParallelL
     parallel_context.destroy()
 
 
-@pytest.mark.parametrize("tp,dp,pp", [pytest.param(i, 1, 1) for i in range(1, min(4, available_gpus()) + 1)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [pytest.param(i, 1, 1, 1) for i in range(1, min(4, available_gpus()) + 1)])
 @pytest.mark.parametrize("tp_mode", list(TensorParallelLinearMode))
 @rerun_if_address_is_in_use()
-def test_tensor_parallel_embedding(tp: int, dp: int, pp: int, tp_mode: TensorParallelLinearMode):
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_tensor_parallel_embedding)(tp_mode=tp_mode)
+def test_tensor_parallel_embedding(tp: int, dp: int, pp: int, sp: int, tp_mode: TensorParallelLinearMode):
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_tensor_parallel_embedding)(tp_mode=tp_mode)
 
 
 def _test_tensor_parallel_embedding(parallel_context: ParallelContext, tp_mode: TensorParallelLinearMode):
diff --git a/tests/test_zero.py b/tests/test_zero.py
index f1127f94..b7503553 100644
--- a/tests/test_zero.py
+++ b/tests/test_zero.py
@@ -22,10 +22,10 @@
 from torch.nn.parallel import DistributedDataParallel
 
 
-@pytest.mark.parametrize("tp,dp,pp", [pytest.param(1, i, 1) for i in range(1, min(4, available_gpus()) + 1)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [pytest.param(1, i, 1, 1) for i in range(1, min(4, available_gpus()) + 1)])
 @rerun_if_address_is_in_use()
-def test_zero_optimizer(tp: int, dp: int, pp: int):
-    init_distributed(pp=pp, dp=dp, tp=tp)(_test_zero_optimizer)()
+def test_zero_optimizer(tp: int, dp: int, pp: int, sp: int):
+    init_distributed(pp=pp, dp=dp, tp=tp, sp=sp)(_test_zero_optimizer)()
 
 
 def _test_zero_optimizer(parallel_context: ParallelContext):
@@ -198,16 +198,16 @@ def _test_zero_optimizer(parallel_context: ParallelContext):
     parallel_context.destroy()
 
 
-@pytest.mark.parametrize("tp,dp,pp", [pytest.param(2, i, 1) for i in range(1, available_gpus() // 2 + 1)])
+@pytest.mark.parametrize("tp,dp,pp,sp", [pytest.param(2, i, 1, 1) for i in range(1, available_gpus() // 2 + 1)])
 @pytest.mark.parametrize("tp_mode", list(TensorParallelLinearMode))
 @pytest.mark.parametrize("async_communication", [False, True])
 @rerun_if_address_is_in_use()
 def test_zero_optimizer_with_tp(
-    tp: int, dp: int, pp: int, tp_mode: TensorParallelLinearMode, async_communication: bool
+    tp: int, dp: int, pp: int, sp: int, tp_mode: TensorParallelLinearMode, async_communication: bool
 ):
     if tp_mode is TensorParallelLinearMode.ALL_REDUCE and async_communication:
         pytest.skip("ALL_REDUCE mode does not support async communication")
-    init_distributed(pp=pp, dp=dp, tp=tp)(_test_zero_optimizer_with_tp)(
+    init_distributed(pp=pp, dp=dp, tp=tp, sp=sp)(_test_zero_optimizer_with_tp)(
         tp_mode=tp_mode, async_communication=async_communication
     )
 

From 07ebf2c75ed20c5c9e08b4fe88f8f26821152595 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Tue, 2 Jul 2024 12:35:17 +0000
Subject: [PATCH 05/19] ddp process group for backwawrd

---
 src/nanotron/helpers.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/nanotron/helpers.py b/src/nanotron/helpers.py
index 1c8bc54e..c5f5af52 100644
--- a/src/nanotron/helpers.py
+++ b/src/nanotron/helpers.py
@@ -381,8 +381,7 @@ def grad_optimizer_builder(named_param_groups):
             named_params_or_groups=named_param_groups,
             # TODO @thomasw21: We need a better API for gradient accumulation/zero etc ...
             optimizer_builder=optimizer_builder,
-            # dp_pg=parallel_context.dp_pg,
-            dp_pg=parallel_context.dp_sp_pg,
+            dp_pg=parallel_context.dp_sp_pg,  # sequence parallel and data parallel process group
         )
 
         # SANITY CHECK: assert that optimizer's named_params point to model's params (check only the first one)
@@ -407,8 +406,9 @@ def grad_optimizer_builder(named_param_groups):
 
         assert isinstance(grad_accumulator, FP32GradientAccumulator)
         grad_accumulator.assign_param_offsets(
-            # dp_rank=dist.get_rank(parallel_context.dp_pg),
-            dp_rank=dist.get_rank(parallel_context.dp_sp_pg),
+            dp_rank=dist.get_rank(
+                parallel_context.dp_sp_pg
+            ),  # sequence parallel and data parallel process group will synchronize the gradient together
             param_name_to_offsets=param_name_to_dp_rank_offsets,
         )
 
@@ -417,8 +417,7 @@ def grad_optimizer_builder(named_param_groups):
         assert isinstance(grad_accumulator, FP32GradientAccumulator)
         model.register_comm_hook(
             state=FP32GradBucketManager(
-                # dp_pg=parallel_context.dp_pg,
-                dp_pg=parallel_context.dp_sp_pg,
+                dp_pg=parallel_context.dp_sp_pg,  # sequence parallel and data parallel process group will synchronize the gradient together
                 accumulator=grad_accumulator,
                 param_id_to_name={
                     id(param): param.get_tied_info().get_full_name_from_module_id_to_prefix(
@@ -705,7 +704,6 @@ def get_consumed_train_samples_of_a_data_stage_from_ckp(
     stage: DatasetStageArgs, metadata: TrainingMetadata
 ) -> Optional[int]:
     start_training_step = stage.start_training_step
-    # TODO: if a new dataset is added, we know that it has consumed 0 tokens? Cannot add new dataset for now.
     return next(
         (s.consumed_train_samples for s in metadata.data_stages if s.start_training_step == start_training_step),
         None,

From 3f504b3d96eaf05ac9b14a10b5c8d7e2b6d81cc0 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Tue, 2 Jul 2024 13:32:08 +0000
Subject: [PATCH 06/19] clean code

---
 src/nanotron/parallel/context.py | 30 +-----------------------------
 1 file changed, 1 insertion(+), 29 deletions(-)

diff --git a/src/nanotron/parallel/context.py b/src/nanotron/parallel/context.py
index b070debb..a7027b77 100644
--- a/src/nanotron/parallel/context.py
+++ b/src/nanotron/parallel/context.py
@@ -85,20 +85,12 @@ def _init_parallel_groups(self):
 
         self.tp_pg = self.create_new_group(ranks.transpose((0, 1, 2, 3, 4)).reshape((-1, self.tensor_parallel_size)))
         self.sp_pg = self.create_new_group(ranks.transpose((1, 2, 3, 4, 0)).reshape((-1, self.sequence_parallel_size)))
-        # only need sync the gradient. Don't need to load different data. Be careful!
-        # Anything related to dp_pg get changed.
-        # Create a group DP+CP. Sync between this group.
-        # temporary fix
-        # self.dp_pg = self.sp_pg
+        # Create a group DP+SP. Sync gradient/avg loss/shard optimizer between this group. things related to dp_pg get changed. But don't need to load different data.
         self.dp_sp_pg = self.create_new_group(
             ranks.transpose((1, 4, 2, 0, 3)).reshape((-1, self.data_parallel_size * self.sequence_parallel_size))
         )  # the last two dimension should correspond to sp and dp.
-        # self.dp_pg = self.create_new_group(ranks.transpose((1, 4, 2, 0, 3)).reshape((-1, self.data_parallel_size*self.sequence_parallel_size))) #the last two dimension should correspond to sp and dp.
         self.dp_pg = self.create_new_group(ranks.transpose((4, 0, 1, 2, 3)).reshape((-1, self.data_parallel_size)))
         self.pp_pg = self.create_new_group(ranks.transpose((3, 4, 0, 1, 2)).reshape((-1, self.pipeline_parallel_size)))
-        # print("dp_sp_pg: ",ranks.transpose((1, 4, 2, 0, 3)).reshape((-1, self.data_parallel_size*self.sequence_parallel_size)))
-        # print("dp_pg: ", ranks.transpose((4, 0, 1, 2, 3)).reshape((-1, self.data_parallel_size)))
-        # print("sp_pg: ", ranks.transpose((1, 2, 3, 4, 0)).reshape((-1, self.sequence_parallel_size)))
         self.expert_pg = self.create_new_group(
             ranks.transpose((2, 3, 4, 0, 1)).reshape((-1, self.expert_parallel_size))
         )
@@ -113,25 +105,6 @@ def _init_parallel_groups(self):
                 for sp_rank in range(self.sequence_parallel_size)
             ]
         )
-
-        # self.tp_pg = self.create_new_group(ranks.transpose((0, 1, 2, 3)).reshape((-1, self.tensor_parallel_size)))
-        # self.dp_pg = self.create_new_group(ranks.transpose((3, 0, 1, 2)).reshape((-1, self.data_parallel_size)))
-        # self.pp_pg = self.create_new_group(ranks.transpose((2, 3, 0, 1)).reshape((-1, self.pipeline_parallel_size)))
-        # self.expert_pg = self.create_new_group(ranks.transpose((1, 2, 3, 0)).reshape((-1, self.expert_parallel_size)))
-
-        # model parallel group = combination of tp and pp and exp for a given dp rank
-        # self.mp_pg = self.create_new_group(
-        #     [ranks[:, :, dp_rank, :].reshape(-1) for dp_rank in range(self.data_parallel_size)]
-        # )
-
-        # self.tp_and_expert_pg = self.create_new_group(
-        #     [
-        #         ranks[:, pp_rank, dp_rank, :].reshape(-1)
-        #         for pp_rank in range(self.pipeline_parallel_size)
-        #         for dp_rank in range(self.data_parallel_size)
-        #     ]
-        # )
-
         self.world_rank_matrix: np.ndarray = ranks
 
     def create_new_group(self, all_groups_ranks: np.ndarray) -> dist.ProcessGroup:
@@ -190,5 +163,4 @@ def get_global_rank(
 
         :return: numpy.int64, The global rank.
         """
-        # return self.world_rank_matrix[sp_rank, ep_rank, pp_rank, 0, tp_rank]
         return self.world_rank_matrix[sp_rank, ep_rank, pp_rank, dp_rank, tp_rank]

From 3f841bad5d4a030d3242e16ef60177e0fbdb1372 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Tue, 2 Jul 2024 14:20:31 +0000
Subject: [PATCH 07/19] update tests to add SP process group

---
 examples/llama/tests/test_conversion.py       | 22 +++---
 tests/helpers/utils.py                        | 10 +--
 .../nanoset/test_build_nanoset_dataloader.py  | 30 ++++----
 tests/test_clip_grads.py                      |  8 +--
 tests/test_data_parallel.py                   |  2 +-
 tests/test_distributed.py                     | 12 ++--
 tests/test_optimizer_params_groups.py         |  2 +-
 tests/test_p2p.py                             |  2 +-
 ..._parameters_accumulate_gradient_in_fp32.py |  6 +-
 tests/test_pipeline_parallel.py               | 10 +--
 tests/test_serialize.py                       | 68 ++++++++++---------
 tests/test_tie_weights.py                     |  8 +--
 tests/test_zero.py                            |  2 +-
 13 files changed, 97 insertions(+), 85 deletions(-)

diff --git a/examples/llama/tests/test_conversion.py b/examples/llama/tests/test_conversion.py
index b5ce3529..7e86de6e 100644
--- a/examples/llama/tests/test_conversion.py
+++ b/examples/llama/tests/test_conversion.py
@@ -105,7 +105,7 @@ def _test_nt_to_hf(parallel_context: ParallelContext, input_ids: torch.Tensor):
 
 
 def test_nt_to_hf(input_ids: torch.Tensor):
-    init_distributed(tp=1, dp=1, pp=1)(_test_nt_to_hf)(input_ids=input_ids)
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_test_nt_to_hf)(input_ids=input_ids)
 
 
 def _test_nt_to_hf_with_files(parallel_context: ParallelContext, input_ids: torch.Tensor, test_context: TestContext):
@@ -130,7 +130,9 @@ def _test_nt_to_hf_with_files(parallel_context: ParallelContext, input_ids: torc
 
 
 def test_nt_to_hf_with_files(input_ids: torch.Tensor):
-    init_distributed(tp=1, dp=1, pp=1)(_test_nt_to_hf_with_files)(input_ids=input_ids, test_context=TestContext())
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_test_nt_to_hf_with_files)(
+        input_ids=input_ids, test_context=TestContext()
+    )
 
 
 def _test_hf_to_nt(parallel_context: ParallelContext, input_ids: torch.Tensor):
@@ -141,11 +143,11 @@ def _test_hf_to_nt(parallel_context: ParallelContext, input_ids: torch.Tensor):
     logits_nt = model_nt.model(input_ids, input_mask).permute(1, 0, 2)
     logits_hf = model_hf(input_ids).logits
     assert logits_nt.size() == logits_hf.size()
-    torch.testing.assert_allclose(logits_hf, logits_nt, atol=ATOL)  
+    torch.testing.assert_allclose(logits_hf, logits_nt, atol=ATOL)
 
 
 def test_hf_to_nt(input_ids: torch.Tensor):
-    init_distributed(tp=1, dp=1, pp=1)(_test_hf_to_nt)(input_ids=input_ids)
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_test_hf_to_nt)(input_ids=input_ids)
 
 
 def _test_hf_to_nt_with_files(parallel_context: ParallelContext, input_ids: torch.Tensor, test_context: TestContext):
@@ -168,7 +170,9 @@ def _test_hf_to_nt_with_files(parallel_context: ParallelContext, input_ids: torc
 
 
 def test_hf_to_nt_with_files(input_ids: torch.Tensor):
-    init_distributed(tp=1, dp=1, pp=1)(_test_hf_to_nt_with_files)(input_ids=input_ids, test_context=TestContext())
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_test_hf_to_nt_with_files)(
+        input_ids=input_ids, test_context=TestContext()
+    )
 
 
 def _test_composed_conversion(parallel_context: ParallelContext):
@@ -196,7 +200,7 @@ def _test_composed_conversion(parallel_context: ParallelContext):
 
 
 def test_composed_conversion():
-    init_distributed(tp=1, dp=1, pp=1)(_test_composed_conversion)()
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_test_composed_conversion)()
 
 
 def _save_parallel_nanotron(parallel_context: ParallelContext, input_ids: torch.Tensor, nt_path: Path):
@@ -239,9 +243,11 @@ def test_tensor_parallel_conversion(input_ids: torch.Tensor):
     hf_path = root / "nanotron"
 
     # Launch both parts.
-    init_distributed(tp=2, dp=1, pp=1)(_save_parallel_nanotron)(input_ids=input_ids, nt_path=nt_path)
+    init_distributed(tp=2, dp=1, pp=1, sp=1)(_save_parallel_nanotron)(input_ids=input_ids, nt_path=nt_path)
     assert (nt_path / "logits.pt").exists()
-    init_distributed(tp=1, dp=1, pp=1)(_convert_from_parallel)(input_ids=input_ids, nt_path=nt_path, hf_path=hf_path)
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_convert_from_parallel)(
+        input_ids=input_ids, nt_path=nt_path, hf_path=hf_path
+    )
     assert (hf_path / "logits.pt").exists()
 
     # Load logits and verify they match.
diff --git a/tests/helpers/utils.py b/tests/helpers/utils.py
index 44813cac..b8f6488a 100644
--- a/tests/helpers/utils.py
+++ b/tests/helpers/utils.py
@@ -107,8 +107,8 @@ def is_dict_equal(first: Dict, second: Dict, sub_paths: Optional[List[str]] = No
     return True, None
 
 
-def get_all_3d_configurations(gpus: int) -> List[Tuple[int, int, int]]:
-    """Given a number of gpus, we want all 3d configurations possible such that pp * dp * tp = gpus"""
+def get_all_4d_configurations(gpus: int) -> List[Tuple[int, int, int, int]]:
+    """Given a number of gpus, we want all 4d configurations possible such that pp * dp * tp * sp = gpus"""
     result = []
     for tp in range(1, gpus + 1):
         if gpus % tp != 0:
@@ -121,8 +121,10 @@ def get_all_3d_configurations(gpus: int) -> List[Tuple[int, int, int]]:
             for pp in range(1, gpus_left_after_dp + 1):
                 if gpus_left_after_dp % pp != 0:
                     continue
-                if tp * dp * pp == gpus:
-                    result.append((pp, dp, tp))
+                gpus_left_after_pp = gpus_left_after_dp // pp
+                for sp in range(1, gpus_left_after_pp + 1):
+                    if tp * dp * pp * sp == gpus:
+                        result.append((tp, dp, pp, sp))
     return result
 
 
diff --git a/tests/nanoset/test_build_nanoset_dataloader.py b/tests/nanoset/test_build_nanoset_dataloader.py
index 2c3ff542..331e4f64 100644
--- a/tests/nanoset/test_build_nanoset_dataloader.py
+++ b/tests/nanoset/test_build_nanoset_dataloader.py
@@ -2,9 +2,6 @@
 from math import isclose
 from pathlib import Path
 
-package_path = Path(__file__).parent.parent
-sys.path.append(str(package_path))
-
 import numpy as np
 import pytest
 from helpers.context import TestContext
@@ -16,7 +13,7 @@
     create_dummy_json_dataset,
     preprocess_dummy_dataset,
 )
-from helpers.utils import available_gpus, get_all_3d_configurations, init_distributed, rerun_if_address_is_in_use
+from helpers.utils import available_gpus, get_all_4d_configurations, init_distributed, rerun_if_address_is_in_use
 from nanotron.data.dataloader_builder import build_nanoset_dataloader
 from nanotron.data.nanoset import Nanoset
 from nanotron.data.utils import count_dataset_indexes, normalize
@@ -24,13 +21,16 @@
 from nanotron.utils import main_rank_first
 from transformers import AutoTokenizer
 
+package_path = Path(__file__).parent.parent
+sys.path.append(str(package_path))
+
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @pytest.mark.parametrize("train_steps", [5, 100])
@@ -38,7 +38,7 @@
 @pytest.mark.parametrize("tokenizer_name_or_path", ["openai-community/gpt2", "unsloth/llama-3-8b-bnb-4bit"])
 @rerun_if_address_is_in_use()
 def test_build_nanoset_dataloader(
-    tp: int, dp: int, pp: int, train_steps: int, sequence_length: int, tokenizer_name_or_path: str
+    tp: int, dp: int, pp: int, sp: int, train_steps: int, sequence_length: int, tokenizer_name_or_path: str
 ):
     test_context = TestContext()
 
@@ -49,7 +49,7 @@ def test_build_nanoset_dataloader(
     for idx, json_path in enumerate(json_paths):
         create_dummy_json_dataset(path_to_json=json_path, dummy_text=f"Nanoset {idx}!", n_samples=(idx + 1) * 50000)
 
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_build_nanoset_dataloader)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_build_nanoset_dataloader)(
         json_paths=json_paths,
         path_to_mmap_files=mmap_dataset_paths,
         train_steps=train_steps,
@@ -155,17 +155,19 @@ def _test_build_nanoset_dataloader(
 
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @pytest.mark.parametrize("skipped_batches", [20, 50])
 @pytest.mark.parametrize("tokenizer_name_or_path", ["openai-community/gpt2", "unsloth/llama-3-8b-bnb-4bit"])
 @rerun_if_address_is_in_use()
-def test_recover_nanoset_dataloader(tp: int, dp: int, pp: int, skipped_batches: int, tokenizer_name_or_path: str):
+def test_recover_nanoset_dataloader(
+    tp: int, dp: int, pp: int, sp: int, skipped_batches: int, tokenizer_name_or_path: str
+):
     test_context = TestContext()
 
     # Create dataset files
@@ -175,7 +177,7 @@ def test_recover_nanoset_dataloader(tp: int, dp: int, pp: int, skipped_batches:
     for idx, json_path in enumerate(json_paths):
         create_dummy_json_dataset(path_to_json=json_path, dummy_text=f"Nanoset {idx}!", n_samples=(idx + 1) * 50000)
 
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_recover_nanoset_dataloader)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_recover_nanoset_dataloader)(
         json_paths=json_paths,
         path_to_mmap_files=mmap_dataset_paths,
         skipped_batches=skipped_batches,
diff --git a/tests/test_clip_grads.py b/tests/test_clip_grads.py
index b4682875..66940294 100644
--- a/tests/test_clip_grads.py
+++ b/tests/test_clip_grads.py
@@ -34,7 +34,7 @@
 @pytest.mark.parametrize("norm_type", [math.inf, 1.0, 2.0])
 @rerun_if_address_is_in_use()
 def test_clip_grads_with_pp(norm_type: float):
-    init_distributed(tp=1, dp=1, pp=2)(_test_clip_grads_with_pp)(norm_type=norm_type)
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_clip_grads_with_pp)(norm_type=norm_type)
 
 
 def _test_clip_grads_with_pp(parallel_context: ParallelContext, norm_type: float):
@@ -203,7 +203,7 @@ def _test_clip_grads_with_pp(parallel_context: ParallelContext, norm_type: float
 @pytest.mark.parametrize("norm_type", [math.inf, 1.0, 2.0])
 @rerun_if_address_is_in_use()
 def test_clip_grads_with_tp(tp_mode: TensorParallelLinearMode, async_communication: bool, norm_type: float):
-    init_distributed(tp=2, dp=1, pp=1)(_test_clip_grads_with_tp)(
+    init_distributed(tp=2, dp=1, pp=1, sp=1)(_test_clip_grads_with_tp)(
         tp_mode=tp_mode, async_communication=async_communication, norm_type=norm_type
     )
 
@@ -345,7 +345,7 @@ def _test_clip_grads_with_tp(
 @pytest.mark.parametrize("norm_type", [math.inf, 1.0, 2.0])
 @rerun_if_address_is_in_use()
 def test_clip_grads_tied_weights(norm_type: float):
-    init_distributed(tp=1, dp=1, pp=2)(_test_clip_grads_tied_weights)(norm_type=norm_type)
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_clip_grads_tied_weights)(norm_type=norm_type)
 
 
 def _test_clip_grads_tied_weights(parallel_context: ParallelContext, norm_type: float):
@@ -438,7 +438,7 @@ def _test_clip_grads_tied_weights(parallel_context: ParallelContext, norm_type:
 @pytest.mark.parametrize("norm_type", [math.inf, 1.0, 2.0])
 @rerun_if_address_is_in_use()
 def test_clip_grads_fp32_accumulator(norm_type: float, half_precision: torch.dtype):
-    init_distributed(tp=1, dp=1, pp=2)(_test_clip_grads_fp32_accumulator)(
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_clip_grads_fp32_accumulator)(
         norm_type=norm_type, half_precision=half_precision
     )
 
diff --git a/tests/test_data_parallel.py b/tests/test_data_parallel.py
index 21ae191a..bc367470 100644
--- a/tests/test_data_parallel.py
+++ b/tests/test_data_parallel.py
@@ -17,7 +17,7 @@
 @pytest.mark.parametrize("accumulation_steps", [1, 3])
 @rerun_if_address_is_in_use()
 def test_ddp_with_afab(accumulation_steps):
-    init_distributed(tp=1, dp=2, pp=1)(_test_ddp_with_afab)(accumulation_steps=accumulation_steps)
+    init_distributed(tp=1, dp=2, pp=1, sp=1)(_test_ddp_with_afab)(accumulation_steps=accumulation_steps)
 
 
 def _test_ddp_with_afab(parallel_context: ParallelContext, accumulation_steps: int):
diff --git a/tests/test_distributed.py b/tests/test_distributed.py
index 7c0d2462..241dd56e 100644
--- a/tests/test_distributed.py
+++ b/tests/test_distributed.py
@@ -3,7 +3,7 @@
 import torch.distributed as dist
 from helpers.utils import (
     available_gpus,
-    get_all_3d_configurations,
+    get_all_4d_configurations,
     init_distributed,
     rerun_if_address_is_in_use,
 )
@@ -36,13 +36,13 @@ def _test_init_parallel_context(parallel_context: ParallelContext):
 
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_init_parallel_context(tp: int, dp: int, pp: int):
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_init_parallel_context)()
\ No newline at end of file
+def test_init_parallel_context(tp: int, dp: int, pp: int, sp: int):
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_init_parallel_context)()
diff --git a/tests/test_optimizer_params_groups.py b/tests/test_optimizer_params_groups.py
index fa835e1c..60d541c3 100644
--- a/tests/test_optimizer_params_groups.py
+++ b/tests/test_optimizer_params_groups.py
@@ -496,7 +496,7 @@ def optimizer_builder(inp_param_groups):
 def test_ddp_optimizer_grad_accumulation_lr_weight_decay_multiple_group(
     half_precision: torch.dtype, accumulation_steps: int
 ):
-    init_distributed(tp=1, dp=2, pp=1)(_test_ddp_optimizer_grad_accumulation_lr_weight_decay_multiple_group)(
+    init_distributed(tp=1, dp=2, pp=1, sp=1)(_test_ddp_optimizer_grad_accumulation_lr_weight_decay_multiple_group)(
         half_precision=half_precision,
         accumulation_steps=accumulation_steps,
     )
diff --git a/tests/test_p2p.py b/tests/test_p2p.py
index ed8245a8..9b260c12 100644
--- a/tests/test_p2p.py
+++ b/tests/test_p2p.py
@@ -14,7 +14,7 @@
 @pytest.mark.parametrize("full", [True, False])
 @rerun_if_address_is_in_use()
 def test_check_send_recv_tensor(send_contiguous: bool, full: bool):
-    init_distributed(tp=1, dp=1, pp=2)(_test_check_send_recv_tensor)(send_contiguous=send_contiguous, full=full)
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_check_send_recv_tensor)(send_contiguous=send_contiguous, full=full)
 
 
 def _test_check_send_recv_tensor(parallel_context: ParallelContext, send_contiguous: bool, full: bool):
diff --git a/tests/test_parameters_accumulate_gradient_in_fp32.py b/tests/test_parameters_accumulate_gradient_in_fp32.py
index ba0debd6..0c7eb152 100644
--- a/tests/test_parameters_accumulate_gradient_in_fp32.py
+++ b/tests/test_parameters_accumulate_gradient_in_fp32.py
@@ -143,7 +143,7 @@ def test_optimizer_can_step_gradient_in_fp32(half_precision: torch.dtype):
 @pytest.mark.parametrize("train_iterations", [1, 3])
 @rerun_if_address_is_in_use()
 def test_ddp_with_grad_accum_in_fp32(half_precision: torch.dtype, accumulation_steps: int, train_iterations: int):
-    init_distributed(tp=1, dp=2, pp=1)(_test_ddp_with_grad_accum_in_fp32)(
+    init_distributed(tp=1, dp=2, pp=1, sp=1)(_test_ddp_with_grad_accum_in_fp32)(
         half_precision=half_precision,
         accumulation_steps=accumulation_steps,
         train_iterations=train_iterations,
@@ -257,7 +257,7 @@ def _test_ddp_with_grad_accum_in_fp32(
         accumulator.backward(loss_fp32_accum)
 
         for name, param in model_ddp_fp32_accum.named_parameters():
-            # Check that half grads has been set to None in sync step, to avoid it being uncorrectly used
+            # Check that half grads has been set to None in sync step, to avoid it being incorrectly used
             half_grad = param.grad
             assert half_grad is None, f"{half_grad} != None"
 
@@ -310,7 +310,7 @@ def _test_ddp_with_grad_accum_in_fp32(
 @pytest.mark.parametrize("reduce_scatter", [True, False])
 @rerun_if_address_is_in_use()
 def test_tied_weights_sync_with_grad_accum_in_fp32(pipeline_engine: PipelineEngine, reduce_scatter: bool):
-    init_distributed(tp=1, dp=2, pp=2)(_test_tied_weights_sync_with_grad_accum_in_fp32)(
+    init_distributed(tp=1, dp=2, pp=2, sp=1)(_test_tied_weights_sync_with_grad_accum_in_fp32)(
         pipeline_engine=pipeline_engine, reduce_scatter=reduce_scatter
     )
 
diff --git a/tests/test_pipeline_parallel.py b/tests/test_pipeline_parallel.py
index a7f8008f..ac8a18a6 100644
--- a/tests/test_pipeline_parallel.py
+++ b/tests/test_pipeline_parallel.py
@@ -22,7 +22,7 @@
 @pytest.mark.skipif(available_gpus() < 2, reason="Testing build_and_set_rank requires at least 2 gpus")
 @rerun_if_address_is_in_use()
 def test_build_and_set_rank():
-    init_distributed(tp=1, dp=1, pp=2)(_test_build_and_set_rank)()
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_build_and_set_rank)()
 
 
 def _test_build_and_set_rank(parallel_context: ParallelContext):
@@ -72,7 +72,7 @@ def test_init_on_device_and_dtype():
 @pytest.mark.parametrize("pp", list(range(2, min(4, available_gpus()) + 1)))
 @rerun_if_address_is_in_use()
 def test_pipeline_engine(pipeline_engine: PipelineEngine, pp: int):
-    init_distributed(tp=1, dp=1, pp=pp)(_test_pipeline_engine)(pipeline_engine=pipeline_engine)
+    init_distributed(tp=1, dp=1, pp=pp, sp=1)(_test_pipeline_engine)(pipeline_engine=pipeline_engine)
 
 
 def _test_pipeline_engine(parallel_context: ParallelContext, pipeline_engine: PipelineEngine):
@@ -217,7 +217,7 @@ def _test_pipeline_engine(parallel_context: ParallelContext, pipeline_engine: Pi
 @pytest.mark.parametrize("pp", list(range(2, min(4, available_gpus()) + 1)))
 @rerun_if_address_is_in_use()
 def test_pipeline_engine_with_tensor_that_does_not_require_grad(pipeline_engine: PipelineEngine, pp: int):
-    init_distributed(pp=pp, dp=1, tp=1)(_test_pipeline_engine_with_tensor_that_does_not_require_grad)(
+    init_distributed(pp=pp, dp=1, tp=1, sp=1)(_test_pipeline_engine_with_tensor_that_does_not_require_grad)(
         pipeline_engine=pipeline_engine
     )
 
@@ -448,7 +448,7 @@ def dummy_infinite_data_loader_with_non_differentiable_tensor(
 @pytest.mark.parametrize("pp", list(range(2, min(4, available_gpus()) + 1)))
 @rerun_if_address_is_in_use()
 def test_pipeline_forward_without_engine(pp: int):
-    init_distributed(pp=pp, dp=1, tp=1)(_test_pipeline_forward_without_engine)()
+    init_distributed(pp=pp, dp=1, tp=1, sp=1)(_test_pipeline_forward_without_engine)()
 
 
 def _test_pipeline_forward_without_engine(parallel_context: ParallelContext):
@@ -623,7 +623,7 @@ def dummy_infinite_data_loader_with_non_differentiable_tensor(
 )
 @rerun_if_address_is_in_use()
 def test_pipeline_engine_diamond(pipeline_engine: PipelineEngine):
-    init_distributed(pp=4, dp=1, tp=1)(_test_pipeline_engine_diamond)(pipeline_engine=pipeline_engine)
+    init_distributed(pp=4, dp=1, tp=1, sp=1)(_test_pipeline_engine_diamond)(pipeline_engine=pipeline_engine)
     pass
 
 
diff --git a/tests/test_serialize.py b/tests/test_serialize.py
index 329ff279..6b28c4c0 100644
--- a/tests/test_serialize.py
+++ b/tests/test_serialize.py
@@ -4,7 +4,7 @@
 from helpers.dummy import dummy_infinite_data_loader, init_dummy_model
 from helpers.utils import (
     available_gpus,
-    get_all_3d_configurations,
+    get_all_4d_configurations,
     init_distributed,
     is_dict_equal,
     rerun_if_address_is_in_use,
@@ -42,18 +42,18 @@ def test_save_and_load_with_changed_topolgy():
 
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_save_and_load_model(tp: int, dp: int, pp: int):
+def test_save_and_load_model(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
     # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_save_and_load_model)(test_context=test_context)
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_and_load_model)(test_context=test_context)
 
 
 def _test_save_and_load_model(parallel_context: ParallelContext, test_context: TestContext):
@@ -84,18 +84,18 @@ def _test_save_and_load_model(parallel_context: ParallelContext, test_context: T
 
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_save_and_load_optimizer(tp: int, dp: int, pp: int):
+def test_save_and_load_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
     # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_save_and_load_optimizer)(test_context=test_context)
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_and_load_optimizer)(test_context=test_context)
 
 
 def _test_save_and_load_optimizer(parallel_context: ParallelContext, test_context: TestContext):
@@ -149,18 +149,20 @@ def _test_save_and_load_optimizer(parallel_context: ParallelContext, test_contex
 
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_save_zero_optimizer_and_load_optimizer(tp: int, dp: int, pp: int):
+def test_save_zero_optimizer_and_load_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
     # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_save_zero_optimizer_and_load_optimizer)(test_context=test_context)
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_zero_optimizer_and_load_optimizer)(
+        test_context=test_context
+    )
 
 
 def _test_save_zero_optimizer_and_load_optimizer(parallel_context: ParallelContext, test_context: TestContext):
@@ -223,18 +225,18 @@ def _test_save_zero_optimizer_and_load_optimizer(parallel_context: ParallelConte
 
 @pytest.mark.skip(reason="Assumption that zero and non zero optimizer have the same serialization format doesn't hold")
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_save_zero_optimizer_and_load_data_parallel_optimizer(tp: int, dp: int, pp: int):
+def test_save_zero_optimizer_and_load_data_parallel_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
     # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_save_zero_optimizer_and_load_data_parallel_optimizer)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_zero_optimizer_and_load_data_parallel_optimizer)(
         test_context=test_context
     )
 
@@ -294,18 +296,18 @@ def _test_save_zero_optimizer_and_load_data_parallel_optimizer(
 
 @pytest.mark.skip(reason="Assumption that zero and non zero optimizer have the same serialization format doesn't hold")
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_save_data_parallel_optimizer_and_load_zero_optimizer(tp: int, dp: int, pp: int):
+def test_save_data_parallel_optimizer_and_load_zero_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
     # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_save_data_parallel_optimizer_and_load_zero_optimizer)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_data_parallel_optimizer_and_load_zero_optimizer)(
         test_context=test_context
     )
 
@@ -361,18 +363,18 @@ def _test_save_data_parallel_optimizer_and_load_zero_optimizer(
 
 
 @pytest.mark.parametrize(
-    "tp,dp,pp",
+    "tp,dp,pp,sp",
     [
-        pytest.param(*all_3d_configs)
+        pytest.param(*all_4d_configs)
         for gpus in range(1, min(available_gpus(), 4) + 1)
-        for all_3d_configs in get_all_3d_configurations(gpus)
+        for all_4d_configs in get_all_4d_configurations(gpus)
     ],
 )
 @rerun_if_address_is_in_use()
-def test_save_optimizer_with_additional_state_dict_keys(tp: int, dp: int, pp: int):
+def test_save_optimizer_with_additional_state_dict_keys(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
     # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp)(_test_save_optimizer_with_additional_state_dict_keys)(
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_optimizer_with_additional_state_dict_keys)(
         test_context=test_context
     )
 
@@ -480,7 +482,7 @@ def _test_save_optimizer_with_additional_state_dict_keys(parallel_context: Paral
 def test_save_and_load_random_states():
     test_context = TestContext()
     # We use DP=2 as we're interested in testing
-    init_distributed(tp=2, dp=1, pp=1)(_test_save_and_load_random_states)(test_context=test_context)
+    init_distributed(tp=2, dp=1, pp=1, sp=1)(_test_save_and_load_random_states)(test_context=test_context)
 
 
 def _test_save_and_load_random_states(parallel_context: ParallelContext, test_context: TestContext):
@@ -519,7 +521,7 @@ def _test_save_and_load_random_states(parallel_context: ParallelContext, test_co
 @rerun_if_address_is_in_use()
 def test_serialize_deserialize_tensormetadata():
     test_context = TestContext()
-    init_distributed(tp=2, dp=1, pp=1)(_test_serialize_deserialize_tensormetadata)(test_context=test_context)
+    init_distributed(tp=2, dp=1, pp=1, sp=1)(_test_serialize_deserialize_tensormetadata)(test_context=test_context)
 
 
 def _test_serialize_deserialize_tensormetadata(parallel_context: ParallelContext, test_context: TestContext):
diff --git a/tests/test_tie_weights.py b/tests/test_tie_weights.py
index eecfc097..d81aeab0 100644
--- a/tests/test_tie_weights.py
+++ b/tests/test_tie_weights.py
@@ -15,7 +15,7 @@
 
 @rerun_if_address_is_in_use()
 def test_tie_weight_in_same_device():
-    init_distributed(tp=1, dp=1, pp=1)(_test_tie_weight_in_same_device)()
+    init_distributed(tp=1, dp=1, pp=1, sp=1)(_test_tie_weight_in_same_device)()
 
 
 def _test_tie_weight_in_same_device(parallel_context: ParallelContext):
@@ -49,7 +49,7 @@ def _test_tie_weight_in_same_device(parallel_context: ParallelContext):
 
 @rerun_if_address_is_in_use()
 def test_tie_weight_in_different_device():
-    init_distributed(tp=1, dp=1, pp=2)(_test_tie_weight_in_different_device)()
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_tie_weight_in_different_device)()
 
 
 def _test_tie_weight_in_different_device(parallel_context: ParallelContext):
@@ -120,7 +120,7 @@ def _test_tie_weight_in_different_device(parallel_context: ParallelContext):
 
 @rerun_if_address_is_in_use()
 def test_tie_weight_across_dp_is_impossible():
-    init_distributed(tp=1, dp=2, pp=1)(_test_tie_weight_across_dp_is_impossible)()
+    init_distributed(tp=1, dp=2, pp=1, sp=1)(_test_tie_weight_across_dp_is_impossible)()
 
 
 def _test_tie_weight_across_dp_is_impossible(parallel_context: ParallelContext):
@@ -158,7 +158,7 @@ def _test_tie_weight_across_dp_is_impossible(parallel_context: ParallelContext):
 
 @rerun_if_address_is_in_use()
 def test_tie_weight_in_different_device_have_gradients_synchronized():
-    init_distributed(tp=1, dp=1, pp=2)(_test_tie_weight_in_different_device_have_gradients_synchronized)()
+    init_distributed(tp=1, dp=1, pp=2, sp=1)(_test_tie_weight_in_different_device_have_gradients_synchronized)()
 
 
 def _test_tie_weight_in_different_device_have_gradients_synchronized(parallel_context: ParallelContext):
diff --git a/tests/test_zero.py b/tests/test_zero.py
index b7503553..c749bc0d 100644
--- a/tests/test_zero.py
+++ b/tests/test_zero.py
@@ -504,7 +504,7 @@ def _test_zero_optimizer_with_tp(
 
 @rerun_if_address_is_in_use()
 def test_sliced_flat_tensor():
-    init_distributed(1, 1, 1)(_test_sliced_flat_tensor)()
+    init_distributed(1, 1, 1, 1)(_test_sliced_flat_tensor)()
 
 
 def _test_sliced_flat_tensor(parallel_context: ParallelContext):

From 9208928b473b68e06f1b1f0a3aa9986d381f8ef7 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Tue, 2 Jul 2024 14:31:56 +0000
Subject: [PATCH 08/19] clean code

---
 src/nanotron/ring_flash_attn/utils.py         | 54 ------------
 .../ring_flash_attn/zigzag_ring_flash_attn.py | 87 -------------------
 2 files changed, 141 deletions(-)

diff --git a/src/nanotron/ring_flash_attn/utils.py b/src/nanotron/ring_flash_attn/utils.py
index c3b27873..dacda6cf 100644
--- a/src/nanotron/ring_flash_attn/utils.py
+++ b/src/nanotron/ring_flash_attn/utils.py
@@ -110,60 +110,6 @@ def wait(self):
         self._ops = []
 
 
-###### my implementation
-# class RingComm:
-#     def __init__(self, process_group: dist.ProcessGroup):
-#         self._process_group = process_group
-#         self._ops = []
-#         self.rank = dist.get_rank(self._process_group)
-#         self.world_size = dist.get_world_size(self._process_group)
-#         self._reqs = []
-
-#         self.send_rank = (self.rank + 1) % self.world_size
-#         self.recv_rank = (self.rank - 1) % self.world_size
-#         self.send_next_next_rank = (self.rank + 2) % self.world_size
-#         self.recv_next_next_rank = (self.rank - 2) % self.world_size
-
-#         if process_group is not None:
-#             self.send_rank = dist.get_global_rank(self._process_group, self.send_rank)
-#             self.recv_rank = dist.get_global_rank(self._process_group, self.recv_rank)
-#             self.send_next_next_rank = dist.get_global_rank(self._process_group, self.send_next_next_rank)
-#             self.recv_next_next_rank = dist.get_global_rank(self._process_group, self.recv_next_next_rank)
-
-#     def send_recv(self, to_send: torch.Tensor, recv_tensor: Optional[torch.Tensor] = None, next_next=False) -> torch.Tensor:
-#         if recv_tensor is None:
-#             res = torch.empty_like(to_send)
-#         else:
-#             res = recv_tensor
-#         if next_next:
-#             send_op = dist.P2POp(dist.isend, to_send, self.send_next_next_rank, group=self._process_group)
-#             recv_op = dist.P2POp(dist.irecv, res, self.recv_next_next_rank, group=self._process_group)
-#         else:
-#             send_op = dist.P2POp(dist.isend, to_send, self.send_rank, group=self._process_group)
-#             recv_op = dist.P2POp(dist.irecv, res, self.recv_rank, group=self._process_group)
-#         self._ops.append(send_op)
-#         self._ops.append(recv_op)
-#         return res
-
-#     def commit(self):
-#         # if self._reqs is not None:
-#         #     raise RuntimeError("commit called twice")
-#         self._reqs.extend(dist.batch_isend_irecv(self._ops))
-
-#     def wait(self):
-#         if self._reqs is None:
-#             raise RuntimeError("wait called before commit")
-#         # for req in self._reqs:
-#         #     req.wait()
-#         # self._reqs = None
-
-#         # use more buffer. This may help
-#         for req in self._reqs[:2]:  # Only wait for the first two requests
-#             req.wait()
-#         self._reqs = self._reqs[2:]  # Remove the first two requests from the list
-#         self._ops = []
-
-
 def extract_local(tensor: torch.Tensor, rank: int, world_size: int, dim: int = 1) -> torch.Tensor:
     value_chunks = tensor.chunk(2 * world_size, dim=dim)
     local_value = torch.cat([value_chunks[rank], value_chunks[2 * world_size - rank - 1]], dim=dim)
diff --git a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
index 6b621e2d..2ac1e4e3 100644
--- a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
+++ b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
@@ -75,93 +75,6 @@ def forward(q, k, v, causal):
     return out, lse
 
 
-# ### my implementation
-# def zigzag_ring_flash_attn_forward(
-#     process_group,
-#     q: torch.Tensor,
-#     k: torch.Tensor,
-#     v: torch.Tensor,
-#     softmax_scale,
-#     dropout_p=0,
-#     causal=True,
-#     window_size=(-1, -1),
-#     alibi_slopes=None,
-#     deterministic=False,
-# ):
-#     assert causal is True, "zigzag ring is meaningless for causal=False"
-#     comm = RingComm(process_group)
-
-#     block_seq_len = q.shape[1] // 2
-#     q1 = q[:, block_seq_len:]
-
-#     out = None
-#     lse = None
-#     next_k_even, next_v_even = None, None
-#     next_k_odd, next_v_odd = None, None
-
-#     def forward(q, k, v, causal):
-#         block_out, _, _, _, _, block_lse, _, _ = _flash_attn_forward(
-#             q,
-#             k,
-#             v,
-#             dropout_p,
-#             softmax_scale,
-#             causal=causal,
-#             window_size=window_size,
-#             alibi_slopes=alibi_slopes,
-#             return_softmax=True and dropout_p > 0,
-#         )
-#         return block_out, block_lse
-
-#     for step in range(comm.world_size):
-#         if step==0:
-#                 # order is important. should wait for the odd
-#                 next_k_odd = comm.send_recv(k) # for 1st
-#                 next_v_odd = comm.send_recv(v)
-#                 next_k_even = comm.send_recv(k,next_next=True) # for 2nd step
-#                 next_v_even = comm.send_recv(v,next_next=True)
-#         else:
-#             # for step+2
-#             if step % 2 == 0:
-#                 next_k_even = comm.send_recv(next_k_odd)
-#                 next_v_even = comm.send_recv(next_v_odd)
-#             else:
-#                 next_k_odd = comm.send_recv(next_k_even)
-#                 next_v_odd = comm.send_recv(next_v_even)
-#         comm.commit()
-
-#         if step == 0:
-#             block_out, block_lse = forward(q, k, v, causal=True)
-#             out, lse = update_out_and_lse(out, lse, block_out, block_lse)
-#         elif step <= comm.rank:
-#             k0 = k[:, :block_seq_len]
-#             v0 = v[:, :block_seq_len]
-#             block_out, block_lse = forward(q, k0, v0, causal=False)
-#             out, lse = update_out_and_lse(out, lse, block_out, block_lse)
-#         else:
-#             block_out, block_lse = forward(q1, k, v, causal=False)
-#             out, lse = update_out_and_lse(
-#                 out,
-#                 lse,
-#                 block_out,
-#                 block_lse,
-#                 slice_=(slice(None), slice(block_seq_len, None)),
-#             )
-
-#         if step + 1 != comm.world_size:
-#             comm.wait()
-#             if step % 2 == 0:
-#                 k = next_k_odd
-#                 v = next_v_odd
-#             else:
-#                 k = next_k_even
-#                 v = next_v_even
-
-#     out = out.to(q.dtype)
-#     lse = lse.squeeze(dim=-1).transpose(1, 2)
-#     return out, lse
-
-
 def zigzag_ring_flash_attn_backward(
     process_group,
     doubt,

From dbb8eb76ecb53b00c6e27b510241b31eb61bc0a9 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Thu, 18 Jul 2024 15:31:57 +0000
Subject: [PATCH 09/19] refactor code

---
 run_generate.py                       |   2 +-
 src/nanotron/generation/decode.py     | 142 +++++++++++++++++++++++++-
 src/nanotron/models/llama.py          |  89 +++++-----------
 src/nanotron/ring_flash_attn/utils.py |  30 ++++++
 4 files changed, 196 insertions(+), 67 deletions(-)

diff --git a/run_generate.py b/run_generate.py
index 1b19db83..5268f799 100644
--- a/run_generate.py
+++ b/run_generate.py
@@ -60,7 +60,7 @@ def get_args():
     parser.add_argument("--pp", type=int, default=0)
     parser.add_argument("--tp", type=int, default=0)
     parser.add_argument("--sp", type=int, default=0)
-    parser.add_argument("--max-new-tokens", type=int, default=128, help="Maximum number of new tokens to generate")
+    parser.add_argument("--max-new-tokens", type=int, default=50, help="Maximum number of new tokens to generate")
     return parser.parse_args()
 
 
diff --git a/src/nanotron/generation/decode.py b/src/nanotron/generation/decode.py
index 6ab71fad..d11eb8d6 100644
--- a/src/nanotron/generation/decode.py
+++ b/src/nanotron/generation/decode.py
@@ -110,7 +110,6 @@ def micro_batcher(
                 padding=tokenizer_config.padding,
                 max_length=tokenizer_config.max_input_length,
                 truncation=tokenizer_config.truncation,
-                # pad_to_multiple_of=8
             )
 
             encodings["attention_mask"] = encodings.attention_mask.to(dtype=torch.bool, device="cuda")
@@ -239,7 +238,6 @@ def decode_text(
                 start_time, elapsed_time_first_iteration = time.perf_counter(), 0
 
             for generation_iter in range(max_new_tokens):
-
                 if is_bench and generation_iter == 0:
                     torch.cuda.synchronize()
                     elapsed_time_first_iteration = start_time - time.perf_counter()
@@ -491,6 +489,146 @@ def generator():
                         )
 
 
+def adjust_padding(input_ids, attention_mask, pad_token_id=128001, pad_to_multiple_of=4, padding_left=True):
+    """
+    This function pad or remove paddiings to make the sequence length a multiple of "pad_to_multiple_of"
+    two modes: padding on the left or right.
+    Use with decode_text_simple for 1M tokens context length.
+    """
+    # Count the number of padding tokens on the left side
+    padding_tokens_count = (input_ids == pad_token_id).sum(dim=1).item()
+    # padding_tokens_count = (input_ids == pad_token_id).sum(dim=1).item()
+    input_length = input_ids.size(1)
+
+    # Calculate the required padding to make the length a multiple of pad_to_multiple_of
+    padding_length = (pad_to_multiple_of - input_length % pad_to_multiple_of) % pad_to_multiple_of
+
+    if (
+        padding_tokens_count >= pad_to_multiple_of - padding_length
+    ):  # pad k tokens = remove 8-k tokens, if pad_to_multiple_of = 8
+        # Remove excessive padding tokens to make the length a multiple of pad_to_multiple_of
+        excess_padding = pad_to_multiple_of - padding_length
+        ## padding is on the left or right.
+        if padding_left:
+            input_ids = input_ids[:, excess_padding:]
+            attention_mask = attention_mask[:, excess_padding:]
+        else:
+            input_ids = input_ids[:, :-excess_padding]
+            attention_mask = attention_mask[:, :-excess_padding]
+    else:
+        # Add padding tokens to the left side
+        if padding_length > 0:
+            padding = torch.full((input_ids.size(0), padding_length), pad_token_id, dtype=torch.long).to(
+                "cuda"
+            )  # [batch_size, padding_length]
+            if padding_left:
+                input_ids = torch.cat([padding, input_ids], dim=1)
+                attention_mask = torch.cat([torch.zeros_like(padding), attention_mask], dim=1)
+            else:
+                input_ids = torch.cat([input_ids, padding], dim=1)
+                attention_mask = torch.cat([attention_mask, torch.zeros_like(padding)], dim=1)
+    return input_ids, attention_mask
+
+
+@torch.inference_mode()
+def decode_text_simple(
+    input_texts: List[str],
+    tokenizer: "PreTrainedTokenizer",
+    model: LlamaModel,
+    parallel_context: ParallelContext,
+    max_new_tokens: int,
+    pad_to_multiple_of: Optional[int] = 8,
+    padding_left: Optional[bool] = False,
+):
+    """
+    Use this function only when dealing with long context.
+    A simpler decode text function without PP aims to support long context(e.g. 1M tokens) inference by adjust padding.
+    For sequence length = 1M, TP=8(reduce scatter mode) and SP=2 is needed. For sequence length <= 512K, TP=8(reduce scatter mode) and SP=1 is enough.
+    Specifically, SP need padding on the right for now(faster with flash_attn_func and easier to implement).
+    TODO: Only tested when batch size = 1.  KV cache is not implemented yet. For faster inference, consider using decode_text.
+    """
+    assert parallel_context.sp_pg.size() == 1 or (
+        parallel_context.sp_pg.size() > 1 and not padding_left
+    ), "For SP, should use padding right for inference."
+    output_texts = []
+    pad_token_id = tokenizer.pad_token_id
+    sampler = GreedySampler(
+        pg=parallel_context.tp_pg
+    )  # Notice that the logics for gathering sharded logits(last dim) are implemented in the sampler.
+    for input_text in input_texts:
+        inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+        input_ids, attention_mask = inputs["input_ids"], inputs["attention_mask"]
+        num_input_tokens = attention_mask.sum().item()
+        sp_world_size = dist.get_world_size(parallel_context.sp_pg)
+        sp_rank = dist.get_rank(parallel_context.sp_pg)
+        # Generate output
+        for i in range(max_new_tokens):
+            input_ids, attention_mask = adjust_padding(
+                input_ids,
+                attention_mask,
+                pad_token_id,
+                pad_to_multiple_of=pad_to_multiple_of,
+                padding_left=padding_left,
+            )
+            if parallel_context.sp_pg.size() > 1:
+                assert (
+                    input_ids.size(1) % (2 * sp_world_size) == 0
+                ), "The sequence length should be a multiple of 2*world_size. Check padding function"
+                chunk_size = input_ids.size(1) // (2 * sp_world_size)  # chunk size
+            local_outputs = model(input_ids=input_ids, input_mask=attention_mask).transpose(
+                0, 1
+            )  # batch_size x seq_length/sp x vocab_size/tp
+            # I don't gather the output along the sequnce level for SP. Because it takes way too much memory.
+            if parallel_context.sp_pg.size() > 1:
+                last_token = attention_mask.sum(dim=1).item() - 1  # the last token before padding
+                last_token_chunk = last_token // chunk_size  # the chunk index of the last token
+                GPU_idx = (
+                    last_token_chunk if last_token_chunk < sp_world_size else 2 * sp_world_size - 1 - last_token_chunk
+                )  # the GPU index of the last token among the SP group
+                last_token_idx = (
+                    last_token % chunk_size + chunk_size
+                    if last_token_chunk >= sp_world_size
+                    else last_token % chunk_size
+                )  # the token index in the GPU
+                if sp_rank == GPU_idx:
+                    next_token_id = sampler(sharded_logits=local_outputs[:, last_token_idx, :])
+                else:
+                    next_token_id = torch.zeros((input_ids.size(0), 1), dtype=torch.long).to("cuda")
+                dist.barrier(group=parallel_context.sp_pg)  # all process have to wait for the sampler to finish
+                dist.broadcast(
+                    next_token_id,
+                    src=dist.get_global_rank(parallel_context.sp_pg, GPU_idx),
+                    group=parallel_context.sp_pg,
+                )  # broadcast the generated token to all process.  Source rank on global process group (regardless of group argument).
+                # replace the padding with generated token(padding on the right side)
+                if last_token + 1 < input_ids.size(1):  # replace the padding with generated token
+                    input_ids[:, last_token + 1] = next_token_id
+                    attention_mask[:, last_token + 1] = 1
+                else:  # insert the generated token when there is no padding to replace
+                    input_ids = torch.cat([input_ids, next_token_id], dim=1)
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(next_token_id)], dim=1)
+            # without SP. We can just use sampler to generate the next token.
+            else:
+                if padding_left:
+                    last_token = -1
+                    next_token_id = sampler(sharded_logits=local_outputs[:, last_token, :])  # batch_size x 1
+                    input_ids = torch.cat([input_ids, next_token_id], dim=1)
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(next_token_id)], dim=1)
+                else:
+                    last_token = attention_mask.sum(dim=1).item() - 1  # the last token before padding
+                    next_token_id = sampler(sharded_logits=local_outputs[:, last_token, :])
+                    if last_token + 1 < input_ids.size(1):  # replace the padding with generated token
+                        input_ids[:, last_token + 1] = next_token_id
+                        attention_mask[:, last_token + 1] = 1
+                    else:  # insert the generated token when there is no padding to replace
+                        input_ids = torch.cat([input_ids, next_token_id], dim=1)
+                        attention_mask = torch.cat([attention_mask, torch.ones_like(next_token_id)], dim=1)
+
+        generation = tokenizer.decode(input_ids[0, num_input_tokens:], skip_special_tokens=True)
+        output_texts.append({"prompt": input_text, "generation": generation})
+    return output_texts
+
+
 @torch.inference_mode()
 def decode_tokenized(
     input_ids: torch.Tensor,
diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index b825bfb1..50fd9bd4 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -40,13 +40,11 @@
     TensorParallelRowLinear,
 )
 from nanotron.random import RandomStates
-from nanotron.ring_flash_attn.ring_flash_attn import ring_flash_attn_func
-from nanotron.ring_flash_attn.utils import normal_split, zigzag_split
+from nanotron.ring_flash_attn.utils import zigzag_split
 from nanotron.ring_flash_attn.zigzag_ring_flash_attn import zigzag_ring_flash_attn_func
 from nanotron.scaling.parametrization import SpectralMupParametrizator, StandardParametrizator
 from nanotron.utils import checkpoint_method
 
-zigzag = True
 logger = logging.get_logger(__name__)
 
 
@@ -99,7 +97,6 @@ def forward(
             self.end *= 2
             self._initialized_buffer = False
         if self._initialized_buffer is False:
-            print(f"Initializing rotary embeddings with end={self.end}")
             self.init_rotary_embeddings()
         dtype = x.dtype
         assert inner_dim % 2 == 0
@@ -135,7 +132,6 @@ def __init__(self, dim: int, end: int, theta: float = 500000.0):
         self.dim = dim
         self.end = end
         self.theta = theta
-        log_rank(f"Initialize LlamaRotaryEmbedding: RoPE Theta = {theta}", logger=logger, level=logging.DEBUG, rank=0)
         self.init_rotary_embeddings()
 
     def init_rotary_embeddings(self):
@@ -247,52 +243,30 @@ def forward(self, hidden_states):  # [seq_length, batch_size, hidden_dim]
 class RingFlashAttention(nn.Module):
     def __init__(self, config: LlamaConfig, pg: dist.ProcessGroup):
         super().__init__()
-        # TODO @thomasw21: GPT has a weird `d_kv` config which I'm guessing is essentically a `d_qkv`
-        assert (
-            config.hidden_size % config.num_attention_heads == 0
-        ), f"Hidden size {config.hidden_size} must be divisible by number of attention heads {config.num_attention_heads}."
-        self.d_qk = config.hidden_size // config.num_attention_heads
-        self.d_v = config.hidden_size // config.num_attention_heads
-        self.is_using_mup = config.is_using_mup
-        self.checkpoint_attention = False  # Because flash_attn already does checkpointing
+        assert config.hidden_size % config.num_attention_heads == 0
+        assert config.hidden_size % config.num_key_value_heads == 0
+        assert dist.get_world_size(pg) > 1, "Ring attention process group size must be greater than 1"
         self.pg = pg
 
-    @checkpoint_method(attr_name="checkpoint_attention")
     def forward(
         self,
         local_q: torch.Tensor,  # [batch_size, q_length, n_local_q_heads, inner_dim]
         local_k: torch.Tensor,  # [batch_size, kv_length, n_local_kv_heads, inner_dim]
         local_v: torch.Tensor,  # [batch_size, kv_length, n_local_kv_heads, inner_dim]
     ):
-        # NOTE: this scale is for µTransfer,
         causal = True
-
-        if zigzag:
-            ring_out, _, _ = zigzag_ring_flash_attn_func(
-                local_q,
-                local_k,
-                local_v,
-                dropout_p=0.0,
-                causal=causal,
-                window_size=(-1, -1),
-                alibi_slopes=None,
-                deterministic=False,
-                return_attn_probs=True,
-                group=self.pg,
-            )
-        else:
-            ring_out, _, _ = ring_flash_attn_func(
-                local_q,
-                local_k,
-                local_v,
-                dropout_p=0.0,
-                causal=causal,
-                window_size=(-1, -1),
-                alibi_slopes=None,
-                deterministic=False,
-                return_attn_probs=True,
-                group=self.pg,
-            )
+        ring_out, _, _ = zigzag_ring_flash_attn_func(
+            local_q,
+            local_k,
+            local_v,
+            dropout_p=0.0,
+            causal=causal,
+            window_size=(-1, -1),
+            alibi_slopes=None,
+            deterministic=False,
+            return_attn_probs=True,
+            group=self.pg,
+        )
         return ring_out
 
 
@@ -486,6 +460,7 @@ def __init__(
         self.prefill_kv_len = (
             config.max_position_embeddings
         )  # TODO @nouamane: compute based on free memory, because in rope we can surpass max_position_embeddings
+        self.layer_idx = layer_idx
 
     def forward(
         self,
@@ -777,6 +752,7 @@ def __init__(
             sp_pg=sp_pg,
             layer_idx=layer_idx,
         )
+        self.layer_idx = layer_idx
 
         self.post_attention_layernorm = TritonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.mlp = MLP(config=config, parallel_config=parallel_config, tp_pg=tp_pg)
@@ -868,7 +844,7 @@ def __init__(
             module_input_keys={"input_ids", "input_mask"},
             module_output_keys={"input_embeds"},
         )
-
+        log_rank(f"Initialize RoPE Theta = {config.rope_theta}", logger=logger, level=logging.INFO, rank=0)
         self.decoder = nn.ModuleList(
             [
                 PipelineBlock(
@@ -912,7 +888,6 @@ def __init__(
             module_input_keys={"x"},
             module_output_keys={"logits"},
         )
-
         self.cast_to_fp32 = PipelineBlock(
             p2p=self.p2p,
             module_builder=lambda: lambda x: x.float(),
@@ -935,22 +910,14 @@ def forward_with_hidden_states(
     ):
         if isinstance(input_ids, torch.Tensor):
             batch_size, seq_length = input_ids.shape
-            position_ids = torch.arange(seq_length, device=input_ids.device).unsqueeze(0).expand(batch_size, -1)
+            position_ids = torch.cumsum(input_mask, dim=-1, dtype=torch.int32) - 1
             # split input if using ring attention
             if self.parallel_context.sp_pg.size() > 1:
                 world_size = self.parallel_context.sp_pg.size()
                 rank = dist.get_rank(self.parallel_context.sp_pg)
-
-                # zigzag split
-                if zigzag:
-                    input_ids, input_mask, position_ids = zigzag_split(
-                        rank, world_size, input_ids, input_mask, position_ids
-                    )
-                # normal split
-                else:
-                    input_ids, input_mask, position_ids = normal_split(
-                        rank, world_size, input_ids, input_mask, position_ids
-                    )
+                input_ids, input_mask, position_ids = zigzag_split(
+                    rank, world_size, input_ids, input_mask, position_ids
+                )
         else:
             position_ids = TensorPointer(input_ids.group_rank)
         # all tensors are optional as most ranks don't need anything from the dataloader.
@@ -1031,17 +998,11 @@ def forward(
         # Megatron by defaults cast everything in fp32. `--f16-lm-cross-entropy` is an option you can use to keep current precision.
         # https://github.com/NVIDIA/Megatron-LM/blob/f267e6186eae1d6e2055b412b00e2e545a8e896a/megatron/model/gpt_model.py#L38
 
-        # ring attention: split the label
+        # ring attention: split the label as well
         if isinstance(label_ids, torch.Tensor) and self.sp_pg.size() > 1:
             world_size = self.sp_pg.size()
             rank = dist.get_rank(self.sp_pg)
-
-            # zigzag split
-            if zigzag:
-                label_ids, label_mask = zigzag_split(rank, world_size, label_ids, label_mask)
-            # normal split
-            else:
-                label_ids, label_mask = normal_split(rank, world_size, label_ids, label_mask)
+            label_ids, label_mask = zigzag_split(rank, world_size, label_ids, label_mask)
         loss = sharded_cross_entropy(
             sharded_logits, label_ids.transpose(0, 1).contiguous(), group=self.tp_pg, dtype=torch.float
         ).transpose(0, 1)
diff --git a/src/nanotron/ring_flash_attn/utils.py b/src/nanotron/ring_flash_attn/utils.py
index dacda6cf..ca6e2cef 100644
--- a/src/nanotron/ring_flash_attn/utils.py
+++ b/src/nanotron/ring_flash_attn/utils.py
@@ -130,3 +130,33 @@ def normal_split(rank: int, world_size: int, *args: torch.Tensor, dim=1) -> Tupl
         local_value = value.chunk(world_size, dim=dim)[rank]
         local_values.append(local_value)
     return tuple(local_values)
+
+
+## a function to merge the tensor in a zigzag way. inverse of zigzag_split
+def zigzag_merge(
+    rank: int, world_size: int, local_tensor: torch.Tensor, process_group: dist.ProcessGroup, dim: int = 1
+) -> Tuple[torch.Tensor, ...]:
+
+    # Split the local tensor into two chunks
+    chunk1, chunk2 = local_tensor.chunk(2, dim=dim)
+
+    # Create placeholders for all chunks
+    all_chunks = [torch.zeros_like(chunk1) for _ in range(2 * world_size)]
+
+    # Gather all chunks from all processes
+    dist.all_gather(all_chunks[:world_size], chunk1, group=process_group)
+    dist.all_gather(all_chunks[world_size:], chunk2, group=process_group)
+
+    # Reverse the order of the second half of chunks
+    all_chunks[world_size:] = all_chunks[world_size:][::-1]
+
+    del chunk1, chunk2
+    torch.cuda.empty_cache()
+
+    # Concatenate all chunks to form the full tensor
+    merged_tensor = torch.cat(all_chunks, dim=dim)
+
+    del all_chunks
+    torch.cuda.empty_cache()
+
+    return merged_tensor

From 16600f09674c5dfd2d544c6ca3fe1cb9c5e274c9 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Thu, 18 Jul 2024 16:45:23 +0000
Subject: [PATCH 10/19] fix tests

---
 src/nanotron/parallel/context.py | 10 ++++------
 tests/helpers/utils.py           |  3 ++-
 tests/test_clip_grads.py         |  2 +-
 tests/test_distributed.py        |  1 +
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/nanotron/parallel/context.py b/src/nanotron/parallel/context.py
index a7027b77..69fd39c4 100644
--- a/src/nanotron/parallel/context.py
+++ b/src/nanotron/parallel/context.py
@@ -20,9 +20,7 @@ def __init__(
         backend: DistributedBackend = "nccl",
     ):
         """Initialize parallel context."""
-        num_gpus_per_model = (
-            tensor_parallel_size * pipeline_parallel_size * expert_parallel_size * sequence_parallel_size
-        )
+        num_gpus_per_model = tensor_parallel_size * pipeline_parallel_size * expert_parallel_size
         world_size = int(os.environ["WORLD_SIZE"])
 
         assert (
@@ -30,11 +28,11 @@ def __init__(
         ), "The total number of processes must be divisible by the data parallel size."
         assert world_size % num_gpus_per_model == 0, (
             "The total number of processes must be divisible by"
-            "the number of GPUs per model (tensor_parallel_size * pipeline_parallel_size)."
+            f"the number of GPUs per model (tensor_parallel_size * pipeline_parallel_size). Got {world_size} and {num_gpus_per_model}."
         )
-        if num_gpus_per_model * data_parallel_size != world_size:
+        if num_gpus_per_model * data_parallel_size * sequence_parallel_size != world_size:
             raise ValueError(
-                f"The number of process requires to run all replicas ({num_gpus_per_model * data_parallel_size})",
+                f"The number of process requires to run all replicas ({num_gpus_per_model * data_parallel_size * sequence_parallel_size })",
                 f"must be equal to the world size ({world_size}).",
             )
 
diff --git a/tests/helpers/utils.py b/tests/helpers/utils.py
index b8f6488a..78fd3e69 100644
--- a/tests/helpers/utils.py
+++ b/tests/helpers/utils.py
@@ -255,11 +255,12 @@ def setup_dist_env(rank, world_size, port):
         os.environ["MASTER_ADDR"] = "localhost"
         os.environ["MASTER_PORT"] = str(port)
 
-    world_size = tp * pp * dp
+    world_size = tp * pp * dp * sp
     setup_dist_env(rank, world_size, port)
     parallel_context = ParallelContext(
         data_parallel_size=dp, pipeline_parallel_size=pp, tensor_parallel_size=tp, sequence_parallel_size=sp
     )
+    print("world_size: ", world_size)
     func(parallel_context, **kwargs)
 
 
diff --git a/tests/test_clip_grads.py b/tests/test_clip_grads.py
index 66940294..73c23f58 100644
--- a/tests/test_clip_grads.py
+++ b/tests/test_clip_grads.py
@@ -429,7 +429,7 @@ def _test_clip_grads_tied_weights(parallel_context: ParallelContext, norm_type:
     # Test that we get the same gradient after clipping
     assert torch.allclose(weight.grad, ref_weight.grad, rtol=1e-7, atol=1e-6)
     assert torch.allclose(bias.grad, ref_bias.grad, rtol=1e-7, atol=1e-6)
-    assert torch.allclose(total_norm, ref_total_norm, rtol=0, atol=0), f"Got {total_norm} and {ref_total_norm}"
+    assert torch.allclose(total_norm, ref_total_norm, rtol=1e-6, atol=1e-6), f"Got {total_norm} and {ref_total_norm}"
 
     parallel_context.destroy()
 
diff --git a/tests/test_distributed.py b/tests/test_distributed.py
index 241dd56e..a543b121 100644
--- a/tests/test_distributed.py
+++ b/tests/test_distributed.py
@@ -17,6 +17,7 @@ def _test_init_parallel_context(parallel_context: ParallelContext):
     assert isinstance(parallel_context.tp_pg, ProcessGroup) if parallel_context.tensor_parallel_size > 1 else True
     assert isinstance(parallel_context.pp_pg, ProcessGroup) if parallel_context.pipeline_parallel_size > 1 else True
     assert isinstance(parallel_context.dp_pg, ProcessGroup) if parallel_context.data_parallel_size > 1 else True
+    assert isinstance(parallel_context.sp_pg, ProcessGroup) if parallel_context.sequence_parallel_size > 1 else True
 
     world_rank = dist.get_rank(parallel_context.world_pg)
     ranks3d = parallel_context.get_local_ranks(world_rank)

From 175a076d9ac594dc43b5312008667162d8f31da2 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Fri, 19 Jul 2024 17:24:52 +0000
Subject: [PATCH 11/19] fix tests

---
 src/nanotron/helpers.py             |  4 ++--
 src/nanotron/parallel/utils.py      |  4 ++--
 src/nanotron/serialize/optimizer.py | 10 ++++++---
 src/nanotron/trainer.py             | 34 +----------------------------
 tests/helpers/dummy.py              |  2 +-
 tests/helpers/utils.py              |  1 -
 tests/test_serialize.py             | 21 +++++++++++-------
 7 files changed, 26 insertions(+), 50 deletions(-)

diff --git a/src/nanotron/helpers.py b/src/nanotron/helpers.py
index 5e2cb7b5..ee9d2cc7 100644
--- a/src/nanotron/helpers.py
+++ b/src/nanotron/helpers.py
@@ -385,7 +385,7 @@ def grad_optimizer_builder(named_param_groups):
             named_params_or_groups=named_param_groups,
             # TODO @thomasw21: We need a better API for gradient accumulation/zero etc ...
             optimizer_builder=optimizer_builder,
-            dp_pg=parallel_context.dp_sp_pg,  # sequence parallel and data parallel process group
+            dp_pg=parallel_context.dp_pg,  # TODO: add sequence parallel support. This will change the topology of the current OS checkpoint.
         )
 
         # SANITY CHECK: assert that optimizer's named_params point to model's params (check only the first one)
@@ -411,7 +411,7 @@ def grad_optimizer_builder(named_param_groups):
         assert isinstance(grad_accumulator, FP32GradientAccumulator)
         grad_accumulator.assign_param_offsets(
             dp_rank=dist.get_rank(
-                parallel_context.dp_sp_pg
+                parallel_context.dp_pg
             ),  # sequence parallel and data parallel process group will synchronize the gradient together
             param_name_to_offsets=param_name_to_dp_rank_offsets,
         )
diff --git a/src/nanotron/parallel/utils.py b/src/nanotron/parallel/utils.py
index b9ac12ae..e0159902 100644
--- a/src/nanotron/parallel/utils.py
+++ b/src/nanotron/parallel/utils.py
@@ -23,10 +23,10 @@ def wrapper(*args, **kwargs):
 
 
 def initial_sync(model: nn.Module, parallel_context: ParallelContext):
-    # Synchronize across dp: basic assumption
+    # Synchronize across dp/sp: basic assumption
     sorted_name_params = sorted(model.named_parameters(), key=lambda x: x[0])
     for name, param in sorted_name_params:
-        dist.all_reduce(param, op=dist.ReduceOp.AVG, group=parallel_context.dp_pg)
+        dist.all_reduce(param, op=dist.ReduceOp.AVG, group=parallel_context.dp_sp_pg)
 
     # Synchronize across tied weights: basic assumption
     for (_, group_ranks), param in sorted(
diff --git a/src/nanotron/serialize/optimizer.py b/src/nanotron/serialize/optimizer.py
index 68a3b1a0..97540472 100644
--- a/src/nanotron/serialize/optimizer.py
+++ b/src/nanotron/serialize/optimizer.py
@@ -41,11 +41,15 @@ def save_optimizer(
     root_folder: Path,
 ):
     """Saves optimizer states
-    - If Zero-0 is used, optimizer states are replicated across all DPs. Only DP-0 saves the states
+    - If Zero-0 is used, optimizer states are replicated across all DPs. Only DP-0 and SP-0 saves the states
     - If Zero-1 is used, optimizer states are sharded across all DPs. Each DP saves its own states
     """
-    if (not optimizer.inherit_from(optim.ZeroDistributedOptimizer)) and dist.get_rank(parallel_context.dp_pg) > 0:
-        # this is Zero-0, so only DP-0 saves the optimizer states
+    if (
+        (not optimizer.inherit_from(optim.ZeroDistributedOptimizer))
+        and dist.get_rank(parallel_context.dp_pg) > 0
+        and dist.get_rank(parallel_context.sp_pg) > 0
+    ):
+        # this is Zero-0, so only DP-0 and SP-0 saves the optimizer states
         return
 
     # TODO: Figure out if I need to save param groups. Right now I'm assuming no as we only store what's trainable
diff --git a/src/nanotron/trainer.py b/src/nanotron/trainer.py
index fde511f8..aceccc3a 100644
--- a/src/nanotron/trainer.py
+++ b/src/nanotron/trainer.py
@@ -479,7 +479,6 @@ def training_step(
             sync_gradients_across_dp(
                 module=self.model,
                 dp_pg=self.parallel_context.dp_sp_pg,
-                # dp_pg=self.parallel_context.dp_pg,
                 reduce_op=dist.ReduceOp.AVG,
                 # TODO @thomasw21: This is too memory hungry, instead we run all_reduce
                 reduce_scatter=False,  # optimizer.inherit_from(ZeroDistributedOptimizer),
@@ -519,7 +518,6 @@ def training_step(
                 [output["loss"] for output in outputs]
             ).sum()  # already divided by n_micro_batches_per_batch
             # sync loss across DP/SP
-            # handle = dist.all_reduce(loss_avg, group=self.parallel_context.dp_pg, async_op=True, op=dist.ReduceOp.AVG)
             handle = dist.all_reduce(
                 loss_avg, group=self.parallel_context.dp_sp_pg, async_op=True, op=dist.ReduceOp.AVG
             )
@@ -715,7 +713,7 @@ def _load_model_checkpoint(self, model: NanotronModel) -> NanotronModel:
                 # Synchronize parameters so that the model is consistent
                 # sync all params across dp
                 for _, param in sorted(model.named_parameters(), key=lambda x: x[0]):
-                    dist.all_reduce(param, op=dist.ReduceOp.AVG, group=self.parallel_context.dp_pg)
+                    dist.all_reduce(param, op=dist.ReduceOp.AVG, group=self.parallel_context.dp_sp_pg)
 
                 # sync tied params across tied groups
                 for (_, group_ranks), param in sorted(
@@ -802,38 +800,8 @@ def _init_model(
             # Check that the model has at least one grad. Necessary for DDP
             check_model_has_grad(model=model, parallel_context=parallel_context)
             # TODO @thomasw21: DDP doesn't support broadcasting complex buffers (and we don't really need that broadcasting anyway)
-
-            # ### debug
-            # tensor_names = [name for name in model.state_dict()]
-            # log_rank(
-            #     f"Params: {tensor_names}, length {len(tensor_names)}",
-            #     logger=logger,
-            #     level=logging.INFO,
-            #     rank=0,
-            # )
-            # log_rank(
-            #     f"Params: {tensor_names}, length {len(tensor_names)}",
-            #     logger=logger,
-            #     level=logging.INFO,
-            #     rank=1,
-            # )
-            # log_rank(
-            #     f"Params: {tensor_names}, length {len(tensor_names)}",
-            #     logger=logger,
-            #     level=logging.INFO,
-            #     rank=2,
-            # )
-            # log_rank(
-            #     f"Params: {tensor_names}, length {len(tensor_names)}",
-            #     logger=logger,
-            #     level=logging.INFO,
-            #     rank=3,
-            # )
-            # ###
-
             model = DistributedDataParallel(
                 model,
-                # process_group=parallel_context.dp_pg,
                 process_group=parallel_context.dp_sp_pg,
                 broadcast_buffers=False,
                 bucket_cap_mb=config.model.ddp_bucket_cap_mb,
diff --git a/tests/helpers/dummy.py b/tests/helpers/dummy.py
index 20332f7d..edcc8aa9 100644
--- a/tests/helpers/dummy.py
+++ b/tests/helpers/dummy.py
@@ -94,7 +94,7 @@ def init_dummy_model(parallel_context: ParallelContext, dtype: torch.dtype = tor
     initial_sync(model=model, parallel_context=parallel_context)
 
     if len(list(model.named_parameters())) > 0:
-        model = DistributedDataParallel(model, process_group=parallel_context.dp_pg)
+        model = DistributedDataParallel(model, process_group=parallel_context.dp_sp_pg)
     else:
         # No parameters, so no need to use DDP to sync parameters gradients
         model = model
diff --git a/tests/helpers/utils.py b/tests/helpers/utils.py
index 78fd3e69..954e9fc5 100644
--- a/tests/helpers/utils.py
+++ b/tests/helpers/utils.py
@@ -260,7 +260,6 @@ def setup_dist_env(rank, world_size, port):
     parallel_context = ParallelContext(
         data_parallel_size=dp, pipeline_parallel_size=pp, tensor_parallel_size=tp, sequence_parallel_size=sp
     )
-    print("world_size: ", world_size)
     func(parallel_context, **kwargs)
 
 
diff --git a/tests/test_serialize.py b/tests/test_serialize.py
index 6b28c4c0..bf32dcce 100644
--- a/tests/test_serialize.py
+++ b/tests/test_serialize.py
@@ -159,8 +159,10 @@ def _test_save_and_load_optimizer(parallel_context: ParallelContext, test_contex
 @rerun_if_address_is_in_use()
 def test_save_zero_optimizer_and_load_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
-    # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_zero_optimizer_and_load_optimizer)(
+    # Currently SP doesn't support zero.
+    if sp != 1:
+        return
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=1)(_test_save_zero_optimizer_and_load_optimizer)(
         test_context=test_context
     )
 
@@ -235,8 +237,9 @@ def _test_save_zero_optimizer_and_load_optimizer(parallel_context: ParallelConte
 @rerun_if_address_is_in_use()
 def test_save_zero_optimizer_and_load_data_parallel_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
-    # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_zero_optimizer_and_load_data_parallel_optimizer)(
+    if sp != 1:
+        return
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=1)(_test_save_zero_optimizer_and_load_data_parallel_optimizer)(
         test_context=test_context
     )
 
@@ -306,8 +309,9 @@ def _test_save_zero_optimizer_and_load_data_parallel_optimizer(
 @rerun_if_address_is_in_use()
 def test_save_data_parallel_optimizer_and_load_zero_optimizer(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
-    # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_data_parallel_optimizer_and_load_zero_optimizer)(
+    if sp != 1:
+        return
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=1)(_test_save_data_parallel_optimizer_and_load_zero_optimizer)(
         test_context=test_context
     )
 
@@ -373,8 +377,9 @@ def _test_save_data_parallel_optimizer_and_load_zero_optimizer(
 @rerun_if_address_is_in_use()
 def test_save_optimizer_with_additional_state_dict_keys(tp: int, dp: int, pp: int, sp: int):
     test_context = TestContext()
-    # We use DP=2 as we're interested in testing that one
-    init_distributed(tp=tp, dp=dp, pp=pp, sp=sp)(_test_save_optimizer_with_additional_state_dict_keys)(
+    if sp != 1:
+        return
+    init_distributed(tp=tp, dp=dp, pp=pp, sp=1)(_test_save_optimizer_with_additional_state_dict_keys)(
         test_context=test_context
     )
 

From 61486e792b8766c18c3c1a2f5ac07669ad281637 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Fri, 19 Jul 2024 17:44:04 +0000
Subject: [PATCH 12/19] another test

---
 tests/test_parameters_accumulate_gradient_in_fp32.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_parameters_accumulate_gradient_in_fp32.py b/tests/test_parameters_accumulate_gradient_in_fp32.py
index 0c7eb152..b955c276 100644
--- a/tests/test_parameters_accumulate_gradient_in_fp32.py
+++ b/tests/test_parameters_accumulate_gradient_in_fp32.py
@@ -348,6 +348,7 @@ def _test_tied_weights_sync_with_grad_accum_in_fp32(
                             pp_rank=get_pp_rank_of(target, module=mdl),
                             dp_rank=dist.get_rank(parallel_context.dp_pg),
                             tp_rank=dist.get_rank(parallel_context.tp_pg),
+                            sp_rank=dist.get_rank(parallel_context.sp_pg),
                         ),
                     ),
                 )

From d5e833245f28b758a7f563875e555c87501ab0ee Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Sun, 21 Jul 2024 14:17:38 +0000
Subject: [PATCH 13/19] import flash attention

---
 src/nanotron/models/llama.py                  | 23 ++++++++-------
 src/nanotron/ring_flash_attn/utils.py         |  4 +--
 .../ring_flash_attn/zigzag_ring_flash_attn.py |  2 +-
 src/nanotron/utils.py                         | 29 +++++++++++++++++--
 4 files changed, 40 insertions(+), 18 deletions(-)

diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index 29d7a0db..3f599f54 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -42,9 +42,19 @@
 )
 from nanotron.random import RandomStates
 from nanotron.ring_flash_attn.utils import zigzag_split
-from nanotron.ring_flash_attn.zigzag_ring_flash_attn import zigzag_ring_flash_attn_func
 from nanotron.scaling.parametrization import SpectralMupParametrizator, StandardParametrizator
-from nanotron.utils import checkpoint_method
+from nanotron.utils import checkpoint_method, supports_flash_attention
+
+if supports_flash_attention():
+    from flash_attn import bert_padding
+    from flash_attn.flash_attn_interface import (
+        flash_attn_varlen_func,
+        flash_attn_with_kvcache,
+    )
+    from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
+
+    from nanotron.ring_flash_attn.zigzag_ring_flash_attn import zigzag_ring_flash_attn_func
+
 
 logger = logging.get_logger(__name__)
 
@@ -293,8 +303,6 @@ def forward(
         q_sequence_mask: torch.Tensor,  # torch.BoolTensor [batch_size, q_length] (can be broadcasted to that size)
         kv_sequence_mask: torch.Tensor,  # torch.BoolTensor [batch_size, kv_length] (can be broadcasted to that size)
     ):
-        from flash_attn.flash_attn_interface import flash_attn_varlen_func
-
         # TODO @thomasw21: Compute once, instead of computing for each layers.
         cu_seqlens_q = torch.zeros((q_sequence_mask.shape[0] + 1), dtype=torch.int32, device=query_states.device)
         cu_seqlens_k = torch.zeros((kv_sequence_mask.shape[0] + 1), dtype=torch.int32, device=query_states.device)
@@ -362,7 +370,6 @@ def __init__(
         sp_pg: dist.ProcessGroup,
         layer_idx: int,
     ):
-        from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
 
         super().__init__()
         # Tensor parallel considerations: We split tensors along head dimension
@@ -469,12 +476,6 @@ def forward(
         sequence_mask,  # [batch_size, seq_length]
         position_ids: Optional[torch.LongTensor] = None,
     ):
-        from flash_attn import bert_padding
-        from flash_attn.flash_attn_interface import (
-            flash_attn_varlen_func,
-            flash_attn_with_kvcache,
-        )
-
         qkv_states = self.qkv_proj(
             hidden_states
         )  # [seq_length, batch_size, n_local_q_heads * d_qk + 2 * n_local_kv_heads * d_qk]
diff --git a/src/nanotron/ring_flash_attn/utils.py b/src/nanotron/ring_flash_attn/utils.py
index ca6e2cef..b902c398 100644
--- a/src/nanotron/ring_flash_attn/utils.py
+++ b/src/nanotron/ring_flash_attn/utils.py
@@ -6,7 +6,7 @@
 
 __all__ = ["update_out_and_lse", "RingComm"]
 
-# copy from https://github.com/zhuzilin/ring-flash-attention
+# reference: https://github.com/zhuzilin/ring-flash-attention
 @torch.jit.script
 def _update_out_and_lse(
     out: torch.Tensor,
@@ -18,8 +18,6 @@ def _update_out_and_lse(
     block_out = block_out.to(torch.float32)
     block_lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1)
 
-    # new_lse = lse + torch.log(1 + torch.exp(block_lse - lse))
-    # torch.exp(lse - new_lse) * out + torch.exp(block_lse - new_lse) * block_out
     # For additional context and discussion, please refer to:
     # https://github.com/zhuzilin/ring-flash-attention/pull/34#issuecomment-2076126795
     out = out - F.sigmoid(block_lse - lse) * (out - block_out)
diff --git a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
index 2ac1e4e3..2b8ab712 100644
--- a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
+++ b/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
@@ -4,7 +4,7 @@
 from .utils import RingComm, update_out_and_lse
 
 
-# copy from https://github.com/zhuzilin/ring-flash-attention
+# reference: https://github.com/zhuzilin/ring-flash-attention
 def zigzag_ring_flash_attn_forward(
     process_group,
     q: torch.Tensor,
diff --git a/src/nanotron/utils.py b/src/nanotron/utils.py
index 14fe1ca8..4a2edc49 100644
--- a/src/nanotron/utils.py
+++ b/src/nanotron/utils.py
@@ -1,11 +1,10 @@
 import functools
 import inspect
-import math
 import os
 import random
 import socket
 from contextlib import ExitStack, contextmanager
-from typing import Callable, ContextManager, List, Optional
+from typing import ContextManager, List, Optional
 
 import torch
 from packaging import version
@@ -52,7 +51,7 @@ def main_rank_first(group: dist.ProcessGroup):
 @contextmanager
 def local_ranks_zero_first(group: Optional[dist.ProcessGroup] = None):
     """Context manager that executes the code in the context with all the local rank zero of the group going first.
-    Usefull to run only once per node first (e.g. to create local files, etc)
+    Useful to run only once per node first (e.g. to create local files, etc)
     """
     is_main = int(os.environ.get("LOCAL_RANK", 0)) == 0
     if is_main:
@@ -123,6 +122,7 @@ def get_untyped_storage(tensor: torch.Tensor) -> torch.UntypedStorage:
     else:
         return tensor.storage().untyped()
 
+
 def tensor_from_untyped_storage(untyped_storage: torch.UntypedStorage, dtype: torch.dtype):
     # TODO @thomasw21: Figure out what's the best Pytorch way of building a tensor from a storage.
     device = untyped_storage.device
@@ -141,3 +141,26 @@ def find_free_port(min_port: int = 2000, max_port: int = 65000) -> int:
                 return port
         except OSError:
             continue
+
+
+def supports_flash_attention():
+    """
+    Check if a GPU supports FlashAttention based on its architecture.
+
+    Parameters:
+    device_id (int): The ID of the GPU device to check.
+
+    Returns:
+    bool: True if the GPU supports FlashAttention, False otherwise.
+    """
+    if not torch.cuda.is_available():
+        return False
+
+    device_id = torch.cuda.current_device()
+    major, minor = torch.cuda.get_device_capability(device_id)
+
+    # Check if the GPU architecture is Ampere (SM 8.x) or newer (SM 9.0)
+    is_sm8x = major == 8 and minor >= 0
+    is_sm90 = major == 9 and minor == 0
+
+    return is_sm8x or is_sm90

From 2a82f5cb3ed53b2044480d9e2e6bedff565b7506 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Sun, 21 Jul 2024 23:36:12 +0000
Subject: [PATCH 14/19] add readme for training

---
 examples/long-context/README.md               |  26 ++++
 examples/long-context/create_config.py        | 127 ++++++++++++++++++
 examples/long-context/images/1M.png           | Bin 0 -> 38025 bytes
 examples/long-context/launch_training.slurm   |  53 ++++++++
 .../templates/llama3_template.jinja2          | 114 ++++++++++++++++
 5 files changed, 320 insertions(+)
 create mode 100644 examples/long-context/README.md
 create mode 100644 examples/long-context/create_config.py
 create mode 100644 examples/long-context/images/1M.png
 create mode 100644 examples/long-context/launch_training.slurm
 create mode 100644 examples/long-context/templates/llama3_template.jinja2

diff --git a/examples/long-context/README.md b/examples/long-context/README.md
new file mode 100644
index 00000000..93fee53c
--- /dev/null
+++ b/examples/long-context/README.md
@@ -0,0 +1,26 @@
+# Long Context with Sequence Parallelism
+
+## Sequence Parallelism
+Sequence parallelism is a way to split the activation value and thus reduce the memory footprint. If your input is long enough (e.g., sequence length > 16K), then you may need to perform sequence parallelism.
+
+Nanotron implements sequence parallelism via ring attention. To use this feature, you only need to set *sp* > 1. However, noting that the TP-Reduce scatter mode not only splits the model's weights/gradients/optimizer states but also the activation value, I suggest trying to increase the TP to 8 first, and then if activation value splitting is still needed, increase the SP.
+
+## Experiment Setup
+Our experimental results show that when training with long sequences (e.g., scaling from 8K to 128K), gradually increasing the sequence length and the size of the RoPE theta speeds up convergence and saves computational resources.
+
+An example of this is when extending the context length from 8K to 1M, this is how I increased the context and RoPE base:
+
+| Sequence Length | Rope Theta      | Train Steps | Batch Accumulation per Replica | Micro Batch Size |
+|-----------------|-----------------|-------------|-------------------------------|------------------|
+| 65536           | 22,400,000.0    | 10          | 32                            | 1                |
+| 131072          | 80,000,000.0    | 10          | 32                            | 1                |
+| 524288          | 1,000,000,000.0 | 10          | 16                            | 1                |
+| 1048576         | 3,600,000,000.0 | 10          | 8                             | 1                |
+
+For this reason, I added a template file in this folder. You can customize the hyper-parameters for each training and then generate the config files by running: `python create_config.py`
+
+Then for training, you only need to execute: `sbatch launch_training.slurm`
+
+## Experiment results
+We successfully extended the model's context length to 1M tokens using less than 0.3B tokens and 50 training steps, achieving 100% accuracy in the needle-in-a-haystack experiment.
+![1M Context Length Example](https://github.com/huggingface/nanotron/raw/main/examples/long-context/images/1M.png)
diff --git a/examples/long-context/create_config.py b/examples/long-context/create_config.py
new file mode 100644
index 00000000..c96ac301
--- /dev/null
+++ b/examples/long-context/create_config.py
@@ -0,0 +1,127 @@
+import os
+
+from jinja2 import Environment, FileSystemLoader
+
+# Choose the template
+template_file = "llama3_template.jinja2"
+
+# Load the template environment
+current_directory = os.getcwd()
+template_directory = os.path.join(current_directory, "templates")
+
+env = Environment(loader=FileSystemLoader(template_directory))
+template = env.get_template(template_file)
+print("Template file: {}".format(template_file))
+print()
+
+
+###### First create a temple file ######
+# You should define the model/tokenizer/dataset in the template file
+# You can use llama3 converter to get the weights and tokenizer
+tokenizer_path = "/fsx/haojun/lighteval_evaluation_model/NanotronLlama3-8B-Instruct"  # replace with your own
+init_weights_path = "/fsx/haojun/lighteval_evaluation_model/NanotronLlama3-8B-Instruct"  # replace with your own
+dataset_folder = "/fsx/haojun/datasets/tokenized_bytes_4B_tokens"  # replace with your own
+###### end ######
+
+
+############ hyper-parameter for experiments ############
+experiment_name = "1M_4stages"
+sequence_lengths = [65536, 131072, 524288, 1048576]  # Model sequence length
+rope_thetas = [22400000.0, 80000000.0, 1000000000.0, 3600000000.0]  # base value of RoPE
+train_steps = [10, 20, 30, 40]  # accumulative steps
+batch_accumulation_per_replicas = [32, 32, 16, 8]  # gradient accumulation steps
+micro_batch_sizes = [1, 1, 1, 1]  # batch size
+sps = [4, 8, 32, 64]  # Sequence parallelism degree
+tp = 8  # Tensor parallelism degree
+checkpoint_intervals = [1, 1, 1, 1]
+
+############ end ############
+
+############ optimizer ############
+lr_warmup_steps = 1
+lr_decay_steps = 1
+learning_rate = 0.00002
+min_decay_lr = 0.00002
+############ end ############
+
+
+############ checkpoints/config path ############
+# model weights output directory
+checkpoints_path = os.path.join(current_directory, "weights", experiment_name)
+checkpoints_paths = [checkpoints_path] * len(sequence_lengths)
+resume_checkpoint_paths = ["null"] + [checkpoints_path] * (len(sequence_lengths) - 1)
+
+# Config files output directory
+output_dir = os.path.join(current_directory, "configs", experiment_name)
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir, exist_ok=True)
+    print(f"Created config directory: {output_dir}")
+############ end ############
+
+# Ensure that we have exactly same number elements in each list to match the requirement
+list_lengths = [
+    len(checkpoints_paths),
+    len(resume_checkpoint_paths),
+    len(sequence_lengths),
+    len(rope_thetas),
+    len(train_steps),
+    len(batch_accumulation_per_replicas),
+    len(micro_batch_sizes),
+    len(sps),
+    len(checkpoint_intervals),
+]
+if not all(length == list_lengths[0] for length in list_lengths):
+    raise ValueError("All input lists must have the same length.")
+
+
+def format_float(value, decimal_places=5):
+    return f"{value:.{decimal_places}f}"
+
+
+for i in range(len(checkpoints_paths)):
+    checkpoints_path = checkpoints_paths[i]
+    resume_checkpoint_path = resume_checkpoint_paths[i]
+    checkpoint_interval = checkpoint_intervals[i]
+    batch_accumulation_per_replica = batch_accumulation_per_replicas[i]
+    sequence_length = sequence_lengths[i]
+    rope_theta = rope_thetas[i]
+    train_step = train_steps[i]
+    micro_batch_size = micro_batch_sizes[i]
+    sp = sps[i]
+
+    # Create the checkpoint path if it doesn't exist
+    if not os.path.exists(checkpoints_path):
+        os.makedirs(checkpoints_path, exist_ok=True)
+        print(f"Created directory: {checkpoints_path}")
+
+    variables = {
+        "tokenizer_path": tokenizer_path,
+        "init_weights_path": init_weights_path,
+        "dataset_folder": dataset_folder,
+        "checkpoints_path": checkpoints_path,
+        "resume_checkpoint_path": resume_checkpoint_path,
+        "checkpoint_interval": checkpoint_interval,
+        "sequence_length": sequence_length,
+        "rope_theta": rope_theta,
+        "train_steps": train_step,
+        "batch_accumulation_per_replica": batch_accumulation_per_replica,
+        "micro_batch_size": micro_batch_size,
+        "learning_rate": format_float(learning_rate),
+        "min_decay_lr": format_float(min_decay_lr),
+        "lr_warmup_steps": lr_warmup_steps,
+        "lr_decay_steps": lr_decay_steps,
+        "sp": sp,
+        "tp": tp,
+    }
+
+    # Render the template with the provided variables
+    config = template.render(variables)
+
+    # # Define the output file name
+    output_file = f"{output_dir}/config_{i}_theta={rope_theta/1e6}M_steps={train_step}_seq_len={sequence_length}.yaml"
+
+    # Save the rendered configuration to a YAML file
+    with open(output_file, "w") as f:
+        f.write(config)
+
+    print(f"Configuration file '{output_file}' has been generated.")
diff --git a/examples/long-context/images/1M.png b/examples/long-context/images/1M.png
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diff --git a/examples/long-context/launch_training.slurm b/examples/long-context/launch_training.slurm
new file mode 100644
index 00000000..427f94f2
--- /dev/null
+++ b/examples/long-context/launch_training.slurm
@@ -0,0 +1,53 @@
+#!/bin/bash
+#SBATCH --job-name=long-context
+#SBATCH --qos=high
+#SBATCH --nodes=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --partition=hopper-prod
+#SBATCH --gres=gpu:8
+#SBATCH --mem=0
+
+# HF stuff
+if [ -f .env ]; then
+    export $(cat .env | xargs)
+fi
+
+# script for a benchmark
+set -x -e
+
+# replace with your own env
+echo "START TIME: $(date)"
+source /admin/home/haojun_zhao/miniconda3/etc/profile.d/conda.sh
+conda activate /admin/home/haojun_zhao/miniconda3/envs/nt_mamba
+echo python3 version = $(python3 --version)
+
+# SLURM stuff
+export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_PORT=9001
+export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
+export OMP_NUM_THREADS=8
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export FI_PROVIDER="efa"
+NUM_GPUS=$(nvidia-smi --list-gpus | wc -l)
+
+echo "NCCL version: $(python -c "import torch;print(torch.cuda.nccl.version())")"
+echo "CUDA version: $(python -c "import torch;print(torch.version.cuda)")"
+
+module load cuda/12.1
+
+current_dir=$(pwd)
+parent_of_parent_dir=$(dirname $(dirname "$current_dir"))
+
+TRAIN_SCRIPT= "$parent_of_parent_dir/run_train.py"
+CONFIG_FILE= "$current_dir/1M_4stages/config_0_theta=22.4M_steps=10_seq_len=65536.yaml"
+
+srun torchrun \
+   --nnodes=4 \
+   --nproc_per_node=8 \
+   --rdzv_backend=c10d \
+   --rdzv-id ${SLURM_JOB_ID} \
+   --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \
+   --max_restarts=0 \
+   --tee=3 \
+   $TRAIN_SCRIPT --config-file $CONFIG_FILE
diff --git a/examples/long-context/templates/llama3_template.jinja2 b/examples/long-context/templates/llama3_template.jinja2
new file mode 100644
index 00000000..904fe8af
--- /dev/null
+++ b/examples/long-context/templates/llama3_template.jinja2
@@ -0,0 +1,114 @@
+checkpoints:
+  checkpoint_interval: {{ checkpoint_interval }}
+  checkpoints_path: "{{ checkpoints_path }}"
+  checkpoints_path_is_shared_file_system: false
+  resume_checkpoint_path: "{{ resume_checkpoint_path }}"
+  save_initial_state: false
+
+data_stages:
+- data:
+    dataset:
+      dataloader_type: cyclic
+      dataset_max_tokens: null
+      dataset_weights: null
+      datasets:
+      - filename_pattern: '.*\.ds'
+        folder: {{ dataset_folder}}
+        seed: null
+        shuffle: false
+        skip_tokens: 0
+      pad_samples_to_global_batch_size: false
+      skip_in_stream: true
+    num_loading_workers: 0
+    seed: 42
+  name: long context
+  start_training_step: 1
+
+
+general:
+  benchmark_csv_path: null
+  consumed_train_samples: null
+  ignore_sanity_checks: true
+  project: debug
+  run: tiny_llama_%date_%jobid
+  seed: 42
+  step: null
+lighteval: null
+logging:
+  iteration_step_info_interval: 1
+  log_level: info
+  log_level_replica: info
+
+model:
+  ddp_bucket_cap_mb: 25
+  dtype: bfloat16
+  init_method:
+    path: {{ init_weights_path }}
+  make_vocab_size_divisible_by: 1
+  model_config:
+    bos_token_id: 128000
+    eos_token_id: 128001
+    hidden_act: silu
+    hidden_size: 4096
+    initializer_range: 0.02
+    intermediate_size: 14336
+    is_llama_config: true
+    max_position_embeddings: {{ sequence_length }}
+    num_attention_heads: 32
+    num_hidden_layers: 32
+    num_key_value_heads: 8
+    pad_token_id: null
+    pretraining_tp: 1
+    rms_norm_eps: 1.0e-05
+    rope_interleaved: false
+    rope_scaling: null
+    rope_theta: {{ rope_theta }}
+    tie_word_embeddings: false
+    use_cache: true
+    vocab_size: 128256
+
+optimizer:
+  accumulate_grad_in_fp32: true
+  clip_grad: 1.0
+  learning_rate_scheduler:
+    learning_rate: {{ learning_rate }}
+    lr_decay_starting_step: null
+    lr_decay_steps: {{ lr_decay_steps }}
+    lr_decay_style: cosine
+    lr_warmup_steps: {{ lr_warmup_steps }}
+    lr_warmup_style: linear
+    min_decay_lr: {{ min_decay_lr }}
+  optimizer_factory:
+    adam_beta1: 0.9
+    adam_beta2: 0.95
+    adam_eps: 1.0e-08
+    name: adamW
+    torch_adam_is_fused: true
+  weight_decay: 0.01
+  zero_stage: 0
+
+parallelism:
+  dp: 1
+  expert_parallel_size: 1
+  pp: 1
+  pp_engine: 1f1b
+  sp: {{ sp }}
+  tp: {{ tp }}
+  tp_linear_async_communication: false
+  tp_mode: REDUCE_SCATTER
+
+profiler: null
+
+tokenizer:
+  tokenizer_max_length: null
+  tokenizer_name_or_path: {{ tokenizer_path }}
+  tokenizer_revision: null
+
+tokens:
+  batch_accumulation_per_replica: {{ batch_accumulation_per_replica }}
+  limit_test_batches: 0
+  limit_val_batches: 0
+  micro_batch_size: {{ micro_batch_size }}
+  sequence_length: {{ sequence_length }}
+  train_steps: {{ train_steps }}
+  val_check_interval: -1

From 87649dffc502f9385d40206583df2e6b5e218d26 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Sun, 21 Jul 2024 23:40:22 +0000
Subject: [PATCH 15/19] correct link

---
 examples/long-context/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/long-context/README.md b/examples/long-context/README.md
index 93fee53c..a801a84d 100644
--- a/examples/long-context/README.md
+++ b/examples/long-context/README.md
@@ -23,4 +23,4 @@ Then for training, you only need to execute: `sbatch launch_training.slurm`
 
 ## Experiment results
 We successfully extended the model's context length to 1M tokens using less than 0.3B tokens and 50 training steps, achieving 100% accuracy in the needle-in-a-haystack experiment.
-![1M Context Length Example](https://github.com/huggingface/nanotron/raw/main/examples/long-context/images/1M.png)
+![1M Context Length Example](./images/1M.png)

From c6bbeda110037cbe18822434a6413771d23c4af6 Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Mon, 22 Jul 2024 00:35:36 +0000
Subject: [PATCH 16/19] evaluation script

---
 .../long-context/needle_in_a_haystack_test.py | 477 ++++++++++++++++++
 1 file changed, 477 insertions(+)
 create mode 100644 examples/long-context/needle_in_a_haystack_test.py

diff --git a/examples/long-context/needle_in_a_haystack_test.py b/examples/long-context/needle_in_a_haystack_test.py
new file mode 100644
index 00000000..728de75e
--- /dev/null
+++ b/examples/long-context/needle_in_a_haystack_test.py
@@ -0,0 +1,477 @@
+"""
+CUDA_LAUNCH_BLOCKING=1 CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun    --nproc_per_node=4    --nnodes=1    --rdzv_backend=c10d    --rdzv_endpoint=localhost:29600    --max_restarts=0    --tee=3    needle_in_a_haystack_test.py --ckpt-path /fsx/haojun/long_context_weights/experiment_gradient_reproduce_LR=2e-5/120
+"""
+
+import argparse
+import glob
+import os
+from dataclasses import fields
+from decimal import Decimal
+from pathlib import Path
+from typing import Optional, Type
+
+import dacite
+import numpy as np
+import torch
+import yaml
+from dacite import from_dict
+from nanotron import distributed as dist
+from nanotron import logging
+from nanotron.config import (
+    LoggingArgs,
+    ParallelismArgs,
+)
+from nanotron.config.config import Config
+from nanotron.config.utils_config import RecomputeGranularity, cast_str_to_pipeline_engine, cast_str_to_torch_dtype
+from nanotron.generation.decode import (
+    decode_text_simple,
+)
+from nanotron.generation.sampler import SamplerType
+from nanotron.logging import log_rank, set_ranks_logging_level
+from nanotron.models import build_model
+from nanotron.parallel import ParallelContext
+from nanotron.parallel.parameters import sanity_check
+from nanotron.parallel.pipeline_parallel.engine import (
+    OneForwardOneBackwardPipelineEngine,
+    PipelineEngine,
+)
+from nanotron.parallel.tensor_parallel.enum import TensorParallelLinearMode
+from nanotron.random import (
+    RandomStates,
+    get_current_random_state,
+    get_synced_random_state,
+    set_random_seed,
+)
+from nanotron.serialize import load_weights
+from nanotron.trainer import CONFIG_TO_MODEL_CLASS, mark_tied_parameters
+from tqdm import tqdm
+
+try:
+    from transformers import AutoTokenizer
+except ImportError:
+    AutoTokenizer = None
+import pickle
+
+logger = logging.get_logger(__name__)
+DEVICE = torch.device("cuda")
+
+# copied this function to avoid strict match
+def get_config_from_dict(
+    config_dict: dict, config_class: Type = Config, skip_unused_config_keys: bool = False, skip_null_keys: bool = False
+):
+    if skip_unused_config_keys:
+        config_dict = {
+            field.name: config_dict[field.name] for field in fields(config_class) if field.name in config_dict
+        }
+    if skip_null_keys:
+        config_dict = {
+            k: {kk: vv for kk, vv in v.items() if vv is not None} if isinstance(v, dict) else v
+            for k, v in config_dict.items()
+            if v is not None
+        }
+    config_dict["data_stages"] = None
+    return from_dict(
+        data_class=config_class,
+        data=config_dict,
+        config=dacite.Config(
+            cast=[Path],
+            type_hooks={
+                torch.dtype: cast_str_to_torch_dtype,
+                PipelineEngine: cast_str_to_pipeline_engine,
+                TensorParallelLinearMode: lambda x: TensorParallelLinearMode[x.upper()],
+                RecomputeGranularity: lambda x: RecomputeGranularity[x.upper()],
+                SamplerType: lambda x: SamplerType[x.upper()],
+            },
+            strict=False,
+        ),
+    )
+
+
+def get_config_from_file(
+    config_path: str,
+    config_class: Type = Config,
+    model_config_class: Optional[Type] = None,
+    skip_unused_config_keys: bool = True,
+    skip_null_keys: bool = True,
+) -> Config:
+    """Get a config object from a file (python or YAML)
+
+    Args:
+        config_path: path to the config file
+        config_type: if the file is a python file, type of the config object to get as a
+            ConfigTypes (Config, LightevalConfig, LightevalSlurm) or str
+            if None, will default to Config
+        skip_unused_config_keys: whether to skip unused first-nesting-level keys in the config file (for config with additional sections)
+        skip_null_keys: whether to skip keys with value None at first and second nesting level
+    """
+    # Open the file and load the file
+    with open(config_path) as f:
+        config_dict = yaml.load(f, Loader=yaml.SafeLoader)
+
+    config = get_config_from_dict(
+        config_dict,
+        config_class=config_class,
+        skip_unused_config_keys=skip_unused_config_keys,
+        skip_null_keys=skip_null_keys,
+    )
+    if model_config_class is not None:
+        if not isinstance(config.model.model_config, (dict, model_config_class)):
+            raise ValueError(
+                f"model_config should be a dictionary or a {model_config_class} and not {config.model.model_config}"
+            )
+        config.model.model_config = model_config_class(**config.model.model_config)
+    return config
+
+
+## Ref: https://github.com/jzhang38/EasyContext
+
+## PREFIX + Haystack + NEEDLE_FORMAT + Haystack + QUESTION_STR
+PREFIX = "This is a very long story book: <book>"
+NEEDLE_FORMAT = "\nThe special magic Singapore number is: {}.\n"
+CITIES = ["Singapore", "Chicago", "Sydney", "Amsterdam"]
+QUESTION_STR = "</book>.\n Based on the content of the book, Question: What is the special magic Singapore number? Answer: The special magic {}"
+PROMPT_TOKENS = 100  # tokens for prefix, needle, question, generation
+
+
+def generate_question_str(num_needles, cities):
+    assert num_needles <= len(cities)
+    assert num_needles == 1, "Only support retrieve 1 city for now."
+    if num_needles == 1:
+        return QUESTION_STR.format(cities[0]) + " number is: "
+    else:
+        return QUESTION_STR.format(", ".join(cities[:num_needles])) + " numbers are: "
+
+
+def generate_random_number(num_digits):
+    lower_bound = 10 ** (num_digits - 1)
+    upper_bound = 10**num_digits - 1
+    return np.random.randint(lower_bound, upper_bound)
+
+
+def load_haystack(tokenizer, max_context_length, haystack_dir):
+    context = ""
+    # do not count <s>
+    while len(tokenizer.encode(context)) - 1 < max_context_length:
+        if dist.get_rank() == 0:
+            print(f"Current Context Length: {len(tokenizer.encode(context))-1}")
+            print(f'Loading files {glob.glob(f"{haystack_dir}/*.txt")[:5]}')
+        for file in glob.glob(f"{haystack_dir}/*.txt"):
+            with open(file, "r") as f:
+                context += f.read()
+            if len(tokenizer.encode(context)) - 1 > max_context_length:
+                break
+    tokenized_haystack = tokenizer.encode(context)
+    return tokenized_haystack
+
+
+## replace the above function by loading from a file to save tokenization time
+def load_tokenized_haystack(context_directory, max_context_length):
+    load_path = f"{context_directory}/tokenized_haystack_{max_context_length}.pt"
+    if os.path.exists(load_path):
+        tokenized_haystack = torch.load(load_path)
+        return tokenized_haystack
+    return None
+
+
+def construct_prompt(
+    tokenized_haystack,
+    tokenized_prefix,
+    tokenized_postfix,
+    tokenized_needle,
+    context_length,
+    tokenized_distractor_list,
+    depth,
+):
+    # insert the needle into depth of the haystack
+    period_tokens = [
+        13,
+        3343,
+    ]  # this is the period token for llama3 tokenizer. https://huggingface.co/meta-llama/Meta-Llama-3-8B/raw/main/tokenizer.json
+    prompt = tokenized_haystack[: context_length - PROMPT_TOKENS]  # test it.
+    if depth == 0:
+        start_index = 0
+    else:
+        start_index = int(len(prompt) * depth)
+        # find the closest period token
+        for i in range(start_index, len(prompt)):
+            if prompt[i] in period_tokens:
+                start_index = i + 1
+                break
+    prompt = prompt[:start_index] + tokenized_needle + prompt[start_index:]
+    # insert distractors
+    for distractor in tokenized_distractor_list:
+        start_index = np.random.randint(0, len(prompt))
+        for i in range(start_index, len(prompt)):
+            if prompt[i] in period_tokens:
+                start_index = i + 1
+                break
+        prompt = prompt[:start_index] + distractor + prompt[start_index:]
+    prompt = tokenized_prefix + prompt + tokenized_postfix
+    return prompt
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ckpt-path", type=Path, required=True, help="Checkpoint path")
+    parser.add_argument("--max-new-tokens", type=int, default=7, help="Maximum number of new tokens to generate")
+    parser.add_argument("--min-context-length", type=int, default=4000)
+    parser.add_argument("--max-context-length", type=int, default=64000)
+    parser.add_argument("--context-interval", type=int, default=4000)
+    parser.add_argument("--depth-interval", type=str, default="0.2")
+    parser.add_argument("--rnd-number-digits", type=int, default=7)
+    parser.add_argument("--num-samples", type=int, default=2)
+    parser.add_argument("--num-distractors", type=int, default=0)
+    parser.add_argument("--num-cities-retrieval", type=int, default=1)
+    parser.add_argument("--tp", type=int, default=8)
+    parser.add_argument("--pp", type=int, default=1)
+    parser.add_argument("--sp", type=int, default=1)
+    parser.add_argument("--haystack-dir", type=str, required=True, help="Haystack directory")
+    return parser.parse_args()
+
+
+depth_interval = Decimal("0.2")
+
+
+def main():
+    args = get_args()
+
+    min_context_length = args.min_context_length
+    max_context_length = args.max_context_length
+    context_interval = args.context_interval
+    num_cities_retrieval = args.num_cities_retrieval
+    depth_interval = Decimal(args.depth_interval)
+    haystack_dir = args.haystack_dir
+
+    rnd_number_digits = args.rnd_number_digits
+    num_samples = args.num_samples
+    num_distractor = args.num_distractors
+
+    config = get_config_from_file((args.ckpt_path / "config.yaml").as_posix())
+    model_config = config.model.model_config
+    tokenizer_path = config.tokenizer.tokenizer_name_or_path
+
+    parallel_config = ParallelismArgs(
+        dp=1,
+        pp=args.pp,
+        tp=args.tp,
+        sp=args.sp,
+        pp_engine=OneForwardOneBackwardPipelineEngine(),
+        tp_mode=TensorParallelLinearMode.REDUCE_SCATTER,
+        # tp_mode=TensorParallelLinearMode.ALL_REDUCE,
+        tp_linear_async_communication=False,
+    )
+
+    # Initialise all process groups
+    parallel_context = ParallelContext(
+        data_parallel_size=parallel_config.dp,
+        pipeline_parallel_size=parallel_config.pp,
+        tensor_parallel_size=parallel_config.tp,
+        sequence_parallel_size=parallel_config.sp,
+    )
+
+    # Set log levels
+    logging_config = LoggingArgs(
+        log_level="info",
+        log_level_replica="info",
+    )
+
+    # Set log levels
+    set_ranks_logging_level(parallel_context=parallel_context, logging_config=logging_config)
+
+    log_rank(f"model_config: {model_config}", logger=logger, level=logging.INFO, rank=0)
+    log_rank(f"tokenizer_path: {tokenizer_path}", logger=logger, level=logging.INFO, rank=0)
+
+    dtype = torch.bfloat16
+
+    # Set random states
+    set_random_seed(42)
+
+    model_config_cls = model_config.__class__.__name__
+    if model_config_cls not in CONFIG_TO_MODEL_CLASS:
+        raise ValueError(
+            f"Unsupported model config {model_config_cls}. Only {CONFIG_TO_MODEL_CLASS.keys()} are supported"
+        )
+
+    # Get synchronized random states
+    if parallel_config.tp_mode is TensorParallelLinearMode.ALL_REDUCE:
+        random_states = RandomStates(
+            {"tp_synced": get_synced_random_state(random_state=get_current_random_state(), pg=parallel_context.tp_pg)}
+        )
+    else:
+        # We don't need to sync across TP when using sequence parallel (REDUCE_SCATTER)
+        random_states = RandomStates({})
+
+    # Reset the model max context length
+    if model_config.max_position_embeddings <= max_context_length:
+        model_config.max_position_embeddings = max_context_length
+
+    # copy from _init_model
+    model = build_model(
+        model_builder=lambda: CONFIG_TO_MODEL_CLASS[model_config_cls](
+            config=model_config,
+            parallel_context=parallel_context,
+            parallel_config=parallel_config,
+            random_states=random_states,
+        ),
+        dtype=dtype,
+        parallel_context=parallel_context,
+    )
+
+    # Mark some parameters as tied
+    # TODO @nouamane: this is only needed for training, can we just mark params as NanotronParameter instead?
+    mark_tied_parameters(model=model, parallel_context=parallel_context, parallel_config=parallel_config)
+
+    # Sanity check model
+    sanity_check(root_module=model)
+
+    # Load checkpoint
+    checkpoint_path = args.ckpt_path
+    log_rank(
+        f"Loading checkpoint from {checkpoint_path}:",
+        logger=logger,
+        level=logging.INFO,
+        rank=0,
+    )
+    load_weights(model=model, parallel_context=parallel_context, root_folder=checkpoint_path)
+
+    model.eval()
+
+    # code for evaluation
+    if AutoTokenizer is not None:
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+        tokenizer.truncation_side = "left"
+
+        question_str = generate_question_str(num_cities_retrieval, CITIES)
+
+        tokenized_prefix = tokenizer.encode(PREFIX)[1:]
+        tokenized_postfix = tokenizer.encode(question_str)[1:]
+        tokenized_haystack = load_haystack(tokenizer, max_context_length - PROMPT_TOKENS, haystack_dir)[1:]
+
+        DISTRACTOR_LIST = [
+            "\nThe special magic New York number is: {}.\n",
+            "\nThe special magic London number is: {}.\n",
+            "\nThe special magic Paris number is: {}.\n",
+            "\nThe special magic Tokyo number is: {}.\n",
+            "\nThe special magic Beijing number is: {}.\n",
+            "\nThe special magic Berlin number is: {}.\n",
+        ]
+
+        random_number_list = [generate_random_number(rnd_number_digits) for i in range(num_samples)]
+
+        depth_values = [float(i) for i in np.arange(Decimal("0.0"), Decimal("1.0") + depth_interval, depth_interval)]
+        distractor_number_list = [int(np.random.randint(10**rnd_number_digits)) for i in range(num_distractor)]
+        distractor_str_list = [
+            DISTRACTOR_LIST[i % len(DISTRACTOR_LIST)].format(distractor_number_list[i]) for i in range(num_distractor)
+        ]
+        tokenized_distractor_list = [tokenizer.encode(distractor_str)[1:] for distractor_str in distractor_str_list]
+
+        results_dict = {}
+
+        if dist.get_rank() == 0:
+            context_lengths = tqdm(range(min_context_length, max_context_length + 1, context_interval))
+        else:
+            context_lengths = range(min_context_length, max_context_length + 1, context_interval)
+
+        for context_length in context_lengths:
+            correct = 0
+            total = 0
+            # Store the result in the dictionary
+            if context_length not in results_dict:
+                results_dict[context_length] = {}
+            for depth in depth_values:
+                depth_correct = 0.0
+                depth_total = 0.0
+                for random_number in random_number_list:
+                    needle_str = NEEDLE_FORMAT.format(random_number)
+                    ground_truth_str = str(random_number)
+                    tokenized_needle = tokenizer.encode(needle_str)[1:]
+                    tokenizer_answer = tokenizer.encode(ground_truth_str)[1:]
+
+                    prompt = construct_prompt(
+                        tokenized_haystack,
+                        tokenized_prefix,
+                        tokenized_postfix,
+                        tokenized_needle,
+                        context_length,
+                        tokenized_distractor_list,
+                        depth,
+                    )
+                    input_ids = torch.tensor(prompt).to(DEVICE)
+                    prompt_text = tokenizer.decode(input_ids, clean_up_tokenization_spaces=False)
+                    ground_truth_str = tokenizer.decode(
+                        torch.tensor(tokenizer_answer).to(DEVICE), clean_up_tokenization_spaces=False
+                    )
+
+                    dummy_inputs = [
+                        prompt_text,
+                    ]
+                    pad_to_multiple_of = (
+                        parallel_context.tensor_parallel_size * parallel_context.sequence_parallel_size * 2
+                    )
+                    outputs = decode_text_simple(
+                        input_texts=dummy_inputs,
+                        model=model.model,
+                        tokenizer=tokenizer,
+                        parallel_context=parallel_context,
+                        max_new_tokens=args.max_new_tokens,
+                        padding_left=False,  # right padding.
+                        pad_to_multiple_of=pad_to_multiple_of,
+                    )
+
+                    for output in outputs:
+                        genenration = output["generation"]
+                        if ground_truth_str in genenration:
+                            correct += 1
+                        total += 1
+                        depth_correct += ground_truth_str in genenration
+                        depth_total += 1
+                        log_rank(
+                            f"Context length: {context_length}, Depth: {depth}, Genenration: {genenration}, Ground Truth: {ground_truth_str}, Passed test: {ground_truth_str in genenration}",
+                            logger=logger,
+                            level=logging.INFO,
+                            rank=0,
+                        )
+
+                # register the result for this depth
+                results_dict[context_length][depth] = depth_correct / depth_total
+                log_rank(
+                    f"Context length: {context_length}, Depth: {depth}, Passed test: {results_dict[context_length][depth]}",
+                    logger=logger,
+                    level=logging.INFO,
+                    rank=0,
+                )
+            # if output_rank:
+            log_rank(
+                f"Context length: {context_length}, Accuracy: {correct/total}",
+                logger=logger,
+                level=logging.INFO,
+                rank=0,
+            )
+            log_rank(
+                "--------------------------------------------------",
+                logger=logger,
+                level=logging.INFO,
+                rank=0,
+            )
+
+        results_dict_path = "results.pkl"
+        with open(results_dict_path, "wb") as f:
+            pickle.dump(results_dict, f)
+        print(f"Evaluation result saved to {results_dict_path}")
+
+    dist.barrier()
+
+    # Get the maximum memory allocated
+    max_memory_allocated = torch.cuda.max_memory_allocated()
+    log_rank(
+        f"Maximum CUDA memory allocated: {max_memory_allocated / 1024 ** 3} GB",
+        logger=logger,
+        level=logging.INFO,
+        rank=0,
+    )
+
+
+if __name__ == "__main__":
+    main()

From 72d94886e662cc7a020b89dfa586fcffc34e060c Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Mon, 22 Jul 2024 09:57:47 +0000
Subject: [PATCH 17/19] change foler location

---
 src/nanotron/models/llama.py                                  | 4 ++--
 .../{ => parallel}/ring_flash_attn/ring_flash_attn.py         | 0
 src/nanotron/{ => parallel}/ring_flash_attn/utils.py          | 0
 .../{ => parallel}/ring_flash_attn/zigzag_ring_flash_attn.py  | 0
 4 files changed, 2 insertions(+), 2 deletions(-)
 rename src/nanotron/{ => parallel}/ring_flash_attn/ring_flash_attn.py (100%)
 rename src/nanotron/{ => parallel}/ring_flash_attn/utils.py (100%)
 rename src/nanotron/{ => parallel}/ring_flash_attn/zigzag_ring_flash_attn.py (100%)

diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index 3f599f54..8c1f7bc3 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -33,6 +33,7 @@
 from nanotron.parallel.parameters import NanotronParameter
 from nanotron.parallel.pipeline_parallel.block import PipelineBlock, TensorPointer
 from nanotron.parallel.pipeline_parallel.p2p import P2P
+from nanotron.parallel.ring_flash_attn.utils import zigzag_split
 from nanotron.parallel.tensor_parallel.functional import sharded_cross_entropy
 from nanotron.parallel.tensor_parallel.nn import (
     TensorParallelColumnLinear,
@@ -41,7 +42,6 @@
     TensorParallelRowLinear,
 )
 from nanotron.random import RandomStates
-from nanotron.ring_flash_attn.utils import zigzag_split
 from nanotron.scaling.parametrization import SpectralMupParametrizator, StandardParametrizator
 from nanotron.utils import checkpoint_method, supports_flash_attention
 
@@ -53,7 +53,7 @@
     )
     from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
 
-    from nanotron.ring_flash_attn.zigzag_ring_flash_attn import zigzag_ring_flash_attn_func
+    from nanotron.parallel.ring_flash_attn.zigzag_ring_flash_attn import zigzag_ring_flash_attn_func
 
 
 logger = logging.get_logger(__name__)
diff --git a/src/nanotron/ring_flash_attn/ring_flash_attn.py b/src/nanotron/parallel/ring_flash_attn/ring_flash_attn.py
similarity index 100%
rename from src/nanotron/ring_flash_attn/ring_flash_attn.py
rename to src/nanotron/parallel/ring_flash_attn/ring_flash_attn.py
diff --git a/src/nanotron/ring_flash_attn/utils.py b/src/nanotron/parallel/ring_flash_attn/utils.py
similarity index 100%
rename from src/nanotron/ring_flash_attn/utils.py
rename to src/nanotron/parallel/ring_flash_attn/utils.py
diff --git a/src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py b/src/nanotron/parallel/ring_flash_attn/zigzag_ring_flash_attn.py
similarity index 100%
rename from src/nanotron/ring_flash_attn/zigzag_ring_flash_attn.py
rename to src/nanotron/parallel/ring_flash_attn/zigzag_ring_flash_attn.py

From 59073399f3747fa81c3c44970a8be2226856a71a Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Mon, 22 Jul 2024 10:20:07 +0000
Subject: [PATCH 18/19] update Readme

---
 examples/long-context/README.md              |   4 ++++
 examples/long-context/images/theta=22p4M.gif | Bin 0 -> 513480 bytes
 2 files changed, 4 insertions(+)
 create mode 100644 examples/long-context/images/theta=22p4M.gif

diff --git a/examples/long-context/README.md b/examples/long-context/README.md
index a801a84d..c0700d98 100644
--- a/examples/long-context/README.md
+++ b/examples/long-context/README.md
@@ -24,3 +24,7 @@ Then for training, you only need to execute: `sbatch launch_training.slurm`
 ## Experiment results
 We successfully extended the model's context length to 1M tokens using less than 0.3B tokens and 50 training steps, achieving 100% accuracy in the needle-in-a-haystack experiment.
 ![1M Context Length Example](./images/1M.png)
+
+To demonstrate how quickly the model can adapt to long contexts, we created a GIF that shows how the model completes a needle-in-a-haystack test over a 64K context length in 10 steps of training.
+
+![64K revolution gif](./images/theta=22p4M.gif)
diff --git a/examples/long-context/images/theta=22p4M.gif b/examples/long-context/images/theta=22p4M.gif
new file mode 100644
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HcmV?d00001


From c81601ae8943a91642e380413992d13ce02559ed Mon Sep 17 00:00:00 2001
From: zzhhjjj <z785566960@gmail.com>
Date: Mon, 22 Jul 2024 10:34:48 +0000
Subject: [PATCH 19/19] update README

---
 examples/long-context/README.md                |   4 ++++
 .../long-context/images/theta=1M_15steps.jpeg  | Bin 0 -> 156273 bytes
 2 files changed, 4 insertions(+)
 create mode 100644 examples/long-context/images/theta=1M_15steps.jpeg

diff --git a/examples/long-context/README.md b/examples/long-context/README.md
index c0700d98..0e688cab 100644
--- a/examples/long-context/README.md
+++ b/examples/long-context/README.md
@@ -28,3 +28,7 @@ We successfully extended the model's context length to 1M tokens using less than
 To demonstrate how quickly the model can adapt to long contexts, we created a GIF that shows how the model completes a needle-in-a-haystack test over a 64K context length in 10 steps of training.
 
 ![64K revolution gif](./images/theta=22p4M.gif)
+
+The setting of the base value is crucial. An example of incorrect setting is setting the base value to 1M and then training for 15 steps. The resulting model can hardly complete the needle-in-a-haystack task.
+
+![ablation study](./images/theta=1M_15steps.jpeg)
diff --git a/examples/long-context/images/theta=1M_15steps.jpeg b/examples/long-context/images/theta=1M_15steps.jpeg
new file mode 100644
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