[cp][flex_attention] integration test trial

torchtitan/distributed/utils.py

@@ -9,13 +9,16 @@
 import os
 from collections.abc import Generator, Iterable
 from datetime import timedelta
+from typing import Optional
 
 import torch
 import torch.distributed._functional_collectives as funcol
 import torch.distributed.distributed_c10d as c10d
 from torch import distributed as dist
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.tensor import DTensor
+from torch.distributed.tensor.experimental._attention import FlexAttentionSharder
+from torch.nn.attention.flex_attention import BlockMask
 
 from torchtitan.tools.logging import logger
 from torchtitan.tools.utils import device_module, device_type
@@ -154,22 +157,37 @@ def create_context_parallel_ctx(
     cp_seq_dims: list[int],
     cp_no_restore_buffers: set[torch.Tensor],
     cp_rotate_method: str,
+    block_mask: Optional[BlockMask] = None,
+    sharder: Optional[FlexAttentionSharder] = None,
 ):
     try:
         from torch.distributed.tensor.experimental import context_parallel
-        from torch.distributed.tensor.experimental._attention import set_rotate_method
+        from torch.distributed.tensor.experimental._attention import (
+            _dispatch_mode,
+            _DispatchMode,
+            set_rotate_method,
+        )
     except ImportError:
         print(
             f"PyTorch version {torch.__version__} does not include the experimental "
             "Context Parallel API. Please update to a newer version."
         )
 
     set_rotate_method(cp_rotate_method)
+    torch.distributed.tensor.experimental._attention._dispatch_mode = (
+        _DispatchMode.TORCH_DISPATCH
+    )
+    assert (
+        torch.distributed.tensor.experimental._attention._dispatch_mode
+        == _DispatchMode.TORCH_DISPATCH
+    )
     return context_parallel(
         cp_mesh,
         buffers=cp_buffers,
         buffer_seq_dims=cp_seq_dims,
         no_restore_buffers=cp_no_restore_buffers,
+        block_mask=block_mask,
+        sharder=sharder,
     )
 
 

torchtitan/experiments/llama4/__init__.py

@@ -40,6 +40,9 @@
         rope_theta=500000,
         num_experts=16,
         interleave_moe_layer_step=1,
+        use_flex_attn=True,
+        attn_mask_type="block_causal",
+        # attn_mask_type="causal",
     ),
     "17bx128e": TransformerModelArgs(
         dim=5120,

torchtitan/experiments/llama4/model/args.py

@@ -55,7 +55,7 @@ class TransformerModelArgs(BaseModelArgs):
     interleave_moe_layer_step: int = 2
     # token-choice
     top_k: int = 1
-    use_grouped_mm: bool = True  # grouped mm or for-loop for the experts computation
+    use_grouped_mm: bool = False  # grouped mm or for-loop for the experts computation
     load_balance_coeff: float | None = 1e-3
 
     def update_from_config(self, job_config: JobConfig, tokenizer: Tokenizer) -> None:
@@ -74,12 +74,13 @@ def update_from_config(self, job_config: JobConfig, tokenizer: Tokenizer) -> Non
                 "FlexAttention is not compatible with selective AC yet. "
                 "See https://github.com/pytorch/pytorch/issues/147879"
             )
-
+        """
         if job_config.parallelism.context_parallel_degree > 1 and self.use_flex_attn:
             raise ValueError(
                 "FlexAttention is not compatible with CP yet. "
                 "We are still working on this."
             )
+        """
 
     def get_nparams_and_flops(
         self, model: nn.Module, seq_len: int

torchtitan/experiments/llama4/train_configs/debug_model.toml

@@ -41,7 +41,7 @@ batch_size = 8
 seq_len = 2048
 max_norm = 1.0  # grad norm clipping
 steps = 10
-compile = false
+compile = true
 dataset = "c4_test"  # supported datasets: c4_test (2K), c4 (177M)
 
 [parallelism]

torchtitan/experiments/llama4/train_configs/llama4_17bx16e.toml

@@ -11,7 +11,7 @@ profile_freq = 100
 
 [metrics]
 log_freq = 10
-enable_tensorboard = false
+enable_tensorboard = true
 save_tb_folder = "tb"
 
 [model]
@@ -27,23 +27,31 @@ eps = 1e-15
 
 [lr_scheduler]
 warmup_steps = 600
+# warmup_steps = 20
 lr_min = 0.1
 
 [training]
-batch_size = 8
+# batch_size = 8
+batch_size = 4
 seq_len = 8192
+# seq_len = 16384
+# seq_len = 32768
+# seq_len = 65536
 max_norm = 1.0  # grad norm clipping
 steps = 3000
+# steps = 100
 compile = false
+# compile = true
 dataset = "c4"
+deterministic = true
 
 [parallelism]
 data_parallel_replicate_degree = 1
 data_parallel_shard_degree = -1
 tensor_parallel_degree = 8
 enable_async_tensor_parallel = false
 pipeline_parallel_degree = 1
-context_parallel_degree = 1
+context_parallel_degree = 4
 
 [checkpoint]
 enable_checkpoint = false

torchtitan/models/llama3/__init__.py

@@ -47,6 +47,17 @@
         multiple_of=1024,
         rope_theta=500000,
     ),
+    "8B_flex_attn": TransformerModelArgs(
+        dim=4096,
+        n_layers=32,
+        n_heads=32,
+        n_kv_heads=8,
+        ffn_dim_multiplier=1.3,
+        multiple_of=1024,
+        rope_theta=500000,
+        use_flex_attn=True,
+        attn_mask_type="block_causal",
+    ),
     "70B": TransformerModelArgs(
         dim=8192,
         n_layers=80,

torchtitan/models/llama3/model.py

@@ -51,12 +51,6 @@ def update_from_config(self, job_config: JobConfig, tokenizer: Tokenizer) -> Non
                 "See https://github.com/pytorch/pytorch/issues/147879"
             )
 
-        if job_config.parallelism.context_parallel_degree > 1 and self.use_flex_attn:
-            raise ValueError(
-                "FlexAttention is not compatible with CP yet. "
-                "We are still working on this."
-            )
-
     def get_nparams_and_flops(self, model: nn.Module, seq_len: int) -> tuple[int, int]:
         nparams = sum(p.numel() for p in model.parameters())
         nparams_embedding = sum(

torchtitan/models/llama3/train_configs/debug_model.toml

@@ -43,7 +43,7 @@ batch_size = 8
 seq_len = 2048
 max_norm = 1.0  # grad norm clipping
 steps = 10
-compile = false
+compile = true
 dataset = "c4_test"  # supported datasets: c4_test (2K), c4 (177M)
 
 [parallelism]

torchtitan/models/llama3/train_configs/llama3_8b.toml

@@ -11,8 +11,9 @@ save_traces_folder = "profile_trace"
 profile_freq = 100
 
 [metrics]
-log_freq = 10
+log_freq = 50
 enable_tensorboard = true
+# enable_tensorboard = false
 save_tb_folder = "tb"
 
 [model]
@@ -27,22 +28,25 @@ lr = 3e-4
 eps = 1e-8
 
 [lr_scheduler]
-warmup_steps = 200  # lr scheduler warm up
+# warmup_steps = 200  # lr scheduler warm up
+warmup_steps = 600
 
 [training]
-batch_size = 1
+batch_size = 4
 seq_len = 8192
 max_norm = 1.0  # grad norm clipping
-steps = 1000
-compile = false
+# steps = 1000
+steps = 3000
+compile = true
 dataset = "c4"
+deterministic = true
 
 [parallelism]
 data_parallel_replicate_degree = 1
 data_parallel_shard_degree = -1
 tensor_parallel_degree = 1
 pipeline_parallel_degree = 1
-context_parallel_degree = 1
+context_parallel_degree = 4
 
 [checkpoint]
 enable_checkpoint = false
@@ -53,7 +57,8 @@ export_dtype = "float32"
 async_mode = "disabled" # ["disabled", "async", "async_with_pinned_mem"]
 
 [activation_checkpoint]
-mode = "selective"  # ["none", "selective", "full"]
+# mode = "selective"  # ["none", "selective", "full"]
+mode = "full"
 selective_ac_option = "op"  # "int" = ac every positive int layer or 'op', ac based on ops policy
 
 [float8]

torchtitan/train.py

@@ -11,10 +11,13 @@
 from typing import Any, Generator, Iterable, Optional
 
 import torch
-from torch.distributed.elastic.multiprocessing.errors import record
 
 import torchtitan.components.ft as ft
 import torchtitan.protocols.train_spec as train_spec_module
+from torch.distributed.elastic.multiprocessing.errors import record
+from torch.distributed.tensor.experimental._attention import (
+    FlexAttentionContiguousSharder,
+)
 
 from torchtitan.components.checkpoint import CheckpointManager
 from torchtitan.components.metrics import (
@@ -133,7 +136,9 @@ def __init__(self, job_config: JobConfig):
 
         # build model (using meta init)
         model_cls = self.train_spec.cls
+        # NOTE (xilunwu): need to store model_args.use_flex_attn for train_step
         model_args = self.train_spec.config[job_config.model.flavor]
+        self.model_args = model_args
         # set the model args from training job configs
         model_args.update_from_config(job_config, tokenizer)
 
@@ -319,13 +324,29 @@ def train_step(self, input_dict: dict[str, torch.Tensor], labels: torch.Tensor):
         # apply context parallelism if cp is enabled
         # ensure CP handles the separate freqs_cis buffer for each pp stage
         inputs = input_dict["input"]
+
+        # TODO: move this into `create_context_parallel_ctx`
+        # init block_mask for flex_attention
+        block_mask = None
+        if self.model_args.use_flex_attn:
+            from torchtitan.models.attention import FlexAttention
+
+            mask_mod = FlexAttention._get_causal_mask_mod()
+            batch_dimension = 1
+            seq_len = inputs.shape[1]
+            block_mask = FlexAttention.compiled_create_block_mask(
+                mask_mod, batch_dimension, None, seq_len, seq_len
+            )
+
         optional_context_parallel_ctx = (
             dist_utils.create_context_parallel_ctx(
                 cp_mesh=world_mesh["cp"],
                 cp_buffers=[inputs, labels] + [m.freqs_cis for m in model_parts],
                 cp_seq_dims=[1, 1] + [0 for _ in model_parts],
                 cp_no_restore_buffers={inputs, labels},
                 cp_rotate_method=self.job_config.parallelism.context_parallel_rotate_method,
+                block_mask=block_mask,
+                sharder=FlexAttentionContiguousSharder(),
             )
             if parallel_dims.cp_enabled
             else None