Merge pull request AI-Hypercomputer#4230 from huytransformer:htn/ag-packing

PiperOrigin-RevId: 937732026

Author: Google-ML-Automation

src/maxtext/utils/train_utils.py

@@ -243,25 +243,35 @@ def create_train_state_fn():
     data_iterator, eval_data_iterator = create_data_iterator(config, mesh)
     rampup_manager = create_rampup_manager(config, checkpoint_manager)
     # Validate context parallelism with packing configuration
+    context_parallel_strategy = config.context_parallel_strategy.lower()
     if config.context_parallel_size > 1 and config.packing:
       if config.dataset_type == "synthetic":
         raise ValueError(
             "Context parallelism with sequence packing is not supported with synthetic data. "
             "Please disable sequence packing (set packing=False)."
         )
-      if config.context_parallel_strategy != "ring":
+      if context_parallel_strategy not in ("all_gather", "ring"):
         raise ValueError(
-            "Context parallelism with 'all_gather' strategy cannot be used with sequence packing. "
-            "Please use 'ring' strategy instead."
+            "Context parallelism with sequence packing supports context_parallel_strategy='all_gather' or 'ring'."
         )
+      if (
+          config.hardware in ("gpu", "gpu_multiprocess")
+          and config.attention == "cudnn_flash_te"
+          and not (context_parallel_strategy == "ring" and config.context_parallel_load_balance)
+      ):
+        raise ValueError("Packing is only supported for load balanced ring attention with context parallelism for GPU.")
 
     # Apply reordering wrapper to data iterators if context parallelism is enabled
     with jax.set_mesh(mesh):
       if config.context_parallel_size > 1 and config.context_parallel_load_balance:
 
         # Determine load balancing reorder strategy based on whether packing is enabled
         if config.context_parallel_reorder_strategy == ReorderStrategy.AUTO:
-          reorder_strategy = ReorderStrategy.STRIPED if config.packing else ReorderStrategy.DUAL_CHUNK_SWAP
+          reorder_strategy = (
+              ReorderStrategy.STRIPED
+              if config.packing and context_parallel_strategy == "ring"
+              else ReorderStrategy.DUAL_CHUNK_SWAP
+          )
         else:
           reorder_strategy = config.context_parallel_reorder_strategy
 

tests/unit/attention_test.py

@@ -865,6 +865,82 @@ def test_tpu_flash_attention_context_parallel(
         f" ici_expert_parallelism={ici_expert_parallelism}.",
     )
 
+  @parameterized.named_parameters(
+      {"testcase_name": "no_load_balance", "context_parallel_load_balance": False},
+      {"testcase_name": "load_balance", "context_parallel_load_balance": True},
+  )
+  @pytest.mark.tpu_only
+  def test_tpu_flash_attention_packed_all_gather_context_parallel(self, context_parallel_load_balance):
+    """Test equivalence between packed dot_product and packed flash attention + all-gather context parallelism."""
+    lnx = jax.random.normal(
+        self.rng,
+        shape=(self.global_batch_size, self.max_target_length, self.embed_dim),
+        dtype=self.dtype,
+    )
+    tokens_per_segment = self.max_target_length // 4
+    segment_ids = jnp.repeat(jnp.arange(1, 5, dtype=jnp.int32), tokens_per_segment)
+    positions = jnp.tile(jnp.arange(tokens_per_segment, dtype=jnp.int32), 4)
+    decoder_segment_ids = jnp.broadcast_to(segment_ids, (self.global_batch_size, self.max_target_length))
+    decoder_positions = jnp.broadcast_to(positions, (self.global_batch_size, self.max_target_length))
+    mha_generic_output, _ = self._attention_as_mha_generic(
+        lnx,
+        lnx,
+        decoder_segment_ids=decoder_segment_ids,
+        inputs_positions=decoder_positions,
+        deterministic=True,
+        model_mode=MODEL_MODE_TRAIN,
+    )
+    generic_state = nnx.state(self._attention_as_mha_generic)
+
+    cfg_cp = pyconfig.initialize(
+        [sys.argv[0], get_test_config_path()],
+        **self.config_arguments,
+        ici_context_parallelism=4,
+        context_parallel_strategy="all_gather",
+        context_parallel_load_balance=context_parallel_load_balance,
+        packing=True,
+    )
+    devices_array_cp = maxtext_utils.create_device_mesh(cfg_cp)
+    mesh_cp = Mesh(devices_array_cp, cfg_cp.mesh_axes)
+    attention_as_mha_flash_cp = Attention(
+        config=cfg_cp,
+        num_query_heads=cfg_cp.num_query_heads,
+        num_kv_heads=cfg_cp.num_kv_heads,
+        head_dim=cfg_cp.head_dim,
+        max_target_length=cfg_cp.max_target_length,
+        max_prefill_predict_length=cfg_cp.max_prefill_predict_length,
+        inputs_q_shape=lnx.shape,
+        inputs_kv_shape=lnx.shape,
+        mesh=mesh_cp,
+        attention_kernel="flash",
+        dtype=self.dtype,
+        dropout_rate=cfg_cp.dropout_rate,
+        model_mode=MODEL_MODE_PREFILL,
+        rngs=self.nnx_rng,
+    )
+    nnx.update(attention_as_mha_flash_cp, generic_state)
+
+    mha_generic_flash_cp_output = attention_test_util.forward_with_context_expert_parallelism(
+        cfg_cp,
+        mesh_cp,
+        attention_as_mha_flash_cp,
+        lnx,
+        decoder_segment_ids,
+        decoder_positions,
+    )
+
+    self.assertTrue(
+        jax.numpy.allclose(
+            jax.device_get(mha_generic_output),
+            jax.device_get(mha_generic_flash_cp_output),
+            rtol=1e-01,
+            atol=1e-01,
+            equal_nan=False,
+        ),
+        msg="Logits from packed generic dot product and packed flash attention + all-gather context parallelism "
+        f"are not close. context_parallel_load_balance={context_parallel_load_balance}.",
+    )
+
   @pytest.mark.tpu_only
   def test_dot_product_cache_axis_order(self):
     all_axis_orders = tuple(itertools.permutations(range(4)))