Add joint tensor and KV cache support to USP method

tests/layers/usp_test.py

@@ -11,30 +11,29 @@
     get_runtime_state,
 )
 from xfuser.core.distributed.parallel_state import destroy_model_parallel, destroy_distributed_environment
+from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+from yunchang.kernels import AttnType
 
 
+def _init_environment():
+    os.environ["RANK"] = "0"
+    os.environ["WORLD_SIZE"] = "1"
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "12355"
+    os.environ["LOCAL_RANK"] = "0"
+    init_distributed_environment(rank=0, world_size=1)
+    initialize_model_parallel(ring_degree=1, ulysses_degree=1)
+
 class TestUSP(unittest.TestCase):
 
     def setUp(self):
-        self._init_environment()
+        _init_environment()
         env_info = PACKAGES_CHECKER.get_packages_info()
         self.HAS_FLASH_ATTN = env_info["has_flash_attn"]
         self.HAS_AITER = env_info["has_aiter"]
-        self.query = torch.randn(29760, 2, 5, 128, device="cuda", dtype=torch.bfloat16)
-        self.key = torch.randn(29760, 2, 5, 128, device="cuda", dtype=torch.bfloat16)
-        self.value = torch.randn(29760, 2, 5, 128, device="cuda", dtype=torch.bfloat16)
-
-
-    def _init_environment(self):
-        os.environ["RANK"] = "0"
-        os.environ["WORLD_SIZE"] = "1"
-        os.environ["MASTER_ADDR"] = "localhost"
-        os.environ["MASTER_PORT"] = "12355"
-        os.environ["LOCAL_RANK"] = "0"
-        init_distributed_environment(rank=0, world_size=1)
-        initialize_model_parallel(ring_degree=1, ulysses_degree=1)
-
-
+        self.query = torch.randn(1, 24, 14867, 128, device="cuda", dtype=torch.bfloat16)
+        self.key = torch.randn(1, 24, 14867, 128, device="cuda", dtype=torch.bfloat16)
+        self.value = torch.randn(1, 24, 14867, 128, device="cuda", dtype=torch.bfloat16)
 
     def tearDown(self):
         destroy_model_parallel()
@@ -123,4 +122,80 @@ def test_ring_attn_aiter(self):
 
         result_diff = (fsdpa_results - aiter_attn_results).abs().max()
         self.assertNotEqual(result_diff, 0) # Different implementations won't produce same output
-        self.assertAlmostEqual(result_diff.item(), 0, places=1) # Difference can be 0.15ish
+        self.assertAlmostEqual(result_diff.item(), 0, places=1) # Difference can be 0.15ish
+
+
+class TestUSPHybridParallel(unittest.TestCase):
+
+    def setUp(self):
+        _init_environment()
+        # Using SDPA here
+        self.HAS_FLASH_ATTN = False
+        self.HAS_AITER = False
+        self.query = torch.randn(1, 24, 14867, 128, device="cuda", dtype=torch.bfloat16)
+        self.key = torch.randn(1, 24, 14867, 128, device="cuda", dtype=torch.bfloat16)
+        self.value = torch.randn(1, 24, 14867, 128, device="cuda", dtype=torch.bfloat16)
+
+        self.hybrid_seq_parallel_attn = xFuserLongContextAttention(
+            attn_type=AttnType.TORCH
+        )
+
+    def tearDown(self):
+        destroy_model_parallel()
+        destroy_distributed_environment()
+
+    def test_usp_hybrid_equivalence(self):
+        """
+        Tests the output from USP is equivalent to hybrid seq parallel attention, i.e
+        yunchang path
+        """
+        usp_results = usp.USP(self.query, self.key, self.value, dropout_p=0.0, is_causal=False)
+        hybrid_results = self.hybrid_seq_parallel_attn(
+            None,
+            self.query.transpose(1, 2),
+            self.key.transpose(1, 2),
+            self.value.transpose(1, 2),
+            dropout_p=0.0,
+            causal=False
+        ).transpose(1, 2)
+
+        result_diff = (usp_results - hybrid_results).abs().max().float().cpu().numpy()
+        self.assertAlmostEqual(result_diff, 0, places=3)
+
+    def test_usp_hybrid_joint_equivalence(self):
+        """
+        Tests the output from USP with joint tensors added is equivalent to hybrid seq
+        parallel attn.
+        """
+        joint_shape = (1, 24, 64, 128)
+
+        joint_query = torch.randn(joint_shape, device="cuda", dtype=torch.bfloat16)
+        joint_key = torch.randn(joint_shape, device="cuda", dtype=torch.bfloat16)
+        joint_value = torch.randn(joint_shape, device="cuda", dtype=torch.bfloat16)
+
+        usp_results = usp.USP(
+            self.query,
+            self.key,
+            self.value,
+            dropout_p=0.0,
+            is_causal=False,
+            joint_query=joint_query,
+            joint_key=joint_key,
+            joint_value=joint_value,
+            joint_strategy="rear"
+        )
+        hybrid_results = self.hybrid_seq_parallel_attn(
+            None,
+            self.query.transpose(1, 2),
+            self.key.transpose(1, 2),
+            self.value.transpose(1, 2),
+            dropout_p=0.0,
+            causal=False,
+            joint_tensor_query=joint_query.transpose(1, 2),
+            joint_tensor_key=joint_key.transpose(1, 2),
+            joint_tensor_value=joint_value.transpose(1, 2),
+            joint_strategy="rear"
+        ).transpose(1, 2)
+
+        result_diff = (usp_results - hybrid_results).abs().max().float().cpu().numpy()
+        self.assertAlmostEqual(result_diff, 0, places=3)

xfuser/model_executor/layers/attention_processor.py

@@ -1231,7 +1231,7 @@ def __call__(
             ):
                 hidden_states = USP(query, key, value, dropout_p=0.0, is_causal=False)
                 hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
-            elif HAS_LONG_CTX_ATTN and get_sequence_parallel_world_size() > 1:
+            elif get_sequence_parallel_world_size() > 1:
                 if get_runtime_state().split_text_embed_in_sp:
                     encoder_query = None
                     encoder_key = None
@@ -1247,27 +1247,19 @@ def __call__(
                         [num_query_tokens, num_encoder_hidden_states_tokens], dim=2
                     )
 
-                    encoder_query = encoder_query.transpose(1, 2)
-                    encoder_key = encoder_key.transpose(1, 2)
-                    encoder_value = encoder_value.transpose(1, 2)
-
-                query = query.transpose(1, 2)
-                key = key.transpose(1, 2)
-                value = value.transpose(1, 2)
-
-                hidden_states = self.hybrid_seq_parallel_attn(
-                    None,
+                hidden_states = USP(
                     query,
                     key,
                     value,
                     dropout_p=0.0,
-                    causal=False,
-                    joint_tensor_query=encoder_query,
-                    joint_tensor_key=encoder_key,
-                    joint_tensor_value=encoder_value,
+                    is_causal=False,
+                    joint_query=encoder_query,
+                    joint_key=encoder_key,
+                    joint_value=encoder_value,
                     joint_strategy="rear",
                 )
 
+                hidden_states = hidden_states.transpose(1, 2)
                 hidden_states = hidden_states.flatten(2, 3)
             else:
                 if HAS_FLASH_ATTN:

xfuser/model_executor/layers/usp.py

@@ -15,10 +15,13 @@
     get_sequence_parallel_world_size,
     get_ulysses_parallel_world_size,
     get_ring_parallel_world_size,
+    get_sequence_parallel_rank,
+    get_ulysses_parallel_rank,
 )
 
 from packaging.version import parse
 from xfuser.envs import PACKAGES_CHECKER
+from xfuser.core.cache_manager.cache_manager import get_cache_manager
 env_info = PACKAGES_CHECKER.get_packages_info()
 HAS_FLASH_ATTN = env_info["has_flash_attn"]
 if HAS_FLASH_ATTN:
@@ -163,6 +166,7 @@ def _ft_c_output_all_to_all(x):
     x = x.reshape(world_size, s // world_size, b, -1, d).permute(2, 0, 3, 1, 4).reshape(b, -1, s // world_size, d)
     return x
 
+
 def _aiter_attn_call(query, key, value, dropout_p, is_causal):
     """
     Performs the necessary tensor permutes and
@@ -217,21 +221,104 @@ def _attention(query, key, value, dropout_p, is_causal):
             query, key, value, dropout_p=dropout_p, is_causal=is_causal
         )
 
-def USP(query, key, value, dropout_p=0.0, is_causal=False):
-    if get_sequence_parallel_world_size() == 1:
-        out = _attention(query, key, value, dropout_p=dropout_p, is_causal=is_causal)
-    elif get_ulysses_parallel_world_size() == 1:
-        out = ring_attn(query, key, value, dropout_p=dropout_p, is_causal=is_causal)
-    elif get_ulysses_parallel_world_size() > 1:
+
+def _preprocess_joint_tensors(joint_key, joint_value):
+    """
+    Preprocess the joint key and value tensors for Ulysses parallelism.
+    """
+    ulysses_world_size = get_ulysses_parallel_world_size()
+    ulysses_rank = get_ulysses_parallel_rank()
+    attn_heads_per_ulysses_rank = (
+        joint_key.shape[1] // ulysses_world_size
+    )
+    joint_key = joint_key.transpose(1,2)
+    joint_value = joint_value.transpose(1,2)
+    joint_key = joint_key[
+        ...,
+        attn_heads_per_ulysses_rank
+        * ulysses_rank : attn_heads_per_ulysses_rank
+        * (ulysses_rank + 1),
+        :, ].transpose(1,2)
+    joint_value = joint_value[
+        ...,
+        attn_heads_per_ulysses_rank
+        * ulysses_rank : attn_heads_per_ulysses_rank
+        * (ulysses_rank + 1),
+        :,
+    ].transpose(1,2)
+    return joint_key, joint_value
+
+def _concat_joint_tensor(tensor, joint_tensor, joint_strategy, dim):
+    """
+    Concatenate the joint tensor to the main tensor based on the joint strategy.
+    """
+    if joint_strategy == "rear":
+        tensor = torch.cat([tensor, joint_tensor], dim=dim)
+    elif joint_strategy == "front":
+        tensor = torch.cat([joint_tensor, tensor], dim=dim)
+    else:
+        raise ValueError(f"Invalid joint_strategy: {joint_strategy}")
+    return tensor
+
+def _update_and_get_kv_cache(key, value, attn_layer):
+    """
+    Update and get the key and value cache for pipeline parallelism.
+    """
+    key, value = get_cache_manager().update_and_get_kv_cache(
+        new_kv=[key.transpose(1, 2), value.transpose(1, 2)],
+        layer=attn_layer,
+        slice_dim=1,
+        layer_type="attn",
+    )
+    key = key.transpose(1, 2).contiguous()
+    value = value.transpose(1, 2).contiguous()
+    return key, value
+
+def USP(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        dropout_p: float = 0.0,
+        is_causal: bool = False,
+        joint_query: torch.Tensor | None = None,
+        joint_key: torch.Tensor | None = None,
+        joint_value: torch.Tensor | None = None,
+        joint_strategy: str | None = None,
+        attn_layer=None,
+    ):
+    """
+    Unified Sequence Parallelism (USP) attention call, supporting combinations of Ulysses and
+    Ring attention. Also supports joint tensors and key-value caching for pipeline parallelism.
+    """
+
+    if joint_strategy:
+        query = _concat_joint_tensor(query, joint_query, joint_strategy, dim=2)
+        joint_key, joint_value = _preprocess_joint_tensors(joint_key, joint_value)
+
+    if get_ulysses_parallel_world_size() > 1:
         query = _ft_c_input_all_to_all(query)
         key = _ft_c_input_all_to_all(key)
         value = _ft_c_input_all_to_all(value)
 
-        if get_ring_parallel_world_size() == 1:
+    if attn_layer:
+        key, value = _update_and_get_kv_cache(key, value, attn_layer)
+    if joint_strategy:
+        key = _concat_joint_tensor(key, joint_key, joint_strategy, dim=2)
+        value = _concat_joint_tensor(value, joint_value, joint_strategy, dim=2)
+
+    if get_sequence_parallel_world_size() == 1: # No SP
+        out = _attention(query, key, value, dropout_p=dropout_p, is_causal=is_causal)
+
+    elif get_ulysses_parallel_world_size() == 1: # Ring only
+        out = ring_attn(query, key, value, dropout_p=dropout_p, is_causal=is_causal)
+
+    else:
+        if get_ring_parallel_world_size() == 1: # Ulysses only
             out = _attention(query, key, value, dropout_p=dropout_p, is_causal=is_causal)
-        else:
+        else: # USP
             out = ring_attn(query, key, value, dropout_p=dropout_p, is_causal=is_causal)
-
         out = _ft_c_output_all_to_all(out)
 
     return out
+
+

xfuser/model_executor/models/transformers/transformer_flux.py

@@ -160,9 +160,12 @@ def __call__(
             key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
             value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
 
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
         uses_pipeline_parallelism = get_runtime_state().num_pipeline_patch > 1
         if not uses_pipeline_parallelism:
-            query, key, value = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
             hidden_states = USP(query, key, value)
             hidden_states = hidden_states.transpose(1, 2)
         else:
@@ -172,26 +175,29 @@ def __call__(
                 encoder_hidden_states_value_proj = None
             else:
                 encoder_hidden_states_query_proj, query = query.split(
-                    [num_encoder_hidden_states_tokens, num_query_tokens], dim=1
+                    [num_encoder_hidden_states_tokens, num_query_tokens], dim=2
                 )
                 encoder_hidden_states_key_proj, key = key.split(
-                    [num_encoder_hidden_states_tokens, num_query_tokens], dim=1
+                    [num_encoder_hidden_states_tokens, num_query_tokens], dim=2
                 )
                 encoder_hidden_states_value_proj, value = value.split(
-                    [num_encoder_hidden_states_tokens, num_query_tokens], dim=1
+                    [num_encoder_hidden_states_tokens, num_query_tokens], dim=2
                 )
-            hidden_states = self.hybrid_seq_parallel_attn(
-                attn if get_runtime_state().num_pipeline_patch > 1 else None,
+            hidden_states = USP(
                 query,
                 key,
                 value,
                 dropout_p=0.0,
-                causal=False,
-                joint_tensor_query=encoder_hidden_states_query_proj,
-                joint_tensor_key=encoder_hidden_states_key_proj,
-                joint_tensor_value=encoder_hidden_states_value_proj,
+                is_causal=False,
+                joint_query=encoder_hidden_states_query_proj,
+                joint_key=encoder_hidden_states_key_proj,
+                joint_value=encoder_hidden_states_value_proj,
                 joint_strategy="front",
+                attn_layer=attn,
             )
+            hidden_states = hidden_states.transpose(1, 2)
+
+
         hidden_states = hidden_states.flatten(2, 3)
         hidden_states = hidden_states.to(query.dtype)