[Hotfix] Fix accuracy and align attention method api with Triton kernel (#5229)

* fix accuracy

* alignment in attention

* fix attention

* fix

* fix bugs

* fix bugs

* fix bugs

colossalai/inference/modeling/layers/attention.py

@@ -1,11 +1,9 @@
 import math
-from typing import Optional
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
 
 
 def copy_to_cache(source, cache, lengths, block_tables, type: str = "prefill"):
@@ -13,12 +11,12 @@ def copy_to_cache(source, cache, lengths, block_tables, type: str = "prefill"):
     Func: copy key/value into key/value cache.
 
     Args:   key/value(source): shape [bsz,seq_len,num_heads,head_size]
-            cache: shape [num_blocks, num_heads, head_size, block_size]
+            cache: shape [num_blocks, num_kv_heads, head_size, block_size]
             lengths: key/value lengths
             block_tables
     """
     num_blocks, num_heads, head_size, block_size = cache.shape
-    bsz, max_seq_len = block_tables.shape
+    bsz, max_blocks_per_seq = block_tables.shape
     needed_blocks = (lengths + block_size - 1) // block_size
 
     if type == "prefill":
@@ -42,13 +40,14 @@ def copy_to_cache(source, cache, lengths, block_tables, type: str = "prefill"):
     return cache
 
 
-def convert_kvcache(cache, lengths, block_tables):
+def convert_kvcache(cache, lengths, block_tables, pad_id=0):
     """
     Func: convert key/value cache for calculation
 
     Args:   cache: shape [num_blocks, num_heads, head_size, block_size]
             lengths: key/value length
             block_tables
+            pad_id: padded_id
     """
     num_blocks, num_heads, head_size, block_size = cache.shape
 
@@ -64,35 +63,29 @@ def convert_kvcache(cache, lengths, block_tables):
 
         _cache = torch.cat(
             (
-                cache1,
+                cache[block_tables[i][: needed_blocks[i] - 1]].permute((0, 3, 1, 2)).reshape(-1, num_heads, head_size),
-                cache2,
+                cache[block_tables[i][needed_blocks[i] - 1], :, :, : num_remaing_tokens[i]].permute(2, 0, 1),
             ),
             dim=0,
         )
         padding = seq_len - _cache.size(0)
         if padding > 0:
-            _cache = F.pad(_cache, (0, 0, 0, 0, 0, 1))
+            _cache = F.pad(_cache, (0, 0, 0, 0, 0, 1), value=pad_id)
         padded_cache.append(_cache)
     return torch.stack(padded_cache, dim=0)
 
 
-class PagedAttention(nn.Module):
+class PagedAttention:
     """
     Pure Torch implementation version of paged_attention.
+        Holds different types of forward function and useful components.
     """
 
-    def __init__(self, num_heads: int, head_size: int, scale: float = 1.0, sliding_window: Optional[int] = None):
+    @staticmethod
-        super().__init__()
+    def pad_and_reshape(tensor, seq_lengths, max_seq_len, num_heads, head_size):
-        self.num_heads = num_heads
+        """
-        self.head_size = head_size
+        Transform 1D no_pad tensor into 2D padded tensor with shape [bsz,seq_len,num_heads,head_size]
-        self.scale = float(scale)
+        """
-        self.sliding_window = sliding_window
-        self._init_rope()
-
-    def _init_rope(self):
-        self.rotary_emb = LlamaRotaryEmbedding(self.head_size)
-
-    def pad_and_reshape(self, tensor, seq_lengths, max_seq_len, num_heads, head_size):
         bsz = len(seq_lengths)
         padded_tensor = torch.zeros(bsz, max_seq_len, num_heads, head_size)
 
@@ -103,22 +96,49 @@ class PagedAttention(nn.Module):
             token_idx += seq_len
         return padded_tensor
 
-    def generate_padding_mask(self, lengths, max_seq_len):
+    @staticmethod
+    def generate_padding_mask(lengths, max_seq_len):
         range_tensor = torch.arange(max_seq_len).expand(len(lengths), max_seq_len)
         padding_mask = range_tensor < lengths.unsqueeze(1)
         return padding_mask
 
+    @staticmethod
+    def repeat_kv(hidden_states: torch.Tensor, n_rep: int = 1) -> torch.Tensor:
+        """
+        Essential component for MQA. Equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
+            Args: hidden_states(batch, num_key_value_heads, seqlen, head_dim)
+                  n_rep: times of repeatition.
+            Output: hidden_states (batch, num_attention_heads, seqlen, head_dim)
+        """
+        if n_rep == 1:
+            return hidden_states
+
+        batch, num_key_value_heads, seq_len, head_dim = hidden_states.shape
+        num_attention_heads = n_rep * num_key_value_heads
+        hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, seq_len, head_dim)
+
+        return hidden_states.reshape(batch, num_attention_heads, seq_len, head_dim)
+
+    @staticmethod
     def nopad_context_forward(
-        self,
         q: torch.Tensor,  # [num_tokens, num_heads, head_size]
-        k: torch.Tensor,
+        k: torch.Tensor,  # [num_tokens, num_kv_heads, head_size]
         v: torch.Tensor,
         k_cache: torch.Tensor,  # [num_blocks, num_heads, head_size, block_size]
         v_cache: torch.Tensor,
         context_lengths: torch.Tensor,  # [num_seqs]
         block_tables: torch.Tensor,  # [num_seqs,max_blocks_per_sequence]
     ):
+        """
+        NOTE: q,k,v are projected and applied rotary embedding, all aligned with triton version.
+        """
+        # Fisrt, do shape verification
         num_tokens, num_heads, head_size = q.shape
+        num_kv_heads = k.shape[-2]
+
+        assert num_heads % num_kv_heads == 0, "num_kv_heads should be divisible by num_heads"
+        num_kv_groups = num_heads // num_kv_heads
+
         block_size = k_cache.shape[-1]
         bsz, max_blocks_per_sequence = block_tables.shape
         max_seq_len = max_blocks_per_sequence * block_size
@@ -127,80 +147,85 @@ class PagedAttention(nn.Module):
         assert context_lengths.shape[0] == block_tables.shape[0]
         shape = (bsz, max_seq_len, num_heads, head_size)
         input_shape = shape[:2]
-        query = self.pad_and_reshape(q, context_lengths, max_seq_len, num_heads, head_size).transpose(1, 2)
+
-        key = self.pad_and_reshape(k, context_lengths, max_seq_len, num_heads, head_size).transpose(1, 2)
+        q = PagedAttention.pad_and_reshape(
-        value = self.pad_and_reshape(v, context_lengths, max_seq_len, num_heads, head_size).transpose(1, 2)
+            q, context_lengths, max_seq_len, num_heads, head_size
+        )  # bsz,seqlen,num_heads,head_size
+        k = PagedAttention.pad_and_reshape(k, context_lengths, max_seq_len, num_heads, head_size)
+        v = PagedAttention.pad_and_reshape(v, context_lengths, max_seq_len, num_heads, head_size)
+
+        copy_to_cache(k, k_cache, lengths=context_lengths, block_tables=block_tables)
+        copy_to_cache(v, v_cache, lengths=context_lengths, block_tables=block_tables)
 
         attn_mask = AttentionMaskConverter._make_causal_mask(input_shape, q.dtype, q.device, past_key_values_length=0)
-        self.generate_padding_mask(context_lengths, max_seq_len)
+        attn_mask = attn_mask + PagedAttention.generate_padding_mask(context_lengths, max_seq_len)
 
-        position_ids = torch.arange(0, max_seq_len, dtype=torch.long, device=query.device)
+        q = q.transpose(1, 2)
-        position_ids = position_ids.unsqueeze(0)
+        k = PagedAttention.repeat_kv(k.transpose(1, 2), num_kv_groups)
+        v = PagedAttention.repeat_kv(v.transpose(1, 2), num_kv_groups)
 
-        cos, sin = self.rotary_emb(value, max_seq_len)
+        # position_ids = torch.arange(0, max_seq_len, dtype=torch.long, device=query.device)
-        query, key = apply_rotary_pos_emb(query, key, cos, sin, position_ids)
+        # position_ids = position_ids.unsqueeze(0)
-
+        # cos, sin = self.rotary_emb(value, max_seq_len)
-        copy_to_cache(key.transpose(1, 2), k_cache, lengths=context_lengths, block_tables=block_tables)
+        # query, key = apply_rotary_pos_emb(query, key, cos, sin, position_ids)
-        copy_to_cache(value.transpose(1, 2), v_cache, lengths=context_lengths, block_tables=block_tables)
-
-        attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(head_size)
 
+        attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(head_size)
         if attn_weights.size() != (bsz, num_heads, max_seq_len, max_seq_len):
             raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,max_seq_len,max_seq_len)}.")
 
         if attn_mask is not None:
             attn_weights += attn_mask
 
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
-        # attn_weights = nn.functional.dropout(attn_weights,p=self.attention_dropout,training=False) maybe useless
+        attn_output = torch.matmul(attn_weights, v)
-        attn_output = torch.matmul(attn_weights, value)
 
         if attn_output.size() != (bsz, num_heads, max_seq_len, head_size):
             raise ValueError(f"Got wrong attn_output, should be in shape {(bsz,num_heads,max_seq_len,head_size)}.")
         attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, max_seq_len, -1)
 
+        del attn_weights
+
         return attn_output
 
+    @staticmethod
     def pad_context_forward(
-        self,
         q: torch.Tensor,  # [batch_size, seq_len, num_heads, head_size]
-        k: torch.Tensor,
+        k: torch.Tensor,  # [batch_size, seq_len, num_kv_heads, head_size]
         v: torch.Tensor,
         k_cache: torch.Tensor,  # [num_blocks, num_heads, head_size, block_size]
         v_cache: torch.Tensor,
         context_lengths: torch.Tensor,  # [num_seqs]
         block_tables: torch.Tensor,  # [num_seqs,max_blocks_per_sequence]
     ):
+        # Firt, do shape verification
         bsz, seq_len, num_heads, head_size = q.shape
+        num_kv_heads = k.shape[-2]
+        assert num_heads % num_kv_heads == 0, "num_kv_heads should be divisible by num_heads"
+        num_kv_groups = num_heads // num_kv_heads
         block_size = k_cache.shape[-1]
         assert q.shape[0] == k.shape[0] == v.shape[0] == block_tables.shape[0]
         block_tables.shape[-1] * block_size
         shape = (bsz, seq_len, num_heads, head_size)
         input_shape = shape[:2]
+
+        # Copy kv to memory(rotary embedded)
+        copy_to_cache(k, k_cache, lengths=context_lengths, block_tables=block_tables)
+        copy_to_cache(v, v_cache, lengths=context_lengths, block_tables=block_tables)
+
         q = q.transpose(1, 2)
-        k = k.transpose(1, 2)
+        k = PagedAttention.repeat_kv(k.transpose(1, 2), num_kv_groups)
-        v = v.transpose(1, 2)
+        v = PagedAttention.repeat_kv(v.transpose(1, 2), num_kv_groups)
 
-        position_ids = torch.arange(0, seq_len, dtype=torch.long, device=q.device)
+        attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(head_size)
-        position_ids = position_ids.unsqueeze(0)
-        cos, sin = self.rotary_emb(v, seq_len)
-        query, key = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
-
-        copy_to_cache(key.transpose(1, 2), k_cache, lengths=context_lengths, block_tables=block_tables)
-        copy_to_cache(v.transpose(1, 2), v_cache, lengths=context_lengths, block_tables=block_tables)
-
-        attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(head_size)
         attn_mask = AttentionMaskConverter._make_causal_mask(input_shape, q.dtype, q.device, past_key_values_length=0)
-        self.generate_padding_mask(context_lengths, seq_len)
+        attn_mask = attn_mask + PagedAttention.generate_padding_mask(context_lengths, seq_len)
 
         if attn_weights.size() != (bsz, num_heads, seq_len, seq_len):
             raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,seq_len,seq_len)}.")
         if attn_mask is not None:
             attn_weights += attn_mask
 
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
-
-        # attn_weights = nn.functional.dropout(attn_weights,p=self.attention_dropout,training=False) maybe useless
         attn_output = torch.matmul(attn_weights, v)
 
         if attn_output.size() != (bsz, num_heads, seq_len, head_size):
@@ -208,62 +233,70 @@ class PagedAttention(nn.Module):
 
         attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, seq_len, -1)
 
+        del attn_weights
+
         return attn_output
 
+    @staticmethod
     def pad_decoding_forward(
-        self,
         q: torch.Tensor,  # [bsz, 1, num_heads, head_size]
-        k: torch.Tensor,
+        k: torch.Tensor,  # [bsz, 1, num_kv_heads, head_size]
         v: torch.Tensor,
         k_cache: torch.Tensor,  # [num_blocks, num_heads, head_size, block_size]
         v_cache: torch.Tensor,
         lengths: torch.Tensor,  # [num_seqs]: input_lengths + output_lengths
         block_tables: torch.Tensor,  # [num_seqs,max_blocks_per_sequence]
     ):
+        # Firt, do shape verification.
         bsz, _, num_heads, head_size = q.shape
+
+        num_kv_heads = k.shape[-2]
+        assert num_heads % num_kv_heads == 0, "num_kv_heads should be divisible by num_heads"
+        num_kv_groups = num_heads // num_kv_heads
         block_size = k_cache.shape[-1]
         seq_len = max(lengths)
 
         assert q.shape[0] == k.shape[0] == v.shape[0] == block_tables.shape[0]
-        max_seq_len = block_tables.shape[-1] * block_size
+        block_tables.shape[-1] * block_size
+
         attn_mask = AttentionMaskConverter._make_causal_mask(
             q.shape[:2], q.dtype, q.device, past_key_values_length=seq_len - 1
         )
-        self.generate_padding_mask(lengths, max_seq_len)
+        attn_mask = attn_mask + PagedAttention.generate_padding_mask(lengths, seq_len).unsqueeze(1).unsqueeze(2)
-        cos, sin = self.rotary_emb(v, max_seq_len)
+        # cos, sin = self.rotary_emb(v, max_seq_len)
+        # position_ids = lengths - 1
+        # position_ids = position_ids.unsqueeze(1)
+        # query, key = apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=2)
 
-        position_ids = lengths - 1
+        copy_to_cache(k, k_cache, lengths=lengths, block_tables=block_tables, type="decoding")
-        position_ids = position_ids.unsqueeze(1)
-
-        query, key = apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=2)
-
-        copy_to_cache(key, k_cache, lengths=lengths, block_tables=block_tables, type="decoding")
         copy_to_cache(v, v_cache, lengths=lengths, block_tables=block_tables, type="decoding")
 
-        key = convert_kvcache(key, k_cache, lengths, block_tables)  # bsz, seqlen,
+        k = convert_kvcache(k_cache, lengths, block_tables)  # bsz, seqlen,
-        value = convert_kvcache(v, v_cache, lengths, block_tables)
+        v = convert_kvcache(v_cache, lengths, block_tables)
 
-        query = query.transpose(1, 2)
+        q = q.transpose(1, 2)
-        key = key.transpose(1, 2)
+        k = PagedAttention.repeat_kv(k.transpose(1, 2), num_kv_groups)
-        value = value.transpose(1, 2)
+        v = PagedAttention.repeat_kv(v.transpose(1, 2), num_kv_groups)
 
-        attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(head_size)
+        attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(head_size)
         if attn_weights.size() != (bsz, num_heads, 1, seq_len):
             raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,1,seq_len)}.")
 
         if attn_mask is not None:
             attn_weights += attn_mask
 
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
-        # attn_weights = nn.functional.dropout(attn_weights,p=self.attention_dropout,training=False) maybe useless
+        attn_output = torch.matmul(attn_weights, v)
-        attn_output = torch.matmul(attn_weights, value)
 
         if attn_output.size() != (bsz, num_heads, 1, head_size):
             raise ValueError(f"Got wrong attn_output, should be in shape {(bsz,num_heads,1,head_size)}.")
         attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, 1, -1)
 
+        del attn_weights
+
         return attn_output
 
+    @staticmethod
     def no_pad_decoding_forward(
         self,
         q: torch.Tensor,  # [num_tokens, num_heads, head_size]

tests/test_infer/test_config_and_struct.py

@@ -3,7 +3,7 @@ import pytest
 import colossalai
 from colossalai.inference.config import InferenceConfig
 from colossalai.inference.struct import BatchInfo, Sequence
-from colossalai.testing import spawn
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 
 def check_config_and_inference():
@@ -74,6 +74,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_if_address_is_in_use()
 def test_config_and_inference():
     spawn(run_dist, 1)
 

tests/test_infer/test_inference_engine.py

@@ -11,7 +11,6 @@ from colossalai.inference.config import InferenceConfig
 from colossalai.inference.core.engine import InferenceEngine
 from colossalai.testing import rerun_if_address_is_in_use, spawn
 
-
 def setup_seed(seed):
     torch.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)

tests/test_infer/test_kvcache_manager.py

@@ -8,7 +8,7 @@ import colossalai
 from colossalai.inference.config import InferenceConfig
 from colossalai.inference.kv_cache import CacheBlock, KVCacheManager
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import parameterize, spawn
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
 
 
 @parameterize(
@@ -155,6 +155,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_if_address_is_in_use()
 def test_cache_manager():
     spawn(run_dist, 1)
 

tests/test_infer/test_models/test_attention.py

@@ -3,15 +3,15 @@ import torch
 from transformers.cache_utils import DynamicCache
 from transformers.modeling_attn_mask_utils import AttentionMaskConverter
 from transformers.models.llama.configuration_llama import LlamaConfig
-from transformers.models.llama.modeling_llama import LlamaAttention
+from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
 
 import colossalai
 from colossalai.inference.modeling.layers.attention import PagedAttention, convert_kvcache, copy_to_cache
-from colossalai.testing import spawn
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 
 def test_copy_to_cache():
-    key = torch.ones((2, 10, 3, 3))
+    key = torch.ones((2, 11, 3, 3))
     key[0, 9, :, :] = 0
     key[1, -2:, :, :] = 0
     cache = torch.zeros(8, 3, 3, 8)
@@ -32,7 +32,8 @@ def test_convert_kvcache():
     key = torch.ones(2, 1, 3, 3) + 1
     lengths = torch.tensor([10, 9])
     block_tables = torch.tensor([[0, 1], [2, 3]])
-    converted_cache = convert_kvcache(key, cache=cache, lengths=lengths, block_tables=block_tables)
+    copy_to_cache(key, cache=cache, lengths=lengths, block_tables=block_tables, type="decoding")
+    converted_cache = convert_kvcache(cache=cache, lengths=lengths, block_tables=block_tables)
     assert converted_cache.shape == (2, 10, 3, 3)
 
 
@@ -40,7 +41,7 @@ def test_context_attention():
     """
     test config: head_num = 4, head_size = 4
     """
-    attn = PagedAttention(4, 4)
+    attn = PagedAttention()
     q = k = v = torch.randn(8, 4, 4)
     k_cache = torch.empty(8, 4, 4, 8)
     v_cache = torch.empty(8, 4, 4, 8)
@@ -61,48 +62,72 @@ def test_context_attention():
 
     # test accuracy with LlamaAttention
     hidden_states = torch.randn(1, 8, 16)
-    proj_q = transformer_attn.q_proj(hidden_states).view(1, 8, 4, 4)
+    proj_q = transformer_attn.q_proj(hidden_states).view(1, 8, 4, 4).transpose(1, 2)
-    proj_k = transformer_attn.k_proj(hidden_states).view(1, 8, 4, 4)
+    proj_k = transformer_attn.k_proj(hidden_states).view(1, 8, 4, 4).transpose(1, 2)
-    proj_v = transformer_attn.v_proj(hidden_states).view(1, 8, 4, 4)
+    proj_v = transformer_attn.v_proj(hidden_states).view(1, 8, 4, 4).transpose(1, 2)
-    pad_attn_output = attn.pad_context_forward(proj_q, proj_k, proj_v, k_cache, v_cache, context_lengths, block_tables)
-    pad_attn_output = transformer_attn.o_proj(pad_attn_output)
 
+    position_ids = torch.arange(0, 8, dtype=torch.long, device=proj_q.device)
+    position_ids = position_ids.unsqueeze(0)
+    cos, sin = transformer_attn.rotary_emb(proj_v, 8)
+    proj_q, proj_k = apply_rotary_pos_emb(proj_q, proj_k, cos, sin, position_ids)
+
+    pad_attn_output = attn.pad_context_forward(
+        proj_q.transpose(1, 2),
+        proj_k.transpose(1, 2),
+        proj_v.transpose(1, 2),
+        k_cache,
+        v_cache,
+        context_lengths,
+        block_tables,
+    )
+    pad_attn_output = transformer_attn.o_proj(pad_attn_output)
     attn_mask = AttentionMaskConverter._make_causal_mask(
         hidden_states.shape[:2], q.dtype, q.device, past_key_values_length=0
     )
+    attn_mask += PagedAttention.generate_padding_mask(context_lengths, 8)
     attn_output, _, _ = transformer_attn.forward(hidden_states, attention_mask=attn_mask)
-    assert torch.allclose(pad_attn_output, attn_output, atol=1e-3, rtol=1e-2)
+    assert torch.allclose(pad_attn_output, attn_output, atol=1e-3, rtol=1e-3)
 
 
 def test_decoding_attention():
     # test the pipeline of decoding attention
-    attn = PagedAttention(4, 4)
+    attn = PagedAttention()
-    q = k = v = torch.randn(2, 1, 4, 4)
+    q = k = v = torch.randn(2, 1, 4, 8)
-    k_cache = torch.empty(8, 4, 4, 8)
+    k_cache = torch.empty(8, 4, 8, 8)
-    v_cache = torch.empty(8, 4, 4, 8)
+    v_cache = torch.empty(8, 4, 8, 8)
-    past_kv = torch.randn(2, 8, 4, 4)
+    past_kv = torch.randn(2, 8, 4, 8)
     context_lenghths = torch.tensor([8, 8])
     lengths = context_lenghths + 1
     block_tables = torch.tensor([[0, 1], [2, 3]])
     copy_to_cache(past_kv, k_cache, lengths=context_lenghths, block_tables=block_tables)
     copy_to_cache(past_kv, v_cache, lengths=context_lenghths, block_tables=block_tables)
     attn.pad_decoding_forward(q, k, v, k_cache, v_cache, lengths=lengths, block_tables=block_tables)
+
     # test decoding accuracy, past_kv is reused
-    config = LlamaConfig(num_attention_heads=4, num_key_value_heads=None, hidden_size=16)
+    config = LlamaConfig(num_attention_heads=4, num_key_value_heads=None, hidden_size=32)
     transformer_attn = LlamaAttention(config)
     transformer_attn.layer_idx = 0
     transformer_attn.training = False
-    hidden_states = torch.randn(2, 1, 16)
+    hidden_states = torch.randn(2, 1, 32)
-    proj_q = transformer_attn.q_proj(hidden_states).view(2, 1, 4, 4)
+    proj_q = transformer_attn.q_proj(hidden_states).view(2, 1, 4, 8).transpose(1, 2)
-    proj_k = transformer_attn.k_proj(hidden_states).view(2, 1, 4, 4)
+    proj_k = transformer_attn.k_proj(hidden_states).view(2, 1, 4, 8).transpose(1, 2)
-    proj_v = transformer_attn.v_proj(hidden_states).view(2, 1, 4, 4)
+    proj_v = transformer_attn.v_proj(hidden_states).view(2, 1, 4, 8).transpose(1, 2)
+
+    cos, sin = transformer_attn.rotary_emb(proj_v, 16)
+    position_ids = lengths - 1
+    position_ids = position_ids.unsqueeze(1)  # NOTE: this may be wrong
+    proj_q, proj_k = apply_rotary_pos_emb(proj_q, proj_k, cos, sin, position_ids, unsqueeze_dim=2)
 
     llama_past_kv = DynamicCache()
     llama_past_kv.update(key_states=past_kv.transpose(1, 2), value_states=past_kv.transpose(1, 2), layer_idx=0)
 
     # past_key_value shape in Llama: bsz, num_heads, seq_len, head_dim
-    pad_attn_output = attn.pad_decoding_forward(proj_q, proj_k, proj_v, k_cache, v_cache, lengths, block_tables)
+    pad_attn_output = attn.pad_decoding_forward(
-    attn_mask = AttentionMaskConverter._make_causal_mask(proj_q.shape[:2], q.dtype, q.device, past_key_values_length=8)
+        proj_q.transpose(1, 2), proj_k.transpose(1, 2), proj_v.transpose(1, 2), k_cache, v_cache, lengths, block_tables
+    )
+    attn_mask = AttentionMaskConverter._make_causal_mask(q.shape[:2], q.dtype, q.device, past_key_values_length=8)
+    attn_mask = attn_mask + PagedAttention.generate_padding_mask(lengths, 9).unsqueeze(1).unsqueeze(2)
+
     pad_attn_output = transformer_attn.o_proj(pad_attn_output)
     position_ids = context_lenghths.unsqueeze(1)
     attn_output, _, _ = transformer_attn.forward(
@@ -112,9 +137,9 @@ def test_decoding_attention():
 
 
 def check_attention_layer():
-    # test_copy_to_cache()
+    test_copy_to_cache()
-    # test_convert_kvcache()
+    test_convert_kvcache()
-    # test_context_attention()
+    test_context_attention()
     test_decoding_attention()
 
 
@@ -124,6 +149,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_if_address_is_in_use()
 def test_attention_layer():
     spawn(run_dist, 1)
 

tests/test_infer/test_request_handler.py

@@ -6,7 +6,7 @@ import colossalai
 from colossalai.inference.config import InferenceConfig
 from colossalai.inference.core.request_handler import RequestHandler, RunningList
 from colossalai.inference.struct import RequestStatus, Sequence
-from colossalai.testing import spawn
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 
 def check_running_list():
@@ -78,6 +78,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_if_address_is_in_use()
 def test_running_list_and_request_handler():
     spawn(run_dist, 1)