We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[tensor] fix some unittests

[tensor] fix some unittests

[tensor] fix some unittests


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `colossalai/kernel/triton/kvcache_copy.py`
Content:
```
1 import torch
2 import triton
3 import triton.language as tl
4 
5 
6 # Triton 2.1.0
7 @triton.jit
8 def _copy_to_kvcache_seqlen1_kernel(
9     KV,  # K or V
10     KVCache,  # KCache or VCache
11     BLOCK_TABLES,
12     context_lengths,
13     stride_kt,
14     stride_kh,
15     stride_kd,
16     stride_cacheb,
17     stride_cacheh,
18     stride_cached,
19     stride_cachebs,
20     stride_bts,
21     stride_btb,
22     block_size,
23     HEAD_DIM: tl.constexpr,
24 ):
25     cur_seq_idx = tl.program_id(0)
26     cur_kv_head_idx = tl.program_id(1)
27 
28     cur_kv_seq_len = tl.load(context_lengths + cur_seq_idx)
29     last_bt_block_idx = cur_kv_seq_len // block_size
30     block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts
31     block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)
32     offsets_in_last_block = (cur_kv_seq_len % block_size) * stride_cachebs
33     offsets_dmodel = tl.arange(0, HEAD_DIM)
34     offsets_kv = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd
35     kv = tl.load(KV + offsets_kv)
36     offsets_kvcache = (
37         block_id * stride_cacheb
38         + cur_kv_head_idx * stride_cacheh
39         + offsets_dmodel * stride_cached
40         + offsets_in_last_block
41     )
42     tl.store(KVCache + offsets_kvcache, kv)
43     return
44 
45 
46 # Used with blocked kv cache.
47 # Copy k or v to block k/v cache during decoding stage
48 def copy_kv_to_blocked_cache(
49     k: torch.Tensor,  #  [bsz, 1, num_kv_heads, head_dim], k or v during decoding stage
50     k_cache: torch.Tensor,  # [num_blocks, num_kv_heads, head_dim, block_size], blocked k or v cache (for now, the shapes of them are the same)
51     context_lengths: torch.Tensor,  # [bsz], past kv seq len (not incorporating the current kv of length 1)
52     block_tables: torch.Tensor,  # [bsz, max_blocks_per_sequence]
53 ):
54     assert k.dim() == 4, "Unsupported shape of k (supposed to be used for decoding stage)"
55     assert k.size(1) == 1, "Unsupported kv seq len (supposed to be used for decoding stage)"
56     assert k.size(-1) == k_cache.size(-2), "Incompatible head dim"
57     assert k.dtype == k_cache.dtype, "Expected consistent dtype for tensor and cache."
58     bsz, _, num_kv_heads, head_dim = k.shape
59     assert context_lengths.shape[0] == block_tables.shape[0] == bsz, (
60         f"Got incompatible batch size (number of seqs):\n"
61         f"  Conext lengths bsz {context_lengths.shape[0]}, Block tables bsz {block_tables.shape[0]}, "
62         f"batch size {bsz}"
63     )
64 
65     # Modify if the shape of kv cahce is changed.
66     block_size = k_cache.size(-1)
67     # [bsz, 1, num_kv_heads, head_dim] -> [bsz, num_kv_heads, head_dim]
68     k = k.squeeze(dim=1)
69 
70     num_warps = 8 if head_dim > 128 else 4
71 
72     grid = (bsz, num_kv_heads)
73     _copy_to_kvcache_seqlen1_kernel[grid](
74         k,
75         k_cache,
76         block_tables,
77         context_lengths,
78         k.stride(0),
79         k.stride(1),
80         k.stride(2),
81         k_cache.stride(0),
82         k_cache.stride(1),
83         k_cache.stride(2),
84         k_cache.stride(3),
85         block_tables.stride(0),
86         block_tables.stride(1),
87         block_size,
88         HEAD_DIM=head_dim,
89         num_warps=num_warps,
90     )
91
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/colossalai/kernel/triton/kvcache_copy.py b/colossalai/kernel/triton/kvcache_copy.py
--- a/colossalai/kernel/triton/kvcache_copy.py
+++ b/colossalai/kernel/triton/kvcache_copy.py
@@ -25,11 +25,11 @@
     cur_seq_idx = tl.program_id(0)
     cur_kv_head_idx = tl.program_id(1)
 
-    cur_kv_seq_len = tl.load(context_lengths + cur_seq_idx)
-    last_bt_block_idx = cur_kv_seq_len // block_size
+    past_kv_seq_len = tl.load(context_lengths + cur_seq_idx) - 1
+    last_bt_block_idx = past_kv_seq_len // block_size
     block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts
     block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)
-    offsets_in_last_block = (cur_kv_seq_len % block_size) * stride_cachebs
+    offsets_in_last_block = (past_kv_seq_len % block_size) * stride_cachebs
     offsets_dmodel = tl.arange(0, HEAD_DIM)
     offsets_kv = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd
     kv = tl.load(KV + offsets_kv)
@@ -43,23 +43,30 @@
     return
 
 
-# Used with blocked kv cache.
-# Copy k or v to block k/v cache during decoding stage
 def copy_kv_to_blocked_cache(
-    k: torch.Tensor,  #  [bsz, 1, num_kv_heads, head_dim], k or v during decoding stage
-    k_cache: torch.Tensor,  # [num_blocks, num_kv_heads, head_dim, block_size], blocked k or v cache (for now, the shapes of them are the same)
-    context_lengths: torch.Tensor,  # [bsz], past kv seq len (not incorporating the current kv of length 1)
-    block_tables: torch.Tensor,  # [bsz, max_blocks_per_sequence]
+    k: torch.Tensor,
+    k_cache: torch.Tensor,
+    kv_lengths: torch.Tensor,
+    block_tables: torch.Tensor,
 ):
+    """
+    Copy keys or values to the blocked key/value cache during decoding stage.
+
+    Parameters:
+    - k (torch.Tensor): [bsz, 1, num_kv_heads, head_dim] - Keys or values during decoding with seq len 1.
+    - k_cache (torch.Tensor): [num_blocks, num_kv_heads, head_dim, block_size] - Blocked key or value cache.
+    - kv_lengths (torch.Tensor): [bsz] - Past key/value sequence lengths plus current sequence length for each sequence.
+    - block_tables (torch.Tensor): [bsz, max_blocks_per_sequence] - Block tables for each sequence.
+    """
     assert k.dim() == 4, "Unsupported shape of k (supposed to be used for decoding stage)"
     assert k.size(1) == 1, "Unsupported kv seq len (supposed to be used for decoding stage)"
     assert k.size(-1) == k_cache.size(-2), "Incompatible head dim"
     assert k.dtype == k_cache.dtype, "Expected consistent dtype for tensor and cache."
     bsz, _, num_kv_heads, head_dim = k.shape
-    assert context_lengths.shape[0] == block_tables.shape[0] == bsz, (
+    assert kv_lengths.shape[0] == block_tables.shape[0] == bsz, (
         f"Got incompatible batch size (number of seqs):\n"
-        f"  Conext lengths bsz {context_lengths.shape[0]}, Block tables bsz {block_tables.shape[0]}, "
-        f"batch size {bsz}"
+        f"  Past kv sequence lengths bsz {kv_lengths.shape[0]}; "
+        f" block tables bsz {block_tables.shape[0]}, input k batch size {bsz}"
     )
 
     # Modify if the shape of kv cahce is changed.
@@ -74,7 +81,7 @@
         k,
         k_cache,
         block_tables,
-        context_lengths,
+        kv_lengths,
         k.stride(0),
         k.stride(1),
         k.stride(2),

{"golden_diff": "diff --git a/colossalai/kernel/triton/kvcache_copy.py b/colossalai/kernel/triton/kvcache_copy.py\n--- a/colossalai/kernel/triton/kvcache_copy.py\n+++ b/colossalai/kernel/triton/kvcache_copy.py\n@@ -25,11 +25,11 @@\n     cur_seq_idx = tl.program_id(0)\n     cur_kv_head_idx = tl.program_id(1)\n \n-    cur_kv_seq_len = tl.load(context_lengths + cur_seq_idx)\n-    last_bt_block_idx = cur_kv_seq_len // block_size\n+    past_kv_seq_len = tl.load(context_lengths + cur_seq_idx) - 1\n+    last_bt_block_idx = past_kv_seq_len // block_size\n     block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts\n     block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)\n-    offsets_in_last_block = (cur_kv_seq_len % block_size) * stride_cachebs\n+    offsets_in_last_block = (past_kv_seq_len % block_size) * stride_cachebs\n     offsets_dmodel = tl.arange(0, HEAD_DIM)\n     offsets_kv = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd\n     kv = tl.load(KV + offsets_kv)\n@@ -43,23 +43,30 @@\n     return\n \n \n-# Used with blocked kv cache.\n-# Copy k or v to block k/v cache during decoding stage\n def copy_kv_to_blocked_cache(\n-    k: torch.Tensor,  #  [bsz, 1, num_kv_heads, head_dim], k or v during decoding stage\n-    k_cache: torch.Tensor,  # [num_blocks, num_kv_heads, head_dim, block_size], blocked k or v cache (for now, the shapes of them are the same)\n-    context_lengths: torch.Tensor,  # [bsz], past kv seq len (not incorporating the current kv of length 1)\n-    block_tables: torch.Tensor,  # [bsz, max_blocks_per_sequence]\n+    k: torch.Tensor,\n+    k_cache: torch.Tensor,\n+    kv_lengths: torch.Tensor,\n+    block_tables: torch.Tensor,\n ):\n+    \"\"\"\n+    Copy keys or values to the blocked key/value cache during decoding stage.\n+\n+    Parameters:\n+    - k (torch.Tensor): [bsz, 1, num_kv_heads, head_dim] - Keys or values during decoding with seq len 1.\n+    - k_cache (torch.Tensor): [num_blocks, num_kv_heads, head_dim, block_size] - Blocked key or value cache.\n+    - kv_lengths (torch.Tensor): [bsz] - Past key/value sequence lengths plus current sequence length for each sequence.\n+    - block_tables (torch.Tensor): [bsz, max_blocks_per_sequence] - Block tables for each sequence.\n+    \"\"\"\n     assert k.dim() == 4, \"Unsupported shape of k (supposed to be used for decoding stage)\"\n     assert k.size(1) == 1, \"Unsupported kv seq len (supposed to be used for decoding stage)\"\n     assert k.size(-1) == k_cache.size(-2), \"Incompatible head dim\"\n     assert k.dtype == k_cache.dtype, \"Expected consistent dtype for tensor and cache.\"\n     bsz, _, num_kv_heads, head_dim = k.shape\n-    assert context_lengths.shape[0] == block_tables.shape[0] == bsz, (\n+    assert kv_lengths.shape[0] == block_tables.shape[0] == bsz, (\n         f\"Got incompatible batch size (number of seqs):\\n\"\n-        f\"  Conext lengths bsz {context_lengths.shape[0]}, Block tables bsz {block_tables.shape[0]}, \"\n-        f\"batch size {bsz}\"\n+        f\"  Past kv sequence lengths bsz {kv_lengths.shape[0]}; \"\n+        f\" block tables bsz {block_tables.shape[0]}, input k batch size {bsz}\"\n     )\n \n     # Modify if the shape of kv cahce is changed.\n@@ -74,7 +81,7 @@\n         k,\n         k_cache,\n         block_tables,\n-        context_lengths,\n+        kv_lengths,\n         k.stride(0),\n         k.stride(1),\n         k.stride(2),\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "import torch\nimport triton\nimport triton.language as tl\n\n\n# Triton 2.1.0\n@triton.jit\ndef _copy_to_kvcache_seqlen1_kernel(\n    KV,  # K or V\n    KVCache,  # KCache or VCache\n    BLOCK_TABLES,\n    context_lengths,\n    stride_kt,\n    stride_kh,\n    stride_kd,\n    stride_cacheb,\n    stride_cacheh,\n    stride_cached,\n    stride_cachebs,\n    stride_bts,\n    stride_btb,\n    block_size,\n    HEAD_DIM: tl.constexpr,\n):\n    cur_seq_idx = tl.program_id(0)\n    cur_kv_head_idx = tl.program_id(1)\n\n    cur_kv_seq_len = tl.load(context_lengths + cur_seq_idx)\n    last_bt_block_idx = cur_kv_seq_len // block_size\n    block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts\n    block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)\n    offsets_in_last_block = (cur_kv_seq_len % block_size) * stride_cachebs\n    offsets_dmodel = tl.arange(0, HEAD_DIM)\n    offsets_kv = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd\n    kv = tl.load(KV + offsets_kv)\n    offsets_kvcache = (\n        block_id * stride_cacheb\n        + cur_kv_head_idx * stride_cacheh\n        + offsets_dmodel * stride_cached\n        + offsets_in_last_block\n    )\n    tl.store(KVCache + offsets_kvcache, kv)\n    return\n\n\n# Used with blocked kv cache.\n# Copy k or v to block k/v cache during decoding stage\ndef copy_kv_to_blocked_cache(\n    k: torch.Tensor,  #  [bsz, 1, num_kv_heads, head_dim], k or v during decoding stage\n    k_cache: torch.Tensor,  # [num_blocks, num_kv_heads, head_dim, block_size], blocked k or v cache (for now, the shapes of them are the same)\n    context_lengths: torch.Tensor,  # [bsz], past kv seq len (not incorporating the current kv of length 1)\n    block_tables: torch.Tensor,  # [bsz, max_blocks_per_sequence]\n):\n    assert k.dim() == 4, \"Unsupported shape of k (supposed to be used for decoding stage)\"\n    assert k.size(1) == 1, \"Unsupported kv seq len (supposed to be used for decoding stage)\"\n    assert k.size(-1) == k_cache.size(-2), \"Incompatible head dim\"\n    assert k.dtype == k_cache.dtype, \"Expected consistent dtype for tensor and cache.\"\n    bsz, _, num_kv_heads, head_dim = k.shape\n    assert context_lengths.shape[0] == block_tables.shape[0] == bsz, (\n        f\"Got incompatible batch size (number of seqs):\\n\"\n        f\"  Conext lengths bsz {context_lengths.shape[0]}, Block tables bsz {block_tables.shape[0]}, \"\n        f\"batch size {bsz}\"\n    )\n\n    # Modify if the shape of kv cahce is changed.\n    block_size = k_cache.size(-1)\n    # [bsz, 1, num_kv_heads, head_dim] -> [bsz, num_kv_heads, head_dim]\n    k = k.squeeze(dim=1)\n\n    num_warps = 8 if head_dim > 128 else 4\n\n    grid = (bsz, num_kv_heads)\n    _copy_to_kvcache_seqlen1_kernel[grid](\n        k,\n        k_cache,\n        block_tables,\n        context_lengths,\n        k.stride(0),\n        k.stride(1),\n        k.stride(2),\n        k_cache.stride(0),\n        k_cache.stride(1),\n        k_cache.stride(2),\n        k_cache.stride(3),\n        block_tables.stride(0),\n        block_tables.stride(1),\n        block_size,\n        HEAD_DIM=head_dim,\n        num_warps=num_warps,\n    )\n", "path": "colossalai/kernel/triton/kvcache_copy.py"}], "after_files": [{"content": "import torch\nimport triton\nimport triton.language as tl\n\n\n# Triton 2.1.0\n@triton.jit\ndef _copy_to_kvcache_seqlen1_kernel(\n    KV,  # K or V\n    KVCache,  # KCache or VCache\n    BLOCK_TABLES,\n    context_lengths,\n    stride_kt,\n    stride_kh,\n    stride_kd,\n    stride_cacheb,\n    stride_cacheh,\n    stride_cached,\n    stride_cachebs,\n    stride_bts,\n    stride_btb,\n    block_size,\n    HEAD_DIM: tl.constexpr,\n):\n    cur_seq_idx = tl.program_id(0)\n    cur_kv_head_idx = tl.program_id(1)\n\n    past_kv_seq_len = tl.load(context_lengths + cur_seq_idx) - 1\n    last_bt_block_idx = past_kv_seq_len // block_size\n    block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts\n    block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)\n    offsets_in_last_block = (past_kv_seq_len % block_size) * stride_cachebs\n    offsets_dmodel = tl.arange(0, HEAD_DIM)\n    offsets_kv = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd\n    kv = tl.load(KV + offsets_kv)\n    offsets_kvcache = (\n        block_id * stride_cacheb\n        + cur_kv_head_idx * stride_cacheh\n        + offsets_dmodel * stride_cached\n        + offsets_in_last_block\n    )\n    tl.store(KVCache + offsets_kvcache, kv)\n    return\n\n\ndef copy_kv_to_blocked_cache(\n    k: torch.Tensor,\n    k_cache: torch.Tensor,\n    kv_lengths: torch.Tensor,\n    block_tables: torch.Tensor,\n):\n    \"\"\"\n    Copy keys or values to the blocked key/value cache during decoding stage.\n\n    Parameters:\n    - k (torch.Tensor): [bsz, 1, num_kv_heads, head_dim] - Keys or values during decoding with seq len 1.\n    - k_cache (torch.Tensor): [num_blocks, num_kv_heads, head_dim, block_size] - Blocked key or value cache.\n    - kv_lengths (torch.Tensor): [bsz] - Past key/value sequence lengths plus current sequence length for each sequence.\n    - block_tables (torch.Tensor): [bsz, max_blocks_per_sequence] - Block tables for each sequence.\n    \"\"\"\n    assert k.dim() == 4, \"Unsupported shape of k (supposed to be used for decoding stage)\"\n    assert k.size(1) == 1, \"Unsupported kv seq len (supposed to be used for decoding stage)\"\n    assert k.size(-1) == k_cache.size(-2), \"Incompatible head dim\"\n    assert k.dtype == k_cache.dtype, \"Expected consistent dtype for tensor and cache.\"\n    bsz, _, num_kv_heads, head_dim = k.shape\n    assert kv_lengths.shape[0] == block_tables.shape[0] == bsz, (\n        f\"Got incompatible batch size (number of seqs):\\n\"\n        f\"  Past kv sequence lengths bsz {kv_lengths.shape[0]}; \"\n        f\" block tables bsz {block_tables.shape[0]}, input k batch size {bsz}\"\n    )\n\n    # Modify if the shape of kv cahce is changed.\n    block_size = k_cache.size(-1)\n    # [bsz, 1, num_kv_heads, head_dim] -> [bsz, num_kv_heads, head_dim]\n    k = k.squeeze(dim=1)\n\n    num_warps = 8 if head_dim > 128 else 4\n\n    grid = (bsz, num_kv_heads)\n    _copy_to_kvcache_seqlen1_kernel[grid](\n        k,\n        k_cache,\n        block_tables,\n        kv_lengths,\n        k.stride(0),\n        k.stride(1),\n        k.stride(2),\n        k_cache.stride(0),\n        k_cache.stride(1),\n        k_cache.stride(2),\n        k_cache.stride(3),\n        block_tables.stride(0),\n        block_tables.stride(1),\n        block_size,\n        HEAD_DIM=head_dim,\n        num_warps=num_warps,\n    )\n", "path": "colossalai/kernel/triton/kvcache_copy.py"}]}