prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  </issue>
  <code>
  [start of colossalai/kernel/triton/kvcache_copy.py]
  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 of colossalai/kernel/triton/kvcache_copy.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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),
  
verification_info: {"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"}]}