[Model][MiniMaxText01] Support MiniMaxText01 model inference (#13454)

Signed-off-by: qscqesze <475517977@qq.com>
Co-authored-by: qingjun <qingjun@minimaxi.com>
Co-authored-by: qscqesze <475517977@qq.com>

docs/source/models/supported_models.md

@@ -503,6 +503,11 @@ See [this page](#generative-models) for more information on how to use generativ
   * `xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc.
   * ✅︎
   * ✅︎
+- * `MiniMaxText01ForCausalLM`
+  * MiniMax-Text
+  * `MiniMaxAI/MiniMax-Text-01`, etc.
+  *
+  * ✅︎
 - * `Zamba2ForCausalLM`
   * Zamba2
   * `Zyphra/Zamba2-7B-instruct`, `Zyphra/Zamba2-2.7B-instruct`, `Zyphra/Zamba2-1.2B-instruct`, etc.

tests/kernels/test_lightning_attn.py

@@ -0,0 +1,286 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.lightning_attn import (
+    linear_decode_forward_triton)
+from vllm.platforms import current_platform
+
+NUM_HEADS = [4, 8]
+HEAD_SIZES = [64]
+BATCH_SIZES = [1, 2]
+SEQ_LENGTHS = [16]
+DTYPES = [torch.float32]
+
+
+def reference_lightning_attention(q, k, v, ed, block_size, kv_history):
+    """Reference implementation of lightning attention core algorithm
+    
+    The difference from the main implementation is that this processes 
+    each step sequentially, instead of using parallelized triton kernels
+    """
+    B, H, S, D = q.shape
+    E = v.shape[-1]
+    dtype = q.dtype
+    output = torch.zeros((B, H, S, E), dtype=dtype, device=q.device)
+
+    # Use clone() to ensure an independent copy
+    if kv_history is None:
+        kv_cache = torch.zeros((B, H, D, E), dtype=dtype, device=q.device)
+    else:
+        kv_cache = kv_history.clone()
+
+    # More efficient implementation
+    # Convert decay factors to matrix form
+    if ed.dim() == 1:
+        decay = torch.exp(-ed).view(1, -1, 1, 1)
+    else:
+        decay = torch.exp(-ed)
+
+    for b in range(B):
+        for step in range(S):
+            # Process all heads at once for this position
+            q_bs = q[b, :, step]  # [H, D]
+            k_bs = k[b, :, step]  # [H, D]
+            v_bs = v[b, :, step]  # [H, E]
+
+            # Calculate KV outer products for all heads
+            for h in range(H):
+                # Calculate KV outer product
+                kv_outer = torch.outer(k_bs[h], v_bs[h])
+
+                # Update KV cache with decay
+                # Note: Using the same order as in the Triton kernel
+                kv_cache[b, h] = decay[0, h, 0, 0] * kv_cache[b, h] + kv_outer
+
+                # Calculate attention output
+                output[b, h, step] = torch.matmul(q_bs[h], kv_cache[b, h])
+
+    # Match the shape returned by the actual implementation
+    # The actual implementation returns a tensor of shape [B, H, 2, D, E]
+    # where dimension 2 contains both KV and KV history
+    kv_reshaped = kv_cache.unsqueeze(2)  # [B, H, 1, D, E]
+    final_kv_cache = torch.cat([kv_reshaped, kv_reshaped],
+                               dim=2)  # [B, H, 2, D, E]
+
+    return output, final_kv_cache
+
+
+def reference_linear_decode(q, k, v, kv_caches, slope_rate, slot_idx):
+    """Reference implementation: linear attention decode function"""
+    B, H, _, D = q.shape
+    output = torch.zeros(B, H * D, dtype=q.dtype, device=q.device)
+
+    # Calculate decay factors once (more efficient)
+    decay = torch.exp(-slope_rate).view(-1, 1, 1)  # [H, 1, 1]
+
+    # Process each batch
+    for b in range(B):
+        slot_id = slot_idx[b].item()
+
+        # Skip padding positions
+        if slot_id == -1:
+            continue
+
+        # Process all heads at once for this batch
+        q_b = q[b, :, 0]  # [H, D]
+        k_b = k[b, :, 0]  # [H, D]
+        v_b = v[b, :, 0]  # [H, D]
+
+        # Process each attention head
+        for h in range(H):
+            # Get current query, key and value
+            q_bh = q_b[h]
+            k_bh = k_b[h]
+            v_bh = v_b[h]
+
+            # Get cache
+            kv_cache_old = kv_caches[b, h]
+
+            # Calculate new key-value outer product
+            kv_outer = torch.outer(k_bh, v_bh)
+
+            # Apply decay and update cache
+            kv_new = kv_outer + decay[h, 0, 0] * kv_cache_old
+
+            # Calculate output
+            out_h = torch.matmul(q_bh, kv_new)
+
+            # Update output and cache
+            output[b, h * D:(h + 1) * D] = out_h
+            kv_caches[b, h] = kv_new
+
+    return output
+
+
+@pytest.mark.parametrize("batch_size", BATCH_SIZES)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@torch.inference_mode()
+def test_linear_decode_forward_triton(
+    batch_size: int,
+    num_heads: int,
+    head_size: int,
+    dtype: torch.dtype,
+):
+    torch.set_default_device("cuda")
+    torch.manual_seed(42)
+    torch.cuda.manual_seed_all(42)
+    current_platform.seed_everything(42)
+    base = 0.01
+    q = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
+    k = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
+    v = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
+
+    kv_caches = base * torch.randn(batch_size,
+                                   num_heads,
+                                   head_size,
+                                   head_size,
+                                   dtype=dtype,
+                                   device="cuda")
+
+    kv_caches_copy = kv_caches.clone()
+
+    slope_rate = torch.zeros(num_heads, device="cuda")
+    for h in range(num_heads):
+        slope_rate[h] = 0.1 * (h + 1)
+
+    slot_idx = torch.arange(batch_size, device="cuda")
+
+    triton_output = linear_decode_forward_triton(q, k, v, kv_caches,
+                                                 slope_rate, slot_idx)
+
+    reference_output = reference_linear_decode(q, k, v, kv_caches_copy,
+                                               slope_rate, slot_idx)
+    torch.testing.assert_close(triton_output,
+                               reference_output,
+                               rtol=1e-1,
+                               atol=1e-1)
+    torch.testing.assert_close(kv_caches, kv_caches_copy, rtol=1e-1, atol=1e-1)
+
+    assert triton_output.shape == (batch_size, num_heads * head_size)
+
+
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@torch.inference_mode()
+def test_linear_decode_forward_triton_with_padding(
+    num_heads: int,
+    head_size: int,
+    dtype: torch.dtype,
+):
+    torch.set_default_device("cuda")
+    torch.manual_seed(42)
+    torch.cuda.manual_seed_all(42)
+    current_platform.seed_everything(42)
+
+    batch_size = 4
+    base = 0.01
+    q = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
+    k = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
+    v = base * torch.randn(batch_size, num_heads, 1, head_size, dtype=dtype)
+
+    kv_caches = base * torch.randn(batch_size,
+                                   num_heads,
+                                   head_size,
+                                   head_size,
+                                   dtype=dtype,
+                                   device="cuda")
+
+    kv_caches_copy = kv_caches.clone()
+
+    slope_rate = torch.zeros(num_heads, device="cuda")
+    for h in range(num_heads):
+        slope_rate[h] = 0.1 * (h + 1)
+
+    slot_idx = torch.tensor([0, 1, -1, 2], device="cuda")
+
+    triton_output = linear_decode_forward_triton(q, k, v, kv_caches,
+                                                 slope_rate, slot_idx)
+
+    reference_output = reference_linear_decode(q, k, v, kv_caches_copy,
+                                               slope_rate, slot_idx)
+
+    padding_mask = (slot_idx
+                    != -1).unsqueeze(1).expand(-1, num_heads * head_size)
+
+    triton_masked = triton_output[padding_mask]
+    reference_masked = reference_output[padding_mask]
+
+    atol, rtol = 1.5e-1, 1.5e-1
+
+    valid_indices = slot_idx != -1
+
+    for i in range(batch_size):
+        if valid_indices[i] > 0:
+            torch.testing.assert_close(kv_caches[i],
+                                       kv_caches_copy[i],
+                                       rtol=rtol,
+                                       atol=atol)
+
+    torch.testing.assert_close(triton_masked,
+                               reference_masked,
+                               rtol=rtol,
+                               atol=atol)
+
+    assert triton_output.shape == (batch_size, num_heads * head_size)
+
+
+@pytest.mark.parametrize("batch_size", BATCH_SIZES)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("seq_len", SEQ_LENGTHS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@torch.inference_mode()
+def test_lightning_attention_reference(
+    batch_size: int,
+    num_heads: int,
+    head_size: int,
+    seq_len: int,
+    dtype: torch.dtype,
+):
+    torch.set_default_device("cuda")
+    torch.manual_seed(42)
+    torch.cuda.manual_seed_all(42)
+    current_platform.seed_everything(42)
+
+    base = 0.01
+    q = base * torch.randn(
+        batch_size, num_heads, seq_len, head_size, dtype=dtype)
+    k = base * torch.randn(
+        batch_size, num_heads, seq_len, head_size, dtype=dtype)
+    v = base * torch.randn(
+        batch_size, num_heads, seq_len, head_size, dtype=dtype)
+
+    ed = torch.zeros(num_heads, device="cuda")
+    for h in range(num_heads):
+        ed[h] = 0.1 * (h + 1)
+
+    kv_history = base * torch.randn(batch_size,
+                                    num_heads,
+                                    head_size,
+                                    head_size,
+                                    dtype=dtype,
+                                    device="cuda")
+
+    kv_history_clone = kv_history.clone()
+
+    ref_output, ref_kv_cache = reference_lightning_attention(
+        q, k, v, ed, 256, kv_history)
+
+    from vllm.model_executor.layers.lightning_attn import lightning_attention
+    actual_output, actual_kv_cache = lightning_attention(
+        q, k, v, ed, 256, kv_history_clone)
+
+    atol, rtol = 1.5e-1, 1.5e-1
+    torch.testing.assert_close(ref_output, actual_output, rtol=rtol, atol=atol)
+    torch.testing.assert_close(ref_kv_cache,
+                               actual_kv_cache,
+                               rtol=rtol,
+                               atol=atol)
+
+    assert ref_output.shape == (batch_size, num_heads, seq_len, head_size)
+    assert ref_kv_cache.shape == actual_kv_cache.shape

tests/models/registry.py

@@ -176,6 +176,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                          trust_remote_code=True),
     "MiniCPM3ForCausalLM": _HfExamplesInfo("openbmb/MiniCPM3-4B",
                                          trust_remote_code=True),
+    "MiniMaxText01ForCausalLM": _HfExamplesInfo("MiniMaxAI/MiniMax-Text-01",
+                                                trust_remote_code=True),
     "MistralForCausalLM": _HfExamplesInfo("mistralai/Mistral-7B-Instruct-v0.1"),
     "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1"),  # noqa: E501
     "QuantMixtralForCausalLM": _HfExamplesInfo("mistral-community/Mixtral-8x22B-v0.1-AWQ"),  # noqa: E501

vllm/config.py

@@ -971,26 +971,34 @@ class ModelConfig:
             return sum(not bc.attention.no_op
                        for bc in block_configs[start:end])
         else:
-            # Hybrid model
+            # Hybrid model Jamba
             layers_block_type_value = getattr(self.hf_config,
                                               "layers_block_type", None)
-            if layers_block_type_value is None:
-                raise ValueError("The model is an hybrid without a "
-                                 "layers_block_type in the hf_config, "
-                                 "cannot determine the num of "
-                                 f"{block_type.value} layers")
+            if layers_block_type_value is not None:
+                if hasattr(self.hf_text_config,
+                           "model_type") and (self.hf_text_config.model_type
+                                              == "zamba2"):
+                    if attn_block_type:
+                        return sum(t == "hybrid"
+                                   for t in layers_block_type_value[start:end])
+                    else:
+                        return self.get_num_layers(parallel_config)
+                return sum(t == block_type.value
+                           for t in layers_block_type_value[start:end])
 
-            if hasattr(self.hf_text_config,
-                       "model_type") and (self.hf_text_config.model_type
-                                          == "zamba2"):
-                if attn_block_type:
-                    return sum(t == "hybrid"
-                               for t in layers_block_type_value[start:end])
-                else:
-                    return self.get_num_layers(parallel_config)
+            # Hybrid model Minimax
+            attn_type_list = getattr(self.hf_config, "attn_type_list", None)
+            if attn_type_list:
+                return sum(t == 1 for t in attn_type_list[start:end])
 
-            return sum(t == block_type.value
-                       for t in layers_block_type_value[start:end])
+            if layers_block_type_value is None and attn_type_list is None:
+                raise ValueError(
+                    "The model is an hybrid without a"
+                    "layers_block_type or an attn_type_list in the hf_config,"
+                    "cannot determine the num of "
+                    f"{block_type.value} layers")
+
+            return sum(t == 1 for t in attn_type_list[start:end])
 
     def get_multimodal_config(self) -> "MultiModalConfig":
         """

vllm/engine/async_llm_engine.py

@@ -303,8 +303,11 @@ class _AsyncLLMEngine(LLMEngine):
             ctx.seq_group_metadata_list = seq_group_metadata_list
             ctx.scheduler_outputs = scheduler_outputs
 
-            finished_requests_ids = self.scheduler[
-                virtual_engine].get_and_reset_finished_requests_ids()
+            if not scheduler_outputs.is_empty():
+                # this will cause mamba_cache/minimax_cache failed
+                # to release finished_requests_ids of the last steps
+                finished_requests_ids = self.scheduler[
+                    virtual_engine].get_and_reset_finished_requests_ids()
 
             # Maybe switch from async mode to sync mode
             if not allow_async_output_proc and len(ctx.output_queue) > 0:

vllm/model_executor/layers/lightning_attn.py

@@ -0,0 +1,651 @@
+# SPDX-License-Identifier: Apache-2.0
+import torch
+import triton
+import triton.language as tl
+from einops import rearrange
+
+
+@triton.jit
+def _fwd_diag_kernel(Q, K, V, Out, S, b: tl.constexpr, h: tl.constexpr, n,
+                     d: tl.constexpr, e: tl.constexpr, BLOCK: tl.constexpr,
+                     NUM_BLOCK, CBLOCK: tl.constexpr):
+    # This kernel computes the diagonal blocks of the attention matrix
+    # Each diagonal block represents attention
+    # where queries attend to keys in the same block
+    off = tl.program_id(0)
+    off_bh = off // NUM_BLOCK  # batch-head index
+    off_block = off % NUM_BLOCK  # block index within the sequence
+    off_cblock = tl.program_id(1)  # sub-block index within a block
+
+    off_h = off_bh % h  # head index
+
+    # Calculate base offsets for the current batch and head
+    qk_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    o_offset = off_bh * n * e
+
+    # Calculate offsets for the current block
+    block_offset = off_block * BLOCK
+    qk_block_offset = block_offset * d
+    v_block_offset = block_offset * e
+    o_block_offset = block_offset * e
+
+    # Calculate offsets for the current sub-block
+    cblock_offset = off_cblock * CBLOCK
+    q_cblock_offset = cblock_offset * d
+    o_cblock_offset = cblock_offset * e
+
+    # Calculate pointers to the query, key, value, and output tensors
+    Q_block_ptr = (Q + qk_offset + qk_block_offset + q_cblock_offset +
+                   tl.arange(0, CBLOCK)[:, None] * d +
+                   tl.arange(0, d)[None, :])
+    K_trans_block_ptr = (K + qk_offset + qk_block_offset +
+                         tl.arange(0, CBLOCK)[None, :] * d +
+                         tl.arange(0, d)[:, None])
+    V_block_ptr = (V + v_offset + v_block_offset +
+                   tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, e)[None, :])
+    O_block_ptr = (Out + o_offset + o_block_offset + o_cblock_offset +
+                   tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, e)[None, :])
+
+    # Load the decay rate for the current head
+    S_block_ptr = S + off_h
+    s = tl.load(S_block_ptr)
+
+    i = off_cblock
+    q_index = tl.arange(0, CBLOCK) + i * CBLOCK
+
+    # Load query values
+    q = tl.load(Q_block_ptr,
+                mask=block_offset + q_index[:, None] < n,
+                other=0.0).to(tl.float32)
+
+    # Initialize output accumulator
+    qkv = tl.zeros([CBLOCK, e], dtype=tl.float32)
+
+    # Process all sub-blocks up to and
+    # including the current one (causal attention)
+    for j in range(i + 1):
+        kv_index = tl.arange(0, CBLOCK) + j * CBLOCK
+        diff = q_index[:, None] - kv_index[None, :]
+        s_index = s * diff
+        # Apply causal mask: only attend to positions before the current one
+        s_index = tl.where(diff >= 0, -s_index, float("-inf"))
+        decay = tl.exp(s_index)
+
+        # Load key and value
+        k_trans = tl.load(
+            K_trans_block_ptr,
+            mask=block_offset + kv_index[None, :] < n,
+            other=0.0,
+        ).to(tl.float32)
+        v = tl.load(
+            V_block_ptr,
+            mask=block_offset + kv_index[:, None] < n,
+            other=0.0,
+        ).to(tl.float32)
+
+        # Compute attention scores and apply decay
+        qk = tl.dot(q, k_trans) * decay
+
+        # Compute weighted values and accumulate
+        qkv += tl.dot(qk, v)
+
+        # Move to the next sub-block
+        K_trans_block_ptr += CBLOCK * d
+        V_block_ptr += CBLOCK * e
+
+    # Store the result
+    tl.store(
+        O_block_ptr,
+        qkv.to(O_block_ptr.dtype.element_ty),
+        mask=block_offset + q_index[:, None] < n,
+    )
+
+
+@triton.jit
+def _fwd_kv_parallel(
+    K,
+    V,
+    K_decay,
+    KV,
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n,
+    d: tl.constexpr,
+    e: tl.constexpr,
+    BLOCK: tl.constexpr,
+    NUM_BLOCK,
+    D_FBLOCK: tl.constexpr,
+    E_FBLOCK: tl.constexpr,
+    NUM_FBLOCK: tl.constexpr,
+    CBLOCK: tl.constexpr,
+    NUM_CBLOCK: tl.constexpr,
+):
+    # This kernel computes the key-value outer
+    # products for each block in parallel
+    off_bh = tl.program_id(0)  # batch-head index
+    off_block = tl.program_id(1)  # block index
+
+    off_h = off_bh % h  # head index
+
+    block_offset = off_block * BLOCK
+
+    # Calculate offsets for the current block
+    k_block_offset = block_offset * d
+    v_block_offset = block_offset * e
+    kv_block_offset = off_block * d * e
+
+    # Calculate base offsets for the current batch and head
+    k_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    kv_offset = off_bh * NUM_BLOCK * d * e
+
+    # Calculate pointers to the key, value, and key-value tensors
+    K_trans_block_ptr = (K + k_offset + k_block_offset +
+                         tl.arange(0, CBLOCK)[None, :] * d +
+                         tl.arange(0, D_FBLOCK)[:, None])
+    V_block_ptr = (V + v_offset + v_block_offset +
+                   tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, E_FBLOCK)[None, :])
+    KV_block_ptr = (KV + kv_offset + kv_block_offset +
+                    tl.arange(0, D_FBLOCK)[:, None] * e +
+                    tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the decay factors for the current head and block
+    k_decay_ptr = (K_decay + off_h * BLOCK + tl.arange(0, CBLOCK)[None, :])
+
+    kv_index = tl.arange(0, CBLOCK)
+
+    # Initialize the key-value outer product accumulator
+    kv = tl.zeros([D_FBLOCK, E_FBLOCK], dtype=tl.float32)
+
+    # Handle the last block which might be smaller than BLOCK
+    if off_block == NUM_BLOCK - 1:
+        split_n = n - (NUM_BLOCK - 1) * BLOCK
+    else:
+        split_n = BLOCK
+    left_shift = tl.cdiv(split_n, CBLOCK) * CBLOCK - split_n
+    num_blocks = min(tl.cdiv(split_n, CBLOCK), NUM_CBLOCK)
+    k_decay_ptr += (NUM_CBLOCK - num_blocks) * CBLOCK
+
+    # Process all sub-blocks in the current block
+    for j in range(num_blocks):
+        left_bound = (1 - j) * left_shift
+        # Load key and value, handling boundary conditions
+        k_trans = tl.load(K_trans_block_ptr - left_shift * d,
+                          mask=kv_index[None, :] >= left_bound,
+                          other=0.0)
+        v = tl.load(V_block_ptr - left_shift * e,
+                    mask=kv_index[:, None] >= left_bound,
+                    other=0.0)
+
+        # Load decay factor and compute weighted key-value outer product
+        k_decay = tl.load(k_decay_ptr)
+        kv += tl.dot(k_trans * k_decay, v)
+
+        # Move to the next sub-block
+        K_trans_block_ptr += CBLOCK * d
+        V_block_ptr += CBLOCK * e
+        k_decay_ptr += CBLOCK
+
+    # Store the result
+    tl.store(KV_block_ptr, kv.to(KV_block_ptr.dtype.element_ty))
+
+
+@triton.jit
+def _fwd_kv_reduce(S, KV, KV_HISTORY, b: tl.constexpr, h: tl.constexpr, n,
+                   d: tl.constexpr, e: tl.constexpr, BLOCK: tl.constexpr,
+                   NUM_BLOCK, D_FBLOCK: tl.constexpr, E_FBLOCK: tl.constexpr):
+    # This kernel reduces the key-value outer products
+    # across blocks and updates the KV history
+    off_bh = tl.program_id(0)  # batch-head index
+    off_h = off_bh % h  # head index
+
+    kv_offset = off_bh * NUM_BLOCK * d * e
+
+    # Calculate pointer to the key-value tensor
+    KV_block_ptr = (KV + kv_offset + tl.arange(0, D_FBLOCK)[:, None] * e +
+                    tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the decay rate for the current head
+    s_ptrs = S + off_h
+    s = tl.load(s_ptrs)
+
+    # Calculate pointer to the key-value history tensor
+    kv_history_offset = off_bh * d * e
+    KV_HISTORY_block_ptr = (KV_HISTORY + kv_history_offset +
+                            tl.arange(0, D_FBLOCK)[:, None] * e +
+                            tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the previous key-value history
+    kv_pre = tl.load(KV_HISTORY_block_ptr).to(tl.float32)
+
+    # Process all blocks in reverse order to compute the prefix sum
+    for i in range(NUM_BLOCK):
+        block_size = min(n - i * BLOCK, BLOCK)
+        # Compute decay factor for the current block
+        block_decay = tl.exp(-s.to(tl.float32) * block_size)
+
+        # Load the current key-value outer product
+        kv_cur = tl.load(KV_block_ptr).to(tl.float32)
+        # Store the previous key-value history to the current block
+        tl.store(KV_block_ptr, kv_pre.to(KV_block_ptr.dtype.element_ty))
+
+        # Update the key-value history with the current block
+        kv_pre = block_decay * kv_pre + kv_cur
+        KV_block_ptr += d * e
+
+    # Store the updated key-value history
+    tl.store(KV_HISTORY_block_ptr, kv_pre)
+
+
+@triton.jit
+def _fwd_none_diag_kernel(
+    Q,
+    Out,
+    S,
+    KV,
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n,
+    d: tl.constexpr,
+    e: tl.constexpr,
+    BLOCK: tl.constexpr,
+    NUM_BLOCK,
+    E_FBLOCK: tl.constexpr,
+    CBLOCK: tl.constexpr,
+    NUM_CBLOCK: tl.constexpr,
+):
+    # This kernel computes the non-diagonal blocks of the attention matrix
+    # Each non-diagonal block represents attention
+    # where queries attend to keys in different blocks
+    off_bh = tl.program_id(0)  # batch-head index
+    off_h = off_bh % h  # head index
+
+    off_nc = tl.program_id(1)
+    off_n = off_nc // NUM_CBLOCK  # block index
+    off_c = off_nc % NUM_CBLOCK  # sub-block index
+    off_e = tl.program_id(2)  # output feature block index
+
+    n_offset = off_n * BLOCK
+    c_offset = off_c * CBLOCK
+    e_offset = off_e * E_FBLOCK
+    block_offset = n_offset + c_offset
+
+    # Calculate offsets for the current batch, head, and block
+    q_offset = off_bh * n * d + (n_offset + c_offset) * d
+    o_offset = off_bh * n * e + (n_offset + c_offset) * e + e_offset
+    kv_offset = off_bh * NUM_BLOCK * d * e + off_n * d * e + e_offset
+
+    # Calculate pointers to the query, output, and key-value tensors
+    Q_block_ptr = (Q + q_offset + tl.arange(0, CBLOCK)[:, None] * d +
+                   tl.arange(0, d)[None, :])
+    O_block_ptr = (Out + o_offset + tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, E_FBLOCK)[None, :])
+    KV_block_ptr = (KV + kv_offset + tl.arange(0, d)[:, None] * e +
+                    tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the decay rate for the current head
+    S_block_ptr = S + off_h
+    s = tl.load(S_block_ptr)
+
+    c_array = tl.arange(0, CBLOCK)
+
+    # Load the key-value outer product for the current block
+    kv = tl.load(KV_block_ptr).to(tl.float32)
+    q_index = block_offset + tl.arange(0, CBLOCK)
+
+    # Load query values
+    q = tl.load(Q_block_ptr, mask=q_index[:, None] < n,
+                other=0.).to(tl.float32)
+
+    # Compute decay factors for the current sub-block
+    q_decay = tl.exp(-s.to(tl.float32) * (off_c * CBLOCK + c_array[:, None]))
+
+    # Compute non-diagonal attention output
+    qkv_none_diag = tl.dot(q, kv) * q_decay
+
+    # Load diagonal attention output (computed by _fwd_diag_kernel)
+    qkv_diag = tl.load(O_block_ptr, mask=q_index[:, None] < n,
+                       other=0.).to(tl.float32)
+
+    # Combine diagonal and non-diagonal attention outputs
+    qkv = qkv_diag + qkv_none_diag
+
+    # Store the result
+    tl.store(O_block_ptr,
+             qkv.to(O_block_ptr.dtype.element_ty),
+             mask=q_index[:, None] < n)
+
+
+class _attention(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, q, k, v, s, kv_history):
+        # Forward pass of the lightning attention algorithm
+        q = q.contiguous()
+        k = k.contiguous()
+        v = v.contiguous()
+        s = s.contiguous()
+
+        # Check CUDA compute capability
+        capability = torch.cuda.get_device_capability()
+        if capability[0] < 8:
+            raise RuntimeError("Flash attention currently only supported",
+                               "for compute capability >= 80")
+
+        # Get input dimensions
+        b, h, n, d = q.shape
+        e = v.shape[-1]
+
+        # Initialize output tensor
+        o = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device)
+
+        # Set block sizes
+        BLOCK = 256
+        NUM_BLOCK = triton.cdiv(n, BLOCK)
+
+        CBLOCK = 32
+        NUM_CBLOCK = BLOCK // CBLOCK
+        assert BLOCK % CBLOCK == 0, "BLOCK must be a multiple of CBLOCK"
+
+        # Compute decay factors for keys
+        array = torch.arange(0, BLOCK, device=q.device) + 1
+        k_decay = torch.exp(-s * (BLOCK - array.reshape(1, -1)))
+
+        # Step 1: Compute diagonal blocks of attention
+        grid = (b * h * NUM_BLOCK, NUM_CBLOCK)
+        _fwd_diag_kernel[grid](q,
+                               k,
+                               v,
+                               o,
+                               s,
+                               b,
+                               h,
+                               n,
+                               d,
+                               e,
+                               BLOCK=BLOCK,
+                               NUM_BLOCK=NUM_BLOCK,
+                               CBLOCK=CBLOCK)
+
+        # Set feature block sizes
+        NUM_FBLOCK = 1
+        D_FBLOCK = d // NUM_FBLOCK
+        assert d % NUM_FBLOCK == 0
+        E_FBLOCK = e // NUM_FBLOCK
+        assert e % NUM_FBLOCK == 0
+
+        CBLOCK = 64
+        NUM_CBLOCK = BLOCK // CBLOCK
+        assert BLOCK % CBLOCK == 0, "BLOCK must be a multiple of CBLOCK"
+
+        # Step 2: Compute key-value outer products for each block in parallel
+        kv = torch.empty((b, h, NUM_BLOCK, d, e),
+                         dtype=torch.float32,
+                         device=q.device)
+        grid = (b * h, NUM_BLOCK)
+        _fwd_kv_parallel[grid](
+            k,
+            v,
+            k_decay,
+            kv,
+            b,
+            h,
+            n,
+            d,
+            e,
+            BLOCK=BLOCK,
+            NUM_BLOCK=NUM_BLOCK,
+            D_FBLOCK=D_FBLOCK,
+            E_FBLOCK=E_FBLOCK,
+            NUM_FBLOCK=NUM_FBLOCK,
+            CBLOCK=CBLOCK,
+            NUM_CBLOCK=NUM_CBLOCK,
+        )
+
+        # Step 3: Reduce key-value outer products
+        # across blocks and update KV history
+        grid = (b * h, NUM_FBLOCK)
+        _fwd_kv_reduce[grid](s,
+                             kv,
+                             kv_history,
+                             b,
+                             h,
+                             n,
+                             d,
+                             e,
+                             BLOCK=BLOCK,
+                             NUM_BLOCK=NUM_BLOCK,
+                             D_FBLOCK=D_FBLOCK,
+                             E_FBLOCK=E_FBLOCK)
+
+        # Step 4: Compute non-diagonal blocks of attention
+        grid = (b * h, NUM_BLOCK * NUM_CBLOCK)
+        _fwd_none_diag_kernel[grid](
+            q,
+            o,
+            s,
+            kv,
+            b,
+            h,
+            n,
+            d,
+            e,
+            BLOCK=BLOCK,
+            NUM_BLOCK=NUM_BLOCK,
+            E_FBLOCK=E_FBLOCK,
+            CBLOCK=CBLOCK,
+            NUM_CBLOCK=NUM_CBLOCK,
+        )
+
+        # Save tensors for backward pass
+        ctx.save_for_backward(q, k, v, s, kv)
+        ctx.BLOCK = BLOCK
+
+        return o, torch.cat([kv, kv_history.unsqueeze(2)], dim=2)
+
+
+# Apply the lightning attention function
+lightning_attention_ = _attention.apply
+
+
+def lightning_attention(q, k, v, ed, block_size=256, kv_history=None):
+    """
+    Apply lightning attention algorithm 
+    to compute attention efficiently.
+    
+    Args:
+        q: Query tensor of shape [batch, heads, seq_len, dim]
+        k: Key tensor of shape [batch, heads, seq_len, dim]
+        v: Value tensor of shape [batch, heads, seq_len, dim_v]
+        ed: Decay rate tensor of shape [heads]
+        block_size: Size of blocks for block-sparse attention
+        kv_history: Optional key-value history from previous computations
+        
+    Returns:
+        output: Attention output
+        kv: Updated key-value history
+    """
+    d = q.shape[-1]
+    e = v.shape[-1]
+
+    if ed.dim() == 1:
+        ed = ed.view(1, -1, 1, 1)
+
+    # Split the computation into chunks for better parallelism
+    m = 128 if d >= 128 else 64
+    assert d % m == 0, f"Dimension d ({d}) must be divisible by m ({m})"
+    arr = [m * i for i in range(d // m + 1)]
+    if arr[-1] != d:
+        arr.append(d)
+    n = len(arr)
+    output = 0
+
+    # Initialize or clone key-value history
+    if kv_history is None:
+        kv_history = torch.zeros((q.shape[0], q.shape[1], d, e),
+                                 dtype=torch.float32,
+                                 device=q.device)
+    else:
+        kv_history = kv_history.clone().contiguous()
+
+    # Process each chunk and accumulate results
+    for i in range(n - 1):
+        s = arr[i]
+        e = arr[i + 1]
+        q1 = q[..., s:e]
+        k1 = k[..., s:e]
+        o, kv = lightning_attention_(q1, k1, v, ed, kv_history)
+        output = output + o
+    return output, kv
+
+
+@triton.jit
+def _linear_attn_decode_kernel(
+    q_ptr,
+    k_ptr,
+    v_ptr,
+    kv_cache_ptr,
+    slope_rate,
+    slot_idx,
+    output_ptr,
+    D: tl.constexpr,
+    qkv_b_stride,
+    qkv_h_stride,
+    cache_b_stride,
+    cache_h_stride,
+    cache_d0_stride,
+    cache_d1_stride,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Kernel for linear attention decoding with KV cache.
+    
+    This kernel computes attention for a single token using the KV cache.
+    """
+    pid_b = tl.program_id(0)  # batch index
+    pid_h = tl.program_id(1)  # head index
+    pid_d = tl.program_id(2)  # dimension block index
+
+    # Load slot index for the current batch
+    slot_id = tl.load(slot_idx + pid_b)
+
+    # Skip if slot_id is -1 (padding)
+    if slot_id == -1:
+        return
+
+    batch_id = pid_b
+    head_id = pid_h
+
+    # Load decay rate for the current head
+    ratio = tl.load(slope_rate + pid_h)
+
+    # Calculate offsets for dimensions
+    qk_d_offsets = tl.arange(0, D)
+    v_d_offsets = tl.arange(0, BLOCK_SIZE) + pid_d * BLOCK_SIZE
+    cache_d_offsets = qk_d_offsets[:, None] * cache_d0_stride + v_d_offsets[
+        None, :] * cache_d1_stride
+
+    # Calculate offsets for the current batch and head
+    q_offset = batch_id * qkv_b_stride + head_id * qkv_h_stride
+    k_offset = batch_id * qkv_b_stride + head_id * qkv_h_stride
+    v_offset = batch_id * qkv_b_stride + head_id * qkv_h_stride
+
+    cache_offset = slot_id * cache_b_stride + head_id * cache_h_stride
+
+    # Create masks for loading tensors
+    qk_mask = qk_d_offsets < D
+    v_mask = v_d_offsets < D
+
+    # Load query, key, and value tensors
+    q = tl.load(q_ptr + q_offset + qk_d_offsets, mask=qk_mask, other=0.0)
+    k = tl.load(k_ptr + k_offset + qk_d_offsets, mask=qk_mask, other=0.0)
+    v = tl.load(v_ptr + v_offset + v_d_offsets, mask=v_mask, other=0.0)
+
+    # Compute key-value outer product
+    kv_outer = k[:, None] * v[None, :]
+    kv_mask = qk_mask[:, None] & v_mask[None, :]
+
+    # Apply decay to previous KV cache
+    ratio = tl.exp(-ratio)
+    kv_ptr = kv_cache_ptr + cache_offset + cache_d_offsets
+    kv_cache_old = tl.load(kv_ptr, mask=kv_mask, other=0.0)
+    kv_outer = kv_outer + ratio * kv_cache_old
+
+    # Compute attention output
+    output = q[:, None].to(tl.float32) * kv_outer
+    output = tl.sum(output, axis=0)
+
+    # Update KV cache and store output
+    tl.store(kv_ptr, kv_outer, mask=kv_mask)
+    tl.store(output_ptr + q_offset + v_d_offsets, output, mask=v_mask)
+
+
+def linear_decode_forward_triton(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    kv_caches: torch.Tensor,
+    slope_rate: torch.Tensor,
+    slot_idx: torch.Tensor,
+    BLOCK_SIZE: int = 32,
+) -> torch.Tensor:
+    """
+    Perform linear attention decoding using Triton kernels.
+    
+    Args:
+        q: Query tensor of shape [B, H, 1, D]
+        k: Key tensor of shape [B, H, 1, D]
+        v: Value tensor of shape [B, H, 1, D]
+        kv_caches: Key-value cache tensor
+        slope_rate: Decay rate tensor
+        slot_idx: Slot indices for batches
+        BLOCK_SIZE: Size of blocks for processing
+        
+    Returns:
+        output: Attention output tensor
+    """
+    B, H, _, D = q.shape
+    assert k.shape == (B, H, 1, D)
+    assert v.shape == (B, H, 1, D)
+
+    # Initialize output tensor
+    output = torch.empty_like(q)
+
+    # Set grid dimensions for the kernel
+    grid = (B, H, D // BLOCK_SIZE)
+
+    # Calculate strides for tensors
+    qkv_b_stride = q.stride(0)
+    qkv_h_stride = q.stride(1)
+
+    cache_b_stride = kv_caches.stride(0)
+    cache_h_stride = kv_caches.stride(1)
+    cache_d0_stride = kv_caches.stride(2)
+    cache_d1_stride = kv_caches.stride(3)
+
+    # Launch the kernel
+    _linear_attn_decode_kernel[grid](
+        q,
+        k,
+        v,
+        kv_caches,
+        slope_rate,
+        slot_idx,
+        output,
+        D,
+        qkv_b_stride,
+        qkv_h_stride,
+        cache_b_stride,
+        cache_h_stride,
+        cache_d0_stride,
+        cache_d1_stride,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+
+    # Reshape output and return
+    output = rearrange(output, "b h n d -> b n (h d)")
+    return output.squeeze(1).contiguous()

vllm/model_executor/models/constant_size_cache.py

@@ -0,0 +1,136 @@
+# SPDX-License-Identifier: Apache-2.0
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Tuple
+
+import torch
+
+from vllm.attention.backends.utils import PAD_SLOT_ID
+
+
+class ConstantSizeCache(ABC):
+    """
+    Abstract base class for managing constant size caches 
+    like Mamba and Minimax.
+    """
+
+    def __init__(self, max_batch_size: int):
+        # Maps between the request id and a dict that maps between the seq_id
+        # and its index inside the cache
+        self.cache_indices_mapping: Dict[str, Dict[int, int]] = {}
+        self.free_cache_indices = list(range(max_batch_size))
+
+    @property
+    @abstractmethod
+    def cache(self) -> Any:
+        """Return the underlying cache tensor(s)"""
+        pass
+
+    @abstractmethod
+    def _copy_cache(self, from_index: int, to_index: int):
+        """Copy cache data from one index to another"""
+        pass
+
+    def current_run_tensors(self, **kwargs) -> Tuple:
+        """
+        Return the tensors for the current run's conv and ssm state.
+        """
+        if "seqlen_agnostic_capture_inputs" not in kwargs:
+            # We get here only on Prefill/Eager mode runs
+            request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
+            finished_requests_ids = kwargs["finished_requests_ids"]
+
+            self._release_finished_requests(finished_requests_ids)
+            state_indices = self._prepare_current_run_cache(
+                request_ids_to_seq_ids, finished_requests_ids)
+
+            state_indices_tensor = torch.as_tensor(state_indices,
+                                                   dtype=torch.int32,
+                                                   device="cuda")
+            cache_tensors = self.cache
+        else:
+            # CUDA graph capturing runs
+            cache_tensors, state_indices_tensor = kwargs[
+                "seqlen_agnostic_capture_inputs"]
+
+        return (cache_tensors, state_indices_tensor)
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        """
+        Copy the relevant state_indices into the CUDA graph input buffer 
+        """
+        assert all(
+            key in kwargs
+            for key in ["request_ids_to_seq_ids", "finished_requests_ids"])
+        finished_requests_ids = kwargs["finished_requests_ids"]
+        request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
+        assert "seqlen_agnostic_capture_inputs" in input_buffers
+        _, input_state_indices_buffer = input_buffers[
+            "seqlen_agnostic_capture_inputs"]
+
+        self._release_finished_requests(finished_requests_ids)
+        state_indices = self._prepare_current_run_cache(
+            request_ids_to_seq_ids, finished_requests_ids)
+        cuda_graph_pad_len = input_state_indices_buffer.shape[0] - len(
+            state_indices)
+        state_indices.extend([PAD_SLOT_ID] * cuda_graph_pad_len)
+
+        input_state_indices_buffer.copy_(
+            torch.as_tensor(state_indices, dtype=torch.int32, device="cuda"))
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        """
+        Provide the CUDA graph capture runs with a buffer in adjusted size.
+        The buffer is used to maintain the Cache during the CUDA graph replay
+        runs.
+        """
+        state_indices_tensor = torch.as_tensor([PAD_SLOT_ID] * batch_size,
+                                               dtype=torch.int32,
+                                               device="cuda")
+        return (self.cache, state_indices_tensor)
+
+    def _assign_seq_id_to_cache_index(self, cur_rid: str, seq_id: int,
+                                      finished_requests_ids) -> int:
+        """
+        Assign (req_id,seq_id) pair to a `destination_index` index, if
+        already occupied, move the occupying index to a free index.
+        """
+        if cur_rid in finished_requests_ids:
+            # set as pad, do not allocate destination index
+            return PAD_SLOT_ID
+        elif cur_rid not in self.cache_indices_mapping:
+            destination_index = self.free_cache_indices.pop()
+            self.cache_indices_mapping[cur_rid] = {seq_id: destination_index}
+            return destination_index
+        elif seq_id not in (seq_ids2indices :=
+                            self.cache_indices_mapping[cur_rid]):
+            # parallel sampling , where n > 1, assume prefill have
+            # already happened, so we copy the
+            # existing cache into the siblings seq_ids caches
+            index_exists = next(iter(seq_ids2indices.values()))
+            # case of decoding n>1, copy prefill cache to decoding indices
+            destination_index = self.free_cache_indices.pop()
+            self._copy_cache(from_index=index_exists,
+                             to_index=destination_index)
+            self.cache_indices_mapping[cur_rid][seq_id] = destination_index
+            return destination_index
+        else:
+            return self.cache_indices_mapping[cur_rid][seq_id]
+
+    def _prepare_current_run_cache(
+            self, request_ids_to_seq_ids: Dict[str, list[int]],
+            finished_requests_ids: List[str]) -> List[int]:
+        return [
+            self._assign_seq_id_to_cache_index(req_id, seq_id,
+                                               finished_requests_ids)
+            for req_id, seq_ids in request_ids_to_seq_ids.items()
+            for seq_id in seq_ids
+        ]
+
+    def _release_finished_requests(self,
+                                   finished_seq_groups_req_ids: List[str]):
+        for req_id in finished_seq_groups_req_ids:
+            if req_id in self.cache_indices_mapping:
+                for seq_id in self.cache_indices_mapping[req_id]:
+                    self.free_cache_indices.append(
+                        self.cache_indices_mapping[req_id][seq_id])
+                self.cache_indices_mapping.pop(req_id)

vllm/model_executor/models/mamba_cache.py

@@ -1,12 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from dataclasses import dataclass
-from typing import Dict, List, Tuple
+from typing import Tuple
 
 import torch
 
 from vllm.attention.backends.utils import PAD_SLOT_ID
 from vllm.config import VllmConfig
+from vllm.model_executor.models.constant_size_cache import ConstantSizeCache
 
 
 @dataclass
@@ -21,7 +22,7 @@ class MambaCacheParams:
                                 self.state_indices_tensor)
 
 
-class MambaCacheManager:
+class MambaCacheManager(ConstantSizeCache):
 
     def __init__(self, vllm_config: VllmConfig, dtype: torch.dtype,
                  num_mamba_layers: int, conv_state_shape: Tuple[int, int],
@@ -32,6 +33,9 @@ class MambaCacheManager:
         if not vllm_config.model_config.enforce_eager:
             max_batch_size = vllm_config.pad_for_cudagraph(max_batch_size)
 
+        # Initialize parent class
+        super().__init__(max_batch_size)
+
         conv_state = torch.empty(size=(num_mamba_layers, max_batch_size) +
                                  conv_state_shape,
                                  dtype=dtype,
@@ -41,126 +45,32 @@ class MambaCacheManager:
                                      dtype=dtype,
                                      device="cuda")
 
-        self.mamba_cache = (conv_state, temporal_state)
+        self._mamba_cache = (conv_state, temporal_state)
 
-        # Maps between the request id and a dict that maps between the seq_id
-        # and its index inside the self.mamba_cache
-        self.mamba_cache_indices_mapping: Dict[str, Dict[int, int]] = {}
-        self.free_cache_indices = list(range(max_batch_size))
+    @property
+    def cache(self):
+        return self._mamba_cache
+
+    def _copy_cache(self, from_index: int, to_index: int):
+        for cache_t in self.cache:
+            cache_t[:, to_index].copy_(cache_t[:, from_index],
+                                       non_blocking=True)
 
     def current_run_tensors(self, **kwargs) -> MambaCacheParams:
         """
         Return the tensors for the current run's conv and ssm state.
         """
-        if "seqlen_agnostic_capture_inputs" not in kwargs:
-            # We get here only on Prefill/Eager mode runs
-            request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
-            finished_requests_ids = kwargs["finished_requests_ids"]
-
-            self._release_finished_requests(finished_requests_ids)
-            state_indices = self._prepare_current_run_mamba_cache(
-                request_ids_to_seq_ids, finished_requests_ids)
-
-            state_indices_tensor = torch.as_tensor(state_indices,
-                                                   dtype=torch.int32,
-                                                   device="cuda")
-            mamba_cache_tensors = self.mamba_cache
-
-        else:
-            # CUDA graph capturing runs
-            (mamba_cache_tensors,
-             state_indices_tensor) = kwargs["seqlen_agnostic_capture_inputs"]
-
-        return MambaCacheParams(mamba_cache_tensors[0], mamba_cache_tensors[1],
+        cache_tensors, state_indices_tensor = super().current_run_tensors(
+            **kwargs)
+        return MambaCacheParams(cache_tensors[0], cache_tensors[1],
                                 state_indices_tensor)
 
-    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
-        """
-        Copy the relevant state_indices into the CUDA graph input buffer 
-        """
-        assert all(
-            key in kwargs
-            for key in ["request_ids_to_seq_ids", "finished_requests_ids"])
-        finished_requests_ids = kwargs["finished_requests_ids"]
-        request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
-        assert "seqlen_agnostic_capture_inputs" in input_buffers
-        _, input_state_indices_buffer = input_buffers[
-            "seqlen_agnostic_capture_inputs"]
-
-        self._release_finished_requests(finished_requests_ids)
-        state_indices = self._prepare_current_run_mamba_cache(
-            request_ids_to_seq_ids, finished_requests_ids)
-        cuda_graph_pad_len = input_state_indices_buffer.shape[0] - len(
-            state_indices)
-        state_indices.extend([PAD_SLOT_ID] * cuda_graph_pad_len)
-
-        input_state_indices_buffer.copy_(
-            torch.as_tensor(state_indices, dtype=torch.int32, device="cuda"))
-
     def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
         """
         Provide the CUDA graph capture runs with a buffer in adjusted size.
         The buffer is used to maintain the Mamba Cache during the CUDA graph
         replay runs.
         """
-        state_indices_tensor = torch.as_tensor([PAD_SLOT_ID] * batch_size,
-                                               dtype=torch.int32,
-                                               device="cuda")
-        return (self.mamba_cache, state_indices_tensor)
-
-    def _copy_mamba_cache(self, from_index: int, to_index: int):
-        assert len(self.mamba_cache) > 0
-        for cache_t in self.mamba_cache:
-            cache_t[:, to_index].copy_(cache_t[:, from_index],
-                                       non_blocking=True)
-
-    def _assign_seq_id_to_cache_index(self, cur_rid: str, seq_id: int,
-                                      finished_requests_ids) -> int:
-        """
-        Assign (req_id,seq_id) pair to a `destination_index` index, if
-        already occupied, move the occupying index to a free index.
-        """
-        if cur_rid in finished_requests_ids:
-            # set as pad, do not allocate destination index
-            return PAD_SLOT_ID
-        elif cur_rid not in self.mamba_cache_indices_mapping:
-            destination_index = self.free_cache_indices.pop()
-            self.mamba_cache_indices_mapping[cur_rid] = {
-                seq_id: destination_index
-            }
-            return destination_index
-        elif seq_id not in (seq_ids2indices :=
-                            self.mamba_cache_indices_mapping[cur_rid]):
-            # parallel sampling , where n > 1, assume prefill have
-            # already happened, so we copy the
-            # existing cache into the siblings seq_ids caches
-            index_exists = next(iter(seq_ids2indices.values()))
-            # case of decoding n>1, copy prefill cache to decoding indices
-            destination_index = self.free_cache_indices.pop()
-            self._copy_mamba_cache(from_index=index_exists,
-                                   to_index=destination_index)
-            self.mamba_cache_indices_mapping[cur_rid][
-                seq_id] = destination_index
-            return destination_index
-        else:
-            # already exists
-            return self.mamba_cache_indices_mapping[cur_rid][seq_id]
-
-    def _prepare_current_run_mamba_cache(
-            self, request_ids_to_seq_ids: Dict[str, list[int]],
-            finished_requests_ids: List[str]) -> List[int]:
-        return [
-            self._assign_seq_id_to_cache_index(req_id, seq_id,
-                                               finished_requests_ids)
-            for req_id, seq_ids in request_ids_to_seq_ids.items()
-            for seq_id in seq_ids
-        ]
-
-    def _release_finished_requests(self,
-                                   finished_seq_groups_req_ids: List[str]):
-        for req_id in finished_seq_groups_req_ids:
-            if req_id in self.mamba_cache_indices_mapping:
-                for seq_id in self.mamba_cache_indices_mapping[req_id]:
-                    self.free_cache_indices.append(
-                        self.mamba_cache_indices_mapping[req_id][seq_id])
-                self.mamba_cache_indices_mapping.pop(req_id)
+        return self._mamba_cache, torch.as_tensor([PAD_SLOT_ID] * batch_size,
+                                                  dtype=torch.int32,
+                                                  device="cuda")

vllm/model_executor/models/minimax_cache.py

@@ -0,0 +1,35 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+import torch
+
+from vllm.model_executor.models.constant_size_cache import ConstantSizeCache
+
+
+@dataclass
+class MinimaxCacheParams:
+    minimax_cache: torch.Tensor = torch.Tensor()
+    state_indices_tensor: torch.Tensor = torch.Tensor()
+
+    def at_layer_idx(self, layer_idx):
+        return MinimaxCacheParams(self.minimax_cache[layer_idx, ...],
+                                  self.state_indices_tensor)
+
+
+class MinimaxCacheManager(ConstantSizeCache):
+
+    def __init__(self, dtype, cache_shape):
+        super().__init__(cache_shape[1])  # max_batch_size is cache_shape[1]
+        self._minimax_cache = torch.empty(size=cache_shape,
+                                          dtype=dtype,
+                                          device="cuda")
+
+    @property
+    def cache(self):
+        return self._minimax_cache
+
+    def _copy_cache(self, from_index: int, to_index: int):
+        assert len(self.cache) > 0
+        for cache_t in self.cache:
+            cache_t[:, to_index].copy_(cache_t[:, from_index],
+                                       non_blocking=True)

vllm/model_executor/models/registry.py

@@ -35,6 +35,7 @@ _TEXT_GENERATION_MODELS = {
     "AquilaModel": ("llama", "LlamaForCausalLM"),
     "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2
     "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
+    "MiniMaxText01ForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
     # baichuan-7b, upper case 'C' in the class name
     "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),
     # baichuan-13b, lower case 'c' in the class name