[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

+# 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

@@ -971,15 +971,10 @@ 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"):
@@ -988,10 +983,23 @@ class ModelConfig:
                                    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])
 
+            # 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])
+
+            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":
         """
         Get the multimodal configuration of the model.

vllm/engine/async_llm_engine.py

@@ -303,6 +303,9 @@ class _AsyncLLMEngine(LLMEngine):
             ctx.seq_group_metadata_list = seq_group_metadata_list
             ctx.scheduler_outputs = scheduler_outputs
 
+            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()
 

vllm/model_executor/layers/lightning_attn.py

+# 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

+# 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

 # 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,
+        return self._mamba_cache, 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)

vllm/model_executor/models/minimax_cache.py

+# 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/minimax_text_01.py

+# SPDX-License-Identifier: Apache-2.0
+"""Inference-only MiniMaxText01 model."""
+import copy
+import math
+import re
+from typing import Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+import torch.distributed
+import torch.nn.functional as F
+from einops import rearrange
+from torch import nn
+from transformers.configuration_utils import PretrainedConfig
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
+from vllm.distributed.parallel_state import (
+    get_pp_group, get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size)
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.lightning_attn import (
+    lightning_attention, linear_decode_forward_triton)
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import HasInnerState, IsHybrid, SupportsV0Only
+from .minimax_cache import MinimaxCacheManager, MinimaxCacheParams
+from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+
+
+def replace_weight_name(name: str,
+                        key: str = None,
+                        to: str = None,
+                        count: int = None,
+                        prefix: str = None) -> str:
+    name = name.replace(key, to) if count is None else \
+        name.replace(key, to, count)
+    return name
+
+
+def weight_loader_with_alias(alias: str):
+
+    def wrapper(func: callable):
+
+        def inner_func(param: torch.Tensor,
+                       loaded_weight: torch.Tensor,
+                       *args,
+                       prefix: str = None,
+                       **kwargs):
+            value = func(param, loaded_weight, *args, **kwargs)
+            return value
+
+        return inner_func
+
+    return wrapper
+
+
+class MiniMaxText01RMSNormTP(CustomOp):
+    name = "MiniMaxText01RMSNormTP"
+
+    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.tp_world = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.weight = nn.Parameter(torch.ones(int(hidden_size /
+                                                  self.tp_world)))
+
+        self.weight.weight_loader = self.weight_loader
+        self.variance_epsilon = eps
+        return
+
+    @staticmethod
+    def weight_loader(
+        param: nn.Parameter,
+        loaded_weight: torch.Tensor,
+    ) -> None:
+        tp_world = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+
+        shard_size = loaded_weight.shape[0] // tp_world
+        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        param.data.copy_(loaded_weight[shard])
+        return
+
+    def _forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        variance = x.pow(2).mean(dim=-1, keepdim=True, dtype=torch.float32)
+        if self.tp_world > 1:
+            variance = tensor_model_parallel_all_reduce(
+                variance) / self.tp_world
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        x = x.to(orig_dtype) * self.weight
+        return x
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        assert residual is None, "RMSNorm does not support residual connection."
+        return self._forward(x)
+
+
+class MiniMaxText01RotaryEmbedding(CustomOp):
+    name = "MiniMaxText01RotaryEmbedding"
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position: int,
+        base: int,
+        is_neox_style: bool,
+        cache_dtype: torch.dtype,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        self.rotary_dim = rotary_dim
+        self.max_position_embeddings = max_position
+        self.base = base
+        self.is_neox_style = is_neox_style
+        self.cache_dtype = cache_dtype
+        cache = self._compute_cos_sin_cache().to(cache_dtype)
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    def _compute_inv_freq(
+        self,
+        base: Union[int, float],
+    ) -> torch.Tensor:
+        """Compute the inverse frequency."""
+        inv_freq = 1.0 / (base**(torch.arange(
+            0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim))
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        """Compute the cos and sin cache."""
+        inv_freq = self._compute_inv_freq(self.base)
+        t = torch.arange(self.max_position_embeddings, dtype=torch.float)
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        from vllm import _custom_ops as ops
+        self.cos_sin_cache = self.cos_sin_cache.to(positions.device)
+        query_cast = query.to(self.cache_dtype)
+        key_cast = key.to(self.cache_dtype)
+        ops.rotary_embedding(positions, query_cast, key_cast, self.head_size,
+                             self.cos_sin_cache, self.is_neox_style)
+        query = query_cast.to(query.dtype)
+        key = key_cast.to(key.dtype)
+        return query, key
+
+
+class MiniMaxText01MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_idx: int = None,
+        prefix: str = "mlp",
+    ) -> None:
+        super().__init__()
+        self.layer_idx = layer_idx
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = SiluAndMul()
+        return
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MiniMaxText01MoE(nn.Module):
+
+    def __init__(
+        self,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        layer_idx: int = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "moe",
+    ) -> None:
+        super().__init__()
+
+        self.layer_idx = layer_idx
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_total_experts = num_experts
+        self.top_k = top_k
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size // self.tp_size
+        self.quant_config = quant_config
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        self.gate = ReplicatedLinear(
+            self.hidden_size,
+            self.num_total_experts,
+            bias=False,
+            params_dtype=torch.float32,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+        self.gate.weight.weight_loader = MiniMaxText01MoE.gate_weight_loader
+
+        self.experts = FusedMoE(
+            num_experts=self.num_total_experts,
+            top_k=self.top_k,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size * self.tp_size,
+            params_dtype=self.params_dtype,
+            reduce_results=True,
+            renormalize=True,
+            quant_config=self.quant_config,
+            tp_size=self.tp_size,
+            prefix=f"{prefix}.experts",
+        )
+        return
+
+    @staticmethod
+    def gate_weight_loader(param: nn.Parameter,
+                           loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+        param.data.copy_(loaded_weight.to(torch.float32))
+        return
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        router_logits_fp32, _ = self.gate(hidden_states.to(torch.float32))
+        final_hidden_states = self.experts(
+            hidden_states, router_logits_fp32.to(hidden_states.dtype))
+        final_hidden = final_hidden_states.view(num_tokens, hidden_size)
+        return final_hidden
+
+
+class MiniMaxText01LinearKernel:
+
+    @staticmethod
+    def jit_linear_forward_prefix(q: torch.Tensor,
+                                  k: torch.Tensor,
+                                  v: torch.Tensor,
+                                  kv_caches: torch.Tensor,
+                                  slope_rate: torch.Tensor,
+                                  block_size: int,
+                                  layer_idx: int = None,
+                                  **kwargs) -> torch.Tensor:
+
+        slope_rate = slope_rate.to(torch.float32)
+        should_pad_dim = q.dim() == 3
+        if should_pad_dim:
+            q = q.unsqueeze(0)
+            k = k.unsqueeze(0)
+            v = v.unsqueeze(0)
+        b, h, n, d = q.shape
+        e = d
+        kv_history = kv_caches.reshape(1, h, d, e).contiguous()
+        output, kv_history = lightning_attention(q,
+                                                 k,
+                                                 v,
+                                                 slope_rate,
+                                                 block_size=block_size,
+                                                 kv_history=kv_history)
+        kv_caches.copy_(kv_history[:, :, -1, :, :].reshape(h, d, e))
+        assert output.shape[0] == 1, "batch size must be 1"
+        return rearrange(output.squeeze(0), "h n d -> n (h d)")
+
+
+class MiniMaxText01LinearAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        hidden_inner_size: int,
+        num_heads: int,
+        head_dim: int,
+        max_position: int,
+        block_size: int,
+        num_hidden_layer: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_idx: int = 0,
+        linear_layer_idx: int = 0,
+        prefix: str = "linear_attn",
+    ) -> None:
+        super().__init__()
+
+        self.layer_idx = layer_idx
+        self.BLOCK = block_size
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.total_num_heads = num_heads
+        self.hidden_inner_size = hidden_inner_size
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        assert self.total_num_heads % self.tp_size == 0
+        self.tp_heads = self.total_num_heads // self.tp_size
+        self.qkv_size = self.num_heads * self.head_dim
+        self.tp_hidden = self.head_dim * self.tp_heads
+
+        self.qkv_proj = ColumnParallelLinear(
+            hidden_size,
+            self.hidden_inner_size * 3,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.output_gate = ColumnParallelLinear(
+            hidden_size,
+            self.hidden_inner_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.output_gate",
+        )
+        self.out_proj = RowParallelLinear(
+            self.hidden_inner_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+        self.norm = MiniMaxText01RMSNormTP(
+            self.hidden_inner_size,
+            eps=1e-5,
+        )
+
+        slope_rate = MiniMaxText01LinearAttention._build_slope_tensor(
+            self.num_heads)
+        if num_hidden_layer <= 1:
+            self.slope_rate = slope_rate * (1 + 1e-5)
+        else:
+            self.slope_rate = slope_rate * (1 - layer_idx /
+                                            (num_hidden_layer - 1) + 1e-5)
+        self.tp_slope = self.slope_rate[self.tp_rank *
+                                        self.tp_heads:(self.tp_rank + 1) *
+                                        self.tp_heads].contiguous()
+
+    @staticmethod
+    def weight_direct_load(param: torch.Tensor,
+                           loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+        param.data.copy_(loaded_weight)
+        return
+
+    @staticmethod
+    def _build_slope_tensor(n_attention_heads: int):
+
+        def get_slopes(n):
+
+            def get_slopes_power_of_2(n):
+                start = 2**(-(2**-(math.log2(n) - 3)))
+                ratio = start
+                return [start * ratio**i for i in range(n)]
+
+            if math.log2(n).is_integer():
+                return get_slopes_power_of_2(n)
+            else:
+                closest_power_of_2 = 2**math.floor(math.log2(n))
+                return (get_slopes_power_of_2(closest_power_of_2) + get_slopes(
+                    2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
+
+        slopes = torch.tensor(get_slopes(n_attention_heads),
+                              dtype=torch.float32).reshape(
+                                  n_attention_heads, 1, 1)
+        return slopes
+
+    def _prefill_and_mix_infer(self, q, k, v, kv_cache, state_indices_tensor,
+                               attn_metadata):
+        hidden = []
+        for _prefill_idx in range(getattr(attn_metadata, "num_prefills", 0)):
+            _start = attn_metadata.query_start_loc[_prefill_idx]
+            _end = attn_metadata.query_start_loc[_prefill_idx + 1]
+            slot_id = state_indices_tensor[_prefill_idx]
+            qs = q[_start:_end].transpose(0, 1).contiguous()
+            ks = k[_start:_end].transpose(0, 1).contiguous()
+            vs = v[_start:_end].transpose(0, 1).contiguous()
+            slot_id = state_indices_tensor[_prefill_idx]
+            slice_layer_cache = kv_cache[slot_id, ...]
+
+            out_slice = MiniMaxText01LinearKernel.jit_linear_forward_prefix(
+                qs,
+                ks,
+                vs,
+                slice_layer_cache,
+                self.tp_slope,
+                self.BLOCK,
+                layer_idx=self.layer_idx)
+            hidden.append(out_slice.contiguous())
+        if attn_metadata.num_decode_tokens > 0:
+            hidden.append(
+                self._decode_infer(q, k, v, kv_cache, state_indices_tensor,
+                                   attn_metadata))
+        hidden = torch.concat(hidden, dim=0).contiguous()
+        return hidden
+
+    def _decode_infer(self, q, k, v, kv_cache, state_indices_tensor,
+                      attn_metadata):
+        q = q[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
+        k = k[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
+        v = v[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
+        slot_id = state_indices_tensor[getattr(attn_metadata, "num_prefills", 0
+                                               ):]
+        hidden = linear_decode_forward_triton(q, k, v, kv_cache, self.tp_slope,
+                                              slot_id, 32)
+        return hidden
+
+    def forward(self, hidden_states: torch.Tensor, positions: torch.Tensor,
+                kv_caches: MinimaxCacheParams, **kwargs) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        qkv32 = qkv.to(torch.float32)
+        qkvact = torch.nn.functional.silu(qkv32)
+        qkvact = qkvact.view((qkv.shape[0], self.tp_heads, -1))
+        q, k, v = torch.split(qkvact, [self.head_dim] * 3, dim=-1)
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        kv_cache = kv_caches.minimax_cache
+        state_indices_tensor = kv_caches.state_indices_tensor
+
+        decode_only = getattr(attn_metadata, "num_prefills", 0) == 0
+        if not decode_only:
+            hidden = self._prefill_and_mix_infer(q, k, v, kv_cache,
+                                                 state_indices_tensor,
+                                                 attn_metadata)
+        else:
+            hidden = self._decode_infer(q, k, v, kv_cache,
+                                        state_indices_tensor, attn_metadata)
+
+        hidden = self.norm._forward(hidden)
+        gate, _ = self.output_gate(hidden_states)
+        hidden = F.sigmoid(gate) * hidden
+        hidden = hidden.to(hidden_states.dtype)
+        hidden, _ = self.out_proj(hidden)
+        return hidden
+
+
+class MiniMaxText01Attention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        head_dim: int,
+        num_kv_heads: int,
+        rotary_dim: int,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        sliding_window: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_idx: int = None,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "mha",
+    ) -> None:
+        super().__init__()
+        self.layer_idx = layer_idx
+
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        return
+
+    def forward(self, hidden_states: torch.Tensor, positions: torch.Tensor,
+                **kwargs) -> torch.Tensor:
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = attn_metadata.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MiniMaxText01DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        expert_num: int = 1,
+        layer_id: int = None,
+        linear_layer_id: Optional[int] = None,
+        prefix: str = "decoder",
+    ) -> None:
+        self._ilayer = layer_id
+        self._irank = get_tensor_model_parallel_rank()
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.expert_num = expert_num
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+
+        head_dim = getattr(config, "head_dim",
+                           config.hidden_size // config.num_attention_heads)
+        if hasattr(config, "max_model_len") and isinstance(
+                config.max_model_len, int):
+            max_position_embeddings = min(config.max_position_embeddings,
+                                          config.max_model_len)
+        if config.attention_type == 0:
+            use_headxdim = True
+            hidden_inner = (head_dim * config.num_attention_heads
+                            if use_headxdim else config.hidden_size)
+            self.self_attn = MiniMaxText01LinearAttention(
+                hidden_size=self.hidden_size,
+                hidden_inner_size=hidden_inner,
+                num_heads=config.num_attention_heads,
+                head_dim=head_dim,
+                max_position=max_position_embeddings,
+                block_size=config.block if hasattr(config, "block") else 256,
+                num_hidden_layer=config.num_hidden_layers,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                linear_layer_idx=linear_layer_id,
+                prefix=prefix)
+        elif config.attention_type == 1:
+            self.self_attn = MiniMaxText01Attention(
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                head_dim=head_dim,
+                rotary_dim=config.rotary_dim
+                if hasattr(config, "rotary_dim") else head_dim,
+                num_kv_heads=config.num_key_value_heads,
+                max_position=max_position_embeddings,
+                rope_theta=rope_theta,
+                sliding_window=config.sliding_window,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                cache_config=cache_config,
+                prefix=prefix)
+        else:
+            raise ValueError(
+                f"Unsupported attention type: {self.config.attention_type}")
+
+        if expert_num == 1:
+            self.mlp = MiniMaxText01MLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                prefix=prefix)
+        else:
+            self.block_sparse_moe = MiniMaxText01MoE(
+                num_experts=expert_num,
+                top_k=config.num_experts_per_tok,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                layer_idx=self._ilayer,
+                quant_config=quant_config,
+                prefix=prefix)
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        if config.attention_type == 0:
+            self.layernorm_attention_alpha = getattr(
+                config, 'layernorm_linear_attention_alpha', 1)
+            self.layernorm_attention_beta = getattr(
+                config, 'layernorm_linear_attention_beta', 1)
+        else:
+            self.layernorm_attention_alpha = getattr(
+                config, 'layernorm_full_attention_alpha', 1)
+            self.layernorm_attention_beta = getattr(
+                config, 'layernorm_full_attention_beta', 1)
+        self.layernorm_mlp_alpha = getattr(config, 'layernorm_mlp_alpha', 1)
+        self.layernorm_mlp_beta = getattr(config, 'layernorm_mlp_beta', 1)
+        self.postnorm = getattr(config, 'postnorm', False)
+        self.shared_moe = False
+
+        shared_intermediate = getattr(config, 'shared_intermediate_size', 0)
+        if shared_intermediate > 0:
+            self.shared_moe = True
+            self.shared_mlp = MiniMaxText01MLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=shared_intermediate,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                prefix=prefix)
+            self.coefficient = ReplicatedLinear(
+                self.hidden_size,
+                1,
+                bias=False,
+                quant_config=quant_config,
+                params_dtype=torch.float32,
+            )
+            self.coefficient.weight.weight_loader = (
+                self.shared_moe_coefficient_loader)
+            self.shared_moe_mode = getattr(config, 'shared_moe_mode',
+                                           'softmax')
+        return
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                positions: torch.Tensor,
+                kv_caches: Union[List[Dict], Optional[torch.Tensor]],
+                attn_metadata: AttentionMetadata,
+                residual: Optional[torch.Tensor],
+                is_warmup: bool = False,
+                **kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        layernorm_input = hidden_states
+        layernorm_output = self.input_layernorm(layernorm_input)
+        residual = layernorm_output if self.postnorm else layernorm_input
+        self_attention_output = self.self_attn(
+            hidden_states=layernorm_output,
+            positions=positions,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata,
+        )
+
+        residual = residual * self.layernorm_attention_alpha
+        self_attention_output = (self_attention_output *
+                                 self.layernorm_attention_beta)
+
+        layernorm_input = residual + self_attention_output
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+        residual = layernorm_output if self.postnorm else layernorm_input
+
+        if self.expert_num == 1:
+            hidden_states = self.mlp(layernorm_output)
+        else:
+            moe_hidden_states = self.block_sparse_moe(
+                copy.deepcopy(layernorm_output))
+            if self.shared_moe:
+                before_moe_dtype = layernorm_output.dtype
+                moe_hidden_fp32 = moe_hidden_states.to(torch.float32)
+                output_mlp = self.shared_mlp(layernorm_output).to(
+                    torch.float32)
+
+                coef, _ = self.coefficient(layernorm_output.to(torch.float32))
+
+                if self.shared_moe_mode == 'softmax':
+                    coef = torch.nn.functional.softmax(coef, dim=-1)
+                    hidden_states = moe_hidden_fp32 * (
+                        1 - coef) + output_mlp * coef
+                elif self.shared_moe_mode == 'sigmoid':
+                    coef = torch.nn.functional.sigmoid(coef)
+                    hidden_states = moe_hidden_fp32 * (
+                        1 - coef) + output_mlp * coef
+
+                hidden_states = hidden_states.to(before_moe_dtype)
+            else:
+                hidden_states = moe_hidden_states
+
+        residual = residual * self.layernorm_mlp_alpha
+        hidden_states = hidden_states * self.layernorm_mlp_beta
+
+        hidden_states = residual + hidden_states
+
+        return hidden_states, None
+
+    @staticmethod
+    def shared_moe_coefficient_loader(param: torch.Tensor,
+                                      loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+
+        param.data.copy_(loaded_weight.to(torch.float32))
+        return
+
+
+class MiniMaxText01Model(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        scheduler_config=None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.decoder_attention_types = getattr(
+            config, "attn_type_list", False) or getattr(
+                config, "decoder_attention_types", False)
+        if not self.decoder_attention_types:
+            self.decoder_attention_types = [1] * config.num_hidden_layers
+        self.num_layers = config.num_hidden_layers
+
+        self._layer_barrier = False
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=self.vocab_size,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        def layer_fn(prefix):
+            layer_idx = int(prefix.split('.')[-1])
+            layer_config = config
+            layer_config.attention_type = self.decoder_attention_types[
+                layer_idx]
+            layer_config.layer_idx = layer_idx
+
+            decoder_kwargs = {
+                "quant_config": quant_config,
+                "layer_id": layer_idx,
+                "cache_config": cache_config
+            }
+
+            if layer_config.attention_type == 0:
+                decoder_kwargs["linear_layer_id"] = sum(
+                    1 for i in range(layer_idx)
+                    if self.decoder_attention_types[i] == 0)
+            else:
+                decoder_kwargs["linear_layer_id"] = None
+
+            if hasattr(config, "num_local_experts") and isinstance(
+                    config.num_local_experts, list):
+                decoder_kwargs["expert_num"] = config.num_local_experts[
+                    layer_idx]
+            elif hasattr(config, "num_local_experts") and isinstance(
+                    config.num_local_experts, int):
+                decoder_kwargs["expert_num"] = config.num_local_experts
+            else:
+                decoder_kwargs["expert_num"] = 1
+
+            return MiniMaxText01DecoderLayer(layer_config,
+                                             **decoder_kwargs,
+                                             prefix=prefix)
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, layer_fn, prefix=f"{prefix}.layers")
+
+        linear_layer_nums = sum(1 for i in range(config.num_hidden_layers)
+                                if self.decoder_attention_types[i] == 0)
+        max_slots_number = scheduler_config.max_num_seqs
+        self.cache_shape = (linear_layer_nums, max_slots_number,
+                            config.num_attention_heads //
+                            get_tensor_model_parallel_world_size(),
+                            config.head_dim, config.head_dim)
+        _dummy = torch.zeros(1)
+        self._dtype = _dummy.dtype
+        del _dummy
+
+        self.minimax_cache = MinimaxCacheManager(dtype=self._dtype,
+                                                 cache_shape=self.cache_shape)
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+        head_dim = getattr(config, "head_dim",
+                           config.hidden_size // config.num_attention_heads)
+        if hasattr(config, "max_model_len") and isinstance(
+                config.max_model_len, int):
+            max_position_embeddings = min(config.max_position_embeddings,
+                                          config.max_model_len)
+        self.rotary_emb = MiniMaxText01RotaryEmbedding(
+            head_dim,
+            rotary_dim=config.rotary_dim
+            if hasattr(config, "rotary_dim") else head_dim,
+            max_position=max_position_embeddings,
+            base=int(rope_theta),
+            is_neox_style=True,
+            cache_dtype=torch.float32,
+        )
+
+        norm_kwargs = {}
+        if hasattr(config, "rms_norm_eps"):
+            norm_kwargs["eps"] = config.rms_norm_eps
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, **norm_kwargs)
+        else:
+            self.norm = PPMissingLayer()
+        self.embed_scale = 1.0
+        return
+
+    def _clear_prefill_cache(self, attn_metadata,
+                             minimax_cache_tensors: torch.Tensor, **kwargs):
+        seq_to_slot_maps = {}
+        seq_id_map = sum(list(kwargs["request_ids_to_seq_ids"].values()), [])
+        for _, seq_to_slot_map in (
+                self.minimax_cache.cache_indices_mapping.items()):
+            seq_to_slot_maps.update(seq_to_slot_map)
+
+        slots_to_clear = []
+        for _prefill_id in range(getattr(attn_metadata, "num_prefills", 0)):
+            seq_id = seq_id_map[_prefill_id]
+            if attn_metadata.context_lens_tensor[
+                    _prefill_id] == 0 and seq_id in seq_to_slot_maps:
+                slots_to_clear.append(seq_to_slot_maps[seq_id])
+
+        if slots_to_clear:
+            slots_tensor = torch.tensor(slots_to_clear,
+                                        device=minimax_cache_tensors.device,
+                                        dtype=torch.long)
+            minimax_cache_tensors[:, slots_tensor, ...] = 0
+
+    def forward(self,
+                input_ids: Optional[torch.Tensor],
+                positions: torch.Tensor,
+                kv_caches: List[torch.Tensor],
+                intermediate_tensors=None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs) -> torch.Tensor:
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        if attn_metadata is None:
+            return None
+        if "request_ids_to_seq_ids" not in kwargs:
+            kwargs["request_ids_to_seq_ids"] = {}
+        if "finished_requests_ids" not in kwargs:
+            kwargs["finished_requests_ids"] = []
+        (
+            minimax_cache_tensors,
+            state_indices_tensor,
+        ) = self.minimax_cache.current_run_tensors(**kwargs)
+        if getattr(attn_metadata, "num_prefills", 0) > 0:
+            self._clear_prefill_cache(attn_metadata, minimax_cache_tensors,
+                                      **kwargs)
+
+        minimax_cache_params = MinimaxCacheParams(minimax_cache_tensors,
+                                                  state_indices_tensor)
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                hidden_states = self.embed_scale * self.embed_tokens(input_ids)
+            else:
+                hidden_states = inputs_embeds
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        kv_cache_index = 0
+        minimax_cache_index = 0
+        attn_metadata.rotary_emb = self.rotary_emb
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            _caches = None
+            if isinstance(layer.self_attn, MiniMaxText01Attention):
+                _caches = kv_caches[kv_cache_index]
+                kv_cache_index += 1
+            if isinstance(layer.self_attn, MiniMaxText01LinearAttention):
+                current_state_layer = minimax_cache_index
+                _caches = minimax_cache_params.at_layer_idx(
+                    current_state_layer)
+                minimax_cache_index += 1
+            hidden_states, residual = layer(
+                hidden_states=hidden_states,
+                positions=positions,
+                kv_caches=_caches,
+                attn_metadata=attn_metadata,
+                residual=residual,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        if residual is not None:
+            hidden_states, _ = self.norm(hidden_states, residual)
+        else:
+            hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid,
+                               SupportsV0Only):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        if not hasattr(config, "sliding_window"):
+            config.sliding_window = None
+
+        self.CONCAT_FFN = True
+
+        self.unpadded_vocab_size = self.config.vocab_size
+        if hasattr(vllm_config.model_config, "max_model_len"):
+            self.config.max_model_len = vllm_config.model_config.max_model_len
+        self.model = MiniMaxText01Model(
+            self.config,
+            quant_config,
+            cache_config=vllm_config.cache_config,
+            scheduler_config=vllm_config.scheduler_config,
+            prefix=maybe_prefix(prefix, "model"))
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                self.config.hidden_size,
+                org_num_embeddings=self.config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            )
+
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    self.config.vocab_size)
+
+            self.sampler = Sampler()
+        else:
+            self.lm_head = PPMissingLayer()
+
+        flash_layer_count = sum(1 for attn_type in self.config.attn_type_list
+                                if attn_type == 1)
+        self.kv_cache = [torch.tensor([]) for _ in range(flash_layer_count)]
+        return
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.model.minimax_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return self.model.minimax_cache.get_seqlen_agnostic_capture_inputs(
+            batch_size)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, self.kv_cache,
+                                   intermediate_tensors, inputs_embeds,
+                                   **kwargs)
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ):
+
+        next_tokens = self.sampler(logits, sampling_metadata)
+
+        return next_tokens
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> None:
+        params_dict = dict(self.named_parameters())
+
+        def which_layer(name: str) -> int:
+            if "layers" in name:
+                after_layer = name.split("layers")[-1]
+                return int(after_layer.split(".")[1])
+            return None
+
+        def is_linear_attn_layer(layer_idx: int) -> bool:
+            if layer_idx is None or not hasattr(self.config, "attn_type_list"):
+                return False
+            return self.config.attn_type_list[layer_idx] == 0
+
+        def is_moe_weight(name: str) -> bool:
+            return "block_sparse_moe" in name and not name.endswith(".bias")
+
+        def get_expert_id(param_name):
+            pattern = r'model\.layers\.\d+\.block_sparse_moe\.experts\.(\d+)\.'
+            match = re.search(pattern, param_name)
+            if match:
+                return match.group(1)
+            return None
+
+        def load_sparse_moe_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+            if isinstance(self.config.num_local_experts, list):
+                expert_params_mapping = [
+                    ("w13_weight"
+                     if weight_name in ["w1", "w3"] else "w2_weight",
+                     f"experts.{expert_id}.{weight_name}.weight", expert_id)
+                    for expert_id in range(max(self.config.num_local_experts))
+                    for weight_name in ["w1", "w2", "w3"]
+                ]
+            else:
+                expert_params_mapping = [
+                    ("w13_scale" if weight_name in ["w1", "w3"] else
+                     "w2_scale", f"{expert_id}.{weight_name}.weight_scale",
+                     expert_id, weight_name)
+                    for expert_id in range(self.config.num_local_experts)
+                    for weight_name in ["w1", "w2", "w3"]
+                ] + [("w13_weight" if weight_name in ["w1", "w3"] else
+                      "w2_weight", f"{expert_id}.{weight_name}.weight",
+                      expert_id, weight_name)
+                     for expert_id in range(self.config.num_local_experts)
+                     for weight_name in ["w1", "w2", "w3"]]
+            for (param_name, weight_name, expert_id,
+                 shard_id) in expert_params_mapping:
+                name_expert_id = get_expert_id(name)
+                if name_expert_id is not None and int(name_expert_id) != int(
+                        expert_id):
+                    continue
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param,
+                              loaded_weight,
+                              weight_name,
+                              expert_id=expert_id,
+                              shard_id=shard_id)
+                break
+            else:
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param, loaded_weight)
+            return
+
+        def is_shared_mlp_weight(name: str) -> bool:
+            return "shared_mlp" in name and not name.endswith(".bias")
+
+        def load_shared_mlp_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+            if not self.CONCAT_FFN:
+                if "gate_proj" in name:
+                    name = name.replace("gate_proj", "w1", 1)
+                elif "up_proj" in name:
+                    name = name.replace("up_proj", "w3", 1)
+                elif "down_proj" in name:
+                    name = name.replace("down_proj", "w2", 1)
+            else:
+                if "gate_proj" in name:
+                    name = name.replace("gate_proj", "gate_up_proj", 1)
+                    loaded_shard_id = 0
+                elif "up_proj" in name:
+                    name = name.replace("up_proj", "gate_up_proj", 1)
+                    loaded_shard_id = 1
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            if not self.CONCAT_FFN:
+                weight_loader(param, loaded_weight)
+            else:
+                if "gate_up_proj" in name:
+                    weight_loader(param, loaded_weight, loaded_shard_id)
+                elif "down_proj" in name:
+                    weight_loader(param, loaded_weight)
+                else:
+                    raise AssertionError(
+                        "MLP weight not in [gate_up_proj, down_proj]")
+            return
+
+        def is_mha_weight(name: str) -> bool:
+            return "self_attn" in name and not name.endswith(".bias")
+
+        def load_linear_attn_weight(name: str, loaded_weight: torch.Tensor,
+                                    self) -> None:
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+
+            weight_loader = getattr(
+                param, "weight_loader",
+                MiniMaxText01LinearAttention.weight_direct_load)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            weight_loader(param, loaded_weight)
+            return
+
+        def load_flash_attn_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+
+            flash_mha_params_mapping = [
+                ("qkv_proj", "q_proj", "q"),
+                ("qkv_proj", "k_proj", "k"),
+                ("qkv_proj", "v_proj", "v"),
+                ("gate_up_proj", "gate_proj", 0),
+                ("gate_up_proj", "up_proj", 1),
+            ]
+            for (param_name, weight_name,
+                 shard_id) in flash_mha_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param, loaded_weight)
+            return
+
+        def is_layer_norm_weight(name: str) -> bool:
+            return "norm" in name and not name.endswith(
+                ".bias") and name in params_dict
+
+        def load_layer_norm_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            weight_loader(param, loaded_weight)
+            return
+
+        def load_basic_weight(name: str, loaded_weight: torch.Tensor,
+                              self) -> None:
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            weight_loader(param, loaded_weight)
+            return
+
+        for name, loaded_weight in weights:
+            weight_at_layer = which_layer(name)
+            if weight_at_layer and weight_at_layer >= len(
+                    self.config.attn_type_list):
+                continue
+
+            if is_layer_norm_weight(name):
+                load_layer_norm_weight(name, loaded_weight, self)
+                continue
+            if is_mha_weight(name):
+                if is_linear_attn_layer(weight_at_layer):
+                    load_linear_attn_weight(name, loaded_weight, self)
+                else:
+                    load_flash_attn_weight(name, loaded_weight, self)
+                continue
+            if is_moe_weight(name):
+                load_sparse_moe_weight(name, loaded_weight, self)
+                continue
+            if is_shared_mlp_weight(name):
+                load_shared_mlp_weight(name, loaded_weight, self)
+                continue
+
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            load_basic_weight(name, loaded_weight, self)
+        return

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