Support for `attention_mask` in forward pass.

This commit implements the following:

- Cleans up unused arguments and definitions.
- Adds support for `attention_mask`.
- Adds support for cached inference.

README.md

@@ -87,7 +87,7 @@ text = tokenizer.batch_decode(outputs)[0]
 print(text)
 ```
 
-**Remark.** In the generation function, our model currently does not support beam search (`num_beams` >1) and `attention_mask' parameters. 
+**Remark.** In the generation function, our model currently does not support beam search (`num_beams` >1).
 Furthermore, in the forward pass of the model, we currently do not support outputing hidden states or attention values, or using custom input embeddings (instead of the model's).
 
 

config.json

@@ -1,13 +1,6 @@
 {
   "_name_or_path": "phi-1-half",
   "activation_function": "gelu_new",
-  "architecture": {
-    "block_cls": "parallel",
-    "mixer": {},
-    "mlp": {
-      "mlp_cls": "mlp"
-    }
-  },
   "architectures": [
     "MixFormerSequentialForCausalLM"
   ],
@@ -15,7 +8,6 @@
     "AutoConfig": "configuration_mixformer_sequential.MixFormerSequentialConfig",
     "AutoModelForCausalLM": "modeling_mixformer_sequential.MixFormerSequentialForCausalLM"
   },
-  "embd_layer": "default",
   "embd_pdrop": 0.0,
   "initializer_range": 0.02,
   "layer_norm_epsilon": 1e-05,
@@ -25,7 +17,6 @@
   "n_inner": null,
   "n_layer": 24,
   "n_positions": 2048,
-  "phyagi_version": "0.0.4.dev",
   "resid_pdrop": 0.0,
   "rotary_dim": 32,
   "tie_word_embeddings": false,

configuration_mixformer_sequential.py

@@ -17,8 +17,6 @@ class MixFormerSequentialConfig(PretrainedConfig):
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
-        "input_emb_layer": "embd_layer",  # `input_emb_layer` key is for backward compatibility
-        "blocks": "architecture",  # `blocks` key is for backward compatibility
     }
 
     def __init__(
@@ -31,8 +29,6 @@ class MixFormerSequentialConfig(PretrainedConfig):
         n_head: Optional[int] = 16,
         rotary_dim: Optional[int] = 32,
         activation_function: Optional[str] = "gelu_new",
-        embd_layer: Optional[str] = "default",
-        architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
         embd_pdrop: Optional[float] = 0.0,
         resid_pdrop: Optional[float] = 0.0,
         layer_norm_epsilon: Optional[float] = 1e-5,
@@ -49,8 +45,6 @@ class MixFormerSequentialConfig(PretrainedConfig):
         self.n_head = n_head
         self.rotary_dim = min(rotary_dim, n_embd // n_head)
         self.activation_function = activation_function
-        self.embd_layer = embd_layer
-        self.architecture = architecture
         self.embd_pdrop = embd_pdrop
         self.resid_pdrop = resid_pdrop
         self.layer_norm_epsilon = layer_norm_epsilon

modeling_mixformer_sequential.py

@@ -1,6 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-
+#
 # BSD 3-Clause License
 # 
 # Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
@@ -50,16 +50,38 @@ from .configuration_mixformer_sequential import MixFormerSequentialConfig
 
 @dataclass
 class InferenceParams:
-    """Inference parameters that are passed to the main model in order
-    to efficienly calculate and store the context during inference.
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
-    max_sequence_len: int
-    max_batch_size: int
-    sequence_len_offset: int = 0
-    batch_size_offset: int = 0
-    key_value_memory_dict: dict = field(default_factory=dict)
-    fused_ft_kernel: bool = False
-    lengths_per_sample: Optional[torch.Tensor] = None
+    """Inference parameters passed to model to efficiently calculate
+    and store context during inference.
+
+    Reference:
+        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
+
+    Args:
+        max_sequence_len: Maximum sequence length.
+        max_batch_size: Maximum batch size.
+        sequence_len_offset: Sequence length offset.
+        batch_size_offset: Batch size offset.
+        key_value_memory_dict: Key value memory dictionary.
+        fused_ft_kernel: Whether to use fused kernel for fast inference.
+        lengths_per_sample: Lengths per sample.
+
+    """
+
+    max_sequence_len: int = field(metadata={"help": "Maximum sequence length."})
+
+    max_batch_size: int = field(metadata={"help": "Maximum batch size."})
+
+    sequence_len_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
+
+    batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
+
+    key_value_memory_dict: Dict[str, Any] = field(
+        default_factory=dict, metadata={"help": "Key value memory dictionary."}
+    )
+
+    fused_ft_kernel: bool = field(default=False, metadata={"help": "Whether to use fused kernel for fast inference."})
+
+    lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."})
 
 
 class Embedding(nn.Module):
@@ -80,14 +102,19 @@ class Embedding(nn.Module):
 
         return hidden_states
 
+
 class RotaryEmbedding(nn.Module):
-    """PyTorch implementation of `flash-attn` RotaryEmbedding layer.
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
+    """Rotary embeddings.
+
+    Reference:
+        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py.
+    
+    """
 
     def __init__(
         self,
         dim: int,
-        base: Optional[int] = 10000,
+        base: int = 10000,
         scale_base: Optional[float] = None,
         device: Optional[str] = None,
         **kwargs,
@@ -119,7 +146,7 @@ class RotaryEmbedding(nn.Module):
         self._cos_k_cached = None
         self._sin_k_cached = None
 
-    def _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0) -> None:
+    def _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: int = 0) -> None:
         # Reset the tables if the sequence length has changed,
         # or if we're on a new device (possibly due to tracing for instance)
         seqlen = x.shape[1] + seqlen_offset
@@ -153,7 +180,7 @@ class RotaryEmbedding(nn.Module):
                 self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
                 self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
 
-    def apply_rotary_emb_qkv(
+    def _apply_rotary_emb_qkv(
         self,
         qkv: torch.FloatTensor,
         sin: torch.FloatTensor,
@@ -189,7 +216,6 @@ class RotaryEmbedding(nn.Module):
 
         # Computes the new keys and queries, recasting to original dtype
         q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
-
         k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
 
         return torch.cat(
@@ -202,47 +228,9 @@ class RotaryEmbedding(nn.Module):
         )
 
     def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Perform the forward pass.
-
-        Args:
-            qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
-            seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.
-
-        Returns:
-            New `qkv` and the cached sinusoids.
-
-        """
-
+        # `qkv` is of shape (batch, seqlen, 3, nheads, headdim)
         self._update_cos_sin_cache(qkv, seqlen_offset)
-
-        return self.apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
-
-def _update_kv_cache(kv, inference_params, layer_idx):
-    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
-    # Pre-allocate memory for key-values for inference.
-    num_heads, head_dim = kv.shape[-2:]
-    if layer_idx not in inference_params.key_value_memory_dict:
-        kv_cache = torch.empty(
-            inference_params.max_batch_size, inference_params.max_sequence_len, 2,
-            num_heads, head_dim, dtype=kv.dtype, device=kv.device
-        )
-        inference_params.key_value_memory_dict[layer_idx] = kv_cache
-    else:
-        kv_cache = inference_params.key_value_memory_dict[layer_idx]
-
-    # Adjust key and value for inference
-    batch_start = inference_params.batch_size_offset
-    batch_end = batch_start + kv.shape[0]
-    sequence_start = inference_params.sequence_len_offset
-    sequence_end = sequence_start + kv.shape[1]
-    assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
-    assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
-
-    assert kv_cache is not None
-    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
-    kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
-    return kv
+        return self._apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
 
 
 class MLP(nn.Module):
@@ -267,17 +255,6 @@ class MLP(nn.Module):
         self.fc2 = nn.Linear(n_inner, config.n_embd)
         self.act = ACT2FN[act_fn]
 
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
-        old_keys = [prefix + "fc_in.weight", prefix + "fc_out.weight", prefix + "fc_in.bias", prefix + "fc_out.bias"]
-        new_keys = [prefix + "fc1.weight", prefix + "fc2.weight", prefix + "fc1.bias", prefix + "fc2.bias"]
-        
-        if all(k in state_dict for k in old_keys) and not all(k in state_dict for k in new_keys):
-            # Older version of `MLP` saved with different key names.
-            for old_key, new_key in zip(old_keys, new_keys):
-                state_dict[new_key] = state_dict.pop(old_key)
-
-        return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
-    
     def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
         hidden_states = self.fc1(hidden_states)
         hidden_states = self.act(hidden_states)
@@ -286,132 +263,114 @@ class MLP(nn.Module):
         return hidden_states
 
 
-class FusedMLP(nn.Module):
-    """Fused Multi-Layer Perceptron from `flash-attn`.
-
+class SelfAttention(nn.Module):
+    """Self-attention layer (compatible with PyTorch).
+    
     Reference:
-        https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.
+        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
 
     """
-    def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None, 
-                 raise_on_missing: bool = False) -> None:
-        super().__init__()
-
-        act_fn = config.activation_function if act_fn is None else act_fn
-        assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
-
-        n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
-        n_inner = n_inner if n_inner is not None else 4 * config.n_embd
-
-        gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"]
-        activation = "gelu_approx" if act_fn in gelu_activations else "relu"
-           
-        self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)
-    
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        return self.mlp(hidden_states)
 
-class SelfAttention(nn.Module):
-    """Implement the scaled dot product attention with softmax.
-    Adapted from https://github.com/Dao-AILab/flash-attention.
-    Arguments
-    ---------
-        softmax_scale: The temperature to use for the softmax attention.
-                      (default: 1/sqrt(d_keys) where d_keys is computed at
-                      runtime)
-        attention_dropout: The dropout rate to apply to the attention
-                           (default: 0.0)
-    """
-    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+    def __init__(
+        self,
+        causal: bool = True,
+        softmax_scale: Optional[float] = None,
+        attention_dropout: float = 0.0,
+    ) -> None:
         super().__init__()
+
         self.causal = causal
         self.softmax_scale = softmax_scale
         self.drop = nn.Dropout(attention_dropout)
 
-    def forward(self, qkv, causal=None, key_padding_mask=None):
-        """Implements the multihead softmax attention.
-        Arguments
-        ---------
-            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
-            causal: if passed, will override self.causal
-            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
-                False means to mask out. (B, S)
-        """
-        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
+    def forward(
+        self,
+        qkv: torch.FloatTensor,
+        causal: bool = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        **kwargs,
+    ) -> torch.FloatTensor:
         causal = self.causal if causal is None else causal
+        batch_size, seq_len = qkv.shape[0], qkv.shape[1]
         q, k, v = qkv.unbind(dim=2)
+
         softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-        scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
-        if key_padding_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype,
-                                      device=scores.device)
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
-            scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+
+        if attention_mask is not None:
+            padding_mask = torch.full((batch_size, seq_len), -10000.0, dtype=scores.dtype, device=scores.device)
+            padding_mask.masked_fill_(attention_mask, 0.0)
+
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+
         if causal:
-            # "triu_tril_cuda_template" not implemented for 'BFloat16'
-            # So we have to construct the mask in float
-            causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            causal_mask = torch.triu(torch.full((seq_len, seq_len), -10000.0, device=scores.device), 1)
             scores = scores + causal_mask.to(dtype=scores.dtype)
+
         attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
-        attention_drop = self.drop(attention)
-        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
+        attention = self.drop(attention)
+
+        output = torch.einsum("bhts,bshd->bthd", attention, v)
+
         return output
 
 
 class CrossAttention(nn.Module):
-    """Implement the scaled dot product attention with softmax.
-    Adapted from https://github.com/Dao-AILab/flash-attention.
-    Arguments
-    ---------
-        softmax_scale: The temperature to use for the softmax attention.
-                      (default: 1/sqrt(d_keys) where d_keys is computed at
-                      runtime)
-        attention_dropout: The dropout rate to apply to the attention
-                           (default: 0.0)
+    """Cross-attention layer (compatible with PyTorch).
+    
+    Reference:
+        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
+    
     """
-    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
+
+    def __init__(
+        self,
+        causal: bool = True,
+        softmax_scale: Optional[float] = None,
+        attention_dropout: float = 0.0,
+    ) -> None:
         super().__init__()
+
         self.causal = causal
         self.softmax_scale = softmax_scale
         self.drop = nn.Dropout(attention_dropout)
 
-    def forward(self, q, kv, causal=None, key_padding_mask=None):
-        """Implements the multihead softmax attention.
-        Arguments
-        ---------
-            q: The tensor containing the query. (B, Sq, H, D)
-            kv: The tensor containing the key and value. (B, Sk, 2, H, D)
-            causal: if passed, will override self.causal
-            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
-                False means to mask out. (B, Sk)
-        """
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
+    def forward(
+        self,
+        q: torch.FloatTensor,
+        kv: torch.FloatTensor,
+        causal: bool = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        **kwargs,
+    ) -> torch.FloatTensor:
         causal = self.causal if causal is None else causal
-        seqlen_k = kv.shape[1]
+        batch_size, seq_len_q = q.shape[0], q.shape[1]
         assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
+
+        seq_len_k = kv.shape[1]
         k, v = kv.unbind(dim=2)
+
         softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-        scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
-        if key_padding_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen_k), -10000.0, dtype=scores.dtype,
-                                      device=scores.device)
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
-            scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+
+        if attention_mask is not None:
+            padding_mask = torch.full((batch_size, seq_len_k), -10000.0, dtype=scores.dtype, device=scores.device)
+            padding_mask.masked_fill_(attention_mask, 0.0)
+
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+
         if causal:
-            # "triu_tril_cuda_template" not implemented for 'BFloat16'
-            # So we have to construct the mask in float
-            causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0,
-                                                device=scores.device), 1)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+            causal_mask = torch.triu(torch.full((seq_len_q, seq_len_k), -10000.0, device=scores.device), 1)
             scores = scores + causal_mask.to(dtype=scores.dtype)
+
         attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
-        attention_drop = self.drop(attention)
-        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
+        attention = self.drop(attention)
+
+        output = torch.einsum("bhts,bshd->bthd", attention, v)
+
         return output
 
+
 def find_mha_dims(
     config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
 ) -> Tuple[int, int]:
@@ -445,152 +404,163 @@ def find_mha_dims(
     return n_head, head_dim
 
 
+def update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
+    """Update the key-value cache for inference.
+
+    Reference:
+        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
+
+    Args:
+        kv: Key-value tensor.
+        inference_params: Inference parameters.
+        layer_idx: Layer index.
+
+    Returns:
+        Updated key-value tensor.
+
+    """
+
+    num_heads, head_dim = kv.shape[-2:]
+
+    if layer_idx not in inference_params.key_value_memory_dict:
+        kv_cache = torch.empty(
+            inference_params.max_batch_size,
+            inference_params.max_sequence_len,
+            2,
+            num_heads,
+            head_dim,
+            dtype=kv.dtype,
+            device=kv.device,
+        )
+        inference_params.key_value_memory_dict[layer_idx] = kv_cache
+    else:
+        if not inference_params.fused_ft_kernel:
+            kv_cache = inference_params.key_value_memory_dict[layer_idx]
+        else:
+            k_cache, v_cache = inference_params.key_value_memory_dict[layer_idx]
+            kv_cache = None
+
+    batch_start = inference_params.batch_size_offset
+    batch_end = batch_start + kv.shape[0]
+    assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
+
+    sequence_start = inference_params.sequence_len_offset
+    sequence_end = sequence_start + kv.shape[1]
+    assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
+
+    if not inference_params.fused_ft_kernel:
+        assert kv_cache is not None
+
+        kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
+        kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
+
+        return kv
+
+    assert inference_params.sequence_len_offset == 0
+    assert kv.dtype in [torch.float16, torch.bfloat16, torch.float32]
+
+    packsize = 4 if kv.dtype == torch.float32 else 8
+
+    if kv_cache is not None:
+        kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
+        k_cache = rearrange(kv_cache[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize).contiguous()
+        v_cache = rearrange(kv_cache[:, :, 1], "b s h d -> b h s d").contiguous()
+        inference_params.key_value_memory_dict[layer_idx] = (k_cache, v_cache)
+    else:
+        k_cache[batch_start:batch_end, :, :, :sequence_end, :] = rearrange(
+            kv[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize
+        )
+        v_cache[batch_start:batch_end, :, :sequence_end, :] = rearrange(kv[:, :, 1], "b s h d -> b h s d")
+
+    return kv
+
+
 class MHA(nn.Module):
-    """Multi-head attention layer.
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
+    """Multi-head attention layer."""
 
     def __init__(
         self,
         config: PretrainedConfig,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[str] = None,
         rotary_dim: Optional[int] = None,
+        rotary_emb_scale_base: Optional[float] = None,
         n_head: Optional[int] = None,
         head_dim: Optional[int] = None,
-        bias: Optional[bool] = True,
-        dropout: Optional[float] = 0.0,
+        bias: bool = True,
+        causal: bool = True,
         softmax_scale: Optional[float] = None,
-        causal: Optional[bool] = True,
+        dropout: float = 0.0,
         layer_idx: Optional[int] = None,
-        rotary_emb_scale_base: Optional[float] = None,
-        return_residual: Optional[bool] = False,
-        checkpointing: Optional[bool] = False,
-        device: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-        fused_dense: Optional[bool] = True,
-        flash_attn: Optional[bool] = True,
-        cutlass_attn: Optional[bool] = False,
-        flash_rotary: Optional[bool] = True,
-        raise_on_missing: Optional[bool] = False
+        return_residual: bool = False,
+        checkpointing: bool = False,
     ) -> None:
         super().__init__()
 
-        factory_kwargs = {"device": device, "dtype": dtype}
-        n_head, head_dim = find_mha_dims(config, n_head, head_dim)
-
-        self.hidden_size = config.n_embd
-        self.n_head = n_head
-        self.head_dim = head_dim
-        self.op_size = n_head * head_dim
-
-        self.causal = causal
-        self.layer_idx = layer_idx
+        # Rotary embedding
         self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
-        self.fused_dense = fused_dense
-        self.flash_attn = flash_attn
-        self.cutlass_attn = cutlass_attn
-        self.flash_rotary = flash_rotary
-        self.return_residual = return_residual
-        self.checkpointing = checkpointing
-
         if self.rotary_emb_dim > 0:
             rotary_kwargs = {"device": device}
             if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
                 rotary_kwargs["scale_base"] = rotary_emb_scale_base
-            
             self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
-        else:
-            pass
+        
+        # MLP
+        self.n_head, self.head_dim = find_mha_dims(config, n_head, head_dim)
+        op_size = self.n_head * self.head_dim
+        hidden_size = config.n_embd
 
-        self.Wqkv = nn.Linear(self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs)
-        self.out_proj = nn.Linear(self.op_size, self.hidden_size, bias=bias, **factory_kwargs)
+        self.Wqkv = nn.Linear(hidden_size, 3 * op_size, bias=bias, device=device, dtype=dtype)
+        self.out_proj = nn.Linear(op_size, hidden_size, bias=bias, device=device, dtype=dtype)
 
+        # Attention
         self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
         self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
 
-    def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
-        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-        Adapted from https://github.com/Dao-AILab/flash-attention."""
-
-        assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
-
-        return _update_kv_cache(kv, inference_params, self.layer_idx)
+        self.layer_idx = layer_idx
+        self.return_residual = return_residual
+        self.checkpointing = checkpointing
 
     def forward(
         self,
         x: torch.FloatTensor,
-        x_kv: Optional[torch.FloatTensor] = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
+        past_key_values: Optional[InferenceParams] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
         cu_seqlens: Optional[torch.LongTensor] = None,
         max_seqlen: Optional[int] = None,
-        mixer_subset: Optional[torch.LongTensor] = None,
-        past_cache: Optional[InferenceParams] = None,
-        **kwargs
+        **kwargs,
     ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        """Perform the forward pass.
-
-        Args:
-            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
-                cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
-                is the is the sum of the sequence lengths in the batch.
-            x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
-            key_padding_mask: boolean mask, True means to keep, False means to mask out.
-                (batch, seqlen). Only applicable when not using FlashAttention.
-            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
-                of the sequences in the batch, used to index into x. Only applicable when using
-                FlashAttention.
-            max_seqlen: int. Maximum sequence length in the batch.
-            mixer_subset: for cross-attention only. If not None, will take a subset of x
-                before applying the query projection. Useful for e.g., ViT where we only care
-                about the CLS token in the last layer.
-            past_cache: For generation only.
-
-        Returns:
-            (batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
-                else (total, hidden_dim) where total is the is the sum of the sequence lengths
-                in the batch.
-
-        """
-
-        if cu_seqlens is not None:
-            assert max_seqlen is not None
-            assert key_padding_mask is None
-            assert self.flash_attn
-            assert self.rotary_emb_dim == 0
-
-        if key_padding_mask is not None:
-            assert cu_seqlens is None
-            assert max_seqlen is None
-            assert not self.flash_attn
-
-        if past_cache is not None:
-            assert key_padding_mask is None
-            assert cu_seqlens is None and max_seqlen is None
-
-        attn_kwargs = {"key_padding_mask": key_padding_mask}
-
-        assert x_kv is None and mixer_subset is None
-
         qkv = self.Wqkv(x)
         qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
 
-        if past_cache is None:
-            if self.rotary_emb_dim > 0:
-                qkv = self.rotary_emb(qkv)
-                context = self.inner_attn(qkv, **attn_kwargs)
+        seqlen_offset = past_key_values.sequence_len_offset if past_key_values is not None else 0
+        if self.rotary_emb_dim > 0:
+            qkv = self.rotary_emb(qkv, seqlen_offset=seqlen_offset)
+
+        if past_key_values is not None:
+            kv = update_kv_cache(qkv[:, :, 1:], past_key_values, self.layer_idx)
+
+        if attention_mask is not None:
+            attention_mask, cu_seqlens, max_seqlen = attention_mask
+            attention_mask = attention_mask.to(qkv.device)
 
+        attention_kwargs = {"attention_mask": attention_mask}
+
+        if past_key_values is None or seqlen_offset == 0:
+            if self.checkpointing:
+                attn_output = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **attention_kwargs)
+            else:
+                attn_output = self.inner_attn(qkv, **attention_kwargs)
         else:
-            if self.rotary_emb_dim > 0:
-                qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
             q = qkv[:, :, 0]
-            kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
-            # If we're processing the prompt, causal=None (use self.causal).
-            # If we're decoding, then causal=False.
-            causal = None if past_cache.sequence_len_offset == 0 else False
-            context = self.inner_cross_attn(q, kv, causal=causal)
+            causal = None if past_key_values.sequence_len_offset == 0 else False
+            attn_output = self.inner_cross_attn(q, kv, causal=causal, **attention_kwargs)
+
+        output = rearrange(attn_output, "... h d -> ... (h d)")
+        output = self.out_proj(output)
 
-        out = rearrange(context, "... h d -> ... (h d)")
-        out = self.out_proj(out)
+        return output if not self.return_residual else (output, x)
 
-        return out if not self.return_residual else (out, x)
 
 class ParallelBlock(nn.Module):
     """Parallel block.
@@ -602,8 +572,6 @@ class ParallelBlock(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        mixer: Optional[Dict[str, Any]] = None,
-        mlp: Optional[Dict[str, Any]] = None,
         block_idx: Optional[int] = None,
     ) -> None:
         super().__init__()
@@ -612,19 +580,20 @@ class ParallelBlock(nn.Module):
         self.resid_dropout = nn.Dropout(config.resid_pdrop)
         self.block_idx = block_idx
 
-        self.mixer = MHA(config=config, **mixer, layer_idx=block_idx)
-        mlp_cls = mlp.pop('mlp_cls')
-        if mlp_cls == 'fused_mlp':
-            self.mlp = FusedMLP(config=config, **mlp)
-        else:
-            self.mlp = MLP(config=config, **mlp)
+        self.mixer = MHA(config, layer_idx=block_idx)
+        self.mlp = MLP(config)
 
-    def forward(self, hidden_states: torch.FloatTensor, 
-                past_cache: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        **kwargs,
+    ) -> torch.FloatTensor:
         residual = hidden_states
         hidden_states = self.ln(hidden_states)
 
-        attn_outputs = self.mixer(hidden_states, past_cache=past_cache)
+        attn_outputs = self.mixer(hidden_states, past_key_values=past_key_values, attention_mask=attention_mask)
         if isinstance(attn_outputs, tuple):
             attn_outputs = attn_outputs[0]
 
@@ -635,6 +604,7 @@ class ParallelBlock(nn.Module):
 
         return hidden_states
 
+
 class CausalLMHead(nn.Module):
     """Causal Language Modeling head.
 
@@ -666,7 +636,7 @@ class CausalLMLoss(nn.Module):
 
     """
 
-    def __init__(self, shift_labels: Optional[bool] = True) -> None:
+    def __init__(self, shift_labels: bool = True) -> None:
         super().__init__()
 
         self.shift_labels = shift_labels
@@ -681,6 +651,7 @@ class CausalLMLoss(nn.Module):
 
         return loss
 
+
 class MixFormerSequentialPreTrainedModel(PreTrainedModel):
     """MixFormer (sequential for DeepSpeed) pre-trained model."""
 
@@ -691,9 +662,35 @@ class MixFormerSequentialPreTrainedModel(PreTrainedModel):
     def __init__(self, *inputs, **kwargs) -> None:
         super().__init__(*inputs, **kwargs)
 
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs) -> Dict[str, Any]:
-        if "use_cache" in kwargs and not kwargs["use_cache"]:
-            return {"input_ids": input_ids}
+    def _init_weights(self, module: nn.Module) -> None:
+        if isinstance(module, (nn.Linear,)):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        if attention_mask is not None and torch.any(~attention_mask.bool()):
+            total_seq_len = torch.sum(attention_mask, dim=1)
+            max_seq_len = torch.max(total_seq_len)
+
+            total_seq_len = torch.cat((torch.tensor([0], device=attention_mask.device), total_seq_len)).unsqueeze(1)
+            cumulative_seq_len = torch.cumsum(total_seq_len, dim=0).squeeze(1).to(torch.int32)
+            attention_mask = (attention_mask.bool(), cumulative_seq_len, max_seq_len.item())
+        else:
+            attention_mask = None
 
         if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
             past_key_values = InferenceParams(
@@ -705,11 +702,15 @@ class MixFormerSequentialPreTrainedModel(PreTrainedModel):
                 key_value_memory_dict={},
             )
         else:
-            # assume past_key_values has cached all but last token in input_ids
+            # Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
             past_key_values.sequence_len_offset = len(input_ids[0]) - 1
             input_ids = input_ids[:, -1].unsqueeze(-1)
 
-        return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "attention_mask": attention_mask,
+        }
 
 
 class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
@@ -717,28 +718,13 @@ class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
 
     _keys_to_ignore_on_load_missing = [""]
     _keys_to_ignore_on_load_unexpected = [r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
+    _no_split_modules = ["ParallelBlock"]
 
     def __init__(self, config: MixFormerSequentialConfig) -> None:
         super().__init__(config)
 
         modules = [Embedding(config)]
-        block_config = config.architecture
-
-        if not isinstance(block_config, list):
-            block_config = [block_config for _ in range(config.n_layer)]
-
-        if config.n_layer != len(block_config):
-            config.n_layer = len(block_config)
-
-        for block_idx, block in enumerate(block_config):
-            # `block_cls` with `legacy` value is for backward compatibility
-            # `path` key is for backward compatibility
-            block = copy.deepcopy(block) or {"block_cls": "parallel"}
-            block_cls = block.pop("path", None) or block.pop("block_cls", None)
-
-            block["block_idx"] = block_idx
-            modules.append(ParallelBlock(config, **block))
-
+        modules += [ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
         modules.append(CausalLMHead(config))
 
         self.layers = nn.Sequential(*modules)
@@ -759,20 +745,26 @@ class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
         self.layers[-1].linear = new_embeddings
 
     def forward(
-        self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None, 
-        past_key_values: Optional[torch.FloatTensor] = None, **kwargs
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs,
     ) -> CausalLMOutputWithPast:
+        if attention_mask is not None and self.training:
+            raise ValueError("`attention_mask` is not supported during training.")
 
-        if not past_key_values:
+        if past_key_values is None and attention_mask is None:
             lm_logits = self.layers(input_ids)
         else:
             hidden_layer = self.layers[0](input_ids)
             for module in self.layers[1:-1]:
-                hidden_layer = module(hidden_layer, past_cache=past_key_values)
+                hidden_layer = module(hidden_layer, past_key_values=past_key_values, attention_mask=attention_mask)
             lm_logits = self.layers[-1](hidden_layer)
 
         loss = None
         if labels is not None:
             loss = self.loss(lm_logits, labels)
-        
+
         return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)