complied Conv

unsloth_compiled_cache/Conv1d.py

@@ -0,0 +1,24 @@
+
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
+from torch import Tensor
+import torch
+from torch.nn import functional as F
+
+
+def forward(self, input: Tensor) -> Tensor:
+    return self._conv_forward(input, self.weight, self.bias).to(input.dtype)

unsloth_compiled_cache/Conv2d.py

@@ -0,0 +1,24 @@
+
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
+from torch import Tensor
+import torch
+from torch.nn import functional as F
+
+
+def forward(self, input: Tensor) -> Tensor:
+    return self._conv_forward(input, self.weight, self.bias).to(input.dtype)

unsloth_compiled_cache/Conv3d.py

@@ -0,0 +1,24 @@
+
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
+from torch import Tensor
+import torch
+from torch.nn import functional as F
+
+
+def forward(self, input: Tensor) -> Tensor:
+    return self._conv_forward(input, self.weight, self.bias).to(input.dtype)

unsloth_compiled_cache/ConvTranspose1d.py

@@ -0,0 +1,36 @@
+
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
+from torch import Tensor
+import torch
+from torch.nn import functional as F
+from transformers.models.mllama.modeling_mllama import (F, List, Optional, Tuple, nn)
+
+def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
+    if self.padding_mode != 'zeros':
+        raise ValueError('Only `zeros` padding mode is supported for ConvTranspose1d')
+
+    assert isinstance(self.padding, tuple)
+    # One cannot replace List by Tuple or Sequence in "_output_padding" because
+    # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+    num_spatial_dims = 1
+    output_padding = self._output_padding(
+        input, output_size, self.stride, self.padding, self.kernel_size,  # type: ignore[arg-type]
+        num_spatial_dims, self.dilation)  # type: ignore[arg-type]
+    return F.conv_transpose1d(
+        input, self.weight, self.bias, self.stride, self.padding,
+        output_padding, self.groups, self.dilation).to(input.dtype)

unsloth_compiled_cache/ConvTranspose2d.py

@@ -0,0 +1,37 @@
+
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
+from torch import Tensor
+import torch
+from torch.nn import functional as F
+from transformers.models.mllama.modeling_mllama import (F, List, Optional, Tuple, nn)
+
+def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
+    if self.padding_mode != 'zeros':
+        raise ValueError('Only `zeros` padding mode is supported for ConvTranspose2d')
+
+    assert isinstance(self.padding, tuple)
+    # One cannot replace List by Tuple or Sequence in "_output_padding" because
+    # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+    num_spatial_dims = 2
+    output_padding = self._output_padding(
+        input, output_size, self.stride, self.padding, self.kernel_size,  # type: ignore[arg-type]
+        num_spatial_dims, self.dilation)  # type: ignore[arg-type]
+
+    return F.conv_transpose2d(
+        input, self.weight, self.bias, self.stride, self.padding,
+        output_padding, self.groups, self.dilation).to(input.dtype)

unsloth_compiled_cache/unsloth_compiled_module_mllama.py

@@ -0,0 +1,782 @@
+
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+import torch
+from unsloth_zoo.loss_utils import fused_linear_cross_entropy
+
+scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
+@torch.compiler.disable(recursive = False)
+def disable_compile_scaled_dot_product_attention(*args, **kwargs):
+    return scaled_dot_product_attention(*args, **kwargs)
+pass
+
+
+torch_compile_options = {'epilogue_fusion': True, 'max_autotune': False, 'shape_padding': True, 'trace.enabled': False, 'triton.cudagraphs': False}
+from torch import Tensor
+import torch
+from torch.nn import functional as F
+from transformers.models.mllama.modeling_mllama import (F, math, Optional, Tuple, torch, nn, ACT2FN, Cache, ROPE_INIT_FUNCTIONS, MllamaTextConfig, MllamaVisionConfig)
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def _prepare_cross_attention_mask(cross_attention_mask: torch.Tensor,
+    num_vision_tokens: int,
+    dtype: str,) -> Tuple[torch.Tensor, torch.Tensor]:
+    # reshape so it can be used by attn module
+    batch_size, text_total_length, *_ = cross_attention_mask.shape
+    cross_attention_mask = cross_attention_mask.repeat_interleave(num_vision_tokens, dim=3)
+    cross_attention_mask = cross_attention_mask.view(batch_size, text_total_length, -1)
+    cross_attention_mask = cross_attention_mask.unsqueeze(1)
+
+    # invert the mask
+    inverted_cross_attn_mask = (1.0 - cross_attention_mask).to(dtype)
+    cross_attention_mask = inverted_cross_attn_mask.masked_fill(inverted_cross_attn_mask.to(torch.bool), torch.finfo(dtype).min)
+
+    # apply full-row bias, which return 4D tensor of shape [B, H, S1, 1] where value is 0 if the a full row in cross attn mask's
+    # last dimension contains negative infinity values, otherwise it's 1
+    negative_inf_value = torch.finfo(dtype).min
+    full_text_row_masked_out_mask = ((cross_attention_mask != negative_inf_value).any(dim=-1).type_as(cross_attention_mask)[..., None])
+    cross_attention_mask *= full_text_row_masked_out_mask
+
+    return cross_attention_mask!=torch.finfo(cross_attention_mask.dtype).min, full_text_row_masked_out_mask
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def _prepare_aspect_ratio_attention_mask(aspect_ratio_mask: torch.Tensor,
+    num_patches: int,
+    target_length: int,
+    dtype: torch.dtype,) -> torch.Tensor:
+    # Expand aspect ratio mask to target_length
+    batch_size, max_num_tiles = aspect_ratio_mask.shape
+    attention_mask = aspect_ratio_mask.view(batch_size, max_num_tiles, 1, 1).to(dtype)
+    attention_mask = attention_mask.repeat(1, 1, target_length, 1)
+
+    # Mask padding patches
+    pad_patches = target_length - num_patches
+    attention_mask[:, :, -pad_patches:] = 0
+
+    # Invert the mask (0 -> 1, 1 -> 0)
+    attention_mask = 1 - attention_mask
+
+    # Reshape to 2D and create 4D attention mask
+    # (batch_size, 1, max_num_tiles * target_length, max_num_tiles * target_length)
+    attention_mask = attention_mask.reshape(batch_size, max_num_tiles * target_length, 1)
+    attention_mask = attention_mask @ attention_mask.transpose(-1, -2) * torch.finfo(dtype).min
+    attention_mask = attention_mask.unsqueeze(1)
+
+    return attention_mask!=torch.finfo(attention_mask.dtype).min
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def MllamaPrecomputedAspectRatioEmbedding_forward(self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
+    embeddings = self.embedding(aspect_ratio_ids)
+    embeddings = embeddings.reshape(-1, self.max_num_tiles, 1, self.hidden_size)
+
+    if self.is_gated:
+        embeddings = embeddings * self.gate.tanh()
+
+    hidden_state = hidden_state + embeddings
+    return hidden_state
+
+class MllamaPrecomputedAspectRatioEmbedding(nn.Module):
+    def __init__(self, config: MllamaVisionConfig, is_gated: bool = True):
+        super().__init__()
+        self.max_num_tiles = config.max_num_tiles
+        self.hidden_size = config.hidden_size
+        self.max_aspect_ratio_id = config.max_aspect_ratio_id
+        self.is_gated = is_gated
+
+        self.embedding = nn.Embedding(self.max_aspect_ratio_id + 1, self.max_num_tiles * self.hidden_size)
+        if is_gated:
+            self.gate = nn.Parameter(torch.zeros(1))
+
+    def forward(self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
+        return MllamaPrecomputedAspectRatioEmbedding_forward(self, hidden_state, aspect_ratio_ids)
+
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def MllamaPrecomputedPositionEmbedding_forward(self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
+    # position embeddings
+    gated_position_embedding = (1 - self.gate.tanh()) * self.embedding
+    hidden_state = hidden_state + gated_position_embedding.view(1, 1, self.num_patches, self.hidden_size)
+
+    # precomputed tile position embeddings
+    tile_position_embedding = self.tile_embedding(aspect_ratio_ids)
+    batch_size = hidden_state.shape[0]
+    tile_position_embedding = tile_position_embedding.reshape(
+        batch_size, self.max_num_tiles, self.num_patches, self.hidden_size
+    )
+    gated_tile_position_embedding = self.gate.tanh() * tile_position_embedding
+    hidden_state = hidden_state + gated_tile_position_embedding
+
+    return hidden_state
+
+class MllamaPrecomputedPositionEmbedding(nn.Module):
+    def __init__(self, config: MllamaVisionConfig):
+        super().__init__()
+        self.max_num_tiles = config.max_num_tiles
+        self.max_aspect_ratio_id = config.max_aspect_ratio_id
+        self.num_patches = (config.image_size // config.patch_size) ** 2 + 1
+        self.hidden_size = config.hidden_size
+        self.scale = config.hidden_size**-0.5
+
+        self.gate = nn.Parameter(torch.zeros(1))
+
+        # position embedding
+        position_embedding = torch.randn(self.num_patches, self.hidden_size)
+        self.embedding = nn.Parameter(self.scale * position_embedding)
+
+        # tile position embedding
+        self.tile_embedding = nn.Embedding(
+            self.max_aspect_ratio_id + 1, self.max_num_tiles * self.num_patches * self.hidden_size
+        )
+
+    def forward(self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
+        return MllamaPrecomputedPositionEmbedding_forward(self, hidden_state, aspect_ratio_ids)
+
+
+@torch.compile(fullgraph = False, dynamic = True, options = torch_compile_options)
+def MllamaVisionMLP_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+    hidden_states = self.fc1(hidden_states)
+    hidden_states = self.activation_fn(hidden_states)
+    hidden_states = self.fc2(hidden_states)
+    return hidden_states
+
+class MllamaVisionMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return MllamaVisionMLP_forward(self, hidden_states)
+
+
+@torch.compiler.disable(recursive = False)
+def MllamaVisionAttention_forward(
+    self,
+    hidden_state: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = None,
+) -> torch.Tensor:
+    query = self.q_proj(hidden_state)
+    key = self.k_proj(hidden_state)
+    value = self.v_proj(hidden_state)
+
+    batch_size, q_seq_len, _ = query.shape
+    _, kv_seq_len, _ = key.shape
+
+    query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+    value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, : key.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_output = torch.matmul(attn_weights, value)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
+
+    output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return output, attn_weights
+
+class MllamaVisionAttention(nn.Module):
+    def __init__(self, config: MllamaVisionConfig):
+        super().__init__()
+
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.attention_heads
+        self.head_dim = config.hidden_size // config.attention_heads
+
+        self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim, bias=False)
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = None,
+    ) -> torch.Tensor:
+        return MllamaVisionAttention_forward(self, hidden_state, attention_mask, output_attentions)
+
+
+@torch.compiler.disable(recursive = False)
+def MllamaVisionSdpaAttention_forward(
+    self,
+    hidden_state: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = None,
+) -> torch.Tensor:
+    # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+    if output_attentions: raise RuntimeError('Unsloth: Not supported')
+
+    query = self.q_proj(hidden_state)
+    key = self.k_proj(hidden_state)
+    value = self.v_proj(hidden_state)
+
+    batch_size, q_seq_len, _ = query.shape
+    _, kv_seq_len, _ = key.shape
+
+    query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim)
+    key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
+    value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
+
+    query = query.transpose(1, 2)
+    key = key.transpose(1, 2)
+    value = value.transpose(1, 2)
+
+    attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
+
+    output = self.o_proj(attn_output)
+
+    return output, None
+
+class MllamaVisionSdpaAttention(MllamaVisionAttention):
+    # Adapted from MllamaVisionAttention
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = None,
+    ) -> torch.Tensor:
+        return MllamaVisionSdpaAttention_forward(self, hidden_state, attention_mask, output_attentions)
+
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def MllamaTextRMSNorm_forward(self, hidden_states):
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.to(torch.float32)
+    variance = hidden_states.pow(2).mean(-1, keepdim=True)
+    hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+    return self.weight * hidden_states.to(input_dtype)
+
+class MllamaTextRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MllamaTextRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        return MllamaTextRMSNorm_forward(self, hidden_states)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+@torch.compiler.disable(recursive = False)
+def MllamaTextCrossAttention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    cross_attention_states: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Cache] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = False,
+    use_cache: bool = None,
+    cache_position: Optional[torch.LongTensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    """Input shape: Batch x Time x Channel"""
+    bsz, q_len, _ = hidden_states.size()
+    query_states = self.q_proj(hidden_states)
+    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    query_states = self.q_norm(query_states)
+
+    if cross_attention_states is not None:
+        key_states = self.k_proj(cross_attention_states)
+        value_states = self.v_proj(cross_attention_states)
+        key_states = key_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        key_states = self.k_norm(key_states)
+        if past_key_value is not None:
+            # if we have a new image + new tokens, we only computed key_states on that new image
+            # we still update the cross key states, past_image, new_image. And use it!
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+            )
+    elif cache_position[0] != 0:
+        key_states, value_states = (
+            past_key_value.key_cache[self.layer_idx],
+            past_key_value.value_cache[self.layer_idx],
+        )
+    else:
+        raise ValueError(
+            "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+        )
+
+    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(bsz, q_len, -1)
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+class MllamaTextCrossAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        config: Optional[MllamaTextConfig] = None,
+        layer_idx: Optional[int] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.num_heads = self.config.num_attention_heads
+        self.num_key_value_heads = self.config.num_key_value_heads
+        self.dropout = config.dropout
+        self.hidden_size = config.hidden_size
+        self.head_dim = config.hidden_size // self.num_heads
+        self.layer_idx = layer_idx
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.q_norm = MllamaTextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = MllamaTextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        use_cache: bool = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        return MllamaTextCrossAttention_forward(self, hidden_states, cross_attention_states, past_key_value, attention_mask, output_attentions, use_cache, cache_position)
+
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+@torch.compiler.disable(recursive = False)
+def MllamaTextCrossSdpaAttention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    cross_attention_states: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Cache] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = False,
+    use_cache: bool = None,
+    cache_position: Optional[torch.LongTensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    """Input shape: Batch x Time x Channel"""
+    if output_attentions: raise RuntimeError('Unsloth: Not supported')
+
+    bsz, q_len, _ = hidden_states.size()
+    query_states = self.q_proj(hidden_states)
+    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    query_states = self.q_norm(query_states)
+
+    if cross_attention_states is not None:
+        key_states = self.k_proj(cross_attention_states)
+        value_states = self.v_proj(cross_attention_states)
+        key_states = key_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        if past_key_value is not None:
+            # if we have a new image + new tokens, we only computed key_states on that new image
+            # we still update the cross key states, past_image, new_image. And use it!
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+            )
+    elif cache_position[0] != 0:
+        key_states, value_states = (
+            past_key_value.key_cache[self.layer_idx],
+            past_key_value.value_cache[self.layer_idx],
+        )
+    else:
+        raise ValueError(
+            "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+        )
+
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    key_states = self.k_norm(key_states)
+
+    # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+    # Reference: https://github.com/pytorch/pytorch/issues/112577.
+    if query_states.device.type == "cuda" and attention_mask is not None:
+        query_states = query_states.contiguous()
+        key_states = key_states.contiguous()
+        value_states = value_states.contiguous()
+
+    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+    is_causal = True if attention_mask is None and q_len > 1 else False
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query_states,
+        key_states,
+        value_states,
+        attn_mask=attention_mask,
+        dropout_p=self.dropout if self.training else 0.0,
+        is_causal=is_causal,
+    )
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(bsz, q_len, -1)
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value
+
+class MllamaTextCrossSdpaAttention(MllamaTextCrossAttention):
+    """
+    Mllama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MllamaTextCrossAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MllamaTextCrossAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        use_cache: bool = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        return MllamaTextCrossSdpaAttention_forward(self, hidden_states, cross_attention_states, past_key_value, attention_mask, output_attentions, use_cache, cache_position)
+
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+def apply_rotary_pos_emb(q, k, cos, sin,  unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+@torch.compiler.disable(recursive = False)
+def MllamaTextSelfAttention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: torch.Tensor,
+    position_embeddings: torch.Tensor,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    past_key_value=None,
+    cache_position=None,
+    **kwargs,
+):
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+    cos, sin = position_embeddings
+    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+    if past_key_value is not None:
+        # sin and cos are specific to RoPE models; cache_position needed for the static cache
+        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.view(bsz, q_len, -1)
+
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+class MllamaTextSelfAttention(nn.Module):
+    def __init__(self, config: MllamaTextConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.num_heads = config.num_attention_heads
+        self.dropout = config.dropout
+        self.hidden_size = config.hidden_size
+        self.num_key_value_heads = config.num_key_value_heads
+        self.head_dim = config.hidden_size // self.num_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.rope_theta = config.rope_theta
+        self.layer_idx = layer_idx
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        position_embeddings: torch.Tensor,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        past_key_value=None,
+        cache_position=None,
+        **kwargs,
+    ):
+        return MllamaTextSelfAttention_forward(self, hidden_states, attention_mask, position_embeddings, output_attentions, use_cache, past_key_value, cache_position, **kwargs)
+
+
+@torch.compiler.disable(recursive = False)
+def MllamaTextSelfSdpaAttention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: torch.Tensor,
+    position_embeddings: torch.Tensor,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    past_key_value=None,
+    cache_position=None,
+    **kwargs,
+):
+    if output_attentions: raise RuntimeError('Unsloth: Not supported')
+
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+    cos, sin = position_embeddings
+    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+    if past_key_value is not None:
+        # sin and cos are specific to RoPE models; cache_position needed for the static cache
+        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    causal_mask = attention_mask
+    if attention_mask is not None:
+        causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+    # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+    # Reference: https://github.com/pytorch/pytorch/issues/112577.
+    if query_states.device.type == "cuda" and causal_mask is not None:
+        query_states = query_states.contiguous()
+        key_states = key_states.contiguous()
+        value_states = value_states.contiguous()
+
+    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+    is_causal = True if causal_mask is None and q_len > 1 else False
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query_states,
+        key_states,
+        value_states,
+        attn_mask=causal_mask,
+        dropout_p=self.dropout if self.training else 0.0,
+        is_causal=is_causal,
+    )
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.view(bsz, q_len, -1)
+
+    attn_output = self.o_proj(attn_output)
+    return attn_output, None, past_key_value
+
+class MllamaTextSelfSdpaAttention(MllamaTextSelfAttention):
+    # Adapted from MllamaTextSelfAttention
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        position_embeddings: torch.Tensor,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        past_key_value=None,
+        cache_position=None,
+        **kwargs,
+    ):
+        return MllamaTextSelfSdpaAttention_forward(self, hidden_states, attention_mask, position_embeddings, output_attentions, use_cache, past_key_value, cache_position, **kwargs)
+
+
+@torch.compile(fullgraph = False, dynamic = True, options = torch_compile_options)
+def MllamaTextMLP_forward(self, x):
+    return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+class MllamaTextMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        # Ignore copy
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return MllamaTextMLP_forward(self, x)
+
+
+@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
+@torch.no_grad()
+def MllamaRotaryEmbedding_forward(self, x, position_ids):
+    if "dynamic" in self.rope_type:
+        self._dynamic_frequency_update(position_ids, device=x.device)
+
+    # Core RoPE block
+    inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+    position_ids_expanded = position_ids[:, None, :].float()
+    # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+    device_type = x.device.type
+    device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+    with torch.autocast(device_type=device_type, enabled=False):
+        freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        cos = emb.cos()
+        sin = emb.sin()
+
+    # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+    cos = cos * self.attention_scaling
+    sin = sin * self.attention_scaling
+
+    return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+class MllamaRotaryEmbedding(nn.Module):
+    def __init__(self, config: MllamaTextConfig, device=None):
+        super().__init__()
+        self.rope_type = config.rope_scaling["rope_type"]
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len, **self.rope_kwargs
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+
+    def forward(self, x, position_ids):
+        return MllamaRotaryEmbedding_forward(self, x, position_ids)