Upload Moondream

config.json

@@ -11,5 +11,5 @@
     "model_type": "phi"
   },
   "torch_dtype": "float16",
-  "transformers_version": "4.36.2"
+  "transformers_version": "4.44.0"
 }

fourier_features.py

@@ -0,0 +1,18 @@
+# Adopted from https://github.com/crowsonkb/k-diffusion/blob/transformer-model-v2/k_diffusion/layers.py
+
+import torch
+import torch.nn as nn
+import math
+
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1.0):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.register_buffer(
+            "weight", torch.randn([out_features // 2, in_features]) * std
+        )
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T
+        return torch.cat([f.cos(), f.sin()], dim=-1)

generation_config.json

@@ -2,5 +2,5 @@
   "_from_model_config": true,
   "bos_token_id": 1,
   "eos_token_id": 2,
-  "transformers_version": "4.36.2"
+  "transformers_version": "4.44.0"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:927694193ed81f83b9b269c0d1ffa8dc823dec90bce4703a54b22ebd6c9632b6
-size 3733912224
+oid sha256:4bf7aed8ba4325d23fa7cd348d795a27f3b272682536f08aca4cdd62cde79293
+size 3736040266

modeling_phi.py

@@ -13,62 +13,113 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-""" PyTorch Phi model."""
-
+"""PyTorch Phi model."""
 
+import math
 from typing import List, Optional, Tuple, Union
 
 import torch
-import torch.nn.functional as F
 import torch.utils.checkpoint
+from packaging import version
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
 )
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    get_torch_version,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
+    is_torchdynamo_compiling,
     logging,
+    replace_return_docstrings,
 )
 from .configuration_moondream import PhiConfig
 
 
-try:  # noqa: SIM105
-    if is_flash_attn_2_available():
-        from flash_attn import flash_attn_func, flash_attn_varlen_func
-        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-except ImportError:
-    # Workaround for https://github.com/huggingface/transformers/issues/28459,
-    # don't move to contextlib.suppress(ImportError)
-    pass
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
 
 
 logger = logging.get_logger(__name__)
 
+_CONFIG_FOR_DOC = "PhiConfig"
 
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(
-        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
-    )
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
+
+# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
+def _prepare_4d_causal_attention_mask_with_cache_position(
+    attention_mask: torch.Tensor,
+    sequence_length: int,
+    target_length: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    min_dtype: float,
+    cache_position: torch.Tensor,
+    batch_size: int,
+):
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+    Args:
+        attention_mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+        sequence_length (`int`):
+            The sequence length being processed.
+        target_length (`int`):
+            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+        dtype (`torch.dtype`):
+            The dtype to use for the 4D attention mask.
+        device (`torch.device`):
+            The device to plcae the 4D attention mask on.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        cache_position (`torch.Tensor`):
+            Indices depicting the position of the input sequence tokens in the sequence.
+        batch_size (`torch.Tensor`):
+            Batch size.
+    """
+    if attention_mask is not None and attention_mask.dim() == 4:
+        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+        causal_mask = attention_mask
+    else:
+        causal_mask = torch.full(
+            (sequence_length, target_length),
+            fill_value=min_dtype,
+            dtype=dtype,
+            device=device,
+        )
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(
+            target_length, device=device
+        ) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = (
+                causal_mask.clone()
+            )  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = (
+                causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+            )
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[
+                :, :, :, :mask_length
+            ].masked_fill(padding_mask, min_dtype)
+
+    return causal_mask
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Phi
 class PhiRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
@@ -77,7 +128,11 @@ class PhiRotaryEmbedding(nn.Module):
         self.max_position_embeddings = max_position_embeddings
         self.base = base
         inv_freq = 1.0 / (
-            self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+            self.base
+            ** (
+                torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
+                / self.dim
+            )
         )
         self.register_buffer("inv_freq", inv_freq, persistent=False)
 
@@ -91,8 +146,8 @@ class PhiRotaryEmbedding(nn.Module):
     def _set_cos_sin_cache(self, seq_len, device, dtype):
         self.max_seq_len_cached = seq_len
         t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
 
         freqs = torch.outer(t, self.inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
@@ -111,7 +166,7 @@ class PhiRotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
+# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Phi
 class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
     """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
@@ -129,8 +184,8 @@ class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
     def _set_cos_sin_cache(self, seq_len, device, dtype):
         self.max_seq_len_cached = seq_len
         t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
         t = t / self.scaling_factor
 
         freqs = torch.outer(t, self.inv_freq)
@@ -140,7 +195,7 @@ class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
+# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Phi
 class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
     """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 
@@ -164,13 +219,17 @@ class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
                 - (self.scaling_factor - 1)
             ) ** (self.dim / (self.dim - 2))
             inv_freq = 1.0 / (
-                base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+                base
+                ** (
+                    torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
+                    / self.dim
+                )
             )
             self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
+            self.max_seq_len_cached, device=device, dtype=torch.int64
+        ).type_as(self.inv_freq)
 
         freqs = torch.outer(t, self.inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
@@ -187,7 +246,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
@@ -256,8 +315,8 @@ class PhiAttention(nn.Module):
         self.layer_idx = layer_idx
         if layer_idx is None:
             logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                 "when creating this class."
             )
 
@@ -322,6 +381,7 @@ class PhiAttention(nn.Module):
         past_key_value: Optional[Cache] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
@@ -373,6 +433,7 @@ class PhiAttention(nn.Module):
                 "sin": sin,
                 "cos": cos,
                 "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
             }
             key_states, value_states = past_key_value.update(
                 key_states, value_states, self.layer_idx, cache_kwargs
@@ -381,10 +442,37 @@ class PhiAttention(nn.Module):
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
 
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states, key_states, value_states, attn_mask=attention_mask
+        # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
+        attn_weights = torch.matmul(
+            query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights += causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(value_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
         )
 
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
         attn_output = attn_output.transpose(1, 2).contiguous()
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
@@ -420,6 +508,7 @@ class PhiFlashAttention2(PhiAttention):
         past_key_value: Optional[Cache] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         # PhiFlashAttention2 attention does not support output_attentions
@@ -473,6 +562,7 @@ class PhiFlashAttention2(PhiAttention):
                 "sin": sin,
                 "cos": cos,
                 "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
             }
             key_states, value_states = past_key_value.update(
                 key_states, value_states, self.layer_idx, cache_kwargs
@@ -511,14 +601,17 @@ class PhiFlashAttention2(PhiAttention):
             key_states = key_states.to(target_dtype)
             value_states = value_states.to(target_dtype)
 
-        attn_output = self._flash_attention_forward(
+        attn_output = _flash_attention_forward(
             query_states,
             key_states,
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=attn_dropout,
             softmax_scale=None,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
         )
 
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
@@ -529,137 +622,148 @@ class PhiFlashAttention2(PhiAttention):
 
         return attn_output, attn_weights, past_key_value
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
-    def _flash_attention_forward(
-        self,
-        query_states,
-        key_states,
-        value_states,
-        attention_mask,
-        query_length,
-        dropout=0.0,
-        softmax_scale=None,
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`int`, *optional*):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
 
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            (
-                query_states,
-                key_states,
-                value_states,
-                indices_q,
-                cu_seq_lens,
-                max_seq_lens,
-            ) = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
+class PhiSdpaAttention(PhiAttention):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.require_contiguous_qkv = version.parse(
+            get_torch_version()
+        ) < version.parse("2.2.0")
 
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
+    """
+    SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `PhiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
 
-            attn_output = pad_input(
-                attn_output_unpad, indices_q, batch_size, query_length
+    # Adapted from PhiAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "PhiModel is using PhiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not "
+                "support `output_attentions=True`. Falling back to the manual attention implementation, but specifying "
+                "the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can "
+                'be removed using the argument `attn_implementation="eager"` when loading the model.'
             )
-        else:
-            attn_output = flash_attn_func(
-                query_states,
-                key_states,
-                value_states,
-                dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
             )
 
-        return attn_output
+        bsz, q_len, _ = hidden_states.size()
 
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
-    def _upad_input(
-        self, query_layer, key_layer, value_layer, attention_mask, query_length
-    ):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
 
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
-            indices_k,
+        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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
         )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
-            indices_k,
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
         )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
-                indices_k,
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
-                query_layer, attention_mask
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
             )
 
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        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 broken in torch==2.1.2 when using non-contiguous inputs and a custom
+        # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
+        # Reference: https://github.com/pytorch/pytorch/issues/112577
+        if (
+            self.require_contiguous_qkv
+            and 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 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.attention_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, self.hidden_size)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
 
 PHI_ATTENTION_CLASSES = {
     "eager": PhiAttention,
     "flash_attention_2": PhiFlashAttention2,
+    "sdpa": PhiSdpaAttention,
 }
 
 
@@ -681,6 +785,8 @@ class PhiDecoderLayer(nn.Module):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
     ) -> Tuple[
         torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
     ]:
@@ -700,6 +806,11 @@ class PhiDecoderLayer(nn.Module):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
             past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
         """
 
         residual = hidden_states
@@ -714,6 +825,7 @@ class PhiDecoderLayer(nn.Module):
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
+            cache_position=cache_position,
         )
         attn_outputs = self.resid_dropout(attn_outputs)
 
@@ -730,6 +842,27 @@ class PhiDecoderLayer(nn.Module):
         return outputs
 
 
+PHI_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`PhiConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
 class PhiPreTrainedModel(PreTrainedModel):
     config_class = PhiConfig
     base_model_prefix = "model"
@@ -737,6 +870,7 @@ class PhiPreTrainedModel(PreTrainedModel):
     _no_split_modules = ["PhiDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn_2 = True
+    _supports_sdpa = True
     _supports_cache_class = True
 
     def _init_weights(self, module):
@@ -761,7 +895,84 @@ class Embedding(nn.Module):
     def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
         return self.wte(input_ids)
 
-
+PHI_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
 class PhiModel(PhiPreTrainedModel):
     """
     Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
@@ -783,7 +994,9 @@ class PhiModel(PhiPreTrainedModel):
                 for layer_idx in range(config.num_hidden_layers)
             ]
         )
+
         self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self._use_sdpa = config._attn_implementation == "sdpa"
 
         self.gradient_checkpointing = False
         # Initialize weights and apply final processing
@@ -795,6 +1008,7 @@ class PhiModel(PhiPreTrainedModel):
     def set_input_embeddings(self, value):
         self.embd.wte = value
 
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -806,6 +1020,7 @@ class PhiModel(PhiPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, BaseModelOutputWithPast]:
         output_attentions = (
             output_attentions
@@ -823,19 +1038,10 @@ class PhiModel(PhiPreTrainedModel):
             return_dict if return_dict is not None else self.config.use_return_dict
         )
 
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
+        if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time"
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
             )
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        past_key_values_length = 0
 
         if self.gradient_checkpointing and self.training:
             if use_cache:
@@ -844,43 +1050,37 @@ class PhiModel(PhiPreTrainedModel):
                 )
                 use_cache = False
 
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_length)
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
+        use_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
+            use_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            logger.warning_once(
+                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)"
             )
-            position_ids = position_ids.unsqueeze(0)
 
         if inputs_embeds is None:
             inputs_embeds = self.embd(input_ids)
 
-        inputs_embeds = self.embed_dropout(inputs_embeds)
-
-        # Attention mask.
-        if self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = (
-                attention_mask
-                if (attention_mask is not None and 0 in attention_mask)
-                else None
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
             )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
             )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask,
+            inputs_embeds,
+            cache_position,
+            past_key_values,
+            output_attentions,
+        )
 
         hidden_states = inputs_embeds
 
@@ -897,19 +1097,22 @@ class PhiModel(PhiPreTrainedModel):
                 layer_outputs = self._gradient_checkpointing_func(
                     decoder_layer.__call__,
                     hidden_states,
-                    attention_mask,
+                    causal_mask,
                     position_ids,
-                    past_key_values,
                     output_attentions,
+                    use_cache,
+                    past_key_values,
+                    cache_position,
                 )
             else:
                 layer_outputs = decoder_layer(
                     hidden_states,
-                    attention_mask=attention_mask,
+                    attention_mask=causal_mask,
                     position_ids=position_ids,
                     past_key_value=past_key_values,
                     output_attentions=output_attentions,
                     use_cache=use_cache,
+                    cache_position=cache_position,
                 )
 
             hidden_states = layer_outputs[0]
@@ -944,6 +1147,86 @@ class PhiModel(PhiPreTrainedModel):
             attentions=all_self_attns,
         )
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = (
+            past_key_values.get_seq_length() if past_key_values is not None else 0
+        )
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if (
+            self.config._attn_implementation == "sdpa"
+            and not using_static_cache
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            min_dtype=min_dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(
+                causal_mask, min_dtype
+            )
+
+        return causal_mask
+
 
 class CausalLMHead(nn.Module):
     """Causal Language Modeling head. Simplified version."""
@@ -958,7 +1241,6 @@ class CausalLMHead(nn.Module):
 
 
 class PhiForCausalLM(PhiPreTrainedModel):
-    _tied_weights_keys = ["lm_head.linear.weight"]
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
     def __init__(self, config):
@@ -976,7 +1258,7 @@ class PhiForCausalLM(PhiPreTrainedModel):
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
     def set_input_embeddings(self, value):
-        self.model.embd.wte = value
+        self.transformer.embd.wte = value
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
     def get_output_embeddings(self):
@@ -994,6 +1276,10 @@ class PhiForCausalLM(PhiPreTrainedModel):
     def get_decoder(self):
         return self.model
 
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1006,6 +1292,8 @@ class PhiForCausalLM(PhiPreTrainedModel):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         r"""
         Args:
@@ -1014,6 +1302,11 @@ class PhiForCausalLM(PhiPreTrainedModel):
                 config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                 (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
 
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
         Returns:
 
         Example:
@@ -1058,13 +1351,16 @@ class PhiForCausalLM(PhiPreTrainedModel):
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
+            cache_position=cache_position,
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
 
         loss = None
         if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
             # Shift so that tokens < n predict n
             shift_logits = logits[..., :-1, :].contiguous()
             shift_labels = labels[..., 1:].contiguous()
@@ -1095,41 +1391,23 @@ class PhiForCausalLM(PhiPreTrainedModel):
         past_key_values=None,
         attention_mask=None,
         inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        num_logits_to_keep=0,
         **kwargs,
     ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if (
-                attention_mask is not None
-                and attention_mask.shape[1] > input_ids.shape[1]
-            ):
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif (
+                input_ids.shape[1] != cache_position.shape[0]
+            ):  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
 
-        position_ids = kwargs.get("position_ids", None)
         if attention_mask is not None and position_ids is None:
             # create position_ids on the fly for batch generation
             position_ids = attention_mask.long().cumsum(-1) - 1
@@ -1137,31 +1415,49 @@ class PhiForCausalLM(PhiPreTrainedModel):
             if past_key_values:
                 position_ids = position_ids[:, -input_ids.shape[1] :]
 
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and (input_ids is None or input_ids.shape[1] == 0):
-            model_inputs = {"inputs_embeds": inputs_embeds}
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
         else:
-            model_inputs = {"input_ids": input_ids}
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs = {
+                "input_ids": input_ids.clone(memory_format=torch.contiguous_format),
+                "inputs_embeds": None,
+            }
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.lm_head.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
 
         model_inputs.update(
             {
                 "position_ids": position_ids,
+                "cache_position": cache_position,
                 "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
+                "use_cache": use_cache,
                 "attention_mask": attention_mask,
+                "num_logits_to_keep": num_logits_to_keep,
             }
         )
         return model_inputs
-
-    @staticmethod
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(
-                    past_state.index_select(0, beam_idx.to(past_state.device))
-                    for past_state in layer_past
-                ),
-            )
-        return reordered_past

moondream.py

@@ -1,10 +1,14 @@
 import torch
-from .vision_encoder import VisionEncoder
-from .configuration_moondream import MoondreamConfig
+
+from typing import List, Union, Literal, Optional
 from transformers import PreTrainedModel
+from PIL import Image
 
-from .modeling_phi import PhiForCausalLM
 from .configuration_moondream import PhiConfig
+from .configuration_moondream import MoondreamConfig
+from .vision_encoder import VisionEncoder
+from .region_model import RegionModel
+from .modeling_phi import PhiForCausalLM
 
 class Moondream(PreTrainedModel):
     config_class = MoondreamConfig
@@ -15,6 +19,7 @@ class Moondream(PreTrainedModel):
         self.vision_encoder = VisionEncoder(
             use_flash_attn=config._attn_implementation == "flash_attention_2"
         )
+        self.region_model = RegionModel()
 
         if type(config.text_config) == dict:
             phi_config = PhiConfig(
@@ -80,12 +85,55 @@ class Moondream(PreTrainedModel):
 
         with torch.no_grad():
             inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
+            attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
             output_ids = self.text_model.generate(
-                inputs_embeds=inputs_embeds, **generate_config
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
             )
 
         return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
 
+    # Note: Not ready for use yet, intended for September release.
+    def caption(
+        self,
+        images: List[Image.Image],
+        tokenizer,
+        length: Optional[Literal["short"]] = None,
+        **kwargs,
+    ):
+        image_embeds = self.encode_image(images)
+
+        templated_prompts = [
+            f"<image>\n\n{'Short caption' if length == 'short' else 'Caption'}:" for _ in images
+        ]
+        inputs_embeds = torch.stack([
+            self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
+            for prompt, image_embed in zip(templated_prompts, image_embeds)
+        ])
+        attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
+
+        generate_config = {
+            "eos_token_id": tokenizer.eos_token_id,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.bos_token_id,
+            "repetition_penalty": 1.2,
+            "max_new_tokens": 512,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config,
+            )
+
+        return [
+            x.strip()
+            for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        ]
+
     def answer_question(
         self,
         image_embeds,
@@ -93,6 +141,7 @@ class Moondream(PreTrainedModel):
         tokenizer,
         chat_history="",
         result_queue=None,
+        max_new_tokens=256,
         **kwargs,
     ):
         prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
@@ -100,7 +149,7 @@ class Moondream(PreTrainedModel):
             image_embeds,
             prompt,
             tokenizer=tokenizer,
-            max_new_tokens=512,
+            max_new_tokens=max_new_tokens,
             **kwargs,
         )[0]
         cleaned_answer = answer.strip()
@@ -176,3 +225,6 @@ class Moondream(PreTrainedModel):
             x.strip()
             for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
         ]
+
+    def detect(self, image: Image.Image, query: str, tokenizer):
+        pass

region_model.py

@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+from .fourier_features import FourierFeatures
+
+class RegionModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.position_features = FourierFeatures(2, 256)
+        self.position_encoder = nn.Linear(256, 2048)
+        self.size_features = FourierFeatures(2, 256)
+        self.size_encoder = nn.Linear(256, 2048)
+
+        self.position_decoder = nn.Linear(2048, 2)
+        self.size_decoder = nn.Linear(2048, 2)
+        self.confidence_decoder = nn.Linear(2048, 1)
+
+    def encode_position(self, position):
+        return self.position_encoder(self.position_features(position))
+
+    def encode_size(self, size):
+        return self.size_encoder(self.size_features(size))
+
+    def decode_position(self, x):
+        return self.position_decoder(x)
+
+    def decode_size(self, x):
+        return self.size_decoder(x)
+
+    def decode_confidence(self, x):
+        return self.confidence_decoder(x)
+
+    def encode(self, position, size):
+        return torch.stack(
+            [self.encode_position(position), self.encode_size(size)], dim=0
+        )
+
+    def decode(self, position_logits, size_logits):
+        return (
+            self.decode_position(position_logits),
+            self.decode_size(size_logits),
+            self.decode_confidence(size_logits),
+        )