Upload Moondream

config.json

@@ -2,7 +2,11 @@
   "architectures": [
     "Moondream"
   ],
-  "model_type": "moondream",
+  "auto_map": {
+    "AutoConfig": "configuration_moondream.MoondreamConfig",
+    "AutoModelForCausalLM": "moondream.Moondream"
+  },
+  "model_type": "moondream1",
   "phi_config": {
     "model_type": "phi-msft"
   },

configuration_moondream.py

@@ -0,0 +1,74 @@
+from transformers import PretrainedConfig
+
+from typing import Optional
+import math
+
+
+class PhiConfig(PretrainedConfig):
+    model_type = "phi-msft"
+
+    def __init__(
+        self,
+        vocab_size: int = 51200,
+        n_positions: int = 2048,
+        n_embd: int = 2048,
+        n_layer: int = 24,
+        n_inner: Optional[int] = None,
+        n_head: int = 32,
+        n_head_kv: Optional[int] = None,
+        rotary_dim: Optional[int] = 32,
+        activation_function: Optional[str] = "gelu_new",
+        flash_attn: bool = False,
+        flash_rotary: bool = False,
+        fused_dense: bool = False,
+        attn_pdrop: float = 0.0,
+        embd_pdrop: float = 0.0,
+        resid_pdrop: float = 0.0,
+        layer_norm_epsilon: float = 1e-5,
+        initializer_range: float = 0.02,
+        tie_word_embeddings: bool = False,
+        pad_vocab_size_multiple: int = 64,
+        gradient_checkpointing: bool = False,
+        **kwargs
+    ):
+        pad_vocab_size = (
+            math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
+        )
+        super().__init__(
+            vocab_size=pad_vocab_size,
+            n_positions=n_positions,
+            n_embd=n_embd,
+            n_layer=n_layer,
+            n_inner=n_inner,
+            n_head=n_head,
+            n_head_kv=n_head_kv,
+            activation_function=activation_function,
+            attn_pdrop=attn_pdrop,
+            embd_pdrop=embd_pdrop,
+            resid_pdrop=resid_pdrop,
+            layer_norm_epsilon=layer_norm_epsilon,
+            initializer_range=initializer_range,
+            pad_vocab_size_multiple=pad_vocab_size_multiple,
+            tie_word_embeddings=tie_word_embeddings,
+            gradient_checkpointing=gradient_checkpointing,
+            **kwargs
+        )
+        self.rotary_dim = min(rotary_dim, n_embd // n_head)
+        self.flash_attn = flash_attn
+        self.flash_rotary = flash_rotary
+        self.fused_dense = fused_dense
+
+    attribute_map = {
+        "max_position_embeddings": "n_positions",
+        "hidden_size": "n_embd",
+        "num_attention_heads": "n_head",
+        "num_hidden_layers": "n_layer",
+    }
+
+
+class MoondreamConfig(PretrainedConfig):
+    model_type = "moondream1"
+
+    def __init__(self, **kwargs):
+        self.phi_config = PhiConfig(**kwargs)
+        super().__init__(**kwargs)

generation_config.json

@@ -0,0 +1,4 @@
+{
+  "_from_model_config": true,
+  "transformers_version": "4.36.2"
+}

modeling_phi.py

@@ -0,0 +1,720 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+#
+# Copyright (c) 2022, Tri Dao, [email protected].
+# Licensed under the BSD 3-Clause License.
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional, Union, Tuple
+
+import math
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .configuration_moondream import PhiConfig
+
+FusedDense = None
+
+
+@dataclass
+class InferenceParams:
+    max_seqlen: int
+    max_batch_size: int
+    seqlen_offset: int = 0
+    batch_size_offset: int = 0
+    key_value_memory_dict: Dict[str, Any] = field(default_factory=dict)
+    lengths_per_sample: torch.Tensor = None
+
+
+class Embedding(nn.Module):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.drop = nn.Dropout(config.embd_pdrop)
+
+    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
+        return self.drop(self.wte(input_ids.view(-1, input_ids.size(-1))))
+
+
+def _apply_rotary_emb(x, cos, sin):
+    seqlen, rotary_dim = x.size(1), cos.size(1) * 2
+    x_rot, x_pass = x[..., :rotary_dim], x[..., rotary_dim:]
+    x1, x2 = x_rot.chunk(2, dim=-1)
+    c, s = cos[:seqlen].unsqueeze(1), sin[:seqlen].unsqueeze(1)
+    x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], dim=-1)
+    return torch.cat([x_rot.to(x.dtype), x_pass], dim=-1)
+
+
+def _apply_rotary_emb_kv(
+    kv: torch.FloatTensor, cos: torch.FloatTensor, sin: torch.FloatTensor
+) -> torch.FloatTensor:
+    seqlen, rotary_dim = kv.shape[1], cos.shape[-1] * 2
+    k_rot = kv[:, :, 0, :, :rotary_dim].chunk(2, dim=-1)
+    k_pass = kv[:, :, 0, :, rotary_dim:]
+    c, s = cos[:seqlen].unsqueeze(1), sin[:seqlen].unsqueeze(1)
+    k_rot = torch.cat(
+        [k_rot[0] * c - k_rot[1] * s, k_rot[0] * s + k_rot[1] * c], dim=-1
+    )
+    return torch.cat(
+        [torch.cat([k_rot, k_pass], dim=-1).unsqueeze(2), kv[:, :, 1:2, :, :]], dim=2
+    )
+
+
+def _apply_rotary_emb_qkv(
+    qkv: torch.FloatTensor, cos: torch.FloatTensor, sin: torch.FloatTensor
+) -> torch.FloatTensor:
+    seqlen, rotary_dim = qkv.shape[1], cos.shape[1] * 2
+
+    c = cos[:seqlen].unsqueeze(1)
+    s = sin[:seqlen].unsqueeze(1)
+
+    qkv_rot = torch.stack(
+        [
+            torch.cat(
+                [
+                    qkv[:, :, i, :, : rotary_dim // 2] * c
+                    - qkv[:, :, i, :, rotary_dim // 2 : rotary_dim] * s,
+                    qkv[:, :, i, :, : rotary_dim // 2] * s
+                    + qkv[:, :, i, :, rotary_dim // 2 : rotary_dim] * c,
+                ],
+                dim=-1,
+            ).to(qkv.dtype)
+            for i in range(2)
+        ],
+        dim=2,
+    )
+
+    qkv_pass = qkv[:, :, :2, :, rotary_dim:].unsqueeze(2)
+    qkv_v = qkv[:, :, 2:3, :, :]
+    return torch.cat([qkv_rot, qkv_pass, qkv_v], dim=2)
+
+
+class RotaryEmbedding(nn.Module):
+    # Enhanced Transformer with Rotary Position Embedding (https://arxiv.org/pdf/2104.09864.pdf)
+    def __init__(
+        self,
+        dim: int,
+        base: int = 10000,
+        scale_base: Optional[float] = None,
+        pos_idx_in_fp32: bool = True,
+        max_position_embeddings: int = 2048,
+        device: Optional[str] = None,
+    ) -> None:
+        super().__init__()
+        # fp32 is preferred since the output of `torch.arange` can be quite large and bf16 would lose a lot of precision
+        self.dim, self.base, self.pos_idx_in_fp32, self.device = (
+            dim,
+            float(base),
+            pos_idx_in_fp32,
+            device,
+        )
+        self.max_position_embeddings = max_position_embeddings
+        if scale_base is not None:
+            raise NotImplementedError
+
+        # Generate and register the non-trainable buffers
+        self.register_buffer(
+            "inv_freq", self._compute_inv_freq(device), persistent=False
+        )
+        self.register_buffer(
+            "scale", self._calculate_scale(dim, scale_base, device), persistent=False
+        )
+        self._update_cos_sin_cache(
+            max_position_embeddings, device=device, dtype=torch.float32
+        )
+
+    def _calculate_scale(self, dim, scale_base, device):
+        return (
+            (
+                (
+                    torch.arange(0, dim, 2, device=device, dtype=torch.float32)
+                    + 0.4 * dim
+                )
+                / (1.4 * dim)
+            )
+            if scale_base is not None
+            else None
+        )
+
+    def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
+        return 1.0 / (
+            self.base
+            ** (
+                torch.arange(0, self.dim, 2, device=device, dtype=torch.float32)
+                / self.dim
+            )
+        )
+
+    def _update_cos_sin_cache(
+        self,
+        seqlen: int,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> None:
+        self._seq_len_cached = seqlen
+        t = torch.arange(
+            seqlen,
+            device=device,
+            dtype=torch.float32 if self.pos_idx_in_fp32 else self.inv_freq.dtype,
+        )
+        inv_freq = (
+            self._compute_inv_freq(device=device)
+            if self.pos_idx_in_fp32 and self.inv_freq.dtype != torch.float32
+            else self.inv_freq
+        )
+
+        freqs = torch.outer(t, inv_freq)
+
+        def apply_scale(freqs, scale, operator, dtype):
+            result = operator(freqs)
+            return (result / scale).to(dtype) if scale is not None else result.to(dtype)
+
+        if scale := self.scale:
+            power = (
+                torch.arange(seqlen, dtype=scale.dtype, device=scale.device)
+                - seqlen // 2
+            ) / self.scale_base
+            scale = scale.to(device=power.device) ** power.unsqueeze(1)
+
+        self._cos_cached = apply_scale(
+            freqs, 1 / scale if scale is not None else None, torch.cos, dtype
+        )
+        self._sin_cached = apply_scale(
+            freqs, 1 / scale if scale is not None else None, torch.sin, dtype
+        )
+        if scale is not None:
+            self._cos_k_cached = apply_scale(freqs, scale, torch.cos, dtype)
+            self._sin_k_cached = apply_scale(freqs, scale, torch.sin, dtype)
+
+    def forward(
+        self,
+        qkv: torch.Tensor,
+        kv: Optional[torch.Tensor] = None,
+        seqlen_offset: int = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        should_update = (
+            self._seq_len_cached < qkv.shape[1] + seqlen_offset
+            or self._cos_cached.device != qkv.device
+            or self._cos_cached.dtype != qkv.dtype
+            or (self.training and self._cos_cached.is_inference())
+        )
+
+        if should_update:
+            self._update_cos_sin_cache(
+                qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype
+            )
+
+        offset_cos = self._cos_cached[seqlen_offset:]
+        offset_sin = self._sin_cached[seqlen_offset:]
+
+        if kv is None:
+            return _apply_rotary_emb_qkv(qkv, offset_cos, offset_sin)
+        else:
+            return _apply_rotary_emb(qkv, offset_cos, offset_sin), _apply_rotary_emb_kv(
+                kv, offset_cos, offset_sin
+            )
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        n_inner: Optional[int] = None,
+        act_fn: Optional[str] = None,
+    ) -> None:
+        super().__init__()
+        n_inner = n_inner or getattr(config, "n_inner", None) or 4 * config.n_embd
+        act_fn = act_fn or config.activation_function
+
+        self.fc1 = nn.Linear(config.n_embd, n_inner)
+        self.fc2 = nn.Linear(n_inner, config.n_embd)
+        self.act = ACT2FN[act_fn]
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        return self.fc2(self.act(self.fc1(hidden_states)))
+
+
+# Flash Attention (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py)
+class SelfAttention(nn.Module):
+    def __init__(
+        self,
+        causal: bool = True,
+        softmax_scale: Optional[float] = None,
+        attention_dropout: float = 0.0,
+    ):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    @torch.autocast("cpu", enabled=False)
+    @torch.autocast("cuda", enabled=False)
+    def forward(
+        self,
+        qkv: torch.FloatTensor,
+        causal: Optional[bool] = None,
+        key_padding_mask: Optional[torch.BoolTensor] = None,
+    ):
+        q, k, v = qkv.chunk(3, dim=-1)
+        scale = self.softmax_scale or 1.0 / q.size(-1) ** 0.5
+
+        scores = (
+            torch.einsum("bthd,bshd->bhts", q.to(torch.float32), k.to(torch.float32))
+            * scale
+        )
+        if causal or self.causal:
+            scores.triu_(1).fill_(-10000.0)
+        if key_padding_mask is not None:
+            scores.masked_fill_(key_padding_mask[:, None, None, :], -10000.0)
+
+        attn = self.drop(torch.softmax(scores, dim=-1).to(v.dtype))
+        return torch.einsum("bhts,bshd->bthd", attn, v)
+
+
+# Flash Attention (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py)
+class CrossAttention(nn.Module):
+    def __init__(self, causal=True, softmax_scale=None, attention_dropout=0.0):
+        super().__init__()
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.drop = nn.Dropout(attention_dropout)
+
+    @torch.autocast("cpu", enabled=False)
+    @torch.autocast("cuda", enabled=False)
+    def forward(
+        self,
+        q: torch.FloatTensor,
+        kv: torch.FloatTensor,
+        causal: bool = None,
+        key_padding_mask: Optional[torch.BoolTensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        seqlen_k = kv.shape[1]
+
+        if kv.shape[3] != q.shape[2]:
+            kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
+        k, v = kv.unbind(dim=2)
+
+        q = q.to(torch.float32)
+        k = k.to(torch.float32)
+
+        causal = self.causal if causal is None else causal
+        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
+
+        # Autocast is manually disabled to avoid `torch.einsum` performing the operation using float16, which might lead to overflow
+        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
+
+        if key_padding_mask is not None:
+            padding_mask = torch.full(
+                (batch_size, seqlen_k),
+                -10000.0,
+                dtype=scores.dtype,
+                device=scores.device,
+            )
+            padding_mask.masked_fill_(key_padding_mask, 0.0)
+            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
+
+        if causal:
+            rows = rearrange(
+                torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1"
+            )
+            cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
+            causal_mask = cols > rows + seqlen_k - seqlen_q
+            scores = scores.masked_fill(causal_mask, -10000.0)
+
+        attention = torch.softmax(scores, dim=-1).to(v.dtype)
+        attention = self.drop(attention)
+        output = torch.einsum("bhts,bshd->bthd", attention, v)
+
+        return output
+
+
+def _find_mha_dims(
+    config: PretrainedConfig,
+    n_head: Optional[int] = None,
+    n_head_kv: Optional[int] = None,
+    head_dim: Optional[int] = None,
+) -> Tuple[int, int]:
+    if n_head is None and head_dim is None:
+        head_dim = config.n_embd // config.n_head
+        n_head = config.n_head
+    elif n_head is None or head_dim is None:
+        raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
+    if n_head_kv is None:
+        n_head_kv = getattr(config, "n_head_kv", None) or n_head
+    return n_head, n_head_kv, head_dim
+
+
+def _update_kv_cache(
+    kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int
+) -> torch.FloatTensor:
+    num_heads, head_dim = kv.shape[-2:]
+    layer_memory = inference_params.key_value_memory_dict.setdefault(
+        layer_idx,
+        torch.empty(
+            inference_params.max_batch_size,
+            inference_params.max_seqlen,
+            2,
+            num_heads,
+            head_dim,
+            dtype=kv.dtype,
+            device=kv.device,
+        ),
+    )
+
+    batch_slice = slice(
+        inference_params.batch_size_offset,
+        inference_params.batch_size_offset + kv.shape[0],
+    )
+    seqlen_slice = slice(
+        inference_params.seqlen_offset, inference_params.seqlen_offset + kv.shape[1]
+    )
+
+    if seqlen_slice.stop >= inference_params.max_seqlen:
+        layer_memory = torch.cat((layer_memory, kv), dim=1)
+        inference_params.key_value_memory_dict[layer_idx] = layer_memory
+
+    layer_memory[batch_slice, seqlen_slice, ...] = kv
+    return layer_memory[batch_slice, : seqlen_slice.stop, ...]
+
+
+# Multi-head attention layer with rotary embeddings
+class MHA(nn.Module):
+    def __init__(
+        self,
+        config,
+        dtype=None,
+        device=None,
+        rotary_dim=None,
+        rotary_base=10000.0,
+        rotary_scale_base=None,
+        n_head=None,
+        n_head_kv=None,
+        head_dim=None,
+        bias=True,
+        causal=True,
+        softmax_scale=None,
+        layer_idx=None,
+        return_residual=False,
+        checkpointing=False,
+    ):
+        super().__init__()
+
+        # Set rotary embedding if specified
+        self.rotary_dim = rotary_dim or getattr(config, "rotary_dim", 0)
+        if self.rotary_dim:
+            self.rotary_emb = RotaryEmbedding(
+                self.rotary_dim,
+                base=rotary_base,
+                scale_base=rotary_scale_base,
+                device=device,
+                max_position_embeddings=config.n_positions,
+            )
+
+        # Determine MHA dims from arguments or config
+        self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
+            config, n_head, n_head_kv, head_dim
+        )
+        op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
+        hidden_size = config.n_embd
+
+        # Choose Linear class based on config, FusedDense is optional
+        LinearClass = (
+            FusedDense if config.fused_dense and FusedDense is not None else nn.Linear
+        )
+        self.Wqkv = LinearClass(
+            hidden_size, op_size, bias=bias, device=device, dtype=dtype
+        )
+        self.out_proj = LinearClass(
+            hidden_size, hidden_size, bias=bias, device=device, dtype=dtype
+        )
+
+        # Initialize attention mechanisms
+        attn_kwargs = {
+            "causal": causal,
+            "softmax_scale": softmax_scale,
+            "attention_dropout": config.attn_pdrop,
+        }
+        self.inner_attn = SelfAttention(**attn_kwargs)
+        self.inner_cross_attn = CrossAttention(**attn_kwargs)
+
+        self.layer_idx = layer_idx
+        self.return_residual = return_residual
+        self.checkpointing = checkpointing
+
+    def _forward_self_attn(
+        self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
+    ) -> torch.FloatTensor:
+        qkv = rearrange(
+            self.Wqkv(x), "... (three h d) -> ... three h d", three=3, d=self.head_dim
+        )
+        if self.rotary_dim > 0:
+            qkv = self.rotary_emb(qkv)
+        attn_func = (
+            torch.utils.checkpoint.checkpoint
+            if self.checkpointing
+            else lambda f, *args, **kwargs: f(*args, **kwargs)
+        )
+        return attn_func(self.inner_attn, qkv, key_padding_mask=key_padding_mask)
+
+    def _forward_cross_attn(
+        self,
+        x: torch.FloatTensor,
+        past_key_values: Optional[InferenceParams],
+        key_padding_mask: Optional[torch.BoolTensor],
+    ) -> torch.FloatTensor:
+        qkv = self.Wqkv(x)
+        q, kv = (
+            qkv[..., : self.n_head * self.head_dim],
+            qkv[..., self.n_head * self.head_dim :],
+        )
+        q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
+        kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
+
+        seqlen_offset = (
+            past_key_values.seqlen_offset if past_key_values is not None else 0
+        )
+        causal = None if seqlen_offset == 0 else False
+        if self.rotary_dim > 0:
+            q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
+
+        if past_key_values is not None:
+            kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
+
+        attn_func = (
+            torch.utils.checkpoint.checkpoint
+            if self.checkpointing
+            else lambda fn, *args, **kwargs: fn(*args, **kwargs)
+        )
+
+        return attn_func(
+            self.inner_cross_attn,
+            q,
+            kv,
+            key_padding_mask=key_padding_mask,
+            causal=causal,
+        )
+
+    def forward(
+        self,
+        x: torch.FloatTensor,
+        past_key_values: Optional[InferenceParams] = None,
+        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+        attention_mask = attention_mask.bool() if attention_mask is not None else None
+        use_cross_attn = self.n_head != self.n_head_kv or past_key_values is not None
+        attn_output_function = (
+            self._forward_cross_attn if use_cross_attn else self._forward_self_attn
+        )
+        attn_output = (
+            attn_output_function(x, past_key_values, attention_mask)
+            if use_cross_attn
+            else attn_output_function(x, attention_mask)
+        )
+        output = self.out_proj(rearrange(attn_output, "... h d -> ... (h d)"))
+        return (output, x) if self.return_residual else output
+
+
+# Parallel block. This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
+class ParallelBlock(nn.Module):
+    def __init__(self, config: PretrainedConfig, block_idx: Optional[int] = None):
+        super().__init__()
+        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+        self.block_idx = block_idx
+        self.mixer = MHA(config, layer_idx=block_idx)
+        self.mlp = MLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+        hidden_states = self.ln(hidden_states)
+
+        attn_outputs = self.mixer(
+            hidden_states,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+        )
+        if isinstance(attn_outputs, tuple):
+            attn_outputs = attn_outputs[0]
+
+        attn_outputs = self.resid_dropout(attn_outputs)
+        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
+        return attn_outputs + feed_forward_hidden_states + residual
+
+
+class CausalLMHead(nn.Module):
+    """Causal Language Modeling head. Simplified version."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.linear = nn.Linear(config.n_embd, config.vocab_size)
+
+    def forward(self, hidden_states):
+        return self.linear(self.ln(hidden_states)).to(torch.float32)
+
+
+# Improving Language Understanding by Generative Pre-Training
+# (https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
+class CausalLMLoss(nn.Module):
+    def __init__(self, shift_labels: bool = True) -> None:
+        super().__init__()
+        self.shift_labels = shift_labels
+        self.loss_fct = nn.CrossEntropyLoss()
+
+    def forward(
+        self, logits: torch.FloatTensor, labels: torch.LongTensor
+    ) -> torch.FloatTensor:
+        if self.shift_labels:
+            logits, labels = logits[..., :-1, :], labels[..., 1:]
+        return self.loss_fct(logits.reshape(-1, logits.size(-1)), labels.reshape(-1))
+
+
+class PhiPreTrainedModel(PreTrainedModel):
+    config_class = PhiConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = False
+    _no_split_modules = ["ParallelBlock"]
+
+    def __init__(self, *inputs, **kwargs) -> None:
+        super().__init__(*inputs, **kwargs)
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor = None,
+        inputs_embeds: torch.FloatTensor = None,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        if input_ids is None and inputs_embeds is None:
+            raise ValueError(
+                "You have to specify either `input_ids` or `inputs_embeds`."
+            )
+
+        max_batch_size = (
+            inputs_embeds.shape[0] if inputs_embeds is not None else input_ids.shape[0]
+        )
+        seqlen_offset = (
+            inputs_embeds.shape[1] + input_ids.shape[1] - 2
+            if inputs_embeds is not None
+            else input_ids.shape[1] - 1
+        )
+
+        args = (
+            {"inputs_embeds": inputs_embeds}
+            if inputs_embeds is not None
+            else {"input_ids": input_ids}
+        )
+
+        if not isinstance(past_key_values, InferenceParams):
+            past_key_values = InferenceParams(
+                max_seqlen=self.config.n_positions,
+                max_batch_size=max_batch_size,
+                seqlen_offset=0,
+                batch_size_offset=0,
+                key_value_memory_dict={},
+                lengths_per_sample=None,
+            )
+        else:
+            past_key_values.seqlen_offset = seqlen_offset
+            args = {"input_ids": input_ids[:, -1].unsqueeze(-1)}
+
+        return {
+            **args,
+            "past_key_values": past_key_values,
+            "attention_mask": attention_mask,
+        }
+
+
+class PhiModel(PhiPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [""]
+    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
+
+    def __init__(self, config: PhiConfig) -> None:
+        super().__init__(config)
+        self.embd = Embedding(config)
+        self.h = nn.ModuleList(
+            [ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
+        )
+        self.gradient_checkpointing = config.gradient_checkpointing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.embd.wte
+
+    def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
+        self.embd.wte = new_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        inputs_embeds: torch.FloatTensor = None,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+    ) -> torch.FloatTensor:
+        if (input_ids is None) == (inputs_embeds is None):
+            raise ValueError("Specify exactly one of `input_ids` or `inputs_embeds`.")
+        hidden_states = self.embd(input_ids) if input_ids is not None else inputs_embeds
+
+        for layer in self.h:
+            func = layer.__call__ if self.gradient_checkpointing else layer
+            args = (hidden_states, past_key_values, attention_mask)
+            hidden_states = (
+                torch.utils.checkpoint.checkpoint(func, *args, use_reentrant=True)
+                if self.gradient_checkpointing
+                else func(*args)
+            )
+
+        return hidden_states
+
+
+class PhiForCausalLM(PhiPreTrainedModel):
+    _keys_to_ignore_on_load_missing, _keys_to_ignore_on_load_unexpected = (
+        [""],
+        [r"transformer\.h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"],
+    )
+
+    def __init__(self, config: PhiConfig) -> None:
+        super().__init__(config)
+        self.transformer = PhiModel(config)
+        self.lm_head = CausalLMHead(config)
+        self.loss = CausalLMLoss()
+        self.post_init()
+
+    def get_output_embeddings(self) -> nn.Linear:
+        return self.lm_head.linear
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
+        self.lm_head.linear = new_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        inputs_embeds: torch.FloatTensor = None,
+        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> CausalLMOutputWithPast:
+        hidden_states = self.transformer(
+            input_ids=input_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+        )
+        lm_logits = self.lm_head(hidden_states)
+        loss = self.loss(lm_logits, labels) if labels is not None else None
+
+        return CausalLMOutputWithPast(
+            loss=loss, logits=lm_logits, past_key_values=past_key_values
+        )

moondream.py

@@ -0,0 +1,100 @@
+import torch
+from .vision_encoder import VisionEncoder
+from .text_model import TextModel
+from .configuration_moondream import MoondreamConfig
+from transformers import PreTrainedModel
+import re
+
+
+class Moondream(PreTrainedModel):
+    config_class = MoondreamConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vision_encoder = VisionEncoder()
+        self.text_model = TextModel(config, None)
+
+    @property
+    def device(self):
+        return self.text_model.model.device
+
+    def encode_image(self, image):
+        return self.vision_encoder(image)
+
+    def input_embeds(self, prompt, image_embeds, tokenizer):
+        def _tokenize(txt):
+            return tokenizer(
+                txt, return_tensors="pt", add_special_tokens=False
+            ).input_ids.to(self.device)
+
+        # Add BOS token
+        embeds = []
+        embeds.append(
+            self.text_model.text_emb(
+                (torch.tensor([[tokenizer.bos_token_id]], device=self.device))
+            )
+        )
+
+        if "<image>" not in prompt:
+            embeds.append(self.text_model.text_emb(_tokenize(prompt)))
+        else:
+            assert prompt.count("<image>") == 1
+            before, after = prompt.split("<image>")
+            embeds.append(self.text_model.text_emb(_tokenize(f"{before}<image>")))
+            embeds.append(image_embeds.to(self.device))
+            embeds.append(self.text_model.text_emb(_tokenize(f"</image>{after}")))
+
+        return torch.cat(embeds, dim=1)
+
+    def generate(
+        self,
+        image_embeds,
+        prompt,
+        tokenizer,
+        eos_text="Human:",
+        max_new_tokens=128,
+        **kwargs,
+    ):
+        eos_tokens = tokenizer(eos_text, add_special_tokens=False)[0].ids
+
+        generate_config = {
+            "eos_token_id": eos_tokens,
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.eos_token_id,
+            "max_new_tokens": max_new_tokens,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
+            output_ids = self.text_model.model.generate(
+                inputs_embeds=inputs_embeds, **generate_config
+            )
+
+        return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+    def answer_question(
+        self,
+        image_embeds,
+        question,
+        tokenizer,
+        chat_history="",
+        result_queue=None,
+        **kwargs,
+    ):
+        prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
+        answer = self.generate(
+            image_embeds,
+            prompt,
+            eos_text="<END>",
+            tokenizer=tokenizer,
+            max_new_tokens=128,
+            **kwargs,
+        )[0]
+        cleaned_answer = re.sub("<$", "", re.sub("END$", "", answer)).strip()
+
+        # Use the result_queue to pass the result if it is provided
+        if result_queue:
+            result_queue.put(cleaned_answer)
+        else:
+            return cleaned_answer

text_model.py

@@ -0,0 +1,20 @@
+from torch import nn
+import transformers
+from .modeling_phi import PhiForCausalLM
+from .configuration_moondream import PhiConfig
+
+transformers.logging.set_verbosity_error()
+
+
+class TextModel(nn.Module):
+    def __init__(self, config, tokenizer) -> None:
+        super().__init__()
+
+        if type(config.phi_config) == dict:
+            phi_config = PhiConfig(**config.phi_config)
+        else:
+            phi_config = config.phi_config
+
+        self.model = PhiForCausalLM(phi_config)
+        self.text_emb = self.model.get_input_embeddings()
+        self.tokenizer = tokenizer

vision_encoder.py

@@ -0,0 +1,150 @@
+import torch
+from torch import nn
+from PIL import Image
+from einops import rearrange
+from torchvision.transforms.v2 import (
+    Compose,
+    Resize,
+    InterpolationMode,
+    ToImage,
+    ToDtype,
+    Normalize,
+)
+import timm
+
+
+class VisualHolder(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.visual = model
+
+    def forward(self, x):
+        return self.visual(x)
+
+
+class ModelHolder(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class LinearPatchEmbedding(nn.Module):
+    def __init__(self, conv):
+        super().__init__()
+        self.linear = nn.Linear(588, 1152)
+        self.linear.weight.data = conv.weight.data.view(1152, -1)
+        if conv.bias is not None:
+            self.linear.bias.data = conv.bias.data
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int = None,
+        out_features: int = None,
+        act_layer: nn.Module = nn.GELU,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+
+        torch.nn.init.kaiming_normal_(
+            self.fc1.weight, mode="fan_in", nonlinearity="relu"
+        )
+        torch.nn.init.kaiming_normal_(
+            self.fc2.weight, mode="fan_in", nonlinearity="relu"
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+class VisionProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        image_embedding_dim = 1152
+        model_dim = 2048
+        hidden_dim = model_dim * 4
+
+        self.mlp1 = MLP(image_embedding_dim, hidden_dim, model_dim)
+        self.mlp2 = MLP(model_dim, hidden_dim, model_dim)
+        self.ln = nn.LayerNorm(model_dim)
+
+    @property
+    def device(self):
+        return self.mlp1.fc1.weight.device
+
+    def forward(self, x):
+        x = self.mlp1(x)
+        x = self.ln(x)
+        x = x + self.mlp2(x)
+        return x
+
+
+class VisionTower(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.encoder = ModelHolder(
+            VisualHolder(timm.create_model("vit_so400m_patch14_siglip_384"))
+        )
+        self.encoder.model.visual.patch_embed = LinearPatchEmbedding(
+            self.encoder.model.visual.patch_embed.proj
+        )
+        self.encoder.model.visual.attn_pool = nn.Identity()
+
+        self.projection = VisionProjection()
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.projection(x)
+        return x
+
+
+class VisionEncoder(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.model = VisionTower()
+        self.preprocess = Compose(
+            [
+                Resize(size=(378, 378), interpolation=InterpolationMode.BICUBIC),
+                ToImage(),
+                ToDtype(torch.float32, scale=True),
+                Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            ]
+        )
+
+    @property
+    def device(self):
+        return self.model.projection.mlp1.fc1.weight.device
+
+    @property
+    def dtype(self):
+        return self.model.projection.mlp1.fc1.weight.dtype
+
+    def __call__(self, image: Image) -> torch.Tensor:
+        with torch.no_grad():
+            image_vec = (
+                self.preprocess(image.convert("RGB"))
+                .unsqueeze(0)
+                .to(self.device, dtype=self.dtype)
+            )
+            image_vec = rearrange(
+                image_vec, "b c (h p1) (w p2) -> b (h w) (c p1 p2)", p1=14, p2=14
+            )
+            return self.model(image_vec)