Re-add modeling_amplify after history purge

modeling_amplify.py

@@ -0,0 +1,291 @@
+"""Standalone AMPLIFY model for HuggingFace Hub (trust_remote_code=True).
+
+This is a self-contained file that can be shipped in a HuggingFace repo so that
+``AutoModel.from_pretrained(..., trust_remote_code=True)`` works without
+installing the ``amplify`` package.
+
+Based on: https://github.com/chandar-lab/AMPLIFY
+"""
+
+from typing import Tuple
+
+import torch
+from torch import nn
+from torch.nn.functional import scaled_dot_product_attention
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.modeling_outputs import MaskedLMOutput
+
+# Optional: flash attention for packed-sequence training.  Not required for
+# standard inference.
+try:
+    from flash_attn.flash_attn_interface import flash_attn_varlen_func  # type: ignore
+except ImportError:
+    flash_attn_varlen_func = None
+
+
+# ---------------------------------------------------------------------------
+# Rotary positional embeddings (inlined from amplify.model.rotary)
+# ---------------------------------------------------------------------------
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device, dtype=torch.float32)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return freqs_cis
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    assert freqs_cis.shape == (x.shape[0], x.shape[1], x.shape[-1])
+    return freqs_cis.contiguous().unsqueeze(2)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+class AMPLIFYConfig(PretrainedConfig):
+    model_type = "AMPLIFY"
+
+    def __init__(
+        self,
+        hidden_size: int = 960,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 15,
+        intermediate_size: int = 3840,
+        embedding_init_range: float = 0.02,
+        decoder_init_range: float = 0.02,
+        norm_eps: float = 1e-05,
+        vocab_size: int = 32,
+        pad_token_id: int = 0,
+        max_length: int = 2048,
+        max_protein_length: int = 50000,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.embedding_init_range = embedding_init_range
+        self.decoder_init_range = decoder_init_range
+        self.norm_eps = norm_eps
+        self.vocab_size = vocab_size
+        self.pad_token_id = pad_token_id
+        self.max_length = max_length
+        self.max_protein_length = max_protein_length
+
+
+# ---------------------------------------------------------------------------
+# Encoder blocks
+# ---------------------------------------------------------------------------
+
+class EncoderBlock(nn.Module):
+    """Standard transformer encoder block with SwiGLU FFN and RoPE."""
+
+    def __init__(self, config: AMPLIFYConfig):
+        super().__init__()
+        self.config = config
+        self.d_head = config.hidden_size // config.num_attention_heads
+
+        # Attention
+        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=False)
+        self.wo = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+
+        # SwiGLU FFN
+        multiple_of = 8
+        intermediate_size = multiple_of * (
+            (int(2 * config.intermediate_size / 3) + multiple_of - 1) // multiple_of
+        )
+        self.c_fc = nn.Linear(config.hidden_size, 2 * intermediate_size, bias=False)
+        self.silu = nn.SiLU()
+        self.mlp_c_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False)
+
+        self.attention_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
+        self.ffn_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        output_attentions: bool,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+    ):
+        batch_size, seq_len, _ = x.shape
+
+        xq, xk, xv = (
+            self.qkv(self.attention_norm(x))
+            .reshape(batch_size, seq_len, self.config.num_attention_heads, self.d_head * 3)
+            .chunk(3, axis=-1)
+        )
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
+
+        attn_weights = None
+
+        if cu_seqlens is not None:
+            assert flash_attn_varlen_func is not None, (
+                "flash_attn is required for packed-sequence attention. "
+                "Install with: pip install flash-attn"
+            )
+            attn = flash_attn_varlen_func(
+                q=xq.squeeze(0),
+                k=xk.squeeze(0),
+                v=xv.squeeze(0),
+                cu_seqlens_q=cu_seqlens.squeeze(),
+                cu_seqlens_k=cu_seqlens.squeeze(),
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                dropout_p=0.0,
+                causal=False,
+            )
+        elif output_attentions:
+            attn_weights = xq.permute(0, 2, 1, 3) @ xk.permute(0, 2, 3, 1) / (xq.size(-1) ** 0.5)
+            if attention_mask is not None:
+                attn_weights = attn_weights * attention_mask
+            attn_weights = attn_weights.softmax(-1)
+            attn = attn_weights @ xv.permute(0, 2, 1, 3)
+            attn = attn.transpose(1, 2)
+        else:
+            attn = scaled_dot_product_attention(
+                query=xq.transpose(1, 2),
+                key=xk.transpose(1, 2),
+                value=xv.transpose(1, 2),
+                attn_mask=attention_mask.bool() if attention_mask is not None else None,
+                dropout_p=0,
+            ).transpose(1, 2)
+
+        attn = self.wo(attn.reshape(batch_size, seq_len, self.config.num_attention_heads * self.d_head))
+
+        x = x + attn
+
+        uv = self.c_fc(self.ffn_norm(x))
+        u, v = torch.chunk(uv, 2, dim=-1)
+        x_mlp = u * self.silu(v)
+        h_mlp = self.mlp_c_proj(x_mlp)
+
+        x = x + h_mlp
+        return x, attn_weights
+
+
+# ---------------------------------------------------------------------------
+# Model
+# ---------------------------------------------------------------------------
+
+class AMPLIFYPreTrainedModel(PreTrainedModel):
+    config_class = AMPLIFYConfig
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.uniform_(
+                -self.config.decoder_init_range, self.config.decoder_init_range
+            )
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.uniform_(
+                -self.config.embedding_init_range, self.config.embedding_init_range
+            )
+
+
+class AMPLIFY(AMPLIFYPreTrainedModel):
+    """AMPLIFY protein language model.
+
+    A transformer encoder for protein sequences using RoPE and SwiGLU,
+    trained with masked language modelling.
+    """
+
+    def __init__(self, config: AMPLIFYConfig, **kwargs):
+        super().__init__(config)
+        self.config = config
+
+        self.encoder = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+
+        self.transformer_encoder = nn.ModuleList()
+        for _ in range(config.num_hidden_layers):
+            self.transformer_encoder.append(EncoderBlock(config))
+
+        self.layer_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
+
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+
+        freqs_cis = precompute_freqs_cis(
+            config.hidden_size // config.num_attention_heads,
+            config.max_protein_length * 2,
+        )
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor = None,
+        max_seqlen: int = None,
+        cu_seqlens: torch.Tensor = None,
+        attention_mask: torch.Tensor = None,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+    ):
+        hidden_states, attentions = [], []
+
+        if isinstance(output_hidden_states, bool) and not output_hidden_states:
+            output_hidden_index = self.config.num_hidden_layers + 1
+        elif isinstance(output_hidden_states, int):
+            output_hidden_index = output_hidden_states
+        else:
+            output_hidden_index = 0
+
+        if attention_mask is not None:
+            attention_mask = (
+                attention_mask.unsqueeze(1)
+                .unsqueeze(1)
+                .repeat(1, self.config.num_attention_heads, attention_mask.size(-1), 1)
+            )
+
+        if cu_seqlens is not None:
+            assert not output_attentions, "Output attentions is not supported when sequences are packed."
+            assert max_seqlen is not None, "Missing max_seqlen. It must be provided when cu_seqlens are not None."
+            assert input_ids.shape[0] == 1, "Cumulative sequence lengths are provided but input_ids are not packed."
+            assert input_ids.is_cuda, "Packing uses flash-attention and is only supported on GPU."
+
+        # RoPE
+        if position_ids is not None:
+            freqs_cis = self.freqs_cis[position_ids]
+        else:
+            freqs_cis = (
+                self.freqs_cis[: input_ids.shape[1]]
+                .unsqueeze(0)
+                .repeat(input_ids.shape[0], 1, 1)
+            )
+
+        x = self.encoder(input_ids)
+
+        for idx, layer in enumerate(self.transformer_encoder):
+            x, attn = layer(x, attention_mask, freqs_cis, output_attentions, max_seqlen, cu_seqlens)
+            if idx >= output_hidden_index:
+                hidden_states.append(x)
+            if output_attentions:
+                attentions.append(attn)
+
+        logits = self.decoder(self.layer_norm(x))
+
+        return MaskedLMOutput(
+            logits=logits,
+            hidden_states=hidden_states,
+            attentions=attentions,
+        )