Delete TheOverthinker_phi-1_5

Moveing files to main directory

TheOverthinker_phi-1_5/added_tokens.json

@@ -1,40 +0,0 @@
-{
-  "\t\t": 50294,
-  "\t\t\t": 50293,
-  "\t\t\t\t": 50292,
-  "\t\t\t\t\t": 50291,
-  "\t\t\t\t\t\t": 50290,
-  "\t\t\t\t\t\t\t": 50289,
-  "\t\t\t\t\t\t\t\t": 50288,
-  "\t\t\t\t\t\t\t\t\t": 50287,
-  "  ": 50286,
-  "   ": 50285,
-  "    ": 50284,
-  "     ": 50283,
-  "      ": 50282,
-  "       ": 50281,
-  "        ": 50280,
-  "         ": 50279,
-  "          ": 50278,
-  "           ": 50277,
-  "            ": 50276,
-  "             ": 50275,
-  "              ": 50274,
-  "               ": 50273,
-  "                ": 50272,
-  "                 ": 50271,
-  "                  ": 50270,
-  "                   ": 50269,
-  "                    ": 50268,
-  "                     ": 50267,
-  "                      ": 50266,
-  "                       ": 50265,
-  "                        ": 50264,
-  "                         ": 50263,
-  "                          ": 50262,
-  "                           ": 50261,
-  "                            ": 50260,
-  "                             ": 50259,
-  "                              ": 50258,
-  "                               ": 50257
-}

TheOverthinker_phi-1_5/config.json

@@ -1,35 +0,0 @@
-{
-  "_name_or_path": "D:\\AI\\oobabooga_windows\\text-generation-webui\\models\\microsoft_phi-1_5",
-  "activation_function": "gelu_new",
-  "architecture": {
-    "block_cls": "parallel",
-    "mixer": {},
-    "mlp": {
-      "mlp_cls": "mlp"
-    }
-  },
-  "architectures": [
-    "MixFormerSequentialForCausalLM"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_mixformer_sequential.MixFormerSequentialConfig",
-    "AutoModelForCausalLM": "modeling_mixformer_sequential.MixFormerSequentialForCausalLM"
-  },
-  "embd_layer": "default",
-  "embd_pdrop": 0.0,
-  "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "model_type": "mixformer-sequential",
-  "n_embd": 2048,
-  "n_head": 32,
-  "n_inner": null,
-  "n_layer": 24,
-  "n_positions": 2048,
-  "phyagi_version": "0.0.4.dev",
-  "resid_pdrop": 0.0,
-  "rotary_dim": 32,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float16",
-  "transformers_version": "4.34.1",
-  "vocab_size": 51200
-}

TheOverthinker_phi-1_5/configuration_mixformer_sequential.py

@@ -1,59 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import math
-from typing import Any, Dict, List, Optional, Union
-
-from transformers import PretrainedConfig
-
-
-class MixFormerSequentialConfig(PretrainedConfig):
-    """MixFormer (sequential for DeepSpeed) configuration."""
-
-    model_type = "mixformer-sequential"
-
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-        "input_emb_layer": "embd_layer",  # `input_emb_layer` key is for backward compatibility
-        "blocks": "architecture",  # `blocks` key is for backward compatibility
-    }
-
-    def __init__(
-        self,
-        vocab_size: Optional[int] = 50304,
-        n_positions: Optional[int] = 2048,
-        n_embd: Optional[int] = 1024,
-        n_layer: Optional[int] = 20,
-        n_inner: Optional[int] = None,
-        n_head: Optional[int] = 16,
-        rotary_dim: Optional[int] = 32,
-        activation_function: Optional[str] = "gelu_new",
-        embd_layer: Optional[str] = "default",
-        architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
-        embd_pdrop: Optional[float] = 0.0,
-        resid_pdrop: Optional[float] = 0.0,
-        layer_norm_epsilon: Optional[float] = 1e-5,
-        initializer_range: Optional[float] = 0.02,
-        tie_word_embeddings: Optional[bool] = False,
-        pad_vocab_size_multiple: Optional[int] = 64,
-        **kwargs
-    ) -> None:
-        self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
-        self.n_positions = n_positions
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_inner = n_inner
-        self.n_head = n_head
-        self.rotary_dim = min(rotary_dim, n_embd // n_head)
-        self.activation_function = activation_function
-        self.embd_layer = embd_layer
-        self.architecture = architecture
-        self.embd_pdrop = embd_pdrop
-        self.resid_pdrop = resid_pdrop
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

TheOverthinker_phi-1_5/generation_config.json

@@ -1,4 +0,0 @@
-{
-  "_from_model_config": true,
-  "transformers_version": "4.34.1"
-}

TheOverthinker_phi-1_5/modeling_mixformer_sequential.py

@@ -1,779 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-# BSD 3-Clause License
-# 
-# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
-# All rights reserved.
-# 
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-# 
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-# 
-# * Redistributions in binary form must reproduce the above copyright notice,
-#   this list of conditions and the following disclaimer in the documentation
-#   and/or other materials provided with the distribution.
-# 
-# * Neither the name of the copyright holder nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-# 
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-from __future__ import annotations
-
-import math
-import copy
-from typing import Any, Dict, Optional, Tuple
-from dataclasses import dataclass, field
-
-import torch
-import torch.nn as nn
-
-from einops import rearrange
-from transformers.activations import ACT2FN
-from transformers import PretrainedConfig, PreTrainedModel
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from .configuration_mixformer_sequential import MixFormerSequentialConfig
-
-@dataclass
-class InferenceParams:
-    """Inference parameters that are passed to the main model in order
-    to efficienly calculate and store the context during inference.
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
-    max_sequence_len: int
-    max_batch_size: int
-    sequence_len_offset: int = 0
-    batch_size_offset: int = 0
-    key_value_memory_dict: dict = field(default_factory=dict)
-    fused_ft_kernel: bool = False
-    lengths_per_sample: Optional[torch.Tensor] = None
-
-
-class Embedding(nn.Module):
-    """Token embedding with dropout."""
-
-    def __init__(self, config: PretrainedConfig) -> None:
-        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:
-        input_shape = input_ids.size()
-        input_ids = input_ids.view(-1, input_shape[-1])
-
-        hidden_states = self.wte(input_ids)
-        hidden_states = self.drop(hidden_states)
-
-        return hidden_states
-
-class RotaryEmbedding(nn.Module):
-    """PyTorch implementation of `flash-attn` RotaryEmbedding layer.
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
-
-    def __init__(
-        self,
-        dim: int,
-        base: Optional[int] = 10000,
-        scale_base: Optional[float] = None,
-        device: Optional[str] = None,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-
-        if scale_base is not None:
-            raise NotImplementedError
-
-        # Generate and save the inverse frequency buffer (non-trainable)
-        self.dim = dim
-        self.base = base
-        self.scale_base = scale_base
-        self.device = device
-
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
-        self.register_buffer("inv_freq", inv_freq)
-
-        scale = (
-            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
-            if scale_base is not None
-            else None
-        )
-        self.register_buffer("scale", scale)
-
-        self._seq_len_cached = 0
-        self._cos_cached = None
-        self._sin_cached = None
-        self._cos_k_cached = None
-        self._sin_k_cached = None
-
-    def _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0) -> None:
-        # Reset the tables if the sequence length has changed,
-        # or if we're on a new device (possibly due to tracing for instance)
-        seqlen = x.shape[1] + seqlen_offset
-
-        # Re-generate the inverse frequency buffer if it's not fp32
-        # (for instance if model.half() was called)
-        if self.inv_freq.dtype != "torch.float32":
-            self.inv_freq = 1.0 / (
-                self.base ** (torch.arange(0, self.dim, 2, device=self.device, dtype=torch.float32) / self.dim)
-            )
-
-        if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
-            self._seq_len_cached = seqlen
-            t = torch.arange(seqlen, device=x.device, dtype=torch.float32)
-
-            # Don't do einsum, it converts fp32 to fp16
-            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            freqs = torch.outer(t, self.inv_freq.to(device=t.device, dtype=torch.float32))
-            if self.scale is None:
-                self._cos_cached = torch.cos(freqs).to(x.dtype)
-                self._sin_cached = torch.sin(freqs).to(x.dtype)
-            else:
-                power = (
-                    torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
-                ) / self.scale_base
-                scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
-
-                # We want the multiplication by scale to happen in fp32
-                self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
-                self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
-                self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
-                self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
-
-    def apply_rotary_emb_qkv(
-        self,
-        qkv: torch.FloatTensor,
-        sin: torch.FloatTensor,
-        cos: torch.FloatTensor,
-        sin_k: Optional[torch.FloatTensor] = None,
-        cos_k: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        _, seqlen, three, _, headdim = qkv.shape
-        assert three == 3
-
-        rotary_seqlen, rotary_dim = cos.shape
-        rotary_dim *= 2
-        assert rotary_dim <= headdim
-        assert seqlen <= rotary_seqlen
-
-        cos_k = cos if cos_k is None else cos_k
-        sin_k = sin if sin_k is None else sin_k
-        assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
-
-        q_rot = qkv[:, :, 0, :, :rotary_dim]
-        q_pass = qkv[:, :, 0, :, rotary_dim:]
-
-        k_rot = qkv[:, :, 1, :, :rotary_dim]
-        k_pass = qkv[:, :, 1, :, rotary_dim:]
-
-        # Splits the queries and keys in half
-        q1, q2 = q_rot.chunk(2, dim=-1)
-        k1, k2 = k_rot.chunk(2, dim=-1)
-        c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
-
-        # Casts to fp32 are necessary to prevent fp16 overflow issues
-        q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
-
-        # Computes the new keys and queries, recasting to original dtype
-        q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
-
-        k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
-
-        return torch.cat(
-            [
-                torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
-                torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
-                qkv[:, :, 2:3, :, :],
-            ],
-            axis=2,
-        )
-
-    def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Perform the forward pass.
-
-        Args:
-            qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
-            seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.
-
-        Returns:
-            New `qkv` and the cached sinusoids.
-
-        """
-
-        self._update_cos_sin_cache(qkv, seqlen_offset)
-
-        return self.apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
-
-def _update_kv_cache(kv, inference_params, layer_idx):
-    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
-    # Pre-allocate memory for key-values for inference.
-    num_heads, head_dim = kv.shape[-2:]
-    if layer_idx not in inference_params.key_value_memory_dict:
-        kv_cache = torch.empty(
-            inference_params.max_batch_size, inference_params.max_sequence_len, 2,
-            num_heads, head_dim, dtype=kv.dtype, device=kv.device
-        )
-        inference_params.key_value_memory_dict[layer_idx] = kv_cache
-    else:
-        kv_cache = inference_params.key_value_memory_dict[layer_idx]
-
-    # Adjust key and value for inference
-    batch_start = inference_params.batch_size_offset
-    batch_end = batch_start + kv.shape[0]
-    sequence_start = inference_params.sequence_len_offset
-    sequence_end = sequence_start + kv.shape[1]
-    assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
-    assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
-
-    assert kv_cache is not None
-    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
-    kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
-    return kv
-
-
-class MLP(nn.Module):
-    """Multi-Layer Perceptron.
-
-    Reference:
-        Attention Is All You Need.
-        https://arxiv.org/pdf/1706.03762.pdf.
-
-    """
-
-    def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None) -> None:
-        super().__init__()
-
-        act_fn = config.activation_function if act_fn is None else act_fn
-        assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
-
-        n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
-        n_inner = n_inner if n_inner is not None else 4 * config.n_embd
-
-        self.fc1 = nn.Linear(config.n_embd, n_inner)
-        self.fc2 = nn.Linear(n_inner, config.n_embd)
-        self.act = ACT2FN[act_fn]
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
-        old_keys = [prefix + "fc_in.weight", prefix + "fc_out.weight", prefix + "fc_in.bias", prefix + "fc_out.bias"]
-        new_keys = [prefix + "fc1.weight", prefix + "fc2.weight", prefix + "fc1.bias", prefix + "fc2.bias"]
-        
-        if all(k in state_dict for k in old_keys) and not all(k in state_dict for k in new_keys):
-            # Older version of `MLP` saved with different key names.
-            for old_key, new_key in zip(old_keys, new_keys):
-                state_dict[new_key] = state_dict.pop(old_key)
-
-        return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
-    
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-
-        return hidden_states
-
-
-class FusedMLP(nn.Module):
-    """Fused Multi-Layer Perceptron from `flash-attn`.
-
-    Reference:
-        https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.
-
-    """
-    def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None, 
-                 raise_on_missing: bool = False) -> None:
-        super().__init__()
-
-        act_fn = config.activation_function if act_fn is None else act_fn
-        assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
-
-        n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
-        n_inner = n_inner if n_inner is not None else 4 * config.n_embd
-
-        gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"]
-        activation = "gelu_approx" if act_fn in gelu_activations else "relu"
-           
-        self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)
-    
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        return self.mlp(hidden_states)
-
-class SelfAttention(nn.Module):
-    """Implement the scaled dot product attention with softmax.
-    Adapted from https://github.com/Dao-AILab/flash-attention.
-    Arguments
-    ---------
-        softmax_scale: The temperature to use for the softmax attention.
-                      (default: 1/sqrt(d_keys) where d_keys is computed at
-                      runtime)
-        attention_dropout: The dropout rate to apply to the attention
-                           (default: 0.0)
-    """
-    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
-        super().__init__()
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
-
-    def forward(self, qkv, causal=None, key_padding_mask=None):
-        """Implements the multihead softmax attention.
-        Arguments
-        ---------
-            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
-            causal: if passed, will override self.causal
-            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
-                False means to mask out. (B, S)
-        """
-        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
-        causal = self.causal if causal is None else causal
-        q, k, v = qkv.unbind(dim=2)
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-        scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
-        if key_padding_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype,
-                                      device=scores.device)
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
-            scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
-        if causal:
-            # "triu_tril_cuda_template" not implemented for 'BFloat16'
-            # So we have to construct the mask in float
-            causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
-            scores = scores + causal_mask.to(dtype=scores.dtype)
-        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
-        attention_drop = self.drop(attention)
-        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
-        return output
-
-
-class CrossAttention(nn.Module):
-    """Implement the scaled dot product attention with softmax.
-    Adapted from https://github.com/Dao-AILab/flash-attention.
-    Arguments
-    ---------
-        softmax_scale: The temperature to use for the softmax attention.
-                      (default: 1/sqrt(d_keys) where d_keys is computed at
-                      runtime)
-        attention_dropout: The dropout rate to apply to the attention
-                           (default: 0.0)
-    """
-    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
-        super().__init__()
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
-
-    def forward(self, q, kv, causal=None, key_padding_mask=None):
-        """Implements the multihead softmax attention.
-        Arguments
-        ---------
-            q: The tensor containing the query. (B, Sq, H, D)
-            kv: The tensor containing the key and value. (B, Sk, 2, H, D)
-            causal: if passed, will override self.causal
-            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
-                False means to mask out. (B, Sk)
-        """
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
-        causal = self.causal if causal is None else causal
-        seqlen_k = kv.shape[1]
-        assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
-        k, v = kv.unbind(dim=2)
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-        scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
-        if key_padding_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen_k), -10000.0, dtype=scores.dtype,
-                                      device=scores.device)
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
-            scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
-        if causal:
-            # "triu_tril_cuda_template" not implemented for 'BFloat16'
-            # So we have to construct the mask in float
-            causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0,
-                                                device=scores.device), 1)
-            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
-            scores = scores + causal_mask.to(dtype=scores.dtype)
-        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
-        attention_drop = self.drop(attention)
-        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
-        return output
-
-def find_mha_dims(
-    config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
-) -> Tuple[int, int]:
-    """Validate and return the number of heads and head dimension for multi-head attention.
-
-    Args:
-        config: Model configuration.
-        n_head: Number of heads.
-        head_dim: Head dimension.
-
-    Returns:
-        Number of heads and head dimension.
-
-    """
-
-    assert all(
-        hasattr(config, attr) for attr in ["n_embd", "n_head"]
-    ), "`config` must have `n_embd` and `n_head` attributes."
-
-    if head_dim is None:
-        assert (
-            config.n_embd % config.n_head == 0
-        ), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
-
-    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`.")
-
-    return n_head, head_dim
-
-
-class MHA(nn.Module):
-    """Multi-head attention layer.
-    Adapted from https://github.com/Dao-AILab/flash-attention."""
-
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        rotary_dim: Optional[int] = None,
-        n_head: Optional[int] = None,
-        head_dim: Optional[int] = None,
-        bias: Optional[bool] = True,
-        dropout: Optional[float] = 0.0,
-        softmax_scale: Optional[float] = None,
-        causal: Optional[bool] = True,
-        layer_idx: Optional[int] = None,
-        rotary_emb_scale_base: Optional[float] = None,
-        return_residual: Optional[bool] = False,
-        checkpointing: Optional[bool] = False,
-        device: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-        fused_dense: Optional[bool] = True,
-        flash_attn: Optional[bool] = True,
-        cutlass_attn: Optional[bool] = False,
-        flash_rotary: Optional[bool] = True,
-        raise_on_missing: Optional[bool] = False
-    ) -> None:
-        super().__init__()
-
-        factory_kwargs = {"device": device, "dtype": dtype}
-        n_head, head_dim = find_mha_dims(config, n_head, head_dim)
-
-        self.hidden_size = config.n_embd
-        self.n_head = n_head
-        self.head_dim = head_dim
-        self.op_size = n_head * head_dim
-
-        self.causal = causal
-        self.layer_idx = layer_idx
-        self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
-        self.fused_dense = fused_dense
-        self.flash_attn = flash_attn
-        self.cutlass_attn = cutlass_attn
-        self.flash_rotary = flash_rotary
-        self.return_residual = return_residual
-        self.checkpointing = checkpointing
-
-        if self.rotary_emb_dim > 0:
-            rotary_kwargs = {"device": device}
-            if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
-                rotary_kwargs["scale_base"] = rotary_emb_scale_base
-            
-            self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
-        else:
-            pass
-
-        self.Wqkv = nn.Linear(self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs)
-        self.out_proj = nn.Linear(self.op_size, self.hidden_size, bias=bias, **factory_kwargs)
-
-        self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
-        self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
-
-    def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
-        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-        Adapted from https://github.com/Dao-AILab/flash-attention."""
-
-        assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
-
-        return _update_kv_cache(kv, inference_params, self.layer_idx)
-
-    def forward(
-        self,
-        x: torch.FloatTensor,
-        x_kv: Optional[torch.FloatTensor] = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
-        cu_seqlens: Optional[torch.LongTensor] = None,
-        max_seqlen: Optional[int] = None,
-        mixer_subset: Optional[torch.LongTensor] = None,
-        past_cache: Optional[InferenceParams] = None,
-        **kwargs
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        """Perform the forward pass.
-
-        Args:
-            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
-                cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
-                is the is the sum of the sequence lengths in the batch.
-            x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
-            key_padding_mask: boolean mask, True means to keep, False means to mask out.
-                (batch, seqlen). Only applicable when not using FlashAttention.
-            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
-                of the sequences in the batch, used to index into x. Only applicable when using
-                FlashAttention.
-            max_seqlen: int. Maximum sequence length in the batch.
-            mixer_subset: for cross-attention only. If not None, will take a subset of x
-                before applying the query projection. Useful for e.g., ViT where we only care
-                about the CLS token in the last layer.
-            past_cache: For generation only.
-
-        Returns:
-            (batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
-                else (total, hidden_dim) where total is the is the sum of the sequence lengths
-                in the batch.
-
-        """
-
-        if cu_seqlens is not None:
-            assert max_seqlen is not None
-            assert key_padding_mask is None
-            assert self.flash_attn
-            assert self.rotary_emb_dim == 0
-
-        if key_padding_mask is not None:
-            assert cu_seqlens is None
-            assert max_seqlen is None
-            assert not self.flash_attn
-
-        if past_cache is not None:
-            assert key_padding_mask is None
-            assert cu_seqlens is None and max_seqlen is None
-
-        attn_kwargs = {"key_padding_mask": key_padding_mask}
-
-        assert x_kv is None and mixer_subset is None
-
-        qkv = self.Wqkv(x)
-        qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
-
-        if past_cache is None:
-            if self.rotary_emb_dim > 0:
-                qkv = self.rotary_emb(qkv)
-                context = self.inner_attn(qkv, **attn_kwargs)
-
-        else:
-            if self.rotary_emb_dim > 0:
-                qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
-            q = qkv[:, :, 0]
-            kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
-            # If we're processing the prompt, causal=None (use self.causal).
-            # If we're decoding, then causal=False.
-            causal = None if past_cache.sequence_len_offset == 0 else False
-            context = self.inner_cross_attn(q, kv, causal=causal)
-
-        out = rearrange(context, "... h d -> ... (h d)")
-        out = self.out_proj(out)
-
-        return out if not self.return_residual else (out, x)
-
-class ParallelBlock(nn.Module):
-    """Parallel block.
-
-    This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
-
-    """
-
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        mixer: Optional[Dict[str, Any]] = None,
-        mlp: Optional[Dict[str, Any]] = None,
-        block_idx: Optional[int] = None,
-    ) -> None:
-        super().__init__()
-
-        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=config, **mixer, layer_idx=block_idx)
-        mlp_cls = mlp.pop('mlp_cls')
-        if mlp_cls == 'fused_mlp':
-            self.mlp = FusedMLP(config=config, **mlp)
-        else:
-            self.mlp = MLP(config=config, **mlp)
-
-    def forward(self, hidden_states: torch.FloatTensor, 
-                past_cache: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
-        residual = hidden_states
-        hidden_states = self.ln(hidden_states)
-
-        attn_outputs = self.mixer(hidden_states, past_cache=past_cache)
-        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))
-
-        hidden_states = attn_outputs + feed_forward_hidden_states + residual
-
-        return hidden_states
-
-class CausalLMHead(nn.Module):
-    """Causal Language Modeling head.
-
-    Reference:
-        Improving Language Understanding by Generative Pre-Training.
-        https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
-
-    """
-
-    def __init__(self, config: PretrainedConfig) -> None:
-        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: torch.FloatTensor) -> torch.FloatTensor:
-        hidden_states = self.ln(hidden_states)
-        logits = self.linear(hidden_states).to(torch.float32)
-
-        return logits
-
-
-class CausalLMLoss(nn.Module):
-    """Causal Language Modeling loss.
-
-    Reference:
-        Improving Language Understanding by Generative Pre-Training.
-        https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
-
-    """
-
-    def __init__(self, shift_labels: Optional[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 = logits[..., :-1, :].contiguous()
-            labels = labels[..., 1:].contiguous()
-
-        loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
-
-        return loss
-
-class MixFormerSequentialPreTrainedModel(PreTrainedModel):
-    """MixFormer (sequential for DeepSpeed) pre-trained model."""
-
-    config_class = MixFormerSequentialConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-
-    def __init__(self, *inputs, **kwargs) -> None:
-        super().__init__(*inputs, **kwargs)
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs) -> Dict[str, Any]:
-        if "use_cache" in kwargs and not kwargs["use_cache"]:
-            return {"input_ids": input_ids}
-
-        if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
-            past_key_values = InferenceParams(
-                max_batch_size=input_ids.shape[0],
-                max_sequence_len=self.config.n_positions,
-                sequence_len_offset=0,
-                batch_size_offset=0,
-                fused_ft_kernel=False,
-                key_value_memory_dict={},
-            )
-        else:
-            # assume past_key_values has cached all but last token in input_ids
-            past_key_values.sequence_len_offset = len(input_ids[0]) - 1
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-
-        return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}
-
-
-class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
-    """MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
-
-    _keys_to_ignore_on_load_missing = [""]
-    _keys_to_ignore_on_load_unexpected = [r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
-    _no_split_modules = ["ParallelBlock"]
-
-    def __init__(self, config: MixFormerSequentialConfig) -> None:
-        super().__init__(config)
-
-        modules = [Embedding(config)]
-        block_config = config.architecture
-
-        if not isinstance(block_config, list):
-            block_config = [block_config for _ in range(config.n_layer)]
-
-        if config.n_layer != len(block_config):
-            config.n_layer = len(block_config)
-
-        for block_idx, block in enumerate(block_config):
-            # `block_cls` with `legacy` value is for backward compatibility
-            # `path` key is for backward compatibility
-            block = copy.deepcopy(block) or {"block_cls": "parallel"}
-            block_cls = block.pop("path", None) or block.pop("block_cls", None)
-
-            block["block_idx"] = block_idx
-            modules.append(ParallelBlock(config, **block))
-
-        modules.append(CausalLMHead(config))
-
-        self.layers = nn.Sequential(*modules)
-        self.loss = CausalLMLoss()
-
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.layers[0].wte
-
-    def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
-        self.layers[0].wte = new_embeddings
-
-    def get_output_embeddings(self) -> nn.Linear:
-        return self.layers[-1].linear
-
-    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
-        self.layers[-1].linear = new_embeddings
-
-    def forward(
-        self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None, 
-        past_key_values: Optional[torch.FloatTensor] = None, **kwargs
-    ) -> CausalLMOutputWithPast:
-
-        if not past_key_values:
-            lm_logits = self.layers(input_ids)
-        else:
-            hidden_layer = self.layers[0](input_ids)
-            for module in self.layers[1:-1]:
-                hidden_layer = module(hidden_layer, past_cache=past_key_values)
-            lm_logits = self.layers[-1](hidden_layer)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss(lm_logits, labels)
-        
-        return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)

TheOverthinker_phi-1_5/pytorch_model.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:324e44d394669786db3285c13b354306b6b1fc3cd6948d3e5d860a501adce930
-size 2836629438

TheOverthinker_phi-1_5/special_tokens_map.json

@@ -1,5 +0,0 @@
-{
-  "bos_token": "<|endoftext|>",
-  "eos_token": "<|endoftext|>",
-  "unk_token": "<|endoftext|>"
-}

TheOverthinker_phi-1_5/tokenizer_config.json

@@ -1,323 +0,0 @@
-{
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "50256": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": false,
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-      "single_word": false,
-      "special": true
-    },
-    "50257": {
-      "content": "                               ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50258": {
-      "content": "                              ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50259": {
-      "content": "                             ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50260": {
-      "content": "                            ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50261": {
-      "content": "                           ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50262": {
-      "content": "                          ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50263": {
-      "content": "                         ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50264": {
-      "content": "                        ",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50265": {
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-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50266": {
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-      "lstrip": false,
-      "normalized": true,
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-      "special": false
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-      "special": false
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-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50269": {
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-      "normalized": true,
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-    "50285": {
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-      "lstrip": false,
-      "normalized": true,
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-      "normalized": true,
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-      "lstrip": false,
-      "normalized": true,
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-      "special": false
-    },
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-      "content": "\t\t\t\t\t\t\t\t",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
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-    "50289": {
-      "content": "\t\t\t\t\t\t\t",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "50290": {
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