Phi2 multipack (#1173)

* phi2 multipack

* update validation and examples for phi

* more updates to phi examples

* make sure to use the correct collator for phi multipack

* phi needs attention mask now for multipack

* if the special token already exists in the tokenizer, don't require in lora modules to save

* fix qlora yml for phi, fix phi test validation

* test qlora too

* make sure flash attention is enabled for the test

* don't use remote code for phi anymore

* reduce sequence len for sample packing phi

examples/phi/phi-ft.yml

@@ -1,8 +1,6 @@
 base_model: microsoft/phi-1_5
-model_type: PhiForCausalLM
+model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-is_llama_derived_model: false
-trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: false
@@ -18,7 +16,7 @@ output_dir: ./phi-sft-out
 
 sequence_len: 2048
 sample_packing: true
-pad_to_sequence_len:
+pad_to_sequence_len: true
 
 adapter:
 lora_model_dir:
@@ -35,7 +33,7 @@ wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
-micro_batch_size: 1
+micro_batch_size: 2
 num_epochs: 4
 optimizer: adamw_torch
 adam_beta2: 0.95
@@ -45,18 +43,20 @@ lr_scheduler: cosine
 learning_rate: 0.000003
 
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: auto
 fp16:
 tf32: true
 
-gradient_checkpointing:
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: True
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
 xformers_attention:
-flash_attention:
+flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 4
@@ -68,7 +68,4 @@ fsdp:
 fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
-  bos_token: "<|endoftext|>"
-  eos_token: "<|endoftext|>"
-  unk_token: "<|endoftext|>"
   pad_token: "<|endoftext|>"

examples/phi/phi-qlora.yml

@@ -1,8 +1,6 @@
 base_model: microsoft/phi-1_5
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-is_llama_derived_model: false
-trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
@@ -16,9 +14,9 @@ dataset_prepared_path:
 val_set_size: 0.05
 output_dir: ./phi-sft-out
 
-sequence_len: 1024
-sample_packing: false  # not CURRENTLY compatible with LoRAs
-pad_to_sequence_len:
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
 
 adapter: qlora
 lora_model_dir:
@@ -35,7 +33,7 @@ wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
-micro_batch_size: 1
+micro_batch_size: 2
 num_epochs: 4
 optimizer: adamw_torch
 adam_beta2: 0.95
@@ -45,18 +43,20 @@ lr_scheduler: cosine
 learning_rate: 0.000003
 
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: auto
 fp16:
 tf32: true
 
-gradient_checkpointing:
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: True
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
 xformers_attention:
-flash_attention:
+flash_attention: true
 
 warmup_steps: 100
 evals_per_epoch: 4
@@ -68,7 +68,4 @@ fsdp:
 fsdp_config:
 resize_token_embeddings_to_32x: true
 special_tokens:
-  bos_token: "<|endoftext|>"
-  eos_token: "<|endoftext|>"
-  unk_token: "<|endoftext|>"
   pad_token: "<|endoftext|>"

examples/phi/phi2-ft.yml

@@ -1,8 +1,6 @@
 base_model: microsoft/phi-2
-model_revision:  834565c  # pin model repo to the previous architecture
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: false
@@ -17,19 +15,16 @@ val_set_size: 0.05
 output_dir: ./phi-sft-out
 
 sequence_len: 2048
-sample_packing: false  # currently unsupported
-pad_to_sequence_len:
+sample_packing: true
+pad_to_sequence_len: true
 
 adapter:
 lora_model_dir:
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.1
-lora_target_linear: true
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_linear:
 lora_fan_in_fan_out:
-lora_modules_to_save:
-  - embd
-  - lm_head
 
 wandb_project:
 wandb_entity:
@@ -38,14 +33,14 @@ wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
-micro_batch_size: 1
+micro_batch_size: 2
 num_epochs: 4
-optimizer: paged_adamw_8bit
+optimizer: adamw_torch
 adam_beta2: 0.95
 adam_epsilon: 0.00001
 max_grad_norm: 1.0
 lr_scheduler: cosine
-learning_rate: 1e-5
+learning_rate: 0.000003
 
 train_on_inputs: false
 group_by_length: false
@@ -54,6 +49,8 @@ fp16:
 tf32: true
 
 gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: True
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:

src/axolotl/core/trainer_builder.py

@@ -930,7 +930,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ]
         ]
         if use_batch_sampler_collator:
-            if self.cfg.model_config_type in ["mixtral", "qwen2"]:
+            if self.cfg.model_config_type in ["mixtral", "qwen2", "falcon", "phi"]:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq

src/axolotl/models/phi/__init__.py

@@ -1,8 +0,0 @@
-"""
-MixFormers model architecture used for phi models
-"""
-
-from .configuration_mixformer_sequential import MixFormerSequentialConfig  # noqa
-from .configuration_phi import PhiConfig  # noqa
-from .modeling_mixformer_sequential import MixFormerSequentialForCausalLM  # noqa
-from .modeling_phi import PhiForCausalLM  # noqa

src/axolotl/models/phi/configuration_mixformer_sequential.py

@@ -1,63 +0,0 @@
-# pylint: skip-file
-
-# 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)

src/axolotl/models/phi/configuration_phi.py

@@ -1,65 +0,0 @@
-# pylint: skip-file
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import math
-from typing import Optional
-
-from transformers import PretrainedConfig
-
-
-class PhiConfig(PretrainedConfig):
-    """Phi configuration."""
-
-    model_type = "phi"
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-    }
-
-    def __init__(
-        self,
-        vocab_size: int = 50304,
-        n_positions: int = 2048,
-        n_embd: int = 1024,
-        n_layer: int = 20,
-        n_inner: Optional[int] = None,
-        n_head: int = 16,
-        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,
-        **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.n_head_kv = n_head_kv
-        self.rotary_dim = min(rotary_dim, n_embd // n_head)
-        self.activation_function = activation_function
-        self.flash_attn = flash_attn
-        self.flash_rotary = flash_rotary
-        self.fused_dense = fused_dense
-        self.attn_pdrop = attn_pdrop
-        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)

src/axolotl/models/phi/modeling_mixformer_sequential.py

@@ -1,930 +0,0 @@
-# pylint: skip-file
-
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-# BSD 3-Clause License
-#
-# Copyright (c) 2022, Tri Dao, [email protected].
-# 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 copy
-import inspect
-from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from einops import rearrange
-from flash_attn.flash_attn_interface import (
-    flash_attn_kvpacked_func,
-    flash_attn_qkvpacked_func,
-    flash_attn_varlen_qkvpacked_func,
-)
-from transformers import PretrainedConfig, PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from ...monkeypatch.utils import get_cu_seqlens_from_pos_ids
-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]  # noqa
-    )
-    assert sequence_end <= (
-        kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2]  # noqa
-    )
-
-    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"]  # noqa
-        activation = "gelu_approx" if act_fn in gelu_activations else "relu"  # noqa
-
-        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, cu_seqlens=None, max_seqlen=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)
-        """
-        causal = self.causal if causal is None else causal
-        if cu_seqlens is not None:
-            return flash_attn_varlen_qkvpacked_func(
-                qkv.squeeze(0),
-                cu_seqlens,
-                max_seqlen,
-                dropout_p=self.drop.p,
-                softmax_scale=self.softmax_scale,
-                causal=causal,
-            )
-        else:
-            return flash_attn_qkvpacked_func(
-                qkv,
-                dropout_p=self.drop.p,
-                softmax_scale=self.softmax_scale,
-                causal=causal,
-            )
-
-
-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)
-        """
-        causal = self.causal if causal is None else causal
-        return flash_attn_kvpacked_func(
-            q,
-            kv,
-            dropout_p=self.drop.p,
-            softmax_scale=self.softmax_scale,
-            causal=causal,
-        )
-
-
-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, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, **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, layer_idx=block_idx)
-        self.mlp = MLP(config)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        past_cache: Optional[torch.FloatTensor] = None,
-        cu_seqlens: Optional[torch.LongTensor] = None,
-        max_seqlen: Optional[int] = None,
-    ) -> torch.FloatTensor:
-        residual = hidden_states
-        hidden_states = self.ln(hidden_states)
-
-        attn_outputs = self.mixer(
-            hidden_states,
-            past_cache=past_cache,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-        )
-        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 PackedSequential(nn.Sequential):
-    def forward(
-        self,
-        input,
-        cu_seqlens: Optional[torch.LongTensor] = None,
-        max_seqlen: Optional[int] = None,
-    ):
-        for module in self:
-            sig = inspect.signature(module.forward)
-            if "cu_seqlens" in sig.parameters:
-                input = module(input, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
-            else:
-                input = module(input)
-        return input
-
-
-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.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 = PackedSequential(*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,
-        position_ids: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> CausalLMOutputWithPast:
-        cu_seqlens: Optional[torch.LongTensor] = None
-        max_seqlen: Optional[int] = None
-        if position_ids is not None:
-            batch_size, seq_length = input_ids.shape
-            position_ids = position_ids.view(-1, seq_length).long()
-            cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
-            cu_seqlens = cu_seqlens.squeeze()
-
-        if not past_key_values:
-            lm_logits = self.layers(
-                input_ids, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
-            )
-        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,
-                    cu_seqlens=cu_seqlens,
-                    max_seqlen=max_seqlen,
-                )
-            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
-        )

src/axolotl/models/phi/modeling_phi.py

@@ -1,1092 +0,0 @@
-# pylint: skip-file
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-#
-# Copyright (c) 2022, Tri Dao, [email protected].
-# Licensed under the BSD 3-Clause License.
-
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass, field
-from typing import Any, Callable, Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from einops import rearrange, repeat
-from torch.utils.checkpoint import checkpoint
-from transformers import PretrainedConfig, PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from .configuration_phi import PhiConfig
-
-try:
-    from flash_attn.bert_padding import pad_input, unpad_input
-    from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
-    from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
-except ImportError:
-    pad_input, unpad_input = None, None
-    FlashRotaryEmbedding = None
-    FlashSelfAttention, FlashCrossAttention = None, None
-
-# this is in a seperate try/except block since sometimes fused_dense isn't available
-# and it shouldn't completely disable flash attn when it isn't
-try:
-    from flash_attn.ops.fused_dense import FusedDense
-except ImportError:
-    FusedDense = None
-
-
-@dataclass
-class InferenceParams:
-    """Inference parameters passed to model to efficiently calculate
-    and store context during inference.
-
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
-
-    Args:
-        max_seqlen: Maximum sequence length.
-        max_batch_size: Maximum batch size.
-        seqlen_offset: Sequence length offset.
-        batch_size_offset: Batch size offset.
-        key_value_memory_dict: Key value memory dictionary.
-        lengths_per_sample: Lengths per sample.
-
-    """
-
-    max_seqlen: int = field(metadata={"help": "Maximum sequence length."})
-
-    max_batch_size: int = field(metadata={"help": "Maximum batch size."})
-
-    seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
-
-    batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
-
-    key_value_memory_dict: Dict[str, Any] = field(
-        default_factory=dict, metadata={"help": "Key value memory dictionary."}
-    )
-
-    lengths_per_sample: torch.Tensor = field(
-        default=None, metadata={"help": "Lengths per sample."}
-    )
-
-
-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
-
-
-def _apply_rotary_emb(
-    x: torch.FloatTensor,
-    cos: torch.FloatTensor,
-    sin: torch.FloatTensor,
-) -> torch.FloatTensor:
-    _, seqlen, _, _ = x.shape
-    _, rotary_dim = cos.shape
-    rotary_dim *= 2
-
-    x_rot = x[:, :, :, :rotary_dim]
-    x_pass = x[:, :, :, rotary_dim:]
-
-    x1, x2 = x_rot.chunk(2, dim=-1)
-    c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(
-        sin[:seqlen], "s d -> s 1 d"
-    )
-    x1, x2, c, s = [t.to(dtype=torch.float32) for t in [x1, x2, c, s]]
-
-    x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype)
-
-    return torch.cat([x_rot, x_pass], axis=-1)
-
-
-def _apply_rotary_emb_kv(
-    kv: torch.FloatTensor,
-    cos: torch.FloatTensor,
-    sin: torch.FloatTensor,
-    cos_k: Optional[torch.FloatTensor] = None,
-    sin_k: Optional[torch.FloatTensor] = None,
-) -> torch.FloatTensor:
-    _, seqlen, _, _, _ = kv.shape
-    _, rotary_dim = cos.shape
-    rotary_dim *= 2
-
-    k_rot = kv[:, :, 0, :, :rotary_dim]
-    k_pass = kv[:, :, 0, :, rotary_dim:]
-
-    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"
-    )
-    k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]]
-
-    k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(kv.dtype)
-
-    return torch.cat(
-        [
-            torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
-            kv[:, :, 1:2, :, :],
-        ],
-        axis=2,
-    )
-
-
-def _apply_rotary_emb_qkv(
-    qkv: torch.FloatTensor,
-    cos: torch.FloatTensor,
-    sin: torch.FloatTensor,
-    cos_k: Optional[torch.FloatTensor] = None,
-    sin_k: Optional[torch.FloatTensor] = None,
-) -> torch.FloatTensor:
-    _, seqlen, _, _, _ = qkv.shape
-    _, rotary_dim = cos.shape
-    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:]
-
-    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"
-    )
-    q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
-
-    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,
-    )
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding (RoPE).
-
-    Reference:
-        RoFormer: 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,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-
-        if scale_base is not None:
-            raise NotImplementedError
-
-        self.dim = dim
-        self.base = float(base)
-        self.scale_base = scale_base
-        self.pos_idx_in_fp32 = pos_idx_in_fp32
-        self.max_position_embeddings = max_position_embeddings
-        self.device = device
-
-        # Generate and save the inverse frequency buffer (non-trainable)
-        inv_freq = self._compute_inv_freq(device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        # Generate and save the scale buffer (non-trainable)
-        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, persistent=False)
-
-        # Initialize cached attributes since ONNX can't rely on dynamic initialization
-        self._update_cos_sin_cache(
-            max_position_embeddings,
-            device=device,
-            dtype=torch.float32,
-        )
-
-    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
-
-        # fp32 is preferred since the output of `torch.arange` can be quite large
-        # and bf16 would lose a lot of precision
-        if self.pos_idx_in_fp32:
-            t = torch.arange(seqlen, device=device, dtype=torch.float32)
-            if self.inv_freq.dtype != torch.float32:
-                inv_freq = self._compute_inv_freq(device=device)
-            else:
-                inv_freq = self.inv_freq
-        else:
-            t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
-            inv_freq = self.inv_freq
-
-        # `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP
-        freqs = torch.outer(t, inv_freq)
-        if self.scale is None:
-            self._cos_cached = torch.cos(freqs).to(dtype)
-            self._sin_cached = torch.sin(freqs).to(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")
-
-            # Force the scale multiplication to happen in fp32
-            self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
-            self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
-            self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
-            self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
-
-    def forward(
-        self,
-        qkv: torch.Tensor,
-        kv: Optional[torch.Tensor] = None,
-        seqlen_offset: int = 0,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        if (
-            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())
-        ):
-            self._update_cos_sin_cache(
-                qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype
-            )
-
-        if kv is None:
-            return _apply_rotary_emb_qkv(
-                qkv,
-                self._cos_cached[seqlen_offset:],
-                self._sin_cached[seqlen_offset:],
-            )
-        else:
-            q = _apply_rotary_emb(
-                qkv,
-                self._cos_cached[seqlen_offset:],
-                self._sin_cached[seqlen_offset:],
-            )
-            kv = _apply_rotary_emb_kv(
-                kv,
-                self._cos_cached[seqlen_offset:],
-                self._sin_cached[seqlen_offset:],
-            )
-
-            return q, 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
-
-        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 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 SelfAttention(nn.Module):
-    """Self-attention layer (compatible with PyTorch).
-
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
-
-    """
-
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ) -> None:
-        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: bool = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
-        q, k, v = qkv.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), -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:
-            causal_mask = torch.triu(
-                torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1
-            )
-            scores = scores + causal_mask.to(dtype=scores.dtype)
-
-        attention = torch.softmax(scores, dim=-1).to(v.dtype)
-        attention = self.drop(attention)
-
-        output = torch.einsum("bhts,bshd->bthd", attention, v)
-
-        return output
-
-
-class CrossAttention(nn.Module):
-    """Cross-attention layer (compatible with PyTorch).
-
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
-
-    """
-
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ) -> None:
-        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,
-        **kwargs,
-    ) -> 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:]
-
-    if layer_idx not in inference_params.key_value_memory_dict:
-        inference_params.key_value_memory_dict[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_start = inference_params.batch_size_offset
-    batch_end = batch_start + kv.shape[0]
-
-    sequence_start = inference_params.seqlen_offset
-    sequence_end = sequence_start + kv.shape[1]
-
-    # When the current sequence length is equal to or larger than the maximum sequence length,
-    # we need to concatenate the current `kv` with the cached `kv` to expand its length
-    if sequence_end >= inference_params.max_seqlen:
-        inference_params.key_value_memory_dict[layer_idx] = torch.concatenate(
-            (inference_params.key_value_memory_dict[layer_idx], kv), dim=1
-        )
-
-    inference_params.key_value_memory_dict[layer_idx][
-        batch_start:batch_end, sequence_start:sequence_end, ...
-    ] = kv
-    kv = inference_params.key_value_memory_dict[layer_idx][
-        batch_start:batch_end, :sequence_end, ...
-    ]
-
-    return kv
-
-
-class MHA(nn.Module):
-    """Multi-head attention layer."""
-
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[str] = None,
-        rotary_dim: Optional[int] = None,
-        rotary_base: float = 10000.0,
-        rotary_scale_base: Optional[float] = None,
-        n_head: Optional[int] = None,
-        n_head_kv: Optional[int] = None,
-        head_dim: Optional[int] = None,
-        bias: bool = True,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        layer_idx: Optional[int] = None,
-        return_residual: bool = False,
-        checkpointing: bool = False,
-    ) -> None:
-        super().__init__()
-
-        # Rotary embedding
-        self.rotary_dim = (
-            rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
-        )
-        if self.rotary_dim > 0:
-            rotary_cls = (
-                FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding
-            )
-            if rotary_cls is None:
-                rotary_cls = RotaryEmbedding
-
-            rotary_kwargs = {}
-            if rotary_cls is RotaryEmbedding:
-                rotary_kwargs["max_position_embeddings"] = config.n_positions
-
-            self.rotary_emb = rotary_cls(
-                self.rotary_dim,
-                base=rotary_base,
-                scale_base=rotary_scale_base,
-                device=device,
-                **rotary_kwargs,
-            )
-
-        # MLP
-        self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
-            config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim
-        )
-        op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
-        hidden_size = config.n_embd
-
-        linear_cls = FusedDense if config.fused_dense else nn.Linear
-        if linear_cls is None:
-            linear_cls = nn.Linear
-
-        self.Wqkv = linear_cls(
-            hidden_size, op_size, bias=bias, device=device, dtype=dtype
-        )
-        self.out_proj = linear_cls(
-            hidden_size, hidden_size, bias=bias, device=device, dtype=dtype
-        )
-
-        # Attention
-        attn_cls = FlashSelfAttention if config.flash_attn else SelfAttention
-        if attn_cls is None:
-            attn_cls = SelfAttention
-
-        cross_attn_cls = FlashCrossAttention if config.flash_attn else CrossAttention
-        if cross_attn_cls is None:
-            cross_attn_cls = CrossAttention
-
-        self.inner_attn = attn_cls(
-            causal=causal,
-            softmax_scale=softmax_scale,
-            attention_dropout=config.attn_pdrop,
-        )
-        self.inner_cross_attn = cross_attn_cls(
-            causal=causal,
-            softmax_scale=softmax_scale,
-            attention_dropout=config.attn_pdrop,
-        )
-
-        self.flash_attn = config.flash_attn and attn_cls is FlashSelfAttention
-        self.layer_idx = layer_idx
-        self.return_residual = return_residual
-        self.checkpointing = checkpointing
-        self._gradient_checkpointing_func = None
-
-    def _forward_self_attn(
-        self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
-    ) -> torch.FloatTensor:
-        qkv = self.Wqkv(x)
-        qkv = rearrange(
-            qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim
-        )
-
-        if self.rotary_dim > 0:
-            qkv = self.rotary_emb(qkv)
-
-        if self.flash_attn:
-            batch_size, seqlen = qkv.shape[0], qkv.shape[1]
-
-            cu_seqlens, max_seqlen = None, None
-            if key_padding_mask is not None:
-                # If `key_padding_mask` is supplied, we need to unpad the input and retrieve
-                # the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
-                qkv, indices, cu_seqlens, max_seqlen = unpad_input(
-                    qkv, key_padding_mask
-                )
-
-            if self.checkpointing and self.training:
-                attn_output = self._gradient_checkpointing_func(
-                    self.inner_attn,
-                    qkv,
-                    cu_seqlens=cu_seqlens,
-                    max_seqlen=max_seqlen,
-                    use_reentrant=False,
-                )
-            else:
-                attn_output = self.inner_attn(
-                    qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
-                ).to(qkv.device)
-
-            # If `key_padding_mask` is supplied, we need to pad the output back to the original shape
-            return (
-                pad_input(attn_output, indices, batch_size, seqlen)
-                if key_padding_mask is not None
-                else attn_output
-            )
-
-        if self.checkpointing and self.training:
-            return self._gradient_checkpointing_func(
-                self.inner_attn,
-                qkv,
-                key_padding_mask=key_padding_mask,
-                use_reentrant=False,
-            )
-
-        return 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:
-        batch_size = x.shape[0]
-
-        qkv = self.Wqkv(x)
-
-        q = qkv[..., : self.n_head * self.head_dim]
-        q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
-
-        kv = qkv[..., self.n_head * 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)
-
-        if self.flash_attn:
-            batch_size, seqlen_q = q.shape[0], q.shape[1]
-            seqlen_k = kv.shape[1]
-
-            cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = (
-                None,
-                None,
-                None,
-                None,
-            )
-            if key_padding_mask is not None:
-                kv, _, cu_seqlens_k, max_seqlen_k = unpad_input(kv, key_padding_mask)
-
-                if seqlen_q == 1:
-                    key_padding_mask = torch.ones(batch_size, 1, device=q.device)
-                elif seqlen_q != seqlen_k:
-                    key_padding_mask = key_padding_mask[:, -seqlen_q:]
-
-                q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
-                    q, key_padding_mask
-                )
-
-            if self.checkpointing and self.training:
-                attn_output = self._gradient_checkpointing_func(
-                    self.inner_cross_attn,
-                    q,
-                    kv,
-                    causal=causal,
-                    cu_seqlens=cu_seqlens_q,
-                    max_seqlen=max_seqlen_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_k=max_seqlen_k,
-                    use_reentrant=False,
-                )
-            else:
-                attn_output = self.inner_cross_attn(
-                    q,
-                    kv,
-                    causal=causal,
-                    cu_seqlens=cu_seqlens_q,
-                    max_seqlen=max_seqlen_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_k=max_seqlen_k,
-                )
-
-            return (
-                pad_input(attn_output, indices_q, batch_size, max_seqlen_q)
-                if key_padding_mask is not None
-                else attn_output
-            )
-
-        if self.checkpointing and self.training:
-            return self._gradient_checkpointing_func(
-                self.inner_cross_attn,
-                q,
-                kv,
-                key_padding_mask=key_padding_mask,
-                causal=causal,
-                use_reentrant=False,
-            )
-
-        return 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,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        if attention_mask is not None:
-            attention_mask = attention_mask.bool()
-        else:
-            attention_mask = None
-
-        # MHA
-        if self.n_head == self.n_head_kv:
-            if past_key_values is None:
-                # If `past_key_values` are not supplied, we run self-attention
-                attn_output = self._forward_self_attn(x, attention_mask)
-            else:
-                # If `past_key_values` are supplied, it means that we might have cached values and
-                # could take advantage of cross-attention
-                attn_output = self._forward_cross_attn(
-                    x, past_key_values, attention_mask
-                )
-        # MQA / GQA
-        else:
-            # Regardless of `past_key_values` being supplied or not, it always use cross-attention
-            # because `q` and `kv` lengths might be different
-            attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
-
-        output = rearrange(attn_output, "... h d -> ... (h d)")
-        output = self.out_proj(output)
-
-        return output if not self.return_residual else (output, 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,
-        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, layer_idx=block_idx)
-        self.mlp = MLP(config)
-        self.checkpointing = False
-        self._gradient_checkpointing_func = None
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        def _forward(
-            mixer,
-            resid_dropout,
-            mlp,
-            ln,
-            hidden_states,
-            past_key_values,
-            attention_mask,
-        ):
-            residual = hidden_states
-            hidden_states = ln(hidden_states)
-
-            attn_outputs = 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 = resid_dropout(attn_outputs)
-            feed_forward_hidden_states = resid_dropout(mlp(hidden_states))
-
-            return attn_outputs + feed_forward_hidden_states + residual
-
-        if self.training and self.checkpointing:
-            return self._gradient_checkpointing_func(
-                _forward,
-                self.mixer,
-                self.resid_dropout,
-                self.mlp,
-                self.ln,
-                hidden_states,
-                past_key_values,
-                attention_mask,
-            )
-
-        return _forward(
-            self.mixer,
-            self.resid_dropout,
-            self.mlp,
-            self.ln,
-            hidden_states,
-            past_key_values,
-            attention_mask,
-        )
-
-
-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: 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 PhiPreTrainedModel(PreTrainedModel):
-    """Phi pre-trained model."""
-
-    config_class = PhiConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["ParallelBlock"]
-
-    def __init__(self, *inputs, **kwargs) -> None:
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module: nn.Module) -> None:
-        if isinstance(module, (nn.Linear,)):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            if module.bias is not None:
-                module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(
-        self, enable: bool = True, gradient_checkpointing_func: Callable = checkpoint
-    ):
-        for module in self.modules():
-            if hasattr(module, "checkpointing"):
-                module._gradient_checkpointing_func = gradient_checkpointing_func
-                module.checkpointing = enable
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids: torch.LongTensor,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
-        **kwargs,
-    ) -> Dict[str, Any]:
-        if past_key_values is None or not (
-            isinstance(past_key_values, InferenceParams)
-        ):
-            past_key_values = InferenceParams(
-                max_seqlen=self.config.n_positions,
-                max_batch_size=input_ids.shape[0],
-                seqlen_offset=0,
-                batch_size_offset=0,
-                key_value_memory_dict={},
-                lengths_per_sample=None,
-            )
-        else:
-            # Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
-            past_key_values.seqlen_offset = input_ids.shape[1] - 1
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-
-        return {
-            "input_ids": input_ids,
-            "past_key_values": past_key_values,
-            "attention_mask": attention_mask,
-        }
-
-
-class PhiModel(PhiPreTrainedModel):
-    """Phi model."""
-
-    _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 = False
-        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,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-    ) -> torch.FloatTensor:
-        hidden_states = self.embd(input_ids)
-
-        for layer in self.h:
-            hidden_states = layer(
-                hidden_states,
-                past_key_values=past_key_values,
-                attention_mask=attention_mask,
-            )
-
-        return hidden_states
-
-
-class PhiForCausalLM(PhiPreTrainedModel):
-    """Phi for Causal Language Modeling."""
-
-    _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,
-        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, past_key_values=past_key_values, attention_mask=attention_mask
-        )
-        lm_logits = self.lm_head(hidden_states)
-
-        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
-        )

src/axolotl/monkeypatch/phi/__init__.py

@@ -0,0 +1,12 @@
+"""
+Patches to support multipack for phi2
+"""
+import transformers
+
+from axolotl.monkeypatch.utils import get_unpad_data
+
+
+def replace_phi_attn_with_multipack_flash_attn():
+    transformers.models.phi.modeling_phi._get_unpad_data = (  # pylint: disable=protected-access
+        get_unpad_data
+    )

src/axolotl/utils/config.py

@@ -364,20 +364,6 @@ def validate_config(cfg):
                 "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
             )
 
-    if cfg.model_type == "MixFormerSequentialForCausalLM" and cfg.adapter is not None:
-        LOG.warning("Use AutoModelForCausalLM for phi/MixFormer models with qLoRA")
-
-    if cfg.model_config_type == "mixformer-sequential":
-        if cfg.sample_packing:
-            if cfg.adapter is not None:
-                LOG.warning(
-                    "phi/MixFormer models are not currently compatible with LoRA and sample_packing"
-                )
-            if cfg.model_type == "AutoModelForCausalLM":
-                raise ValueError(
-                    "`model_type: MixFormerSequentialForCausalLM` required for sample_packing"
-                )
-
     if cfg.datasets:
         for idx, ds_cfg in enumerate(cfg.datasets):
             if not ds_cfg.type:

src/axolotl/utils/data.py

@@ -397,7 +397,7 @@ def load_tokenized_prepared_datasets(
             LOG.info("shuffle merged datasets")
             dataset = dataset.shuffle(seed=seed)
 
-        dataset, _ = process_datasets_for_packing(cfg, dataset, None, tokenizer)
+        dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
         if cfg.local_rank == 0:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")

src/axolotl/utils/lora_embeddings.py

@@ -7,8 +7,6 @@ def get_linear_embedding_layers(model_type):
     """
     returns the linear embedding layers needed for loras, dependent on the model arch
     """
-    if model_type == "phi-msft":
-        return ["embd.wte", "lm_head.linear"]
     if model_type == "gpt_neox":
         return ["embed_in", "embed_out"]
     if model_type == "falcon":

src/axolotl/utils/models.py

@@ -169,6 +169,7 @@ def load_tokenizer(cfg):
             # pylint: disable=too-many-boolean-expressions
             if (
                 (getattr(tokenizer, k) is None or getattr(tokenizer, k) != val)
+                and (len(tokenizer.encode(val)) > 1)
                 and cfg.adapter
                 and (
                     not cfg.lora_modules_to_save
@@ -342,6 +343,12 @@ def load_model(
         LOG.info("patching falcon with flash attention")
         replace_falcon_attn_with_multipack_flash_attn()
 
+    if cfg.model_config_type == "phi" and cfg.flash_attention and cfg.sample_packing:
+        from axolotl.monkeypatch.phi import replace_phi_attn_with_multipack_flash_attn
+
+        LOG.info("patching phi with flash attention")
+        replace_phi_attn_with_multipack_flash_attn()
+
     if cfg.model_config_type == "qwen2" and cfg.flash_attention and cfg.sample_packing:
         from axolotl.monkeypatch.qwen2 import (
             replace_qwen2_attn_with_multipack_flash_attn,
@@ -448,7 +455,7 @@ def load_model(
                 "flash_attention_2"
             )
         else:
-            if model_config.model_type in ["mixtral", "qwen2", "falcon"]:
+            if model_config.model_type in ["mixtral", "qwen2", "falcon", "phi"]:
                 model_kwargs["attn_implementation"] = "flash_attention_2"
                 model_config._attn_implementation = (  # pylint: disable=protected-access
                     "flash_attention_2"
@@ -458,10 +465,6 @@ def load_model(
                 model_config._attn_implementation = (  # pylint: disable=protected-access
                     "eager"
                 )
-        if model_config.model_type == "phi-msft":
-            model_config.flash_attn = True
-            model_config.flash_rotary = True
-            model_config.fused_dense = True
 
     try:
         if (
@@ -518,16 +521,6 @@ def load_model(
         #         device=cfg.device,
         #     )
         #     model.train() # sets to train instead of eval mode
-        elif model_type == "PhiForCausalLM" or model_config.model_type == "phi-msft":
-            from axolotl.models.phi import PhiForCausalLM
-
-            model = PhiForCausalLM.from_pretrained(
-                base_model,
-                config=model_config,
-                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
-                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
-                **model_kwargs,
-            )
         elif model_type == "MambaLMHeadModel":
             # FIXME this is janky at best and hacked together to make it work
             MambaLMHeadModel = fix_mamba_attn_for_loss()  # pylint: disable=invalid-name

src/axolotl/utils/trainer.py

@@ -106,19 +106,16 @@ def drop_long_seq(sample, sequence_len=2048):
     return len(sample["input_ids"]) <= sequence_len and len(sample["input_ids"]) > 0
 
 
-def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
+def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
     drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
     with zero_first(is_main_process()):
         if cfg.is_preprocess:
             max_input_len = np.max(get_dataset_lengths(train_dataset))
             LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
 
-        # Phi doesn't want the attention_mask feature when training
         if (
-            "CodeGenTokenizer" in tokenizer.__class__.__name__
-            or (cfg.is_mistral_derived_model and cfg.flash_attention)
-            or cfg.model_config_type == "mamba"
-        ):
+            cfg.is_mistral_derived_model and cfg.flash_attention
+        ) or cfg.model_config_type == "mamba":
             LOG.info("dropping attention_mask column")
             train_dataset = train_dataset.remove_columns("attention_mask")
             if eval_dataset:

tests/e2e/patched/test_phi_multipack.py

@@ -0,0 +1,123 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestPhiMultipack(unittest.TestCase):
+    """
+    Test case for Phi2 models
+    """
+
+    @with_temp_dir
+    def test_ft_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "microsoft/phi-1_5",
+                "model_type": "PhiForCausalLM",
+                "tokenizer_type": "AutoTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "pad_to_sequence_len": True,
+                "load_in_8bit": False,
+                "adapter": None,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "dataset_shard_num": 10,
+                "dataset_shard_idx": 0,
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "eval_steps": 10,
+                "save_steps": 10,
+                "bf16": "auto",
+            }
+        )
+
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()
+
+    @with_temp_dir
+    def test_qlora_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "microsoft/phi-1_5",
+                "model_type": "PhiForCausalLM",
+                "tokenizer_type": "AutoTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "pad_to_sequence_len": True,
+                "load_in_8bit": False,
+                "adapter": "qlora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "dataset_shard_num": 10,
+                "dataset_shard_idx": 0,
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "eval_steps": 10,
+                "save_steps": 10,
+                "bf16": "auto",
+            }
+        )
+
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()

tests/e2e/test_phi.py

@@ -7,9 +7,6 @@ import os
 import unittest
 from pathlib import Path
 
-import pytest
-from transformers.utils import is_torch_bf16_gpu_available
-
 from axolotl.cli import load_datasets
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
@@ -27,17 +24,15 @@ class TestPhi(unittest.TestCase):
     Test case for Phi2 models
     """
 
-    @pytest.mark.skip(reason="fixme later")
     @with_temp_dir
-    def test_phi2_ft(self, temp_dir):
+    def test_phi_ft(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "microsoft/phi-2",
-                "trust_remote_code": True,
+                "base_model": "microsoft/phi-1_5",
                 "model_type": "AutoModelForCausalLM",
                 "tokenizer_type": "AutoTokenizer",
-                "sequence_len": 512,
+                "sequence_len": 2048,
                 "sample_packing": False,
                 "load_in_8bit": False,
                 "adapter": None,
@@ -64,13 +59,9 @@ class TestPhi(unittest.TestCase):
                 "max_steps": 10,
                 "save_steps": 10,
                 "eval_steps": 10,
-                "save_safetensors": True,
+                "bf16": "auto",
             }
         )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -78,25 +69,24 @@ class TestPhi(unittest.TestCase):
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "pytorch_model.bin").exists()
 
-    @pytest.mark.skip(reason="multipack no longer supported atm")
     @with_temp_dir
-    def test_ft_packed(self, temp_dir):
+    def test_phi_qlora(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "microsoft/phi-2",
-                "trust_remote_code": True,
-                "model_type": "PhiForCausalLM",
+                "base_model": "microsoft/phi-1_5",
+                "model_type": "AutoModelForCausalLM",
                 "tokenizer_type": "AutoTokenizer",
-                "sequence_len": 512,
-                "sample_packing": True,
+                "sequence_len": 2048,
+                "sample_packing": False,
                 "load_in_8bit": False,
-                "adapter": None,
+                "adapter": "qlora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
                 "val_set_size": 0.1,
                 "special_tokens": {
-                    "unk_token": "<|endoftext|>",
-                    "bos_token": "<|endoftext|>",
-                    "eos_token": "<|endoftext|>",
                     "pad_token": "<|endoftext|>",
                 },
                 "datasets": [
@@ -112,18 +102,18 @@ class TestPhi(unittest.TestCase):
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
+                "optimizer": "paged_adamw_8bit",
                 "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "max_steps": 10,
+                "save_steps": 10,
+                "eval_steps": 10,
+                "bf16": "auto",
             }
         )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
-
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "pytorch_model.bin").exists()
+        assert (Path(temp_dir) / "adapter_model.bin").exists()

tests/test_validation.py

@@ -742,11 +742,11 @@ class ValidationCheckModelConfig(BaseValidation):
 
         check_model_config(cfg, model_config)
 
-    def test_phi2_add_tokens_adapter(self):
+    def test_phi_add_tokens_adapter(self):
         cfg = DictDefault(
             {"adapter": "qlora", "load_in_4bit": True, "tokens": ["<|imstart|>"]}
         )
-        model_config = DictDefault({"model_type": "phi-msft"})
+        model_config = DictDefault({"model_type": "phi"})
 
         with pytest.raises(
             ValueError,
@@ -759,7 +759,7 @@ class ValidationCheckModelConfig(BaseValidation):
                 "adapter": "qlora",
                 "load_in_4bit": True,
                 "tokens": ["<|imstart|>"],
-                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "lora_modules_to_save": ["embd.wte", "lm_head.linear"],
             }
         )
 
@@ -774,7 +774,7 @@ class ValidationCheckModelConfig(BaseValidation):
                 "adapter": "qlora",
                 "load_in_4bit": True,
                 "tokens": ["<|imstart|>"],
-                "lora_modules_to_save": ["embd.wte", "lm_head.linear"],
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
             }
         )