verl/models/transformers/llama.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import sys
from typing import Callable, Optional, Tuple

import torch

if sys.version_info >= (3, 11):
    pass
else:
    pass

from transformers.cache_utils import Cache
from transformers.modeling_flash_attention_utils import _flash_attention_forward
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
from transformers.utils import logging

from verl.utils.ulysses import (
    gather_heads_scatter_seq,
    gather_seq_scatter_heads,
    get_ulysses_sequence_parallel_world_size,
    validate_ulysses_config,
)

logger = logging.get_logger(__name__)


def llama_flash_attn_forward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Cache] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    cache_position: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    """
    Adapted from transformers 4.47.1 to support Ulysses sequence parallelism.

    NOTE: This function is used for transformers versions in the range [4.45.0, 4.47.1].
    """
    output_attentions = False

    bsz, q_len, _ = hidden_states.size()

    query_states = self.q_proj(hidden_states)
    key_states = self.k_proj(hidden_states)
    value_states = self.v_proj(hidden_states)

    # Flash attention requires the input to have the shape
    # batch_size x seq_length x head_dim x hidden_dim
    # therefore we just need to keep the original shape
    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

    # trade off: repeat first and then all to all
    # key_states = repeat_kv(key_states, self.num_key_value_groups)
    # value_states = repeat_kv(value_states, self.num_key_value_groups)

    ########## AlltoAll for Ulysses ##########
    ulysses_sp_size = get_ulysses_sequence_parallel_world_size()

    if ulysses_sp_size > 1:
        validate_ulysses_config(self.num_heads, ulysses_sp_size)

        # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim)
        query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1)
        key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1)
        value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1)

    full_q_len = query_states.size(2)  # full seq length

    if position_embeddings is None:
        logger.warning_once(
            "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
            "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
            "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
            "removed and `position_embeddings` will be mandatory."
        )
        cos, sin = self.rotary_emb(value_states, position_ids)
    else:
        cos, sin = position_embeddings
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

    if past_key_value is not None:
        # sin and cos are specific to RoPE models; cache_position needed for the static cache
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

    # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
    # to be able to avoid many of these transpose/reshape/view.
    query_states = query_states.transpose(1, 2)
    key_states = key_states.transpose(1, 2)
    value_states = value_states.transpose(1, 2)

    dropout_rate = self.attention_dropout if self.training else 0.0

    # In PEFT, usually we cast the layer norms in float32 for training stability reasons
    # therefore the input hidden states gets silently casted in float32. Hence, we need
    # cast them back in the correct dtype just to be sure everything works as expected.
    # This might slowdown training & inference so it is recommended to not cast the LayerNorms
    # in fp32. (LlamaRMSNorm handles it correctly)

    input_dtype = query_states.dtype
    if input_dtype == torch.float32:
        if torch.is_autocast_enabled():
            target_dtype = torch.get_autocast_gpu_dtype()
        # Handle the case where the model is quantized
        elif hasattr(self.config, "_pre_quantization_dtype"):
            target_dtype = self.config._pre_quantization_dtype
        else:
            target_dtype = self.q_proj.weight.dtype

        logger.warning_once(f"The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in {target_dtype}.")

        query_states = query_states.to(target_dtype)
        key_states = key_states.to(target_dtype)
        value_states = value_states.to(target_dtype)

    attn_output = _flash_attention_forward(
        query_states,
        key_states,
        value_states,
        attention_mask,
        full_q_len,
        position_ids=position_ids,
        dropout=dropout_rate,
        sliding_window=getattr(self, "sliding_window", None),
        use_top_left_mask=self._flash_attn_uses_top_left_mask,
        is_causal=self.is_causal,
        **kwargs,
    )

    attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous()
    ########## AlltoAll for Ulysses ##########
    if ulysses_sp_size > 1:
        attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2)
    attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value


def llama_attn_forward(
    self,
    hidden_states: torch.Tensor,
    position_embeddings: Tuple[torch.Tensor, torch.Tensor],
    attention_mask: Optional[torch.Tensor],
    past_key_value: Optional[Cache] = None,
    cache_position: Optional[torch.LongTensor] = None,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    """
    Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0.

    NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.50.0.
    """
    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
    from transformers.models.llama.modeling_llama import eager_attention_forward

    bsz, q_len, _ = hidden_states.shape

    query_states = self.q_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
    key_states = self.k_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
    value_states = self.v_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

    ########## AlltoAll for Ulysses ##########
    ulysses_sp_size = get_ulysses_sequence_parallel_world_size()

    if ulysses_sp_size > 1:
        validate_ulysses_config(self.config.num_attention_heads, ulysses_sp_size)

        query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1)
        key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1)
        value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1)

    full_q_len = query_states.size(2)

    cos, sin = position_embeddings
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

    if past_key_value is not None:
        # sin and cos are specific to RoPE models; cache_position needed for the static cache
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

    attention_interface: Callable = eager_attention_forward
    if self.config._attn_implementation != "eager":
        if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
            logger.warning_once('`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.')
        else:
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

    attn_output, attn_weights = attention_interface(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        dropout=0.0 if not self.training else self.attention_dropout,
        scaling=self.scaling,
        **kwargs,
    )

    attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous()
    ########## AlltoAll for Ulysses ##########
    if ulysses_sp_size > 1:
        attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2)
    attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
    attn_output = self.o_proj(attn_output)
    return attn_output, attn_weights