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More model refactoring! (#35359)

Browse Source 
* cohere

* style

* phi3

* style

* small fix

* small fix

* phi3 longrope

* oups

* Update rope (only for phi3 still)

* Update test_modeling_rope_utils.py

* Update modeling_phi3.py

* fix

* fix copies

* style

* Fix copied from bad renaming
This commit is contained in:
Cyril Vallez 2025-01-09 11:09:09 +01:00 committed by GitHub
parent 137965ca7d
commit 965a2fb320
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36 changed files with 1253 additions and 1243 deletions
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15
src/transformers/modeling_rope_utils.py
View File

@ -279,25 +279,20 @@ def _compute_longrope_parameters(
# `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
# values to compute the default attention scaling factor, instead of using `factor`.
if hasattr(config, "original_max_position_embeddings"):
if seq_len and seq_len < config.original_max_position_embeddings:
expanded_max_position_embeddings = config.original_max_position_embeddings
else:
expanded_max_position_embeddings = config.max_position_embeddings
max_position_embeddings = config.original_max_position_embeddings
factor = expanded_max_position_embeddings / max_position_embeddings
original_max_position_embeddings = config.original_max_position_embeddings
factor = config.max_position_embeddings / config.original_max_position_embeddings
else:
max_position_embeddings = config.max_position_embeddings
expanded_max_position_embeddings = max_position_embeddings * factor
original_max_position_embeddings = config.max_position_embeddings
# Sets the attention factor as suggested in the paper
if attention_factor is None:
if factor <= 1.0:
attention_factor = 1.0
else:
attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
attention_factor = math.sqrt(1 + math.log(factor) / math.log(original_max_position_embeddings))
# Compute the inverse frequencies -- scaled based on the target sequence length
if expanded_max_position_embeddings > max_position_embeddings:
if seq_len and seq_len > original_max_position_embeddings:
ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
else:
ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)

6
src/transformers/models/aria/modeling_aria.py
View File

@ -723,11 +723,7 @@ class AriaPreTrainedModel(PreTrainedModel):
class AriaTextRotaryEmbedding(nn.Module):
def __init__(
self,
config: AriaTextConfig,
device=None,
):
def __init__(self, config: AriaTextConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/bamba/modeling_bamba.py
View File

@ -120,11 +120,7 @@ class HybridMambaAttentionDynamicCache(modeling_jamba.HybridMambaAttentionDynami
class BambaRotaryEmbedding(nn.Module):
def __init__(
self,
config: BambaConfig,
device=None,
):
def __init__(self, config: BambaConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

534
src/transformers/models/cohere/modeling_cohere.py
View File

@ -1,3 +1,9 @@
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/cohere/modular_cohere.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_cohere.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 Cohere team. All rights reserved.
#
@ -20,13 +26,10 @@
# This file is based on the LLama model definition file in transformers
"""PyTorch Cohere model."""
import math
from typing import List, Optional, Tuple, Union
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
@ -34,31 +37,21 @@ from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
LossKwargs,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
replace_return_docstrings,
)
from .configuration_cohere import CohereConfig
if is_flash_attn_2_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "CohereConfig"
@ -79,49 +72,17 @@ class CohereLayerNorm(nn.Module):
return hidden_states.to(input_dtype)
ALL_LAYERNORM_LAYERS.append(CohereLayerNorm)
class CohereRotaryEmbedding(nn.Module):
# Note: the forward pass of this RoPE is slightly different from Llama's, resulting in different `sin`/`cos` for
# the same parameterization. The differences are highlighted with a comment.
def __init__(
self,
dim=None,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
rope_type="default",
config: Optional[CohereConfig] = None,
):
def __init__(self, config: CohereConfig, device=None):
super().__init__()
# TODO (joao): remove the `if` below, only used for BC
self.rope_kwargs = {}
if config is None:
logger.warning_once(
"`CohereRotaryEmbedding` can now be fully parameterized by passing the model config through the "
"`config` argument. All other arguments will be removed in v4.46"
)
self.rope_kwargs = {
"rope_type": rope_type,
"factor": scaling_factor,
"dim": dim,
"base": base,
"max_position_embeddings": max_position_embeddings,
}
self.rope_type = rope_type
self.max_seq_len_cached = max_position_embeddings
self.original_max_seq_len = max_position_embeddings
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
# BC: "rope_type" was originally "type"
if config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
@ -161,7 +122,7 @@ class CohereRotaryEmbedding(nn.Module):
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.repeat_interleave(freqs, 2, dim=-1) # This line differs from Llama's implementation
emb = torch.repeat_interleave(freqs, 2, dim=-1) # diff from Llama: we interleave() instead of cat()
cos = emb.cos()
sin = emb.sin()
@ -172,6 +133,60 @@ class CohereRotaryEmbedding(nn.Module):
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class CohereMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def rotate_half(x):
# Split and rotate. Note that this function is different from e.g. Llama.
x1 = x[..., ::2]
@ -210,36 +225,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)
class CohereMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
# Ignore copy
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class CohereAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
@ -247,334 +232,97 @@ class CohereAttention(nn.Module):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.o_proj = nn.Linear(
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
)
self.use_qk_norm = config.use_qk_norm
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
if self.use_qk_norm:
# When sharding the model using Tensor Parallelism, need to be careful to use n_local_heads
self.q_norm = CohereLayerNorm(hidden_size=(self.num_heads, self.head_dim), eps=config.layer_norm_eps)
self.q_norm = CohereLayerNorm(
hidden_size=(config.num_attention_heads, self.head_dim), eps=config.layer_norm_eps
)
self.k_norm = CohereLayerNorm(
hidden_size=(self.num_key_value_heads, self.head_dim), eps=config.layer_norm_eps
hidden_size=(config.num_key_value_heads, self.head_dim), eps=config.layer_norm_eps
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
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, # necessary, but kept here for BC
**kwargs,
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self.q_proj(hidden_states).view(hidden_shape)
key_states = self.k_proj(hidden_states).view(hidden_shape)
value_states = self.v_proj(hidden_states).view(hidden_shape)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
if self.use_qk_norm:
if self.use_qk_norm: # main diff from Llama
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 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; position_ids 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)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# NO LONGER EXIST Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Cohere
# TODO cyril: modular
class CohereFlashAttention2(CohereAttention):
"""
Cohere flash attention module. This module inherits from `CohereAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
# Ignore copy
def 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, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if isinstance(past_key_value, StaticCache):
raise ValueError(
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
)
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)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
if self.use_qk_norm:
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 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; position_ids 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. (CohereLayerNorm 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"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {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,
q_len,
dropout=dropout_rate,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=self.is_causal,
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class CohereSdpaAttention(CohereAttention):
"""
Cohere attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`CohereAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = 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, # necessary, but kept here for BC
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"CohereModel is using CohereSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
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)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
if self.use_qk_norm:
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 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
# sin and cos are specific to RoPE models; position_ids 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)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
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]
causal_mask = attention_mask
# if attention_mask is not None and cache_position is not None:
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if causal_mask is None and q_len > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
COHERE_ATTENTION_CLASSES = {
"eager": CohereAttention,
"flash_attention_2": CohereFlashAttention2,
"sdpa": CohereSdpaAttention,
}
return attn_output, attn_weights
class CohereDecoderLayer(nn.Module):
def __init__(self, config: CohereConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = COHERE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.self_attn = CohereAttention(config=config, layer_idx=layer_idx)
self.mlp = CohereMLP(config)
self.input_layernorm = CohereLayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
@ -583,11 +331,12 @@ class CohereDecoderLayer(nn.Module):
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
@ -595,13 +344,13 @@ class CohereDecoderLayer(nn.Module):
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
@ -613,7 +362,7 @@ class CohereDecoderLayer(nn.Module):
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states_attention, self_attn_weights, present_key_value = self.self_attn(
hidden_states_attention, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
@ -622,6 +371,7 @@ class CohereDecoderLayer(nn.Module):
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
# Fully Connected
@ -631,19 +381,16 @@ class CohereDecoderLayer(nn.Module):
hidden_states = residual + hidden_states_attention + hidden_states_mlp
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
COHERE_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings etc.).
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
@ -661,7 +408,6 @@ COHERE_START_DOCSTRING = r"""
"The bare Cohere Model outputting raw hidden-states without any specific head on top.",
COHERE_START_DOCSTRING,
)
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->Cohere
class CoherePreTrainedModel(PreTrainedModel):
config_class = CohereConfig
base_model_prefix = "model"
@ -754,6 +500,10 @@ COHERE_INPUTS_DOCSTRING = r"""
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@ -761,8 +511,6 @@ COHERE_INPUTS_DOCSTRING = r"""
"The bare Cohere Model outputting raw hidden-states without any specific head on top.",
COHERE_START_DOCSTRING,
)
# copied from transformers.models.llama.modeling_llama.LlamaModel with Llama->Cohere, LLAMA->COHERE
# TODO cyril: modular
class CohereModel(CoherePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`CohereDecoderLayer`]
@ -771,7 +519,6 @@ class CohereModel(CoherePreTrainedModel):
config: CohereConfig
"""
# Ignore copy
def __init__(self, config: CohereConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
@ -800,7 +547,7 @@ class CohereModel(CoherePreTrainedModel):
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
@ -1023,11 +770,13 @@ class CohereModel(CoherePreTrainedModel):
return causal_mask
# TODO: re-enable check: Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with Llama->Cohere
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
# Ignore copy
def __init__(self, config):
super().__init__(config)
self.model = CohereModel(config)
@ -1035,6 +784,7 @@ class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.logit_scale = config.logit_scale
self.tie_word_embeddings = config.tie_word_embeddings
# Initialize weights and apply final processing
self.post_init()
@ -1056,7 +806,6 @@ class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
def get_decoder(self):
return self.model
# Ignore copy
@add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
@ -1064,7 +813,7 @@ class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
@ -1073,7 +822,7 @@ class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
**loss_kwargs,
**kwargs: Unpack[KwargsForCausalLM],
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
@ -1123,16 +872,17 @@ class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
logits = logits * self.logit_scale
logits = logits * self.logit_scale # main diff from Llama
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]

393
src/transformers/models/cohere/modular_cohere.py Normal file
View File

@ -0,0 +1,393 @@
# coding=utf-8
# Copyright 2024 Cohere team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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
# limitations under the License.
# This file is based on the LLama model definition file in transformers
"""PyTorch Cohere model."""
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...cache_utils import Cache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import CausalLMOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import LossKwargs, logging
from ..llama.modeling_llama import (
LlamaAttention,
LlamaForCausalLM,
LlamaMLP,
LlamaModel,
LlamaRotaryEmbedding,
eager_attention_forward,
)
from .configuration_cohere import CohereConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "CohereConfig"
class CohereLayerNorm(nn.Module):
def __init__(self, hidden_size=None, eps=1e-5, bias=False):
"""The hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dim"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
mean = hidden_states.mean(-1, keepdim=True)
variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
hidden_states = (hidden_states - mean) * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = self.weight.to(torch.float32) * hidden_states
return hidden_states.to(input_dtype)
ALL_LAYERNORM_LAYERS.append(CohereLayerNorm)
class CohereRotaryEmbedding(LlamaRotaryEmbedding):
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.repeat_interleave(freqs, 2, dim=-1) # diff from Llama: we interleave() instead of cat()
cos = emb.cos()
sin = emb.sin()
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
cos = cos * self.attention_scaling
sin = sin * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def rotate_half(x):
# Split and rotate. Note that this function is different from e.g. Llama.
x1 = x[..., ::2]
x2 = x[..., 1::2]
rot_x = torch.stack([-x2, x1], dim=-1).flatten(-2)
return rot_x
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
dtype = q.dtype
q = q.float()
k = k.float()
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)
class CohereMLP(LlamaMLP):
def __init__(self, config):
super().__init__(config)
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
class CohereAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: CohereConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.use_qk_norm = config.use_qk_norm
if self.use_qk_norm:
# When sharding the model using Tensor Parallelism, need to be careful to use n_local_heads
self.q_norm = CohereLayerNorm(
hidden_size=(config.num_attention_heads, self.head_dim), eps=config.layer_norm_eps
)
self.k_norm = CohereLayerNorm(
hidden_size=(config.num_key_value_heads, self.head_dim), eps=config.layer_norm_eps
)
def 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: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape)
key_states = self.k_proj(hidden_states).view(hidden_shape)
value_states = self.v_proj(hidden_states).view(hidden_shape)
if self.use_qk_norm: # main diff from Llama
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 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; position_ids 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(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class CohereDecoderLayer(nn.Module):
def __init__(self, config: CohereConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = CohereAttention(config=config, layer_idx=layer_idx)
self.mlp = CohereMLP(config)
self.input_layernorm = CohereLayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states_attention, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
# Fully Connected
hidden_states_mlp = self.mlp(hidden_states)
# Add everything together
hidden_states = residual + hidden_states_attention + hidden_states_mlp
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class CohereModel(LlamaModel):
def __init__(self, config: CohereConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[CohereDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.rotary_emb = CohereRotaryEmbedding(config=config)
self.norm = CohereLayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
class CohereForCausalLM(LlamaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.model = CohereModel(config)
self.logit_scale = config.logit_scale
self.tie_word_embeddings = config.tie_word_embeddings
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
**kwargs: Unpack[KwargsForCausalLM],
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
Returns:
Example:
```python
>> from transformers import AutoTokenizer, CohereForCausalLM
>> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
>> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
>> prompt = "Hey, are you conscious? Can you talk to me?"
>> inputs = tokenizer(prompt, return_tensors="pt")
>> # Generate
>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
logits = logits * self.logit_scale # main diff from Llama
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)

95
src/transformers/models/cohere2/modeling_cohere2.py
View File

@ -28,10 +28,13 @@ import torch.nn as nn
from ...activations import ACT2FN
from ...cache_utils import Cache, HybridCache
from ...generation import GenerationMixin
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import (
LossKwargs,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
@ -46,50 +49,20 @@ if is_flash_attn_2_available():
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "Cohere2Config"
class Cohere2RotaryEmbedding(nn.Module):
# Note: the forward pass of this RoPE is slightly different from Llama's, resulting in different `sin`/`cos` for
# the same parameterization. The differences are highlighted with a comment.
def __init__(
self,
dim=None,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
rope_type="default",
config: Optional[Cohere2Config] = None,
):
def __init__(self, config: Cohere2Config, device=None):
super().__init__()
# TODO (joao): remove the `if` below, only used for BC
self.rope_kwargs = {}
if config is None:
logger.warning_once(
"`Cohere2RotaryEmbedding` can now be fully parameterized by passing the model config through the "
"`config` argument. All other arguments will be removed in v4.46"
)
self.rope_kwargs = {
"rope_type": rope_type,
"factor": scaling_factor,
"dim": dim,
"base": base,
"max_position_embeddings": max_position_embeddings,
}
self.rope_type = rope_type
self.max_seq_len_cached = max_position_embeddings
self.original_max_seq_len = max_position_embeddings
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
# BC: "rope_type" was originally "type"
if config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
@ -129,7 +102,7 @@ class Cohere2RotaryEmbedding(nn.Module):
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.repeat_interleave(freqs, 2, dim=-1) # This line differs from Llama's implementation
emb = torch.repeat_interleave(freqs, 2, dim=-1) # diff from Llama: we interleave() instead of cat()
cos = emb.cos()
sin = emb.sin()
@ -157,6 +130,18 @@ class Cohere2LayerNorm(nn.Module):
return hidden_states.to(input_dtype)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def rotate_half(x):
# Split and rotate. Note that this function is different from e.g. Llama.
x1 = x[..., ::2]
@ -195,18 +180,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
config: Cohere2Config,
query: torch.Tensor,
@ -425,7 +398,6 @@ class Cohere2MLP(nn.Module):
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
# Ignore copy
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
@ -436,7 +408,6 @@ class Cohere2DecoderLayer(nn.Module):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Cohere2Attention(config, layer_idx)
self.mlp = Cohere2MLP(config)
self.input_layernorm = Cohere2LayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
self.config = config
@ -521,7 +492,8 @@ class Cohere2DecoderLayer(nn.Module):
COHERE2_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings etc.).
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
@ -874,11 +846,13 @@ class Cohere2Model(Cohere2PreTrainedModel):
return causal_mask
# TODO: re-enable check: Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with Llama->Cohere2
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
# Ignore copy
def __init__(self, config: Cohere2Config):
super().__init__(config)
self.model = Cohere2Model(config)
@ -886,6 +860,7 @@ class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.logit_scale = config.logit_scale
self.tie_word_embeddings = config.tie_word_embeddings
# Initialize weights and apply final processing
self.post_init()
@ -907,7 +882,6 @@ class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
def get_decoder(self):
return self.model
# Ignore copy
@add_start_docstrings_to_model_forward(COHERE2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
@ -915,7 +889,7 @@ class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
@ -924,7 +898,7 @@ class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
**loss_kwargs,
**kwargs: Unpack[KwargsForCausalLM],
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
@ -974,16 +948,17 @@ class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
logits = logits * self.logit_scale
logits = logits * self.logit_scale # main diff from Llama
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]

6
src/transformers/models/falcon/modeling_falcon.py
View File

@ -110,11 +110,7 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Falcon
class FalconRotaryEmbedding(nn.Module):
def __init__(
self,
config: FalconConfig,
device=None,
):
def __init__(self, config: FalconConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/gemma/modeling_gemma.py
View File

@ -92,11 +92,7 @@ class GemmaMLP(nn.Module):
class GemmaRotaryEmbedding(nn.Module):
def __init__(
self,
config: GemmaConfig,
device=None,
):
def __init__(self, config: GemmaConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/gemma2/modeling_gemma2.py
View File

@ -324,11 +324,7 @@ class Gemma2DecoderLayer(nn.Module):
class Gemma2RotaryEmbedding(nn.Module):
def __init__(
self,
config: Gemma2Config,
device=None,
):
def __init__(self, config: Gemma2Config, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

7
src/transformers/models/glm/modeling_glm.py
View File

@ -62,7 +62,6 @@ class GlmMLP(nn.Module):
self.config = config
self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.activation_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
@ -256,11 +255,7 @@ class GlmRMSNorm(nn.Module):
class GlmRotaryEmbedding(nn.Module):
def __init__(
self,
config: GlmConfig,
device=None,
):
def __init__(self, config: GlmConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/gpt_neox/modeling_gpt_neox.py
View File

@ -491,11 +491,7 @@ class GPTNeoXSdpaAttention(GPTNeoXAttention):
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->GPTNeoX
class GPTNeoXRotaryEmbedding(nn.Module):
def __init__(
self,
config: GPTNeoXConfig,
device=None,
):
def __init__(self, config: GPTNeoXConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
View File

@ -225,11 +225,7 @@ class GPTNeoXJapaneseAttention(nn.Module):
# Copied from transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXRotaryEmbedding with GPTNeoX->GPTNeoXJapanese
class GPTNeoXJapaneseRotaryEmbedding(nn.Module):
def __init__(
self,
config: GPTNeoXJapaneseConfig,
device=None,
):
def __init__(self, config: GPTNeoXJapaneseConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/granite/modeling_granite.py
View File

@ -309,11 +309,7 @@ class GraniteDecoderLayer(nn.Module):
class GraniteRotaryEmbedding(nn.Module):
def __init__(
self,
config: GraniteConfig,
device=None,
):
def __init__(self, config: GraniteConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/granitemoe/modeling_granitemoe.py
View File

@ -158,11 +158,7 @@ ALL_LAYERNORM_LAYERS.append(GraniteMoeRMSNorm)
# Copied from transformers.models.granite.modeling_granite.GraniteRotaryEmbedding with Granite->GraniteMoe
class GraniteMoeRotaryEmbedding(nn.Module):
def __init__(
self,
config: GraniteMoeConfig,
device=None,
):
def __init__(self, config: GraniteMoeConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/jetmoe/modeling_jetmoe.py
View File

@ -386,11 +386,7 @@ class JetMoeRMSNorm(nn.Module):
# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with Gemma->JetMoe
class JetMoeRotaryEmbedding(nn.Module):
def __init__(
self,
config: JetMoeConfig,
device=None,
):
def __init__(self, config: JetMoeConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/llama/modeling_llama.py
View File

@ -80,11 +80,7 @@ ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
class LlamaRotaryEmbedding(nn.Module):
def __init__(
self,
config: LlamaConfig,
device=None,
):
def __init__(self, config: LlamaConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

16
src/transformers/models/mimi/modeling_mimi.py
View File

@ -365,11 +365,7 @@ class MimiLayerScale(nn.Module):
# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Mimi
class MimiRotaryEmbedding(nn.Module):
def __init__(
self,
config: MimiConfig,
device=None,
):
def __init__(self, config: MimiConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"
@ -1063,7 +1059,7 @@ class MimiTransformerModel(nn.Module):
attentions=all_self_attns,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Mimi
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
@ -1073,6 +1069,14 @@ class MimiTransformerModel(nn.Module):
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Mimi. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None

6
src/transformers/models/mistral/modeling_mistral.py
View File

@ -270,11 +270,7 @@ class MistralDecoderLayer(nn.Module):
class MistralRotaryEmbedding(nn.Module):
def __init__(
self,
config: MistralConfig,
device=None,
):
def __init__(self, config: MistralConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/mixtral/modeling_mixtral.py
View File

@ -392,11 +392,7 @@ class MixtralDecoderLayer(nn.Module):
class MixtralRotaryEmbedding(nn.Module):
def __init__(
self,
config: MixtralConfig,
device=None,
):
def __init__(self, config: MixtralConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

26
src/transformers/models/moshi/modeling_moshi.py
View File

@ -308,11 +308,7 @@ class MoshiLinear(nn.Module):
# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Moshi
class MoshiRotaryEmbedding(nn.Module):
def __init__(
self,
config: MoshiConfig,
device=None,
):
def __init__(self, config: MoshiConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"
@ -1292,7 +1288,7 @@ class MoshiDepthDecoder(MoshiPreTrainedModel, GenerationMixin):
attentions=all_self_attns,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Moshi
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
@ -1302,6 +1298,14 @@ class MoshiDepthDecoder(MoshiPreTrainedModel, GenerationMixin):
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Moshi. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
@ -1596,7 +1600,7 @@ class MoshiModel(MoshiPreTrainedModel):
attentions=all_self_attns,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Moshi
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
@ -1606,6 +1610,14 @@ class MoshiModel(MoshiPreTrainedModel):
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Moshi. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None

6
src/transformers/models/olmo/modeling_olmo.py
View File

@ -274,11 +274,7 @@ class OlmoDecoderLayer(nn.Module):
class OlmoRotaryEmbedding(nn.Module):
def __init__(
self,
config: OlmoConfig,
device=None,
):
def __init__(self, config: OlmoConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/olmo2/modeling_olmo2.py
View File

@ -275,11 +275,7 @@ class Olmo2DecoderLayer(nn.Module):
class Olmo2RotaryEmbedding(nn.Module):
def __init__(
self,
config: Olmo2Config,
device=None,
):
def __init__(self, config: Olmo2Config, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/olmoe/modeling_olmoe.py
View File

@ -158,11 +158,7 @@ ALL_LAYERNORM_LAYERS.append(OlmoeRMSNorm)
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Olmoe
class OlmoeRotaryEmbedding(nn.Module):
def __init__(
self,
config: OlmoeConfig,
device=None,
):
def __init__(self, config: OlmoeConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/persimmon/modeling_persimmon.py
View File

@ -57,11 +57,7 @@ _CONFIG_FOR_DOC = "PersimmonConfig"
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Persimmon
class PersimmonRotaryEmbedding(nn.Module):
def __init__(
self,
config: PersimmonConfig,
device=None,
):
def __init__(self, config: PersimmonConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/phi/modeling_phi.py
View File

@ -270,11 +270,7 @@ class PhiDecoderLayer(nn.Module):
class PhiRotaryEmbedding(nn.Module):
def __init__(
self,
config: PhiConfig,
device=None,
):
def __init__(self, config: PhiConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

908
src/transformers/models/phi3/modeling_phi3.py
View File

File diff suppressed because it is too large Load Diff

311
src/transformers/models/phi3/modular_phi3.py Normal file
View File

@ -0,0 +1,311 @@
# coding=utf-8
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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
# limitations under the License.
"""PyTorch Phi-3 model."""
from typing import Callable, Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import logging
from ..mistral.modeling_mistral import (
MistralDecoderLayer,
MistralForCausalLM,
MistralForSequenceClassification,
MistralForTokenClassification,
MistralPreTrainedModel,
MistralRotaryEmbedding,
apply_rotary_pos_emb,
eager_attention_forward,
)
from .configuration_phi3 import Phi3Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct"
_CONFIG_FOR_DOC = "Phi3Config"
class Phi3MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.activation_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
up_states = self.gate_up_proj(hidden_states)
gate, up_states = up_states.chunk(2, dim=-1)
up_states = up_states * self.activation_fn(gate)
return self.down_proj(up_states)
class Phi3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Phi3Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.num_key_value_heads = config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.is_causal = True
op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim)
self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False)
def 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: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
qkv = self.qkv_proj(hidden_states)
query_pos = self.config.num_attention_heads * self.head_dim
query_states = qkv[..., :query_pos]
key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
query_states = query_states.view(hidden_shape).transpose(1, 2)
key_states = key_states.view(hidden_shape).transpose(1, 2)
value_states = value_states.view(hidden_shape).transpose(1, 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,
sliding_window=getattr(self.config, "sliding_window", None),
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class Phi3DecoderLayer(MistralDecoderLayer):
def __init__(self, config: Phi3Config, layer_idx: int):
super().__init__(config, layer_idx)
self.config = config
self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx)
self.mlp = Phi3MLP(config)
self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`):
input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
`[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_value (`Cache`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + self.resid_attn_dropout(hidden_states) # main diff with Llama
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + self.resid_mlp_dropout(hidden_states) # main diff with Llama
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class Phi3RotaryEmbedding(MistralRotaryEmbedding):
def __init__(self, config: Phi3Config, device=None):
super().__init__(config, device)
def _longrope_frequency_update(self, position_ids, device):
"""Longrope uses long factor if sequence is larger than original pretraining length, short otherwise."""
seq_len = torch.max(position_ids) + 1
if hasattr(self.config, "original_max_position_embeddings"):
original_max_position_embeddings = self.config.original_max_position_embeddings
else:
original_max_position_embeddings = self.config.max_position_embeddings
if seq_len > original_max_position_embeddings:
if not hasattr(self, "long_inv_freq"):
self.long_inv_freq, _ = self.rope_init_fn(
self.config, device, seq_len=original_max_position_embeddings + 1
)
self.register_buffer("inv_freq", self.long_inv_freq, persistent=False)
else:
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
elif self.rope_type == "longrope":
self._longrope_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
cos = cos * self.attention_scaling
sin = sin * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class Phi3PreTrainedModel(MistralPreTrainedModel):
_version = "0.0.5"
class Phi3ForCausalLM(MistralForCausalLM, Phi3PreTrainedModel):
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
num_logits_to_keep=None,
**kwargs,
):
# Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
# process
# When the first time input length reached long and short factor switching point, enforce re-compute cache
# It will cause downside of slower at this single token position, however, better than current failure.
if (
past_key_values
and self.config.rope_scaling
and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
):
past_length = cache_position[0]
if past_length <= self.config.original_max_position_embeddings:
past_key_values = None
model_inputs = Phi3PreTrainedModel().prepare_inputs_for_generation(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
position_ids=position_ids,
use_cache=use_cache,
num_logits_to_keep=num_logits_to_keep,
**kwargs,
)
return model_inputs
class Phi3ForSequenceClassification(MistralForSequenceClassification):
pass
class Phi3ForTokenClassification(MistralForTokenClassification):
pass

10
src/transformers/models/phimoe/modeling_phimoe.py
View File

@ -1173,7 +1173,7 @@ class PhimoeModel(PhimoePreTrainedModel):
router_logits=all_router_logits,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Phimoe
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
@ -1183,6 +1183,14 @@ class PhimoeModel(PhimoePreTrainedModel):
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Phimoe. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None

6
src/transformers/models/qwen2/modeling_qwen2.py
View File

@ -283,11 +283,7 @@ class Qwen2DecoderLayer(nn.Module):
class Qwen2RotaryEmbedding(nn.Module):
def __init__(
self,
config: Qwen2Config,
device=None,
):
def __init__(self, config: Qwen2Config, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

16
src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
View File

@ -167,11 +167,7 @@ class Qwen2MoeRMSNorm(nn.Module):
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2Moe
class Qwen2MoeRotaryEmbedding(nn.Module):
def __init__(
self,
config: Qwen2MoeConfig,
device=None,
):
def __init__(self, config: Qwen2MoeConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"
@ -1064,7 +1060,7 @@ class Qwen2MoeModel(Qwen2MoePreTrainedModel):
router_logits=all_router_logits,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Qwen2Moe
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
@ -1074,6 +1070,14 @@ class Qwen2MoeModel(Qwen2MoePreTrainedModel):
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Qwen2Moe. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None

10
src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
View File

@ -1160,7 +1160,7 @@ class Qwen2VLModel(Qwen2VLPreTrainedModel):
attentions=all_self_attns,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Qwen2VL
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
@ -1170,6 +1170,14 @@ class Qwen2VLModel(Qwen2VLPreTrainedModel):
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Qwen2VL. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None

6
src/transformers/models/stablelm/modeling_stablelm.py
View File

@ -63,11 +63,7 @@ _CONFIG_FOR_DOC = "StableLmConfig"
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->StableLm
class StableLmRotaryEmbedding(nn.Module):
def __init__(
self,
config: StableLmConfig,
device=None,
):
def __init__(self, config: StableLmConfig, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

6
src/transformers/models/starcoder2/modeling_starcoder2.py
View File

@ -274,11 +274,7 @@ class Starcoder2DecoderLayer(nn.Module):
class Starcoder2RotaryEmbedding(nn.Module):
def __init__(
self,
config: Starcoder2Config,
device=None,
):
def __init__(self, config: Starcoder2Config, device=None):
super().__init__()
self.rope_kwargs = {}
# BC: "rope_type" was originally "type"

3
tests/models/phi3/test_modeling_phi3.py
View File

@ -459,6 +459,9 @@ class Phi3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
"long_factor": [5.0 for _ in range(n_factors)],
}
input_tensor = ids_tensor([1, 4090], config.vocab_size)
# Make sure we don't have padding tokens. If this is the case, then the actual number of "true" tokens may be shorter
# than `config.original_max_position_embeddings + 5`, invalidating this test
input_tensor[input_tensor == config.pad_token_id] += 1
model = Phi3ForCausalLM(config)
model.to(torch_device)
model.eval()

24
tests/utils/test_modeling_rope_utils.py
View File

@ -311,10 +311,10 @@ class RopeTest(unittest.TestCase):
self.assertEqual(config.rope_theta, 10000.0)
self.assertFalse(hasattr(config, "partial_rotary_factor"))
# longrope applies scaling on EACH inv frequency, `short_factor` or `long_factor`, depending on `factor`
# longrope applies scaling on EACH inv frequency, `short_factor` or `long_factor`, depending on the seq_len
dim = config.hidden_size // config.num_attention_heads
short_factor = [2.0] * (dim // 2) # scaling applied when factor == 1.0
long_factor = torch.ones(dim // 2).cumsum(0).tolist() # scaling applied when factor > 1.0
short_factor = [2.0] * (dim // 2) # scaling applied when seq_len <= max_position_embeddings
long_factor = torch.ones(dim // 2).cumsum(0).tolist() # scaling applied when seq_len > max_position_embeddings
rope_fn = ROPE_INIT_FUNCTIONS["default"]
default_inv_freq, _ = rope_fn(config=config, device=torch_device)
@ -353,26 +353,18 @@ class RopeTest(unittest.TestCase):
# Verify that "TypeError: '<' not supported between instances of 'NoneType' and 'int'" is not raised.
rope_config_validation(config)
# Check 2: Factor == 1.0 -> short factor is applied to the default frequencies
factor = 1.0
# Check 2: seq_len == 0 -> short factor is applied to the default frequencies
config.rope_scaling = {
"rope_type": "longrope",
"factor": factor,
"factor": 1.0,
"short_factor": short_factor,
"long_factor": long_factor,
}
inv_freq, _ = rope_fn(config=config, device=torch_device)
inv_freq, _ = rope_fn(config=config, device=torch_device, seq_len=0)
torch.testing.assert_close(inv_freq, default_inv_freq / torch.tensor(short_factor).to(torch_device))
# Check 3: Factor > 1.0 -> long factor is applied to the default frequencies
factor = 10.0
config.rope_scaling = {
"rope_type": "longrope",
"factor": factor,
"short_factor": short_factor,
"long_factor": long_factor,
}
inv_freq, _ = rope_fn(config=config, device=torch_device)
# Check 3: seq_len > max_position_embeddings -> long factor is applied to the default frequencies
inv_freq, _ = rope_fn(config=config, device=torch_device, seq_len=config.max_position_embeddings + 1)
torch.testing.assert_close(inv_freq, default_inv_freq / torch.tensor(long_factor).to(torch_device))
def test_llama3_rope_numerically(self):

2
utils/check_config_attributes.py
View File

@ -51,6 +51,8 @@ SPECIAL_CASES_TO_ALLOW = {
# generation configs (TODO joao)
"Gemma2Config": ["tie_word_embeddings", "cache_implementation"],
"Cohere2Config": ["cache_implementation"],
# Dropout with this value was declared but never used
"Phi3Config": ["embd_pdrop"],
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used to compute the property `self.layers_block_type`