# coding=utf-8
# Copyright 2024 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 Mllama model."""
from typing import Optional, Tuple, List
import torch
import torch.utils.checkpoint
from torch import nn
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "ipex":
import intel_extension_for_pytorch as ipex
else:
import flash_attn_2_cuda
from transformers.activations import ACT2FN
import torch.nn.functional as F
from text_generation_server.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
FastLinear,
)
from text_generation_server.layers.attention import (
Seqlen,
)
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
FlashLlamaForCausalLM,
)
def _prepare_aspect_ratio_attention_mask(
aspect_ratio_mask: torch.Tensor,
num_patches: int,
target_length: int,
dtype: torch.dtype,
) -> torch.Tensor:
# Expand aspect ratio mask to target_length
batch_size, max_num_tiles = aspect_ratio_mask.shape
attention_mask = aspect_ratio_mask.view(batch_size, max_num_tiles, 1, 1).to(dtype)
attention_mask = attention_mask.repeat(1, 1, target_length, 1)
# Mask padding patches
pad_patches = target_length - num_patches
attention_mask[:, :, -pad_patches:] = 0
# Invert the mask (0 -> 1, 1 -> 0)
attention_mask = 1 - attention_mask
# Reshape to 2D and create 4D attention mask
# (batch_size, 1, max_num_tiles * target_length, max_num_tiles * target_length)
attention_mask = attention_mask.reshape(
batch_size, max_num_tiles * target_length, 1
)
attention_mask = (
attention_mask @ attention_mask.transpose(-1, -2) * torch.finfo(dtype).min
)
attention_mask = attention_mask.unsqueeze(1)
return attention_mask
# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
min_dtype: float,
cache_position: torch.Tensor,
batch_size: int,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to plcae the 4D attention mask on.
min_dtype (`float`):
The minimum value representable with the dtype `dtype`.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
causal_mask = torch.full(
(sequence_length, target_length),
fill_value=min_dtype,
dtype=dtype,
device=device,
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(
target_length, device=device
) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = (
causal_mask.clone()
) # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = (
causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[
:, :, :, :mask_length
].masked_fill(padding_mask, min_dtype)
return causal_mask
def _prepare_cross_attention_mask(
cross_attention_mask: torch.Tensor,
num_vision_tokens: int,
dtype: str,
) -> Tuple[torch.Tensor, torch.Tensor]:
# reshape so it can be used by attn module
batch_size, text_total_length, *_ = cross_attention_mask.shape
cross_attention_mask = cross_attention_mask.repeat_interleave(
num_vision_tokens, dim=3
)
cross_attention_mask = cross_attention_mask.view(batch_size, text_total_length, -1)
cross_attention_mask = cross_attention_mask.unsqueeze(1)
# invert the mask
inverted_cross_attn_mask = (1.0 - cross_attention_mask).to(dtype)
cross_attention_mask = inverted_cross_attn_mask.masked_fill(
inverted_cross_attn_mask.to(torch.bool), torch.finfo(dtype).min
)
# apply full-row bias, which return 4D tensor of shape [B, H, S1, 1] where value is 0 if the a full row in cross attn mask's
# last dimension contains negative infinity values, otherwise it's 1
negative_inf_value = torch.finfo(dtype).min
full_text_row_masked_out_mask = (
(cross_attention_mask != negative_inf_value)
.any(dim=-1)
.type_as(cross_attention_mask)[..., None]
)
cross_attention_mask *= full_text_row_masked_out_mask
return cross_attention_mask, full_text_row_masked_out_mask
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->MllamaVision
class MllamaVisionMLP(nn.Module):
def __init__(self, *, prefix, config, weights):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = TensorParallelColumnLinear.load(
prefix=f"{prefix}.fc1", weights=weights, config=config, bias=True
)
self.fc2 = TensorParallelRowLinear.load(
prefix=f"{prefix}.fc2", weights=weights, config=config, bias=True
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class MllamaVisionSdpaAttention(nn.Module):
def __init__(self, *, prefix, config, weights):
super().__init__()
self.embed_dim = config.hidden_size
self.head_dim = config.hidden_size // config.attention_heads
self.num_heads = config.attention_heads // weights.process_group.size()
self.qkv_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
def forward(
self,
hidden_state: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
qkv = self.qkv_proj(hidden_state)
query, key, value = qkv.split(
[
self.head_dim * self.num_heads,
self.head_dim * self.num_heads,
self.head_dim * self.num_heads,
],
dim=2,
)
batch_size, q_seq_len, _ = query.shape
_, kv_seq_len, _ = key.shape
query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim)
key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
attn_output = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
output = self.o_proj(attn_output)
return output
class MllamaVisionEncoderLayer(nn.Module):
def __init__(self, *, prefix, config, weights, is_gated: bool):
super().__init__()
self.hidden_size = config.hidden_size
self.num_attention_heads = config.attention_heads
self.is_gated = is_gated
self.intermediate_size = config.intermediate_size
self.self_attn = MllamaVisionSdpaAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.mlp = MllamaVisionMLP(
prefix=f"{prefix}.mlp", config=config, weights=weights
)
self.input_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=1e-05
)
self.post_attention_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=1e-05
)
# there used to be an if else here, no code path
if is_gated:
self.gate_attn = nn.Parameter(
weights.get_tensor(f"{prefix}.gate_attn"), requires_grad=False
)
self.gate_ffn = nn.Parameter(
weights.get_tensor(f"{prefix}.gate_ffn"), requires_grad=False
)
def forward(
self,
hidden_state: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
):
# Self Attention
residual = hidden_state
hidden_state = self.input_layernorm(hidden_state)
hidden_state = self.self_attn(hidden_state, attention_mask=attention_mask)
gate_attn = 1 if not self.is_gated else self.gate_attn.tanh()
hidden_state = residual + gate_attn * hidden_state
# Feed forward
residual = hidden_state
hidden_state = self.post_attention_layernorm(hidden_state)
hidden_state = self.mlp(hidden_state)
gate_ffn = 1 if not self.is_gated else self.gate_ffn.tanh()
hidden_state = residual + gate_ffn * hidden_state
return hidden_state
class MllamaVisionEncoder(nn.Module):
def __init__(self, *, prefix, config, weights, is_gated: bool, num_layers: int):
super().__init__()
self.config = config
self.layers = [
MllamaVisionEncoderLayer(
prefix=f"{prefix}.layers.{i}",
config=config,
weights=weights,
is_gated=is_gated,
)
for i in range(num_layers)
]
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
):
encoder_states = [hidden_states]
for encoder_layer in self.layers:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
)
hidden_states = layer_outputs
encoder_states.append(hidden_states)
return hidden_states, encoder_states
class MllamaPrecomputedAspectRatioEmbedding(nn.Module):
def __init__(self, *, prefix, config, weights):
super().__init__()
self.max_num_tiles = config.max_num_tiles
self.hidden_size = config.hidden_size
self.max_aspect_ratio_id = config.max_aspect_ratio_id
self.embedding = TensorParallelEmbedding(
prefix=f"{prefix}.embedding", weights=weights
)
self.gate = nn.Parameter(
weights.get_tensor(f"{prefix}.gate"), requires_grad=False
)
def forward(
self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor
) -> torch.Tensor:
embeddings = self.embedding(aspect_ratio_ids)
embeddings = embeddings.reshape(-1, self.max_num_tiles, 1, self.hidden_size)
# Always gated.
embeddings = embeddings * self.gate.tanh()
hidden_state = hidden_state + embeddings
return hidden_state
class MllamaPrecomputedPositionEmbedding(nn.Module):
def __init__(self, *, prefix, config, weights):
super().__init__()
self.max_num_tiles = config.max_num_tiles
self.max_aspect_ratio_id = config.max_aspect_ratio_id
self.num_patches = (config.image_size // config.patch_size) ** 2 + 1
self.hidden_size = config.hidden_size
self.scale = config.hidden_size**-0.5
self.gate = nn.Parameter(
weights.get_tensor(f"{prefix}.gate"), requires_grad=False
)
# position embedding
embedding = nn.Parameter(
weights.get_tensor(f"{prefix}.embedding"), requires_grad=False
)
self.gated_position_embedding = (1 - self.gate.tanh()) * embedding
self.tile_embedding = TensorParallelEmbedding(
prefix=f"{prefix}.tile_embedding", weights=weights
)
def forward(
self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor
) -> torch.Tensor:
# position embeddings
hidden_state = hidden_state + self.gated_position_embedding.view(
1, 1, self.num_patches, self.hidden_size
)
# precomputed tile position embeddings
tile_position_embedding = self.tile_embedding(aspect_ratio_ids)
batch_size = hidden_state.shape[0]
tile_position_embedding = tile_position_embedding.reshape(
batch_size, self.max_num_tiles, self.num_patches, self.hidden_size
)
gated_tile_position_embedding = self.gate.tanh() * tile_position_embedding
hidden_state = hidden_state + gated_tile_position_embedding
return hidden_state
class MllamaVisionModel(nn.Module):
def __init__(self, *, prefix, config, weights):
super().__init__()
self.image_size = config.image_size
self.patch_size = config.patch_size
self.max_num_tiles = config.max_num_tiles
self.hidden_size = config.hidden_size
self.num_channels = config.num_channels
self.intermediate_layers_indices = config.intermediate_layers_indices
self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
self.scale = config.hidden_size**-0.5
self.dtype = weights.dtype
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.hidden_size,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
bias=False,
)
self.patch_embedding.weight = nn.Parameter(
weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False
)
self.class_embedding = nn.Parameter(
weights.get_tensor(f"{prefix}.class_embedding"), requires_grad=False
)
self.gated_positional_embedding = MllamaPrecomputedPositionEmbedding(
prefix=f"{prefix}.gated_positional_embedding",
config=config,
weights=weights,
)
self.pre_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(
prefix=f"{prefix}.pre_tile_positional_embedding",
config=config,
weights=weights,
)
self.post_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(
prefix=f"{prefix}.post_tile_positional_embedding",
config=config,
weights=weights,
)
## layer norms
self.layernorm_pre = nn.LayerNorm.load(
prefix=f"{prefix}.layernorm_pre",
weights=weights,
# torch default
eps=1e-05,
)
self.layernorm_post = nn.LayerNorm.load(
prefix=f"{prefix}.layernorm_post",
weights=weights,
# torch default
eps=1e-05,
)
## encoders
self.transformer = MllamaVisionEncoder(
prefix=f"{prefix}.transformer",
config=config,
weights=weights,
is_gated=False,
num_layers=config.num_hidden_layers,
)
self.global_transformer = MllamaVisionEncoder(
prefix=f"{prefix}.global_transformer",
config=config,
weights=weights,
is_gated=True,
num_layers=config.num_global_layers,
)
def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor:
batch_size, _, hidden_size = hidden_state.shape
class_embedding = self.class_embedding.expand(batch_size, 1, hidden_size)
hidden_state = torch.cat([class_embedding, hidden_state], dim=1)
return hidden_state
def forward(
self,
pixel_values: torch.Tensor,
aspect_ratio_ids: torch.Tensor,
attention_mask: torch.Tensor,
) -> torch.Tensor:
(
batch_size,
num_concurrent_media,
num_tiles,
num_channels,
height,
width,
) = pixel_values.shape
pixel_values = pixel_values.reshape(
batch_size * num_concurrent_media * num_tiles, num_channels, height, width
)
aspect_ratio_ids = aspect_ratio_ids.reshape(
batch_size * num_concurrent_media, -1
)
# patch embedding
patch_embeds = self.patch_embedding(pixel_values)
hidden_state = patch_embeds.flatten(2).transpose(1, 2)
# tile embeddings
_, num_patches, dim = hidden_state.shape
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media, num_tiles, -1, dim
)
hidden_state = self.pre_tile_positional_embedding(
hidden_state, aspect_ratio_ids
)
# apply cls token
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media * num_tiles, num_patches, dim
)
hidden_state = self.apply_class_embedding(hidden_state)
num_patches += 1
# apply position embeddings
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media, num_tiles, num_patches, dim
)
hidden_state = self.gated_positional_embedding(hidden_state, aspect_ratio_ids)
# apply encoder
hidden_state = self.layernorm_pre(hidden_state)
# Compute the number of tokens to pad
num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8
# Compute padding tuple for pad function
padding = (
0,
0,
0,
num_padding_patches,
) # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2)
# Pad the tensor
hidden_state = F.pad(hidden_state, padding, mode="constant", value=0)
slice_index = -num_padding_patches if num_padding_patches > 0 else None
if attention_mask is not None:
attention_mask = attention_mask.reshape(
batch_size * num_concurrent_media, -1
)
attention_mask = _prepare_aspect_ratio_attention_mask(
aspect_ratio_mask=attention_mask,
num_patches=self.num_patches,
target_length=hidden_state.shape[2],
dtype=self.dtype,
)
hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1, dim)
hidden_state, all_intermediate_hidden_states = self.transformer(
hidden_state,
attention_mask=attention_mask,
)
intermediate_hidden_states = [
hidden_state
for idx, hidden_state in enumerate(all_intermediate_hidden_states)
if idx in self.intermediate_layers_indices
]
intermediate_hidden_states = torch.stack(intermediate_hidden_states, dim=-1)
# apply global encoder
hidden_state = self.layernorm_post(hidden_state)
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media,
num_tiles,
num_patches + num_padding_patches,
dim,
)
hidden_state = self.post_tile_positional_embedding(
hidden_state, aspect_ratio_ids
)
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media,
num_tiles * (num_patches + num_padding_patches),
dim,
)
hidden_state, _ = self.global_transformer(
hidden_state, attention_mask=attention_mask
)
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media,
num_tiles,
num_patches + num_padding_patches,
dim,
)
hidden_state = hidden_state[:, :, :slice_index]
# adding intermediate layer outputs
hidden_state = hidden_state.reshape(
batch_size, num_concurrent_media, num_tiles, num_patches, dim
)
intermediate_hidden_states = intermediate_hidden_states.reshape(
batch_size * num_concurrent_media,
num_tiles,
num_patches + num_padding_patches,
-1,
)
intermediate_hidden_states = intermediate_hidden_states[:, :, :slice_index]
intermediate_hidden_states = intermediate_hidden_states.reshape(
batch_size, num_concurrent_media, num_tiles, num_patches, -1
)
hidden_state = torch.cat([hidden_state, intermediate_hidden_states], dim=-1)
return hidden_state
class MllamaTextCrossAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, *, prefix, config, weights, layer_idx):
super().__init__()
self.config = config
self.num_heads = self.config.num_attention_heads
self.num_key_value_heads = self.config.num_key_value_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.head_size = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.layer_idx = layer_idx
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
self.num_key_value_heads // weights.process_group.size()
)
self.q_proj = TensorParallelColumnLinear.load(
config,
prefix=f"{prefix}.q_proj",
weights=weights,
bias=False,
)
self.k_proj = TensorParallelColumnLinear.load(
config,
prefix=f"{prefix}.k_proj",
weights=weights,
bias=False,
)
self.v_proj = TensorParallelColumnLinear.load(
config,
prefix=f"{prefix}.v_proj",
weights=weights,
bias=False,
)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.q_norm = MllamaTextRMSNorm.load(
prefix=f"{prefix}.q_norm", weights=weights, eps=config.rms_norm_eps
)
self.k_norm = MllamaTextRMSNorm.load(
prefix=f"{prefix}.k_norm", weights=weights, eps=config.rms_norm_eps
)
self.softmax_scale = self.head_size**-0.5
def forward(
self,
hidden_states: torch.Tensor,
cross_attention_states: Optional[torch.Tensor] = None,
# past_key_value=None,
# attention_mask: Optional[torch.Tensor] = None,
# cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# hidden_states = hidden_states.unsqueeze(0)
# bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
query_states = query_states.view(-1, self.num_heads, self.head_size)
query_states = self.q_norm(query_states)
(
cross_attention_states,
cu_seqlen_q,
cu_seqlen_k,
max_q,
max_k,
indices,
) = cross_attention_states
key_states = self.k_proj(cross_attention_states)
value_states = self.v_proj(cross_attention_states)
key_states = key_states.view(-1, self.num_key_value_heads, self.head_size)
value_states = value_states.view(-1, self.num_key_value_heads, self.head_size)
key_states = self.k_norm(key_states)
# key_states = key_states.repeat(1, self.num_key_value_groups, 1)
# value_states = value_states.repeat(1, self.num_key_value_groups, 1)
causal = False
# logger.info(
# f"Q: {query_states.shape} -K {key_states.shape} - V{value_states.shape}"
# )
if SYSTEM == "ipex":
attn_output = torch.empty_like(query_states)
ipex.llm.functional.varlen_attention(
(
query_states.contiguous()
if query_states.device.type == "xpu"
else query_states
),
(
key_states.contiguous()
if key_states.device.type == "xpu"
else key_states
),
(
value_states.contiguous()
if value_states.device.type == "xpu"
else value_states
),
attn_output,
cu_seqlen_q,
cu_seqlen_k,
max_q,
max_k,
0.0,
self.softmax_scale,
False,
causal,
False,
None,
)
else:
attn_output = flash_attn_2_cuda.varlen_fwd(
query_states,
key_states,
value_states,
None,
cu_seqlen_q,
cu_seqlen_k,
None,
None,
None, # block_tables
None,
max_q,
max_k,
0.0,
self.softmax_scale,
False,
causal, # Causal
-1, # window_size_left,
-1,
0.0, # softcap
False,
None,
)[0]
attn_output = self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
return attn_output
# Copied from transformers.models.gemma2.modeling_gemma2.Gemma2MLP with Gemma2->MllamaText
class MllamaTextMLP(nn.Module):
def __init__(self, *, prefix, config, weights):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
shape = x.shape
gate_up_states = self.gate_up_proj(x)
gate_up_states = gate_up_states.view(*shape[:-1], 2, self.intermediate_size)
result = self.down_proj(
self.act_fn(gate_up_states[:, 0]) * gate_up_states[:, 1]
)
return result
class FlashLlamaCrossLayer(torch.nn.Module):
"""Cross-attention transformer block with tanh-gated attention and feedforward."""
def __init__(self, *, prefix, config, weights, index) -> None:
layer_idx = index
super().__init__()
self.cross_attn = MllamaTextCrossAttention(
prefix=f"{prefix}.cross_attn",
config=config,
weights=weights,
layer_idx=layer_idx,
)
self.input_layernorm = MllamaTextRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.cross_attn_attn_gate = torch.nn.Parameter(
weights.get_tensor(f"{prefix}.cross_attn_attn_gate"), requires_grad=False
)
self.mlp = MllamaTextMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.post_attention_layernorm = MllamaTextRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.cross_attn_mlp_gate = torch.nn.Parameter(
weights.get_tensor(f"{prefix}.cross_attn_mlp_gate"), requires_grad=False
)
self.layer_idx = layer_idx
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
seqlen,
max_s,
adapter_data,
cross_attention_states, # [ IB, ...]
) -> Tuple[torch.Tensor, torch.Tensor]:
if cross_attention_states is None:
return hidden_states, residual
if residual is not None:
hidden_states += residual
indices = cross_attention_states[-1]
out_hidden_states = hidden_states[:]
if len(indices) > 0:
assert max(indices) < hidden_states.shape[0]
hidden_states = hidden_states[indices]
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.cross_attn(
hidden_states=hidden_states,
# attention_mask=cross_attention_mask,
cross_attention_states=cross_attention_states,
)
hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states
out_hidden_states[indices] = hidden_states
hidden_states = out_hidden_states
return hidden_states, None
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MllamaText
class MllamaTextRMSNorm(nn.Module):
def __init__(self, weight, eps):
super().__init__()
self.weight = weight
self.variance_epsilon = eps
@classmethod
def load(cls, *, prefix, weights, eps):
weight = nn.Parameter(
weights.get_tensor(f"{prefix}.weight"), requires_grad=False
)
return cls(weight=weight, eps=eps)
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class MllamaForConditionalGeneration(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
config.vision_config.quantize = None
config.vision_config.speculator = config.speculator
config.text_config.quantize = config.quantize
config.text_config.speculator = config.speculator
config.text_config._attn_implementation = "sdpa"
self.hidden_size = config.text_config.hidden_size
self.vision_model = MllamaVisionModel(
prefix="vision_model", config=config.vision_config, weights=weights
)
self.multi_modal_projector = FastLinear.load(
prefix="multi_modal_projector", config=config, weights=weights, bias=True
)
self.text_model = FlashLlamaForCausalLM(
prefix="language_model", config=config.text_config, weights=weights
)
self.config = config
self.dtype = weights.dtype
self.device = weights.device
def vision_forward(self, pixel_values, aspect_ratio_ids, aspect_ratio_mask):
if aspect_ratio_ids is None:
raise ValueError(
"`aspect_ratio_ids` must be provided if `pixel_values` is provided"
)
# logger.info(f"PIxel values {pixel_values.shape}")
batch_size = pixel_values.shape[0]
vision_states = self.vision_model(
pixel_values, aspect_ratio_ids, aspect_ratio_mask
)
cross_attention_states = self.multi_modal_projector(vision_states).reshape(
-1, vision_states.shape[-2], self.hidden_size
)
_, _, h = cross_attention_states.shape
cross_attention_states = cross_attention_states.view(batch_size, -1, h)
# logger.info(f"cross {cross_attention_states.shape}")
return cross_attention_states
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
seqlen: Seqlen,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor],
adapter_data: Optional[torch.Tensor] = None,
# XXX: Putting these as optional so that the cuda warmup calls can go through.
cross_attention_states: Optional[torch.Tensor] = None,
image_indices=None,
):
if cross_attention_states is not None:
seqlen_q = len(image_indices)
n_images = cross_attention_states.shape[0]
seqlen_k = cross_attention_states.shape[1]
device = cross_attention_states.device
if cu_seqlen_prefill is not None:
offset = 0
cu_q = []
indices = []
for index in image_indices:
cu_q.append(offset)
length = seqlen.input_lengths[index].item()
assert index < seqlen.cu_seqlen_q.shape[0]
input_ids_offset = seqlen.cu_seqlen_q[index]
indices.extend(range(input_ids_offset, input_ids_offset + length))
offset += length
cu_q.append(offset)
cu_seqlen_q = torch.Tensor(cu_q).to(device=device, dtype=torch.int32)
assert max(indices) < input_ids.shape[0]
cu_seqlen_k = (
torch.arange(
n_images + 1,
device=device,
dtype=torch.int32,
)
* seqlen_k
)
max_q = cu_seqlen_q[-1].item()
max_k = seqlen_k
else:
cu_seqlen_q = torch.arange(
seqlen_q + 1, device=device, dtype=torch.int32
)
seqlen_k = cross_attention_states.shape[1]
n_images = cross_attention_states.shape[0]
cu_seqlen_k = (
torch.arange(
n_images + 1,
device=device,
dtype=torch.int32,
)
* seqlen_k
)
max_q = seqlen_q
max_k = seqlen_k
indices = image_indices[:]
cross_attention_states = (
cross_attention_states,
cu_seqlen_q,
cu_seqlen_k,
max_q,
max_k,
indices,
)
outputs = self.text_model(
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=kv_cache,
block_tables=block_tables,
slots=slots,
seqlen=seqlen,
max_s=max_s,
prefill_cache_indices=prefill_cache_indices,
lm_head_indices=lm_head_indices,
adapter_data=adapter_data,
cross_attention_states=cross_attention_states,
)
return outputs