1394 lines
58 KiB
Python
1394 lines
58 KiB
Python
# coding=utf-8
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# Copyright 2025 The LLAMA4 and HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from dataclasses import dataclass
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from typing import Callable, Optional, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers.models.llama4.configuration_llama4 import Llama4VisionConfig
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...masking_utils import create_causal_mask, create_chunked_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, CausalLMOutputWithPast, ModelOutput
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
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from ...utils.generic import check_model_inputs
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from .configuration_llama4 import Llama4Config, Llama4TextConfig
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logger = logging.get_logger(__name__)
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class Llama4TextExperts(nn.Module):
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def __init__(self, config: Llama4TextConfig):
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super().__init__()
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self.num_experts = config.num_local_experts
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self.intermediate_size = config.intermediate_size
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self.hidden_size = config.hidden_size
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self.expert_dim = self.intermediate_size
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self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, 2 * self.expert_dim))
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self.down_proj = nn.Parameter(torch.empty((self.num_experts, self.expert_dim, self.hidden_size)))
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""
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This should really not be run on a single machine, as we are reaching compute bound:
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- the inputs are expected to be "sorted" per expert already.
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- the weights are viewed with another dim, to match num_expert, 1, shape * num_tokens, shape
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Args:
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hidden_states (torch.Tensor): (batch_size * token_num, hidden_size)
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selected_experts (torch.Tensor): (batch_size * token_num, top_k)
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routing_weights (torch.Tensor): (batch_size * token_num, top_k)
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Returns:
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torch.Tensor
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"""
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hidden_states = hidden_states.view(self.gate_up_proj.shape[0], -1, self.hidden_size)
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gate_up = torch.bmm(hidden_states, self.gate_up_proj)
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gate, up = gate_up.chunk(2, dim=-1) # not supported for DTensors
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next_states = torch.bmm((up * self.act_fn(gate)), self.down_proj)
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next_states = next_states.view(-1, self.hidden_size)
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return next_states
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# Phi3MLP
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class Llama4TextMLP(nn.Module):
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def __init__(self, config, intermediate_size=None):
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super().__init__()
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if intermediate_size is None:
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intermediate_size = config.intermediate_size
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self.config = config
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self.gate_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)
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self.up_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)
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self.down_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False)
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self.activation_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.activation_fn(self.gate_proj(x)) * self.up_proj(x)
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return self.down_proj(down_proj)
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class Llama4TextL2Norm(torch.nn.Module):
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def __init__(self, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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return self._norm(x.float()).type_as(x)
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def extra_repr(self):
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return f"eps={self.eps}"
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class Llama4TextRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-5):
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"""
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Llama4RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(hidden_size))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float()).type_as(x)
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return output * self.weight
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.eps}"
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class Llama4Router(nn.Linear):
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def __init__(self, config):
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super().__init__(config.hidden_size, config.num_local_experts, bias=False)
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self.num_experts = config.num_local_experts
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self.top_k = config.num_experts_per_tok
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def forward(self, hidden_states):
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router_logits = super().forward(hidden_states)
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router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=1)
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router_scores = torch.full_like(router_logits, float("-inf")).scatter_(1, router_indices, router_top_value)
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router_scores = torch.nn.functional.sigmoid(router_scores.float()).to(router_scores.dtype)
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return router_scores, router_logits
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@use_kernel_forward_from_hub("Llama4TextMoe")
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class Llama4TextMoe(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.top_k = config.num_experts_per_tok
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self.hidden_dim = config.hidden_size
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self.num_experts = config.num_local_experts
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self.experts = Llama4TextExperts(config)
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self.router = Llama4Router(config)
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self.shared_expert = Llama4TextMLP(config)
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def forward(self, hidden_states):
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hidden_states = hidden_states.reshape(-1, self.hidden_dim)
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router_scores, router_logits = self.router(hidden_states)
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routed_in = hidden_states.repeat(router_scores.shape[1], 1)
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routed_in = routed_in * router_scores.reshape(-1, 1)
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routed_out = self.experts(routed_in)
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out = self.shared_expert(hidden_states)
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out.add_(routed_out.reshape(router_scores.shape[1], -1, routed_out.shape[-1]).sum(dim=0))
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return out, router_logits
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class Llama4TextRotaryEmbedding(nn.Module):
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def __init__(self, config: Llama4TextConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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self.rope_type = "llama3" if config.rope_scaling is not None else "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.to(x.device) @ position_ids_expanded).transpose(1, 2)
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freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # Convert to complex representation
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freqs_cis = freqs_cis * self.attention_scaling
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return freqs_cis
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def apply_rotary_emb(
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xq: torch.Tensor,
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xk: torch.Tensor,
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freqs_cis: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
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xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
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xq_out = torch.view_as_real(xq_ * freqs_cis[:, :, None, :]).flatten(3)
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xk_out = torch.view_as_real(xk_ * freqs_cis[:, :, None, :]).flatten(3)
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return xq_out.type_as(xq), xk_out.type_as(xk)
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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# Adapted from transformers.models.llama.modeling_llama.eager_attention_forward -> llama4 doesn't cast attn weights to fp32
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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# Adapted from transformers.models.llama.modeling_llama.eager_attention_forward -> llama4 doesn't cast attn weights to fp32
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def vision_eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * module.head_dim**-0.5
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Llama4TextAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Llama4TextConfig, layer_idx):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attn_scale = config.attn_scale
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self.floor_scale = config.floor_scale
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self.attn_temperature_tuning = config.attn_temperature_tuning
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.use_rope = config.no_rope_layers[layer_idx]
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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if self.config.use_qk_norm and self.use_rope:
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self.qk_norm = Llama4TextL2Norm(config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape)
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key_states = self.k_proj(hidden_states).view(*input_shape, -1, self.head_dim)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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if self.use_rope: # the 16E model skips rope for long context on certain layers
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query_states, key_states = apply_rotary_emb(
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query_states, key_states, position_embeddings.to(query_states.device)
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)
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if hasattr(self, "qk_norm"): # the 128E model does not use qk_norm
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query_states = self.qk_norm(query_states)
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key_states = self.qk_norm(key_states)
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# Use temperature tuning from https://huggingface.co/papers/2501.19399) to NoROPE layers
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if self.attn_temperature_tuning and not self.use_rope:
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attn_scales = (
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torch.log(torch.floor((cache_position.float() + 1.0) / self.floor_scale) + 1.0) * self.attn_scale + 1.0
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)
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attn_scales = attn_scales.view((1, input_shape[-1], 1, 1)).expand((*input_shape, 1, 1)) # batch size > 1
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query_states = (query_states * attn_scales).to(query_states.dtype)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Llama4TextDecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config, layer_idx):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.layer_idx = layer_idx
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self.attention_type = config.layer_types[layer_idx]
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self.self_attn = Llama4TextAttention(config, layer_idx)
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self.is_moe_layer = layer_idx in config.moe_layers
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if self.is_moe_layer: # the 128E model interleaves dense / sparse
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self.feed_forward = Llama4TextMoe(config)
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else:
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self.feed_forward = Llama4TextMLP(config, intermediate_size=config.intermediate_size_mlp)
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self.input_layernorm = Llama4TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Llama4TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[tuple[torch.Tensor]] = None,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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attention_states, _ = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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past_key_value=past_key_value,
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use_cache=use_cache,
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cache_position=cache_position,
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**kwargs,
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)
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hidden_states = residual + attention_states
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# Fully Connected
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|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.feed_forward(hidden_states)
|
|
if self.is_moe_layer:
|
|
hidden_states, _ = hidden_states
|
|
hidden_states = residual + hidden_states.view(residual.shape)
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class Llama4PreTrainedModel(PreTrainedModel):
|
|
config: Llama4Config
|
|
supports_gradient_checkpointing = True
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = False
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = True
|
|
|
|
def _init_weights(self, module):
|
|
std = (
|
|
self.config.initializer_range
|
|
if hasattr(self.config, "initializer_range")
|
|
else self.config.text_config.initializer_range
|
|
)
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.weight.data.fill_(1.0)
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, Llama4TextRMSNorm):
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, Llama4TextExperts):
|
|
module.gate_up_proj.data.normal_(mean=0.0, std=std)
|
|
module.down_proj.data.normal_(mean=0.0, std=std)
|
|
elif isinstance(module, Llama4VisionModel):
|
|
module.class_embedding.data.normal_(std=module.scale)
|
|
module.positional_embedding_vlm.data.normal_(std=module.scale)
|
|
|
|
|
|
@auto_docstring
|
|
class Llama4TextModel(Llama4PreTrainedModel):
|
|
_no_split_modules = ["Llama4TextDecoderLayer"]
|
|
base_model_prefix = "model"
|
|
config: Llama4TextConfig
|
|
_can_record_outputs = {
|
|
"attentions": Llama4TextAttention,
|
|
"hidden_states": Llama4TextDecoderLayer,
|
|
"router_logits": Llama4TextMoe,
|
|
}
|
|
|
|
def __init__(self, config: Llama4TextConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
|
self.layers = nn.ModuleList(
|
|
[Llama4TextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = Llama4TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = Llama4TextRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Union[tuple, BaseModelOutputWithPast]:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids.to(self.embed_tokens.weight.device))
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
# It may already have been prepared by e.g. `generate`
|
|
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
|
# Prepare mask arguments
|
|
mask_kwargs = {
|
|
"config": self.config,
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
# Create the masks
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"chunked_attention": create_chunked_causal_mask(**mask_kwargs),
|
|
}
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
freq_cis = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=freq_cis,
|
|
**kwargs,
|
|
)
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values if use_cache else None,
|
|
)
|
|
|
|
|
|
class Llama4ForCausalLM(Llama4PreTrainedModel, GenerationMixin):
|
|
_no_split_modules = ["Llama4TextDecoderLayer"]
|
|
base_model_prefix = "language_model"
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
config: Llama4TextConfig
|
|
|
|
def __init__(self, config: Llama4TextConfig):
|
|
super().__init__(config)
|
|
self.model = Llama4TextModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Union[tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
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]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, Llama4ForCausalLM
|
|
|
|
>>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf")
|
|
|
|
>>> 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."
|
|
```"""
|
|
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,
|
|
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
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Base class for Llava causal language model (or autoregressive) outputs.
|
|
"""
|
|
)
|
|
class Llama4CausalLMOutputWithPast(ModelOutput):
|
|
r"""
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Language modeling loss (for next-token prediction).
|
|
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
|
`past_key_values` input) to speed up sequential decoding.
|
|
image_hidden_states (`torch.FloatTensor`, *optional*):
|
|
A `torch.FloatTensor` of size (batch_size, num_images, sequence_length, hidden_size)`.
|
|
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor]] = None
|
|
image_hidden_states: Optional[torch.FloatTensor] = None
|
|
|
|
|
|
class Llama4VisionMLP2(torch.nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
self.fc1 = nn.Linear(self.intermediate_size, config.projector_input_dim, bias=False)
|
|
self.fc2 = nn.Linear(config.projector_output_dim, config.projector_output_dim, bias=False)
|
|
self.activation_fn = nn.GELU() # ACT2FN[config.hidden_act]
|
|
self.dropout = config.projector_dropout
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.fc1(hidden_states)
|
|
hidden_states = self.activation_fn(hidden_states)
|
|
hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
return self.activation_fn(self.fc2(hidden_states))
|
|
|
|
|
|
class Llama4MultiModalProjector(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.linear_1 = nn.Linear(
|
|
config.vision_config.vision_output_dim,
|
|
config.text_config.hidden_size,
|
|
bias=False,
|
|
)
|
|
|
|
def forward(self, image_features):
|
|
hidden_states = self.linear_1(image_features)
|
|
return hidden_states
|
|
|
|
|
|
def pixel_shuffle(input_tensor, shuffle_ratio):
|
|
# input_tensor: [batch_size, num_patches, channels]
|
|
batch_size, num_patches, channels = input_tensor.shape
|
|
patch_size = int(math.sqrt(num_patches))
|
|
|
|
input_tensor = input_tensor.view(batch_size, patch_size, patch_size, -1)
|
|
batch_size, height, width, channels = input_tensor.size()
|
|
|
|
reshaped_tensor = input_tensor.view(batch_size, height, int(width * shuffle_ratio), int(channels / shuffle_ratio))
|
|
reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
|
|
|
|
reshaped_tensor = reshaped_tensor.view(
|
|
batch_size, int(height * shuffle_ratio), int(width * shuffle_ratio), int(channels / (shuffle_ratio**2))
|
|
)
|
|
reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
|
|
|
|
output_tensor = reshaped_tensor.view(batch_size, -1, reshaped_tensor.shape[-1])
|
|
return output_tensor
|
|
|
|
|
|
class Llama4VisionPixelShuffleMLP(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.pixel_shuffle_ratio = config.pixel_shuffle_ratio
|
|
self.inner_dim = int(config.projector_input_dim // (self.pixel_shuffle_ratio**2))
|
|
self.output_dim = config.projector_output_dim
|
|
self.mlp = Llama4VisionMLP2(config)
|
|
|
|
def forward(self, encoded_patches: torch.Tensor) -> torch.Tensor:
|
|
encoded_patches = pixel_shuffle(encoded_patches, self.pixel_shuffle_ratio)
|
|
return self.mlp(encoded_patches)
|
|
|
|
|
|
# TODO there is a different RoPE for vision encoder, defined as below
|
|
def reshape_for_broadcast(freqs_ci: torch.Tensor, query: torch.Tensor):
|
|
ndim = query.ndim
|
|
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(query.shape)]
|
|
return freqs_ci.view(*shape)
|
|
|
|
|
|
def vision_apply_rotary_emb(
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
freqs_ci: torch.Tensor,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
query_ = torch.view_as_complex(query.float().reshape(*query.shape[:-1], -1, 2))
|
|
key_ = torch.view_as_complex(key.float().reshape(*key.shape[:-1], -1, 2))
|
|
freqs_ci = reshape_for_broadcast(freqs_ci=freqs_ci, query=query_) # freqs_ci[:,:,None,:]
|
|
freqs_ci = freqs_ci.to(query_.device)
|
|
query_out = torch.view_as_real(query_ * freqs_ci).flatten(3)
|
|
key_out = torch.view_as_real(key_ * freqs_ci).flatten(3)
|
|
return query_out.type_as(query), key_out.type_as(key) # but this drops to 8e-3
|
|
|
|
|
|
class Llama4VisionAttention(nn.Module):
|
|
def __init__(self, config: Llama4VisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_dim = config.hidden_size // config.num_attention_heads
|
|
self.num_key_value_groups = 1
|
|
self.attention_dropout = config.attention_dropout
|
|
self.scaling = self.head_dim**-0.5
|
|
|
|
self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=True)
|
|
self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=True)
|
|
self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=True)
|
|
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim, bias=True)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
freqs_ci: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[Cache] = 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)
|
|
|
|
query_states, key_states = vision_apply_rotary_emb(query_states, key_states, freqs_ci=freqs_ci)
|
|
|
|
query_states = query_states.transpose(1, 2)
|
|
key_states = key_states.transpose(1, 2)
|
|
value_states = value_states.transpose(1, 2)
|
|
|
|
attention_interface: Callable = vision_eager_attention_forward
|
|
# flex disable because breaks on TP 8, embed is 88 not power of 2
|
|
if self.config._attn_implementation not in ["eager", "flex_attention"]:
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
None,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=None, # TODO Might be enforced here for TP compatibility as scaling is not just sqrt(head_dim)
|
|
is_causal=False, # HAS TO BE ENFORCED
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class Llama4VisionMLP(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.activation_fn = nn.GELU() # ACT2FN[config.hidden_act]
|
|
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=True)
|
|
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, 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 Llama4VisionEncoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: Llama4VisionConfig):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = Llama4VisionAttention(config)
|
|
self.mlp = Llama4VisionMLP(config)
|
|
|
|
self.input_layernorm = nn.LayerNorm(config.hidden_size)
|
|
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_state: torch.Tensor,
|
|
freqs_ci: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
):
|
|
# Self Attention
|
|
residual = hidden_state
|
|
|
|
hidden_state = self.input_layernorm(hidden_state)
|
|
|
|
hidden_state, attn_weights = self.self_attn(
|
|
hidden_state,
|
|
freqs_ci=freqs_ci,
|
|
attention_mask=attention_mask,
|
|
)
|
|
hidden_state = residual + hidden_state
|
|
|
|
# Feed forward
|
|
residual = hidden_state
|
|
hidden_state = self.post_attention_layernorm(hidden_state)
|
|
hidden_state = self.mlp(hidden_state)
|
|
hidden_state = residual + hidden_state
|
|
|
|
outputs = (hidden_state,)
|
|
|
|
if output_attentions:
|
|
outputs += (attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
class Llama4VisionEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
|
[`Llama4VisionEncoderLayer`].
|
|
|
|
Args:
|
|
config: Llama4VisionConfig
|
|
"""
|
|
|
|
def __init__(self, config: Llama4VisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layers = nn.ModuleList([Llama4VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
self.config = config
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
freqs_ci: torch.Tensor, # TODO move this to an attribute instead of keeping it around
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
r"""
|
|
Args:
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
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
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
for encoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
layer_outputs = encoder_layer(
|
|
hidden_state=hidden_states,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
freqs_ci=freqs_ci,
|
|
)
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
class Llama4UnfoldConvolution(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
kernel_size = config.patch_size
|
|
if isinstance(kernel_size, int):
|
|
kernel_size = (kernel_size, kernel_size)
|
|
self.unfold = torch.nn.Unfold(kernel_size=kernel_size, stride=config.patch_size)
|
|
self.linear = nn.Linear(
|
|
config.num_channels * kernel_size[0] * kernel_size[1],
|
|
config.hidden_size,
|
|
bias=False,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.unfold(hidden_states)
|
|
hidden_states = hidden_states.permute(0, 2, 1)
|
|
hidden_states = self.linear(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class Llama4VisionRotaryEmbedding(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
idx = config.image_size // config.patch_size
|
|
img_idx = torch.arange(idx**2, dtype=torch.int32).reshape(idx**2, 1)
|
|
img_idx = torch.cat([img_idx, img_idx[:1]], dim=0)
|
|
img_idx[-1, -1] = -2 # ID_CLS_TOKEN
|
|
frequencies_x = img_idx % idx # get the coordinates of the 2d matrix along x
|
|
frequencies_y = img_idx // idx # get the coordinates of the 2d matrix along y
|
|
freq_dim = config.hidden_size // config.num_attention_heads // 2
|
|
rope_freq = 1.0 / (config.rope_theta ** (torch.arange(0, freq_dim, 2)[: (freq_dim // 2)].float() / freq_dim))
|
|
freqs_x = ((frequencies_x + 1)[..., None] * rope_freq[None, None, :]).repeat_interleave(2, dim=-1)
|
|
freqs_y = ((frequencies_y + 1)[..., None] * rope_freq[None, None, :]).repeat_interleave(2, dim=-1)
|
|
freqs = torch.cat([freqs_x, freqs_y], dim=-1).float().contiguous()[..., ::2]
|
|
freqs = freqs.masked_fill(img_idx.reshape(-1, 1, 1) < 0, 0)
|
|
freq_cis = torch.view_as_complex(torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1))
|
|
self.freqs_ci = freq_cis # idx**2, idx**2, idx * 2
|
|
|
|
def forward(self, hidden_states):
|
|
return self.freqs_ci.to(hidden_states.device)
|
|
|
|
|
|
class Llama4VisionModel(Llama4PreTrainedModel):
|
|
base_model_prefix = "vision_model"
|
|
_no_split_modules = ["Llama4VisionEncoderLayer"]
|
|
config: Llama4VisionConfig
|
|
|
|
def __init__(self, config: Llama4VisionConfig):
|
|
super().__init__(config)
|
|
self.image_size = config.image_size
|
|
self.patch_size = config.patch_size
|
|
self.hidden_size = config.hidden_size
|
|
self.num_channels = config.num_channels
|
|
|
|
self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
|
|
self.scale = config.hidden_size**-0.5
|
|
|
|
self.patch_embedding = Llama4UnfoldConvolution(config)
|
|
|
|
self.class_embedding = nn.Parameter(self.scale * torch.randn(self.hidden_size))
|
|
self.positional_embedding_vlm = nn.Parameter(self.scale * torch.randn(self.num_patches, self.hidden_size))
|
|
self.rotary_embedding = Llama4VisionRotaryEmbedding(config)
|
|
|
|
# layer norms
|
|
self.layernorm_pre = nn.LayerNorm(self.hidden_size)
|
|
self.layernorm_post = nn.LayerNorm(self.hidden_size)
|
|
|
|
# encoders
|
|
self.model = Llama4VisionEncoder(config)
|
|
self.vision_adapter = Llama4VisionPixelShuffleMLP(config)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
"""
|
|
This function is used to fetch the first embedding layer to activate grads on inputs.
|
|
"""
|
|
return self.patch_embedding
|
|
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[BaseModelOutput, tuple[torch.Tensor, ...]]:
|
|
r"""
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, MllamaVisionModel
|
|
|
|
>>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
|
|
>>> model = MllamaVisionModel.from_pretrained(checkpoint)
|
|
>>> processor = AutoProcessor.from_pretrained(checkpoint)
|
|
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
|
|
>>> output = model(**inputs)
|
|
|
|
>>> print(output.last_hidden_state.shape)
|
|
torch.Size([1, 1, 4, 1025, 7680])
|
|
```
|
|
"""
|
|
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
|
|
|
|
# num_concurrent_media and num_chunks are both currently 1
|
|
batch_size_times_num_tiles, num_channels, height, width = pixel_values.shape
|
|
num_concurrent_media = 1
|
|
num_chunks = 1
|
|
hidden_state = self.patch_embedding(pixel_values)
|
|
_, num_patches, hidden_dim = hidden_state.shape
|
|
|
|
# Add cls token
|
|
hidden_state = hidden_state.reshape(
|
|
batch_size_times_num_tiles * num_concurrent_media * num_chunks, num_patches, hidden_dim
|
|
)
|
|
class_embedding = self.class_embedding.expand(hidden_state.shape[0], 1, hidden_state.shape[-1])
|
|
hidden_state = torch.cat([hidden_state, class_embedding], dim=1)
|
|
num_patches += 1
|
|
|
|
# Position embeddings
|
|
hidden_state = hidden_state.reshape(
|
|
batch_size_times_num_tiles * num_concurrent_media, num_chunks, num_patches, hidden_dim
|
|
)
|
|
positional_embedding = self.positional_embedding_vlm.to(dtype=hidden_state.dtype, device=hidden_state.device)
|
|
hidden_state = hidden_state + positional_embedding
|
|
|
|
hidden_state = self.layernorm_pre(hidden_state)
|
|
|
|
hidden_state = hidden_state.view(batch_size_times_num_tiles, -1, hidden_dim)
|
|
freqs_ci = self.rotary_embedding(pixel_values)
|
|
|
|
output = self.model(
|
|
hidden_state,
|
|
attention_mask=None,
|
|
output_hidden_states=output_hidden_states,
|
|
output_attentions=output_attentions,
|
|
freqs_ci=freqs_ci,
|
|
)
|
|
|
|
hidden_state = output.last_hidden_state
|
|
|
|
hidden_state = self.layernorm_post(hidden_state)
|
|
|
|
hidden_state = hidden_state[:, :-1, :]
|
|
|
|
# now, we use Llama4VisionPixelShuffle + mlp to project embeddings
|
|
hidden_state = self.vision_adapter(hidden_state)
|
|
|
|
hidden_states = output.hidden_states if output_hidden_states else None
|
|
|
|
if output_attentions:
|
|
attentions = output[2]
|
|
else:
|
|
attentions = None
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_state, hidden_states, attentions] if v is not None)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_state,
|
|
hidden_states=hidden_states,
|
|
attentions=attentions,
|
|
)
|
|
|
|
|
|
class Llama4ForConditionalGeneration(Llama4PreTrainedModel, GenerationMixin):
|
|
_no_split_modules = ["Llama4TextDecoderLayer", "Llama4VisionEncoderLayer"]
|
|
_tp_plan = {}
|
|
base_model_prefix = ""
|
|
config: Llama4Config
|
|
|
|
def __init__(self, config: Llama4Config):
|
|
super().__init__(config)
|
|
self.vision_model = Llama4VisionModel(config.vision_config)
|
|
|
|
self.multi_modal_projector = Llama4MultiModalProjector(config)
|
|
self.language_model = Llama4ForCausalLM(config.text_config)
|
|
self.vocab_size = config.text_config.vocab_size
|
|
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
|
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.language_model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.language_model.set_input_embeddings(value)
|
|
|
|
def get_output_embeddings(self):
|
|
return self.language_model.get_output_embeddings()
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.language_model.set_output_embeddings(new_embeddings)
|
|
|
|
def set_decoder(self, decoder):
|
|
self.language_model.set_decoder(decoder)
|
|
|
|
def get_decoder(self):
|
|
return self.language_model.get_decoder()
|
|
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
vision_feature_layer: Union[int, list[int]],
|
|
vision_feature_select_strategy: str,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Obtains image last hidden states from the vision tower and apply al projection.
|
|
|
|
Args:
|
|
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
|
|
The tensors corresponding to the input images.
|
|
vision_feature_layer (`Union[int, list[int]]`):
|
|
The index of the layer to select the vision feature. If multiple indices are provided,
|
|
the vision feature of the corresponding indices will be concatenated to form the
|
|
vision features.
|
|
vision_feature_select_strategy (`str`):
|
|
The feature selection strategy used to select the vision feature from the vision backbone.
|
|
Can be one of `"default"` or `"full"`
|
|
Returns:
|
|
image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
|
|
"""
|
|
if vision_feature_select_strategy not in ["default", "full"]:
|
|
raise ValueError(f"Unexpected select feature strategy: {self.vision_feature_select_strategy}")
|
|
kwargs = {k: v for k, v in kwargs.items() if v is not None}
|
|
image_outputs = self.vision_model(pixel_values, output_hidden_states=False, **kwargs)
|
|
hidden_state = image_outputs.last_hidden_state
|
|
return hidden_state
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
vision_feature_layer: Optional[Union[int, list[int]]] = None,
|
|
vision_feature_select_strategy: Optional[str] = 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,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
image_sizes: torch.Tensor = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Union[tuple, Llama4CausalLMOutputWithPast]:
|
|
r"""
|
|
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]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, LlavaForConditionalGeneration
|
|
|
|
>>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
|
|
>>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
|
|
|
|
>>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
|
|
```"""
|
|
|
|
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
|
|
vision_feature_layer = (
|
|
vision_feature_layer
|
|
if vision_feature_layer is not None
|
|
else self.config.vision_config.vision_feature_layer
|
|
)
|
|
vision_feature_select_strategy = (
|
|
vision_feature_select_strategy
|
|
if vision_feature_select_strategy is not None
|
|
else self.config.vision_config.vision_feature_select_strategy
|
|
)
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if pixel_values is not None and inputs_embeds is not None:
|
|
raise ValueError(
|
|
"You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
|
|
)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if pixel_values is not None:
|
|
image_features = self.get_image_features(
|
|
pixel_values=pixel_values,
|
|
vision_feature_layer=vision_feature_layer,
|
|
vision_feature_select_strategy=vision_feature_select_strategy,
|
|
image_sizes=image_sizes,
|
|
)
|
|
|
|
vision_flat = image_features.view(-1, image_features.size(-1))
|
|
projected_vision_flat = self.multi_modal_projector(vision_flat)
|
|
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
|
|
n_image_tokens = (special_image_mask).sum()
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
|
|
if n_image_tokens != projected_vision_flat.size(0):
|
|
raise ValueError(
|
|
f"Mismatch: final_mask wants {n_image_tokens} embeddings, "
|
|
f"but multi_modal_projector returned {projected_vision_flat.size(0)}"
|
|
)
|
|
projected_vision_flat = projected_vision_flat.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, projected_vision_flat)
|
|
|
|
outputs = self.language_model(
|
|
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,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
|
|
logits = outputs[0]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Shift so that tokens < n predict n
|
|
if attention_mask is not None:
|
|
# we use the input attention mask to shift the logits and labels, because it is 2D.
|
|
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
|
|
shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
|
|
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
|
|
shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
|
|
else:
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
loss = loss_fct(
|
|
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return Llama4CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
cache_position=None,
|
|
logits_to_keep=None,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
|
|
|
model_inputs = self.language_model.prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
|
|
if cache_position[0] == 0:
|
|
# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
|
|
# Otherwise we need pixel values to be passed to model
|
|
model_inputs["pixel_values"] = pixel_values
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = [
|
|
"Llama4PreTrainedModel",
|
|
"Llama4TextModel",
|
|
"Llama4VisionModel",
|
|
"Llama4ForCausalLM",
|
|
"Llama4ForConditionalGeneration",
|
|
]
|