521 lines
21 KiB
Python
521 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2024 Mistral and the HuggingFace Inc. team. All rights reserved.
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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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"""PyTorch Pixtral model."""
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from collections.abc import Callable
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from typing import Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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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
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from ...modeling_rope_utils import 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 auto_docstring, can_return_tuple, logging
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from .configuration_pixtral import PixtralVisionConfig
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logger = logging.get_logger(__name__)
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def position_ids_in_meshgrid(patch_embeds_list, max_width):
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positions = []
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for patch in patch_embeds_list:
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height, width = patch.shape[-2:]
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mesh = torch.meshgrid(torch.arange(height), torch.arange(width), indexing="ij")
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h_grid, v_grid = torch.stack(mesh, dim=-1).reshape(-1, 2).chunk(2, -1)
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ids = h_grid * max_width + v_grid
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positions.append(ids[:, 0])
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return torch.cat(positions)
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class PixtralRotaryEmbedding(nn.Module):
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"""
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The key with pixtral embedding is just that you have a frequency for each pixel positions.
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If you have height x width pixels (or embedding pixels), then the frequency used for ROPE
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is given by indexing the pre_computed frequency on the width and height.
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What you output is of dimension (batch, height * width, dim) with dim the embed dim.
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This simply means that for each image hidden state, you are going to add
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a corresponding positional embedding, based on its index in the grid.
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"""
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def __init__(self, config, device=None):
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super().__init__()
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self.rope_type = "default"
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self.dim = config.head_dim
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self.base = config.rope_theta
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max_patches_per_side = config.image_size // config.patch_size
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freqs = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim))
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h = torch.arange(max_patches_per_side, device=freqs.device)
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w = torch.arange(max_patches_per_side, device=freqs.device)
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freqs_h = torch.outer(h, freqs[::2]).float()
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freqs_w = torch.outer(w, freqs[1::2]).float()
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inv_freq = torch.cat(
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[
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freqs_h[:, None, :].repeat(1, max_patches_per_side, 1),
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freqs_w[None, :, :].repeat(max_patches_per_side, 1, 1),
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],
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dim=-1,
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).reshape(-1, self.dim // 2) # we reshape to only index on the position indexes, not tuple of indexes
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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# TODO maybe make it torch compatible later on. We can also just slice
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self.register_buffer("inv_freq", torch.cat((inv_freq, inv_freq), dim=-1), persistent=False)
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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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freqs = self.inv_freq[position_ids]
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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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emb = freqs
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cos = emb.cos()
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sin = emb.sin()
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
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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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attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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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)
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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 PixtralAttention(nn.Module):
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"""
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Multi-headed attention compatible with ALL_ATTENTION_FUNCTIONS.
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"""
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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self.is_causal = False
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self.scaling = self.head_dim**-0.5
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self.is_causal = False
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self.dropout = config.attention_dropout
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.o_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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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_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
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output_attentions: Optional[bool] = False,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""Input shape: Batch x Time x Channel"""
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batch_size, patches, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=0)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and output_attentions:
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
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'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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# Since we use packing, if flash_attention_2 is selected we rely on position_ids
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if self.config._attn_implementation == "flash_attention_2":
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kwargs["position_ids"] = kwargs["position_ids"].to(hidden_states.device, non_blocking=True)
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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.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(batch_size, patches, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights
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# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Pixtral
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class PixtralMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Pixtral
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class PixtralRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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PixtralRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class PixtralAttentionLayer(GradientCheckpointingLayer):
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def __init__(self, config):
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super().__init__()
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self.attention_norm = PixtralRMSNorm(config.hidden_size, eps=1e-5)
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self.feed_forward = PixtralMLP(config)
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self.attention = PixtralAttention(config)
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self.ffn_norm = PixtralRMSNorm(config.hidden_size, eps=1e-5)
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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: torch.Tensor,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.FloatTensor]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`):
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Input to the layer of shape `(batch, seq_len, embed_dim)`.
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attention_mask (`torch.FloatTensor`):
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Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
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output_attentions (`bool`, *optional*, defaults to `False`):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.attention_norm(hidden_states)
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hidden_states, attn_weights = self.attention(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_embeddings=position_embeddings,
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output_attentions=output_attentions,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.ffn_norm(hidden_states)
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hidden_states = self.feed_forward(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs
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class PixtralTransformer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.layers = torch.nn.ModuleList()
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for _ in range(config.num_hidden_layers):
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self.layers.append(PixtralAttentionLayer(config))
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self.gradient_checkpointing = False
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def forward(
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self,
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inputs_embeds,
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attention_mask: Optional[torch.Tensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Union[tuple, BaseModelOutput]:
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r"""
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Args:
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Embeddings which serve as input to the Transformer.
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
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for more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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encoder_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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hidden_states = inputs_embeds
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for encoder_layer in self.layers:
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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layer_outputs = encoder_layer(
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hidden_states,
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attention_mask,
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position_embeddings=position_embeddings,
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output_attentions=output_attentions,
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**kwargs,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_attentions = all_attentions + (layer_outputs[1],)
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
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)
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@auto_docstring
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class PixtralPreTrainedModel(PreTrainedModel):
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config: PixtralVisionConfig
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base_model_prefix = "model"
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main_input_name = "pixel_values"
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supports_gradient_checkpointing = True
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_supports_attention_backend = True
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_supports_flash_attn = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_no_split_modules = ["PixtralAttentionLayer"]
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_supports_flash_attn = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_attention_backend = True
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def _init_weights(self, module):
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std = self.config.initializer_range
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, PixtralRMSNorm):
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module.weight.data.fill_(1.0)
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def generate_block_attention_mask(patch_embeds_list, tensor):
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dtype = tensor.dtype
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device = tensor.device
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seq_len = tensor.shape[1]
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d_min = torch.finfo(dtype).min
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causal_mask = torch.full((seq_len, seq_len), fill_value=d_min, dtype=dtype, device=device)
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block_end_idx = torch.tensor(patch_embeds_list).cumsum(-1)
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block_start_idx = torch.tensor([0] + patch_embeds_list[:-1]).cumsum(-1)
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for start, end in zip(block_start_idx, block_end_idx):
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causal_mask[start:end, start:end] = 0
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causal_mask = causal_mask[None, None, :, :].expand(tensor.shape[0], 1, -1, -1)
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return causal_mask
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@auto_docstring
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class PixtralVisionModel(PixtralPreTrainedModel):
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base_model_prefix = "vision_encoder"
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def __init__(self, config):
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super().__init__(config)
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self.config = config
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self.patch_conv = nn.Conv2d(
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in_channels=config.num_channels,
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out_channels=config.hidden_size,
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kernel_size=config.patch_size,
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stride=config.patch_size,
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bias=False,
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)
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self.patch_size = config.patch_size
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self.ln_pre = PixtralRMSNorm(config.hidden_size, eps=1e-5)
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self.transformer = PixtralTransformer(config)
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self.patch_positional_embedding = PixtralRotaryEmbedding(config)
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self.post_init()
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def get_input_embeddings(self):
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return self.patch_conv
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@can_return_tuple
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@auto_docstring
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def forward(
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self,
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pixel_values: torch.Tensor,
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image_sizes: Optional[torch.Tensor] = None,
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output_hidden_states: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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*args,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Union[tuple, BaseModelOutput]:
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if image_sizes is None:
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batch_size, _, height, width = pixel_values.shape
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image_sizes = [(height, width)] * batch_size
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# pass images through initial convolution independently
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patch_embeds = self.patch_conv(pixel_values)
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patch_embeds_list = [
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embed[..., : (size[0] // self.patch_size), : (size[1] // self.patch_size)]
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for embed, size in zip(patch_embeds, image_sizes)
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]
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# flatten to a single sequence
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patch_embeds = torch.cat([p.flatten(1).T for p in patch_embeds_list], dim=0).unsqueeze(0)
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patch_embeds = self.ln_pre(patch_embeds)
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# positional embeddings
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position_ids = position_ids_in_meshgrid(
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patch_embeds_list, max_width=self.config.image_size // self.config.patch_size
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)
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kwargs["position_ids"] = position_ids
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position_embeddings = self.patch_positional_embedding(patch_embeds, position_ids)
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if self.config._attn_implementation == "flash_attention_2":
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# We only rely on position_ids when using flash_attention_2
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attention_mask = None
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else:
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attention_mask = generate_block_attention_mask(
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[p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
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)
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return self.transformer(
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patch_embeds,
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attention_mask=attention_mask,
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position_embeddings=position_embeddings,
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output_hidden_states=output_hidden_states,
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output_attentions=output_attentions,
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return_dict=True,
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**kwargs,
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)
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__all__ = ["PixtralVisionModel", "PixtralPreTrainedModel"]
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