mirror of
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Fix signatures for processing kwargs (#35105)
* add conversion script * remove pg2 refs * fixup style * small update * get correct scaling * add back missing bos * fix missing config keys * might revert this pos_embeddings * fixup 9b config * fix 9b * fixup 9b conversion for good + add back num_hidden_layers * add correct query scaling for 2b, 9b, 27b * fixup 27b conversion * Additional variant: 27b-896 * Use CPU for conversion to reduce GPU RAM requirements * fix causal mask generation + formatting * fix in-training causal mask generation edge case * trigger CI * update config * update config * update config * update config * update config * update config * update config * update config * update config * move conversion file to main model dir * handle multi-images + bos token * address comments for input ids * revert ci fixes * [run-slow] paligemma * fix * [run-slow] paligemma * skip end 2 end * [run-slow] paligemma --------- Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Pablo Montalvo
GitHub
parent
e27465c801
commit
a5bb528471
@ -0,0 +1,415 @@
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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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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"""Convert PaliGemma2 checkpoints from the original repository."""
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import argparse
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import collections
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import jax.numpy as jnp
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import ml_dtypes
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import numpy as np
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import torch
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from transformers import (
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AutoTokenizer,
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Gemma2Config,
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PaliGemmaConfig,
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PaliGemmaForConditionalGeneration,
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PaliGemmaProcessor,
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SiglipImageProcessor,
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)
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from transformers.tokenization_utils_base import AddedToken
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from transformers.utils import logging
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device = "cpu"
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logging.set_verbosity_info()
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logger = logging.get_logger(__name__)
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# TODO add sequence length variations here
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PALIGEMMA2_VARIANTS = ["2b-224", "2b-448", "2b-896", "9b-224", "9b-448", "9b-896", "27b-224", "27b-448", "27b-896"]
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VARIANT_CONFIGS = {
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"2b": {
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"num_positions": 256,
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"hidden_size": 2304,
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"num_hidden_layers": 26,
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"intermediate_size": 9216,
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"num_key_value_heads": 4,
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"num_attention_heads": 8,
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"head_dim": 256,
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"query_pre_attn_scalar": 256,
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},
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"9b": {
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"num_positions": 1024,
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"hidden_size": 3584,
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"num_hidden_layers": 42,
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"intermediate_size": 14336,
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"num_key_value_heads": 8,
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"num_attention_heads": 16,
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"head_dim": 256,
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"query_pre_attn_scalar": 256,
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},
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"27b": {
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"num_positions": 4096,
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"hidden_size": 4608,
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"num_hidden_layers": 46,
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"intermediate_size": 36864,
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"num_key_value_heads": 16,
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"num_attention_heads": 32,
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"head_dim": 128,
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"query_pre_attn_scalar": 4608 // 32, # scaling is different for the 28b
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},
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}
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DTYPES = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}
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def get_paligemma2_config(variant: str, precision: str):
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config = {
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"image_token_index": None,
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"pad_token_id": 0,
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"bos_token_id": 2,
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"eos_token_id": 1,
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}
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base_variant = variant.split("-")[0]
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if variant in PALIGEMMA2_VARIANTS:
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image_size = int(variant.split("-")[1])
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variant_config = VARIANT_CONFIGS[base_variant]
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patch_size = 14
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num_image_tokens = (image_size**2) // (patch_size**2)
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config["projection_dim"] = variant_config["hidden_size"]
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config["image_token_index"] = 257152
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config["num_hidden_layers"] = variant_config["num_hidden_layers"] # For generate
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text_config = Gemma2Config.from_pretrained("google/gemma-2-2b-it").to_dict()
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sup_text_config = {
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"model_type": "gemma2",
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"vocab_size": 257152,
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"num_hidden_layers": variant_config["num_hidden_layers"],
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"num_key_value_heads": variant_config["num_key_value_heads"],
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"head_dim": variant_config["head_dim"],
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"torch_dtype": precision,
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"hidden_size": variant_config["hidden_size"],
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"hidden_activation": "gelu_pytorch_tanh",
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"num_attention_heads": variant_config["num_attention_heads"],
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"intermediate_size": variant_config["intermediate_size"],
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"is_encoder_decoder": False,
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"query_pre_attn_scalar": variant_config["query_pre_attn_scalar"],
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}
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text_config.update(sup_text_config)
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vision_config = {
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"num_positions": variant_config["num_positions"], # not useful, to remove
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"torch_dtype": precision,
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"image_size": image_size,
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"patch_size": patch_size,
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"num_image_tokens": num_image_tokens,
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"hidden_size": 1152,
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"intermediate_size": 4304,
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"num_hidden_layers": 27,
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"num_attention_heads": 16,
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"projection_dim": variant_config["hidden_size"],
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"hidden_act": "gelu_pytorch_tanh",
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"vision_use_head": False,
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}
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final_config = PaliGemmaConfig(text_config=text_config, vision_config=vision_config, **config)
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else:
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raise ValueError(f"Identifier {variant} not supported. Available: {PALIGEMMA2_VARIANTS}")
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return final_config
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def slice_state_dict(state_dict, config):
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# fmt: off
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# patch embeddings
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state_dict["vision_tower.vision_model.embeddings.patch_embedding.weight"] = state_dict.pop("img/embedding/kernel").transpose(
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3, 2, 0, 1
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)
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state_dict["vision_tower.vision_model.embeddings.patch_embedding.bias"] = state_dict.pop("img/embedding/bias")
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# positional embeddings
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state_dict["vision_tower.vision_model.embeddings.position_embedding.weight"] = state_dict.pop("img/pos_embedding").reshape(
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-1, config.vision_config.hidden_size
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)
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# extract vision layers to be sliced at index 0. There are 27 layers in the base model.
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encoderblock_layernorm0_scale = state_dict.pop("img/Transformer/encoderblock/LayerNorm_0/scale")
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encoderblock_layernorm0_bias = state_dict.pop("img/Transformer/encoderblock/LayerNorm_0/bias")
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encoderblock_layernorm1_scale = state_dict.pop("img/Transformer/encoderblock/LayerNorm_1/scale")
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encoderblock_layernorm1_bias = state_dict.pop("img/Transformer/encoderblock/LayerNorm_1/bias")
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encoderblock_mlp_dense0_kernel= state_dict.pop("img/Transformer/encoderblock/MlpBlock_0/Dense_0/kernel")
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encoderblock_mlp_dense0_bias= state_dict.pop("img/Transformer/encoderblock/MlpBlock_0/Dense_0/bias")
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encoderblock_mlp_dense1_kernel= state_dict.pop("img/Transformer/encoderblock/MlpBlock_0/Dense_1/kernel")
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encoderblock_mlp_dense1_bias= state_dict.pop("img/Transformer/encoderblock/MlpBlock_0/Dense_1/bias")
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encoderblock_attention_0_key_kernel = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/kernel")
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encoderblock_attention_0_key_bias = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/bias")
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encoderblock_attention_0_value_kernel = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/kernel")
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encoderblock_attention_0_value_bias = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/bias")
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encoderblock_attention_0_query_kernel = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/kernel")
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encoderblock_attention_0_query_bias = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/bias")
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encoderblock_attention_0_out_kernel = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/kernel")
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encoderblock_attention_0_out_bias = state_dict.pop("img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/bias")
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for i in range(config.vision_config.num_hidden_layers):
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.layer_norm1.weight"] = encoderblock_layernorm0_scale[i].transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.layer_norm1.bias"] = encoderblock_layernorm0_bias[i]
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.layer_norm2.weight"] = encoderblock_layernorm1_scale[i].transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.layer_norm2.bias"] = encoderblock_layernorm1_bias[i]
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.weight"] = encoderblock_mlp_dense0_kernel[i].transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.bias"] = encoderblock_mlp_dense0_bias[i]
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.weight"] = encoderblock_mlp_dense1_kernel[i].transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.bias"] = encoderblock_mlp_dense1_bias[i]
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.weight"] = encoderblock_attention_0_key_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.bias"] = encoderblock_attention_0_key_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.weight"] = encoderblock_attention_0_value_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.bias"] = encoderblock_attention_0_value_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.weight"] = encoderblock_attention_0_query_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.bias"] = encoderblock_attention_0_query_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.weight"] = encoderblock_attention_0_out_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose()
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state_dict[f"vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.bias"] = encoderblock_attention_0_out_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1)
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state_dict["vision_tower.vision_model.post_layernorm.weight"] = state_dict.pop("img/Transformer/encoder_norm/scale").transpose()
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state_dict["vision_tower.vision_model.post_layernorm.bias"] = state_dict.pop("img/Transformer/encoder_norm/bias")
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# multimodal projector
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state_dict['multi_modal_projector.linear.weight'] = state_dict.pop("img/head/kernel").transpose()
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state_dict['multi_modal_projector.linear.bias'] = state_dict.pop("img/head/bias")
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# text decoder (gemma)
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embedding_vector = state_dict.pop("llm/embedder/input_embedding")
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state_dict["language_model.model.embed_tokens.weight"] = embedding_vector
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# pop the einsum attention + mlp representations. There are 26 layers in gemma2-2b.
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llm_attention_attn_vec_einsum = state_dict.pop("llm/layers/attn/attn_vec_einsum/w")
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# (26, 2, 4, 2304, 256) for 2b-224, 4 kv heads and 26 layers
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llm_attention_kv_einsum = state_dict.pop("llm/layers/attn/kv_einsum/w")
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llm_attention_q_einsum = state_dict.pop("llm/layers/attn/q_einsum/w")
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llm_mlp_gating_einsum = state_dict.pop("llm/layers/mlp/gating_einsum")
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llm_mlp_linear = state_dict.pop("llm/layers/mlp/linear")
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# TODO verify correctness of layer norm loading
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llm_input_layernorm = state_dict.pop("llm/layers/pre_attention_norm/scale")
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llm_pre_feedforward_layernorm = state_dict.pop("llm/layers/pre_ffw_norm/scale")
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llm_post_attention_layernorm = state_dict.pop("llm/layers/post_attention_norm/scale")
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llm_post_feedforward_layernorm = state_dict.pop("llm/layers/post_ffw_norm/scale")
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for i in range(config.text_config.num_hidden_layers):
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# llm_attention_q_einsum[i].shape = (8, 2048, 256)
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# q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size)
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"""
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q shape (8, 2304, 256)
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k shape (4, 2304, 256)
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v shape (4, 2304, 256)
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o shape (8, 256, 2304)
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"""
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q_transpose = (0, 2, 1)
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k_transpose = (0, 2, 1)
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v_transpose = (0, 2, 1)
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o_transpose = (2, 0, 1)
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q_weight_matrices = llm_attention_q_einsum[i].transpose(*q_transpose)
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q_proj_weight_reshaped = q_weight_matrices
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q_proj_weight_reshaped = q_proj_weight_reshaped.reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size)
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state_dict[f"language_model.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped
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# Shape: (4, 2304, 256)
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k_weight_matrices = llm_attention_kv_einsum[i, 0].transpose(*k_transpose)
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k_proj_weight_reshaped = k_weight_matrices.reshape(
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config.text_config.num_key_value_heads * config.text_config.head_dim,
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config.text_config.hidden_size
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)
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state_dict[f"language_model.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped
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# llm_attention_kv_einsum[i, 1].shape = (num_key_value_heads, hidden_size, head_dim)
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v_weight_matrices = llm_attention_kv_einsum[i, 1].transpose(*v_transpose) # Shape: (4, 2304, 256)
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v_proj_weight_reshaped = v_weight_matrices.reshape(
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config.text_config.num_key_value_heads * config.text_config.head_dim,
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config.text_config.hidden_size
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)
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state_dict[f"language_model.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped
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# output projection.
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# llm_attention_attn_vec_einsum[i].shape = (8, 256, 2304)
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o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].transpose(*o_transpose).reshape(config.text_config.hidden_size, config.text_config.num_attention_heads * config.text_config.head_dim)
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state_dict[f"language_model.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped
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# mlp layers
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gate_proj_weight = llm_mlp_gating_einsum[i, 0]
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state_dict[f"language_model.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose()
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up_proj_weight = llm_mlp_gating_einsum[i, 1]
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state_dict[f"language_model.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose()
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state_dict[f"language_model.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose()
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state_dict[f"language_model.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i]
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state_dict[f"language_model.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i]
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state_dict[f"language_model.model.layers.{i}.pre_feedforward_layernorm.weight"] = llm_pre_feedforward_layernorm[i]
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state_dict[f"language_model.model.layers.{i}.post_feedforward_layernorm.weight"] = llm_post_feedforward_layernorm[i]
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state_dict["language_model.model.norm.weight"] = state_dict.pop("llm/final_norm/scale")
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state_dict["language_model.lm_head.weight"] = embedding_vector # weights are tied.
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[k for k in state_dict.keys() if not k.startswith('vision') and not k.startswith('language')]
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# fmt: on
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for key, value in state_dict.items():
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if not isinstance(value, torch.Tensor):
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try:
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if value.dtype == jnp.bfloat16:
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value = jnp.array(value).astype(jnp.float32)
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value = np.array(value)
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state_dict[key] = torch.from_numpy(value).to(torch.bfloat16)
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else:
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state_dict[key] = torch.from_numpy(value)
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except Exception as initial_exception:
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raise ValueError(f"Conversion failed from jax weights with {initial_exception}. Check your inputs.")
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return state_dict
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def flatten_nested_dict(params, parent_key="", sep="/", precision: int = "float32"):
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items = []
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for k, v in params.items():
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k = k.removeprefix("params/")
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new_key = parent_key + sep + k if parent_key else k
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if isinstance(v, collections.abc.MutableMapping):
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items.extend(flatten_nested_dict(v, parent_key=new_key, sep=sep, precision=precision).items())
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else:
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if precision == "bfloat16":
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try:
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v = v.view(ml_dtypes.bfloat16)
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except Exception as initial_exception:
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raise ValueError(f"Conversion failed from bfloat16 with {initial_exception}, check your inputs.")
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items.append((new_key, v))
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return dict(items)
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@torch.no_grad()
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def convert_paligemma2_checkpoint(
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checkpoint_path,
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pytorch_dump_folder_path,
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variant: str,
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precision: str,
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do_convert_weights=False,
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):
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"""
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Read checkpoints from flax npz files, rename/reshape, send result to state dict and verify logits if needed.
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"""
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config = get_paligemma2_config(variant, precision=precision)
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if do_convert_weights:
|
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tokenizer_id = "google/paligemma-3b-pt-224" # same tokenizer as paligemma 1
|
||||
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
|
||||
image_token = AddedToken("<image>", normalized=False, special=True)
|
||||
tokens_to_add = {"additional_special_tokens": [image_token]}
|
||||
tokenizer.add_special_tokens(tokens_to_add)
|
||||
|
||||
# tokenizer.padding_side = 'right' # uncomment for testing purposes only.
|
||||
|
||||
image_processor = SiglipImageProcessor.from_pretrained("google/paligemma-3b-pt-224")
|
||||
image_processor.size = {"width": config.vision_config.image_size, "height": config.vision_config.image_size}
|
||||
image_processor.image_seq_length = config.vision_config.num_image_tokens
|
||||
|
||||
processor = PaliGemmaProcessor(image_processor=image_processor, tokenizer=tokenizer)
|
||||
data = jnp.load(checkpoint_path)
|
||||
state_dict = flatten_nested_dict(data, precision=precision)
|
||||
del data
|
||||
state_dict_transformers = slice_state_dict(state_dict, config)
|
||||
del state_dict
|
||||
del config.hidden_size # this key is unused
|
||||
model = PaliGemmaForConditionalGeneration(config).to(device).eval()
|
||||
model.load_state_dict(state_dict_transformers)
|
||||
del state_dict_transformers
|
||||
model.config.text_config._attn_implementation = "sdpa"
|
||||
|
||||
# model expansion to get random embeds of image tokens
|
||||
pad_shape = 64 # for performance reasons
|
||||
pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
|
||||
mu = torch.mean(pre_expansion_embeddings, dim=0).float()
|
||||
n = pre_expansion_embeddings.size()[0]
|
||||
sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
|
||||
dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
|
||||
|
||||
# We add an image token so we resize the model
|
||||
model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
|
||||
model.language_model.model.embed_tokens.weight.data[257152:] = torch.stack(
|
||||
tuple(
|
||||
(dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[257152:].shape[0]))
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
model.language_model.lm_head.weight.data[257152:] = torch.stack(
|
||||
tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[257152:].shape[0]))),
|
||||
dim=0,
|
||||
)
|
||||
# convert to needed precision
|
||||
|
||||
model.to(DTYPES[precision])
|
||||
model.save_pretrained(pytorch_dump_folder_path, safe_serialization=True)
|
||||
processor.save_pretrained(pytorch_dump_folder_path)
|
||||
|
||||
else:
|
||||
processor = PaliGemmaProcessor.from_pretrained(pytorch_dump_folder_path, do_rescale=False)
|
||||
model = (
|
||||
PaliGemmaForConditionalGeneration.from_pretrained(pytorch_dump_folder_path, attn_implementation="sdpa")
|
||||
.to(device)
|
||||
.eval()
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--checkpoint_path",
|
||||
required=True,
|
||||
type=str,
|
||||
help="Path to the .npz checkpoint",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--pytorch_dump_folder_path",
|
||||
required=True,
|
||||
type=str,
|
||||
help="Path to the output directory where model and processor will be saved.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--precision",
|
||||
choices=["float32", "bfloat16", "float16"],
|
||||
type=str,
|
||||
help="Precision identifier for model conversion - should match the base checkpoint precision.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--variant",
|
||||
default="2b-224",
|
||||
choices=PALIGEMMA2_VARIANTS,
|
||||
type=str,
|
||||
help="String identifier of the paligemma2 variant to convert.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--do_convert_weights", action="store_true", help="Whether or not to reload and convert the weights."
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
convert_paligemma2_checkpoint(
|
||||
checkpoint_path=args.checkpoint_path,
|
||||
pytorch_dump_folder_path=args.pytorch_dump_folder_path,
|
||||
variant=args.variant,
|
||||
precision=args.precision,
|
||||
do_convert_weights=args.do_convert_weights,
|
||||
)
|
||||
@ -21,7 +21,7 @@ import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
|
||||
from ...cache_utils import Cache, StaticCache
|
||||
from ...cache_utils import Cache, HybridCache, StaticCache
|
||||
from ...generation import GenerationMixin
|
||||
from ...modeling_utils import PreTrainedModel
|
||||
from ...utils import (
|
||||
@ -341,7 +341,14 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
return self.language_model.tie_weights()
|
||||
|
||||
def _update_causal_mask(
|
||||
self, attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training: bool = False
|
||||
self,
|
||||
attention_mask,
|
||||
token_type_ids,
|
||||
past_key_values,
|
||||
cache_position,
|
||||
input_ids=None,
|
||||
inputs_embeds=None,
|
||||
is_training: bool = False,
|
||||
):
|
||||
if self.config.text_config._attn_implementation == "flash_attention_2":
|
||||
if attention_mask is not None and 0.0 in attention_mask:
|
||||
@ -349,11 +356,13 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
return None
|
||||
|
||||
using_static_cache = isinstance(past_key_values, StaticCache)
|
||||
dtype = inputs_embeds.dtype
|
||||
min_dtype = torch.finfo(dtype).min
|
||||
sequence_length = inputs_embeds.shape[1]
|
||||
min_dtype = torch.finfo(self.dtype).min
|
||||
inputs_lead_dim = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
|
||||
sequence_length = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
||||
if using_static_cache:
|
||||
target_length = past_key_values.get_max_cache_shape()
|
||||
elif isinstance(past_key_values, HybridCache):
|
||||
target_length = past_key_values.get_max_cache_shape()
|
||||
else:
|
||||
target_length = (
|
||||
attention_mask.shape[-1]
|
||||
@ -366,7 +375,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
return attention_mask
|
||||
|
||||
causal_mask = torch.full(
|
||||
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
||||
(sequence_length, target_length), fill_value=min_dtype, dtype=self.dtype, device=cache_position.device
|
||||
)
|
||||
# Causal diagonal mask only if training, otherwise attend to the whole prefix. Training-specific attn for prefix is handled below
|
||||
if sequence_length != 1:
|
||||
@ -376,7 +385,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
causal_mask[:, :sequence_length] = 0.0
|
||||
|
||||
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
||||
causal_mask = causal_mask[None, None, :, :].expand(inputs_embeds.shape[0], 1, -1, -1)
|
||||
causal_mask = causal_mask[None, None, :, :].expand(inputs_lead_dim, 1, -1, -1)
|
||||
if attention_mask is not None:
|
||||
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
||||
mask_length = attention_mask.shape[-1]
|
||||
@ -405,7 +414,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
image_outputs = self.vision_tower(pixel_values)
|
||||
selected_image_feature = image_outputs.last_hidden_state
|
||||
image_features = self.multi_modal_projector(selected_image_feature)
|
||||
image_features = image_features / (self.config.hidden_size**0.5)
|
||||
image_features = image_features / (self.config.text_config.hidden_size**0.5)
|
||||
return image_features
|
||||
|
||||
@add_start_docstrings_to_model_forward(PALIGEMMA_INPUTS_DOCSTRING)
|
||||
@ -516,9 +525,8 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
labels = torch.where(input_ids == self.pad_token_id, self.config.ignore_index, labels)
|
||||
|
||||
causal_mask = self._update_causal_mask(
|
||||
attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training
|
||||
attention_mask, token_type_ids, past_key_values, cache_position, input_ids, inputs_embeds, is_training
|
||||
)
|
||||
|
||||
outputs = self.language_model(
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
@ -579,6 +587,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
token_type_ids=None,
|
||||
use_cache=True,
|
||||
num_logits_to_keep=None,
|
||||
labels=None,
|
||||
**kwargs,
|
||||
):
|
||||
# Overwritten -- custom `position_ids` and `pixel_values` handling
|
||||
@ -598,10 +607,14 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
|
||||
# position_ids in Paligemma are 1-indexed
|
||||
if model_inputs.get("position_ids") is not None:
|
||||
model_inputs["position_ids"] += 1
|
||||
|
||||
# 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. NOTE: use_cache=False needs pixel_values always
|
||||
if cache_position[0] == 0:
|
||||
model_inputs["pixel_values"] = pixel_values
|
||||
|
||||
is_training = token_type_ids is not None and labels is not None
|
||||
if cache_position[0] == 0 and isinstance(past_key_values, HybridCache):
|
||||
causal_mask = self._update_causal_mask(
|
||||
attention_mask, token_type_ids, past_key_values, cache_position, input_ids, inputs_embeds, is_training
|
||||
)
|
||||
model_inputs["attention_mask"] = causal_mask
|
||||
return model_inputs
|
||||
|
||||
@ -269,7 +269,7 @@ class PaliGemmaProcessor(ProcessorMixin):
|
||||
logger.warning(
|
||||
"You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special "
|
||||
"image tokens in the text, as many tokens as there are images per each text. It is recommended to "
|
||||
"add `<image>` tokens in the very beginning of your text and `<bos>` token after that. For this call, we will infer how many images "
|
||||
"add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images "
|
||||
"each text has and add special tokens."
|
||||
)
|
||||
|
||||
@ -304,9 +304,16 @@ class PaliGemmaProcessor(ProcessorMixin):
|
||||
]
|
||||
images = make_batched_images(images)
|
||||
else:
|
||||
text = [sample.replace(IMAGE_TOKEN, IMAGE_TOKEN * self.image_seq_length) for sample in text]
|
||||
input_strings = [f"{sample}\n" for sample in text]
|
||||
|
||||
expanded_samples = []
|
||||
for sample in text:
|
||||
expanded_sample = sample.replace(IMAGE_TOKEN, IMAGE_TOKEN * self.image_seq_length)
|
||||
bos_rfind_index = expanded_sample.rfind(IMAGE_TOKEN)
|
||||
bos_index = bos_rfind_index + len(IMAGE_TOKEN) if bos_rfind_index != -1 else 0
|
||||
expanded_sample = (
|
||||
expanded_sample[:bos_index] + self.tokenizer.bos_token + expanded_sample[bos_index:]
|
||||
)
|
||||
expanded_samples.append(expanded_sample)
|
||||
input_strings = [f"{sample}\n" for sample in expanded_samples]
|
||||
pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
|
||||
|
||||
# max_length has to account for the image tokens
|
||||
|
||||
@ -347,6 +347,11 @@ class PaliGemmaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTes
|
||||
def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
|
||||
pass
|
||||
|
||||
# TODO (joao, raushan): fix me -- the problem is in `cache_position[0] == 0`, i.e. dynamic control flow
|
||||
@unittest.skip("PaliGemma is not compatible with end-to-end generation compilation")
|
||||
def test_generate_compile_fullgraph(self):
|
||||
pass
|
||||
|
||||
|
||||
@slow
|
||||
@require_torch
|
||||
|
||||
@ -63,8 +63,8 @@ class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
tokenizer = self.get_component("tokenizer")
|
||||
|
||||
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
||||
text_multi_images = "<image><image><bos>Dummy text!"
|
||||
text_single_image = "<image><bos>Dummy text!"
|
||||
text_multi_images = "<image><image>Dummy text!"
|
||||
text_single_image = "<image>Dummy text!"
|
||||
text_no_image = "Dummy text!"
|
||||
|
||||
image = self.prepare_image_inputs()
|
||||
@ -85,7 +85,7 @@ class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
self.assertTrue(out_noimage[k].tolist() == out_multiimages[k].tolist())
|
||||
|
||||
text_batched = ["Dummy text!", "Dummy text!"]
|
||||
text_batched_with_image = ["<image><bos>Dummy text!", "<image><bos>Dummy text!"]
|
||||
text_batched_with_image = ["<image>Dummy text!", "<image>Dummy text!"]
|
||||
out_images = processor(text=text_batched_with_image, images=[image, image], return_tensors="np")
|
||||
out_noimage_nested = processor(text=text_batched, images=[[image], [image]], return_tensors="np")
|
||||
out_noimage = processor(text=text_batched, images=[image, image], return_tensors="np")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user