[VLM][Model] Support image input for Chameleon (#6633)
This commit is contained in:
Roger Wang
@@ -16,9 +16,10 @@ _GENERATION_MODELS = {
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"BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"), # baichuan-7b
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"BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"), # baichuan-13b
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"BloomForCausalLM": ("bloom", "BloomForCausalLM"),
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"ChameleonForCausalLM":
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("chameleon", "ChameleonForConditionalGeneration"
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), #TODO(ywang96): fix model name when huggingface fixes it
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#TODO(ywang96): remove this when huggingface fixes the model repo
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"ChameleonForCausalLM": ("chameleon", "ChameleonForConditionalGeneration"),
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"ChameleonForConditionalGeneration":
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("chameleon", "ChameleonForConditionalGeneration"),
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"ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
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"ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
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"CohereForCausalLM": ("commandr", "CohereForCausalLM"),
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@@ -1,13 +1,17 @@
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from functools import cached_property
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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from typing import (Any, Dict, Iterable, List, Literal, Optional, Tuple,
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TypedDict)
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import torch
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import torch.nn.functional as F
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from PIL import Image
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig
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from vllm.config import CacheConfig, MultiModalConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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@@ -22,10 +26,114 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors, SamplerOutput
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from vllm.transformers_utils.configs import ChameleonConfig
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.image import (cached_get_tokenizer,
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repeat_and_pad_image_tokens)
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from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData
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from vllm.transformers_utils.configs import (ChameleonConfig,
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ChameleonVQVAEConfig)
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from vllm.utils import print_warning_once
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from .interfaces import SupportsVision
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logger = init_logger(__name__)
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# These configs are not part of the model config but the preprocessor
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# and processor files, so we hardcode them in the model file for now.
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CHAMELEON_CROP_SIZE_HEIGHT = CHAMELEON_CROP_SIZE_WIDTH = 512
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CHAMELEON_IMAGE_SEQ_LENGTH = 1024
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CHAMELEON_IMAGE_TOKEN_ID = 8711
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CHAMELEON_IMAGE_START_TOKEN_ID = 8197
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CHAMELEON_IMAGE_END_TOKEN_ID = 8196
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CHAMELEON_SEP_TOKEN_ID = 8710
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class ChameleonImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: torch.Tensor
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"""Shape: `(batch_size, num_channels, height, width)`"""
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def get_max_chameleon_image_tokens(ctx: InputContext):
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return CHAMELEON_IMAGE_SEQ_LENGTH
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def dummy_seq_data_for_chameleon(
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seq_len: int,
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*,
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image_token_id: int,
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image_feature_size_override: Optional[int] = None,
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):
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if image_feature_size_override is None:
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image_feature_size = CHAMELEON_IMAGE_SEQ_LENGTH
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else:
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image_feature_size = image_feature_size_override
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token_ids = [image_token_id] * image_feature_size
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token_ids += [0] * (seq_len - image_feature_size)
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return SequenceData(token_ids)
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def dummy_image_for_chameleon(
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image_width_override: Optional[int] = None,
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image_height_override: Optional[int] = None,
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):
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width = CHAMELEON_CROP_SIZE_WIDTH
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height = CHAMELEON_CROP_SIZE_HEIGHT
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if image_width_override is not None:
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width = image_width_override
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if image_height_override is not None:
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height = image_height_override
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image = Image.new("RGB", (width, height), color=0)
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return {"image": image}
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def dummy_data_for_chameleon(ctx: InputContext, seq_len: int):
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seq_data = dummy_seq_data_for_chameleon(
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seq_len,
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image_token_id=CHAMELEON_IMAGE_TOKEN_ID,
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)
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mm_data = dummy_image_for_chameleon()
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return seq_data, mm_data
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def input_processor_for_chameleon(ctx: InputContext, llm_inputs: LLMInputs):
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"""
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Processing input prompt to insert required tokens for image placeholder.
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See https://github.com/huggingface/transformers/blob/0fdea8607d7e01eb0e38a1ebeb7feee30a22f0cf/src/transformers/models/chameleon/processing_chameleon.py#L58
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""" # noqa
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multi_modal_data = llm_inputs.get("multi_modal_data")
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if multi_modal_data is None or "image" not in multi_modal_data:
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return llm_inputs
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model_config = ctx.model_config
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tokenizer = cached_get_tokenizer(model_config.tokenizer)
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new_prompt, new_token_ids = repeat_and_pad_image_tokens(
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tokenizer,
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llm_inputs.get("prompt"),
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llm_inputs["prompt_token_ids"],
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image_token_id=CHAMELEON_IMAGE_TOKEN_ID,
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repeat_count=CHAMELEON_IMAGE_SEQ_LENGTH,
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pad_token_left=CHAMELEON_IMAGE_START_TOKEN_ID,
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pad_token_right=CHAMELEON_IMAGE_END_TOKEN_ID,
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)
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# Appending sep token for chat mode to follow default processor
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# behavior
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new_prompt += tokenizer.sep_token
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new_token_ids += [CHAMELEON_SEP_TOKEN_ID]
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# NOTE: Create a defensive copy of the original inputs
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return LLMInputs(prompt_token_ids=new_token_ids,
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prompt=new_prompt,
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multi_modal_data=multi_modal_data)
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class ChameleonLayerNorm(nn.LayerNorm):
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@@ -318,12 +426,333 @@ class ChameleonSwinDecoderLayer(nn.Module):
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return hidden_states, residual
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# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEVectorQuantizer #noqa
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class ChameleonVQVAEVectorQuantizer(nn.Module):
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def __init__(self, config: ChameleonVQVAEConfig):
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super().__init__()
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self.num_embeddings = config.num_embeddings
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self.embedding_dim = config.embed_dim
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self.beta = getattr(config, "beta", 0.25)
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self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim)
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self.re_embed = self.num_embeddings
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def forward(self, hidden_state: torch.Tensor):
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hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
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hidden_state_flattened = hidden_state.view(-1, self.embedding_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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distances = (
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torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) +
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torch.sum(self.embedding.weight**2, dim=1) -
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2 * torch.einsum("bd,dn->bn", hidden_state_flattened,
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self.embedding.weight.transpose(0, 1)))
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min_encoding_indices = torch.argmin(distances, dim=1)
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hidden_state_quant = self.embedding(min_encoding_indices).view(
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hidden_state.shape)
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# compute loss for embedding
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loss = torch.mean((hidden_state_quant.detach() - hidden_state)**
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2) + self.beta * torch.mean(
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(hidden_state_quant - hidden_state.detach())**2)
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# preserve gradients
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hidden_state_quant = hidden_state + (hidden_state_quant -
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hidden_state).detach()
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# reshape back to match original input shape
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hidden_state_quant = hidden_state_quant.permute(0, 3, 1,
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2).contiguous()
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return hidden_state_quant, loss, min_encoding_indices
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# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderConvDownsample #noqa
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class ChameleonVQVAEEncoderConvDownsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.conv = nn.Conv2d(in_channels,
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in_channels,
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kernel_size=3,
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stride=2,
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padding=0)
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def forward(self, hidden_states: torch.Tensor):
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# no asymmetric padding in torch conv, must do it ourselves
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hidden_states = F.pad(hidden_states,
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pad=(0, 1, 0, 1),
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mode="constant",
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value=0)
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hidden_states = self.conv(hidden_states)
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return hidden_states
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# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderResnetBlock #noqa
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class ChameleonVQVAEEncoderResnetBlock(nn.Module):
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def __init__(
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self,
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config: ChameleonVQVAEConfig,
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in_channels: int,
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out_channels=None,
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conv_shortcut=False,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = in_channels if out_channels is None \
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else out_channels
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self.use_conv_shortcut = conv_shortcut
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self.norm1 = torch.nn.GroupNorm(num_groups=32,
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num_channels=in_channels,
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eps=1e-6,
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affine=True)
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self.conv1 = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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self.norm2 = torch.nn.GroupNorm(num_groups=32,
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num_channels=out_channels,
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eps=1e-6,
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affine=True)
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self.dropout = torch.nn.Dropout(config.dropout)
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self.conv2 = torch.nn.Conv2d(out_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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else:
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self.nin_shortcut = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, hidden_states: torch.Tensor):
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residual = hidden_states
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hidden_states = self.norm1(hidden_states)
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hidden_states *= torch.sigmoid(hidden_states)
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hidden_states = self.conv1(hidden_states)
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hidden_states = self.norm2(hidden_states)
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hidden_states *= torch.sigmoid(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.conv2(hidden_states)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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residual = self.conv_shortcut(residual)
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else:
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residual = self.nin_shortcut(residual)
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return residual + hidden_states
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# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderAttnBlock #noqa
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class ChameleonVQVAEEncoderAttnBlock(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.in_channels = in_channels
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self.norm = torch.nn.GroupNorm(num_groups=32,
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num_channels=in_channels,
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eps=1e-6,
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affine=True)
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self.q = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, hidden_states: torch.Tensor):
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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query_states = self.q(hidden_states)
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key_states = self.k(hidden_states)
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value_states = self.v(hidden_states)
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# compute attention
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batch_size, channels, height, width = query_states.shape
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query_states = query_states.reshape(batch_size, channels,
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height * width).permute(0, 2, 1)
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key_states = key_states.reshape(batch_size, channels, height * width)
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attn_weights = torch.bmm(query_states, key_states)
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attn_weights = attn_weights * (int(channels)**(-0.5))
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attn_weights = F.softmax(attn_weights, dim=2)
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# attend to values
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value_states = value_states.reshape(batch_size, channels,
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height * width)
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attn_weights = attn_weights.permute(0, 2, 1)
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attn_output = torch.bmm(value_states,
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attn_weights).reshape(batch_size, channels,
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height, width)
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attn_output = self.proj_out(attn_output)
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return residual + attn_output
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# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoder #noqa
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class ChameleonVQVAEEncoder(nn.Module):
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def __init__(self, config: ChameleonVQVAEConfig):
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super().__init__()
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self.num_resolutions = len(config.channel_multiplier)
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self.num_res_blocks = config.num_res_blocks
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base_channels = config.base_channels
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resolution = config.resolution
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in_channels = config.in_channels
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double_latent = config.double_latent
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latent_channels = config.latent_channels
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channel_multiplier = config.channel_multiplier
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self.conv_in = torch.nn.Conv2d(in_channels,
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base_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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curr_res = resolution
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in_channel_multiplier = (1, ) + tuple(channel_multiplier)
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self.in_channel_multiplier = in_channel_multiplier
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = base_channels * in_channel_multiplier[i_level]
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block_out = base_channels * channel_multiplier[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(
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ChameleonVQVAEEncoderResnetBlock(
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config=config,
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in_channels=block_in,
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out_channels=block_out,
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))
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block_in = block_out
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if (config.attn_resolutions is not None
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and curr_res in config.attn_resolutions
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and config.attn_type == "vanilla"):
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attn.append(ChameleonVQVAEEncoderAttnBlock(block_in))
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = ChameleonVQVAEEncoderConvDownsample(block_in)
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curr_res = curr_res // 2
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self.down.append(down)
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self.mid = nn.Module()
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self.mid.block_1 = ChameleonVQVAEEncoderResnetBlock(
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config=config,
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in_channels=block_in,
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out_channels=block_in,
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)
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self.mid.attn_1 = ChameleonVQVAEEncoderAttnBlock(
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block_in) if config.attn_type == "vanilla" else nn.Identity()
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self.mid.block_2 = ChameleonVQVAEEncoderResnetBlock(
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config=config,
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in_channels=block_in,
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out_channels=block_in,
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)
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self.norm_out = torch.nn.GroupNorm(num_groups=32,
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num_channels=block_in,
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eps=1e-6,
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affine=True)
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self.conv_out = torch.nn.Conv2d(
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block_in,
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2 * latent_channels if double_latent else latent_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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def forward(self, pixel_values: torch.Tensor):
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# downsampling
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hidden_states = [self.conv_in(pixel_values)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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hidden_state = self.down[i_level].block[i_block](
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hidden_states[-1], )
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if len(self.down[i_level].attn) > 0:
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hidden_state = self.down[i_level].attn[i_block](
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hidden_state)
|
||||
hidden_states.append(hidden_state)
|
||||
if i_level != self.num_resolutions - 1:
|
||||
hidden_states.append(self.down[i_level].downsample(
|
||||
hidden_states[-1]))
|
||||
|
||||
# middle
|
||||
last_hidden_state = hidden_states[-1]
|
||||
last_hidden_state = self.mid.block_1(last_hidden_state)
|
||||
last_hidden_state = self.mid.attn_1(last_hidden_state)
|
||||
last_hidden_state = self.mid.block_2(last_hidden_state)
|
||||
|
||||
# end
|
||||
last_hidden_state = self.norm_out(last_hidden_state)
|
||||
last_hidden_state *= torch.sigmoid(last_hidden_state)
|
||||
last_hidden_state = self.conv_out(last_hidden_state)
|
||||
return last_hidden_state
|
||||
|
||||
|
||||
# Adapted from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAE #noqa
|
||||
class ChameleonVQVAE(nn.Module):
|
||||
|
||||
def __init__(self, config: ChameleonVQVAEConfig):
|
||||
super().__init__()
|
||||
self.encoder = ChameleonVQVAEEncoder(config)
|
||||
self.quantize = ChameleonVQVAEVectorQuantizer(config)
|
||||
self.quant_conv = torch.nn.Conv2d(config.latent_channels,
|
||||
config.embed_dim, 1)
|
||||
self.post_quant_conv = torch.nn.Conv2d(config.embed_dim,
|
||||
config.latent_channels, 1)
|
||||
self.eval() # Chameleon's VQ model is frozen
|
||||
|
||||
def encode(
|
||||
self, pixel_values: torch.Tensor
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
hidden_states = self.encoder(pixel_values)
|
||||
hidden_states = self.quant_conv(hidden_states)
|
||||
quant, emb_loss, indices = self.quantize(hidden_states)
|
||||
return quant, emb_loss, indices
|
||||
|
||||
|
||||
# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonImageVocabularyMapping #noqa
|
||||
class ChameleonImageVocabularyMapping:
|
||||
"""
|
||||
A class for mapping discrete image tokens from VQGAN to BPE tokens.
|
||||
"""
|
||||
|
||||
def __init__(self, vocab_map):
|
||||
def __init__(self, vocab_map: Dict[str, int]):
|
||||
self.vocab_map = vocab_map
|
||||
self.image_token_id = vocab_map.get("<image>")
|
||||
|
||||
@@ -401,13 +830,23 @@ class ChameleonModel(nn.Module):
|
||||
for _ in range(config.num_hidden_layers)
|
||||
])
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
# TODO: Support image input
|
||||
# self.vqmodel = ChameleonVQModel(config.vq_config)
|
||||
self.vqmodel = ChameleonVQVAE(config.vq_config)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def get_image_tokens(self, pixel_values: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Tokenizes images into discrete tokens with VQGAN module. Converts
|
||||
obtained image tokens into BPE tokens and wraps with "boi" and "eoi"
|
||||
special tokens.
|
||||
"""
|
||||
batch_size = pixel_values.shape[0]
|
||||
_, _, image_toks = self.vqmodel.encode(pixel_values)
|
||||
bpe_toks = self.vocabulary_mapping.convert_img2bpe(image_toks)
|
||||
bpe_toks = bpe_toks.view(batch_size, -1)
|
||||
return bpe_toks
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.Tensor],
|
||||
@@ -434,16 +873,22 @@ class ChameleonModel(nn.Module):
|
||||
return hidden_states
|
||||
|
||||
|
||||
class ChameleonForConditionalGeneration(nn.Module):
|
||||
@MULTIMODAL_REGISTRY.register_image_input_mapper()
|
||||
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_chameleon_image_tokens)
|
||||
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_chameleon)
|
||||
@INPUT_REGISTRY.register_input_processor(input_processor_for_chameleon)
|
||||
class ChameleonForConditionalGeneration(nn.Module, SupportsVision):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: ChameleonConfig,
|
||||
multimodal_config: MultiModalConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.multimodal_config = multimodal_config
|
||||
self.model = ChameleonModel(config, cache_config, quant_config)
|
||||
self.unpadded_vocab_size = config.vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
@@ -458,6 +903,36 @@ class ChameleonForConditionalGeneration(nn.Module):
|
||||
config.vocab_size, logit_scale)
|
||||
self.sampler = Sampler()
|
||||
|
||||
def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
expected_dims = (3, CHAMELEON_CROP_SIZE_HEIGHT,
|
||||
CHAMELEON_CROP_SIZE_WIDTH)
|
||||
actual_dims = tuple(data.shape[1:])
|
||||
|
||||
if actual_dims != expected_dims:
|
||||
expected_expr = ("batch_size", *map(str, expected_dims))
|
||||
raise ValueError(
|
||||
f"The expected shape of pixel values is {expected_expr}. "
|
||||
f"You supplied {tuple(data.shape)}.")
|
||||
|
||||
return data
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object) -> Optional[ChameleonImagePixelInputs]:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
|
||||
if pixel_values is None:
|
||||
return None
|
||||
|
||||
if not isinstance(pixel_values, torch.Tensor):
|
||||
raise ValueError("Incorrect type of pixel values. "
|
||||
f"Got type: {type(pixel_values)}")
|
||||
|
||||
return ChameleonImagePixelInputs(
|
||||
type="pixel_values",
|
||||
data=self._validate_pixel_values(pixel_values),
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
@@ -468,10 +943,17 @@ class ChameleonForConditionalGeneration(nn.Module):
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
|
||||
# TODO (ywang96): Support image input
|
||||
# image_tokens = self.process_image_input(**kwargs)
|
||||
# image_mask = input_ids == self.vocabulary_mapping.image_token_id
|
||||
# input_ids[special_image_mask] = image_tokens.flatten().to(input_ids.dtype) #noqa
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
|
||||
if image_input is not None:
|
||||
assert self.model.vqmodel is not None
|
||||
image_tokens = self.model.get_image_tokens(image_input["data"].to(
|
||||
self.config.torch_dtype))
|
||||
image_token_id = self.model.vocabulary_mapping.image_token_id
|
||||
special_image_mask = input_ids == image_token_id
|
||||
image_tokens = image_tokens.to(input_ids.device, input_ids.dtype)
|
||||
input_ids = input_ids.masked_scatter(special_image_mask,
|
||||
image_tokens)
|
||||
|
||||
hidden_states = self.model(input_ids, positions, kv_caches,
|
||||
attn_metadata)
|
||||
@@ -511,43 +993,52 @@ class ChameleonForConditionalGeneration(nn.Module):
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
# Skip loading vqgan
|
||||
# TODO: add support for the vision model
|
||||
if "vqmodel" in name:
|
||||
continue
|
||||
if ("rotary_emb.cos_cached" in name
|
||||
or "rotary_emb.sin_cached" in name):
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
use_default_weight_loading = False
|
||||
if "vqmodel" in name:
|
||||
if self.model.vqmodel is not None:
|
||||
# We only do sharding for language model and
|
||||
# not vqvae for now.
|
||||
use_default_weight_loading = True
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
if name.endswith("kv_scale"):
|
||||
remapped_kv_scale_name = name.replace(
|
||||
".kv_scale", ".attn.kv_scale")
|
||||
if remapped_kv_scale_name not in params_dict:
|
||||
print_warning_once(
|
||||
f"Found kv scale in the checkpoint (e.g. {name}), "
|
||||
"but not found the expected name in the model "
|
||||
f"(e.g. {remapped_kv_scale_name}). kv-scale is "
|
||||
"not loaded.")
|
||||
for (param_name, weight_name,
|
||||
shard_id) in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
else:
|
||||
name = remapped_kv_scale_name
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
if name.endswith("kv_scale"):
|
||||
remapped_kv_scale_name = name.replace(
|
||||
".kv_scale", ".attn.kv_scale")
|
||||
if remapped_kv_scale_name not in params_dict:
|
||||
print_warning_once(
|
||||
"Found kv scale in the checkpoint (e.g. "
|
||||
f"{name}), but not found the expected name in "
|
||||
f"the model (e.g. {remapped_kv_scale_name}). "
|
||||
"kv-scale is not loaded.")
|
||||
continue
|
||||
else:
|
||||
name = remapped_kv_scale_name
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
if use_default_weight_loading and name in params_dict:
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
|
||||
Reference in New Issue
Block a user