Apply Github PR #127 to all HF models
Browse files
modeling_internvl_chat.py
CHANGED
@@ -43,7 +43,7 @@ class InternVLChatModel(PreTrainedModel):
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def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
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super().__init__(config)
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-
assert version_cmp(transformers.__version__, '4.
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image_size = config.force_image_size or config.vision_config.image_size
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patch_size = config.vision_config.patch_size
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self.patch_size = patch_size
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@@ -112,7 +112,7 @@ class InternVLChatModel(PreTrainedModel):
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B, N, C = input_embeds.shape
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input_embeds = input_embeds.reshape(B * N, C)
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-
if torch.distributed.get_rank() == 0:
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print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
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input_ids = input_ids.reshape(B * N)
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def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
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super().__init__(config)
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+
assert version_cmp(transformers.__version__, '4.37.0', 'ge')
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image_size = config.force_image_size or config.vision_config.image_size
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patch_size = config.vision_config.patch_size
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self.patch_size = patch_size
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B, N, C = input_embeds.shape
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input_embeds = input_embeds.reshape(B * N, C)
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+
if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
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print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
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input_ids = input_ids.reshape(B * N)
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