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https://github.com/huggingface/text-generation-inference.git
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update according to review comment
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
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@ -24,7 +24,6 @@ from text_generation_server.models.custom_modeling.vlm import (
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load_text_model,
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load_text_model,
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load_vision_model,
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load_vision_model,
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)
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)
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from text_generation_server.layers.attention import Seqlen
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class PaliGemmaForConditionalGeneration(nn.Module):
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class PaliGemmaForConditionalGeneration(nn.Module):
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@ -93,7 +92,7 @@ class PaliGemmaForConditionalGeneration(nn.Module):
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# insert image features into input embeddings
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# insert image features into input embeddings
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inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
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inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
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input_lengths = Seqlen(input_lengths=input_lengths)
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hidden_states = self.text_model.model(
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hidden_states = self.text_model.model(
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inputs_embeds=inputs_embeds,
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inputs_embeds=inputs_embeds,
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position_ids=position_ids,
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position_ids=position_ids,
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@ -35,7 +35,6 @@ from text_generation_server.layers import (
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TensorParallelRowLinear,
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TensorParallelRowLinear,
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)
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)
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from text_generation_server.utils.weights import DefaultWeightsLoader
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from text_generation_server.utils.weights import DefaultWeightsLoader
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from text_generation_server.layers.attention import Seqlen
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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@ -825,7 +824,7 @@ class Idefics2ForConditionalGeneration(nn.Module):
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inputs_embeds = self._merge_input_ids_with_image_features(
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inputs_embeds = self._merge_input_ids_with_image_features(
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input_ids, inputs_embeds, image_hidden_states
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input_ids, inputs_embeds, image_hidden_states
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)
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)
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input_lengths = Seqlen(input_lengths=input_lengths)
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hidden_states = self.text_model.model(
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hidden_states = self.text_model.model(
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inputs_embeds=inputs_embeds,
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inputs_embeds=inputs_embeds,
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position_ids=position_ids,
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position_ids=position_ids,
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@ -31,7 +31,6 @@ from text_generation_server.layers import (
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TensorParallelColumnLinear,
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TensorParallelColumnLinear,
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TensorParallelRowLinear,
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TensorParallelRowLinear,
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)
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)
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from text_generation_server.layers.attention import Seqlen
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def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
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def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
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@ -269,7 +268,7 @@ class LlavaNextForConditionalGeneration(nn.Module):
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inputs_embeds = self._merge_input_ids_with_image_features(
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inputs_embeds = self._merge_input_ids_with_image_features(
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input_ids, inputs_embeds, image_features
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input_ids, inputs_embeds, image_features
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)
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)
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input_lengths = Seqlen(input_lengths=input_lengths)
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hidden_states = self.text_model.model(
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hidden_states = self.text_model.model(
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inputs_embeds=inputs_embeds,
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inputs_embeds=inputs_embeds,
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position_ids=position_ids,
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position_ids=position_ids,
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@ -14,6 +14,7 @@ from text_generation_server.models.flash_causal_lm import (
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FlashCausalLM,
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FlashCausalLM,
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)
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)
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from transformers import AutoProcessor
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from transformers import AutoProcessor
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from text_generation_server.layers.attention import Seqlen
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tracer = trace.get_tracer(__name__)
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tracer = trace.get_tracer(__name__)
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@ -342,6 +343,7 @@ class VlmCausalLM(FlashCausalLM):
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else:
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else:
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cuda_graph = None
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cuda_graph = None
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if cu_seqlen_prefill is not None or cuda_graph is None:
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if cu_seqlen_prefill is not None or cuda_graph is None:
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input_lengths = Seqlen(input_lengths=input_lengths)
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logits, speculative_logits = self.model.forward(
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logits, speculative_logits = self.model.forward(
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input_ids=input_ids,
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input_ids=input_ids,
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position_ids=position_ids,
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position_ids=position_ids,
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