Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
267 lines
9.9 KiB
Python
267 lines
9.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import math
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from typing import Iterable, List, Optional, Set, Tuple
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import torch
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import torch.nn as nn
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from vllm.attention import AttentionMetadata
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
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BartParallelLMHead,
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BartScaledWordEmbedding)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .utils import AutoWeightsLoader
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class Florence2LanguageModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model)
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self.encoder = BartEncoder(config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.encoder")
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self.decoder = BartDecoder(config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.decoder")
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if self.config.tie_word_embeddings:
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self.encoder.embed_tokens.weight = self.shared.weight
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self.decoder.embed_tokens.weight = self.shared.weight
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def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
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encoder_input_ids: torch.Tensor,
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encoder_positions: torch.Tensor, kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata) -> torch.Tensor:
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r"""
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Args:
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input_ids
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Indices of *decoder* input sequence tokens in the vocabulary.
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Padding will be ignored by default should you
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provide it.
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positions
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Positions of *decoder* input sequence tokens.
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encoder_input_ids
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Indices of *encoder* input sequence tokens in the vocabulary.
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encoder_positions:
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Positions of *encoder* input sequence tokens.
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kv_caches:
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Layer-wise list of KV cache tensors
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attn_metadata:
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vLLM Attention metadata structure
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Returns:
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Model output torch.Tensor
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"""
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encoder_hidden_states = None
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if encoder_input_ids.numel() > 0:
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# Run encoder attention if a non-zero number of encoder tokens
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# are provided as input
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encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
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positions=encoder_positions,
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kv_caches=kv_caches,
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attn_metadata=attn_metadata)
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# decoder outputs consists of
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# (dec_features, past_key_value, dec_hidden, dec_attn)
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decoder_outputs = self.decoder(
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decoder_input_ids=input_ids,
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decoder_positions=positions,
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encoder_hidden_states=encoder_hidden_states,
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kv_caches=kv_caches,
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attn_metadata=attn_metadata)
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return decoder_outputs
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class Florence2LanguageForConditionalGeneration(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.config = config
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self.model = Florence2LanguageModel(vllm_config=vllm_config,
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prefix=f"{prefix}.model")
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embed_scale = math.sqrt(
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config.d_model) if config.scale_embedding else 1.0
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self.vocab_size = config.vocab_size
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self.lm_head = BartParallelLMHead(self.vocab_size,
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config.d_model,
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embed_scale=embed_scale)
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self.logits_processor = LogitsProcessor(self.vocab_size,
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config.vocab_size)
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self.sampler = get_sampler()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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encoder_input_ids: torch.Tensor,
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encoder_positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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**kwargs,
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) -> torch.Tensor:
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r"""
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Args:
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input_ids
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torch.Tensor of *decoder* input token ids.
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positions
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torch.Tensor of *decoder* position indices.
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encoder_input_ids
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torch.Tensor of *encoder* input token ids.
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encoder_positions
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torch.Tensor of *encoder* position indices
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kv_caches:
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Layer-wise list of KV cache tensors
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attn_metadata:
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vLLM Attention metadata structure
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Returns:
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Output torch.Tensor
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"""
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return self.model(input_ids, positions, encoder_input_ids,
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encoder_positions, kv_caches, attn_metadata)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(self, logits: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> SamplerOutput:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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if "final_logits_bias" in name:
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continue
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if self.config.tie_word_embeddings and "embed_tokens" in name:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class Florence2ForConditionalGeneration(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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# TODO(Isotr0py): Add vision backbone
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self.language_model = Florence2LanguageForConditionalGeneration(
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vllm_config=vllm_config.with_hf_config(config.text_config),
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prefix=f"{prefix}.language_model",
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)
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@property
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def sampler(self):
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return self.language_model.sampler
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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*,
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encoder_input_ids: torch.Tensor,
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encoder_positions: torch.Tensor,
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**kwargs,
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) -> torch.Tensor:
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r"""
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Args:
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input_ids
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torch.Tensor of *decoder* input token ids.
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positions
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torch.Tensor of *decoder* position indices.
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encoder_input_ids
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torch.Tensor of *encoder* input token ids.
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encoder_positions
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torch.Tensor of *encoder* position indices
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kv_caches:
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Layer-wise list of KV cache tensors
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attn_metadata:
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vLLM Attention metadata structure
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Returns:
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Output torch.Tensor
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"""
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return self.language_model(input_ids, positions, encoder_input_ids,
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encoder_positions, kv_caches, attn_metadata)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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return self.language_model.compute_logits(hidden_states,
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sampling_metadata)
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> SamplerOutput:
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return self.language_model.sample(logits, sampling_metadata)
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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skip_prefixes = [
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'image_projection', "vision_tower", "image_proj_norm",
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"image_pos_embed", "visual_temporal_embed"
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]
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loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
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return loader.load_weights(weights)
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