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* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
101 lines
4.6 KiB
Markdown
101 lines
4.6 KiB
Markdown
## Run gpt-2b + LoRA using GptManager / cpp runtime
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First build a model with LoRA and inflight-batching enabled.
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```bash
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git-lfs clone https://huggingface.co/qychen/luotuo-lora-7b-0.1
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git-lfs clone https://huggingface.co/kunishou/Japanese-Alpaca-LoRA-7b-v0
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BASE_MODEL=llama-7b-hf
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python examples/llama/convert_checkpoint.py --model_dir ${BASE_MODEL} \
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--output_dir /tmp/llama_7b_with_lora_qkv/trt_ckpt/fp16/1-gpu/ \
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--dtype float16 \
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--hf_lora_dir Japanese-Alpaca-LoRA-7b-v0 \
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--max_lora_rank 8 \
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--lora_target_modules "attn_q" "attn_k" "attn_v"
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trtllm-build --checkpoint_dir /tmp/llama_7b_with_lora_qkv/trt_ckpt/fp16/1-gpu/ \
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--output_dir /tmp/llama_7b_with_lora_qkv/trt_engines/fp16/1-gpu/ \
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--remove_input_padding enable \
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--gpt_attention_plugin float16 \
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--context_fmha enable \
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--paged_kv_cache enable \
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--gemm_plugin float16 \
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--lora_plugin float16 \
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--max_batch_size 128 \
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--max_input_len 512 \
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--max_output_len 50 \
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```
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To pass LoRAs into the cpp runtime they must be converted to the format below.
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The script below will convert a huggingface LoRA model to the correct numpy tensors.
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```bash
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python3 tensorrt_llm/examples/hf_lora_convert.py -i Japanese-Alpaca-LoRA-7b-v0 -o Japanese-Alpaca-LoRA-7b-v0-weights --storage-type float16
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python3 tensorrt_llm/examples/hf_lora_convert.py -i luotuo-lora-7b-0.1 -o luotuo-lora-7b-0.1-weights --storage-type float16
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```
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See tensorrtllm_backend [docs](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/inflight_batcher_llm/README.md) for a Multi-LoRA example using Triton.
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### LoRA tensor format details
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To run inference with LoRA weights using GptManager, InferenceRequests must have LoraWeights (lora_weights) and LoraConfig (lora_config) parameters.
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`LoraWeights` contains the weights for all the LoRAs. Currently this should include weight for all tp and pp ranks.
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The weights tensor has the shape `[ num_lora_modules_layers, D x Hi + Ho x D ]`. the last dimension holds the in / out adapter weights for the associated module (e.g. attn_qkv) and model layer.
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Each of the in / out tensors are first flattened and then concatenated together in the format above.
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The first dimension (of size `num_lora_module_layers`) has an entry for each module-layer (ie there is an entry for attn_q layer1 and another for attn_k layer1).
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`D=adapter_size (i.e. R value), Hi=hidden_size_in, Ho=hidden_size_out.`
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`LoraConfig` is a configuration tensor which identifies the moduleId, layerId, and adapter size of each element of `LoraWeights`.
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It has the shape `[num_lora_modules_layers, 3]`.
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The last dimension holds `[ module_id, layer_idx, adapter_size D (i.e. R value) ]`
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Reference: This feature supports LoRAs as described in https://arxiv.org/pdf/2106.09685.pdf
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#### Example LoRA tensors
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Here is an example of loraWeights and loraConfig tensors for a model with tp=1, pp=1, 4 layers, and a hidden size of 4.
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The tensors below are for a LoRA which has a `q` and `k` adapter.
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```
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# loraConfig
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[
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[1, 0, 2]
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[2, 0, 4]
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[1, 1, 2]
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[2, 1, 4]
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[1, 2, 2] # Note that the final 2 layers only adapt `q`
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[1, 3, 8]
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]
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# Note: The loraConfig tensor configures the loraWeights tensor.
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# The contents of each row of loraWeights is specified be the corresponding row in loraConfig
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# loraWeights
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# Note: that 'in weights' and 'out weights' are 'A' and 'B' in the LoRA paper.
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[
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[ <2 x 4 in weights>, <4 x 2 out weights> <padding> ] # `q` adapter for layer 0
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[ <4 x 4 in weights>, <4 x 4 out weights> <padding> ] # `k` adapter for layer 0
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[ <2 x 4 in weights>, <4 x 2 out weights> <padding> ] # `q` adapter for layer 1
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[ <4 x 4 in weights>, <4 x 4 out weights> <padding> ] # `k` adapter for layer 1
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[ <2 x 4 in weights>, <4 x 2 out weights> <padding> ] # `q` adapter for layer 2
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[ <8 x 4 in weights>, <4 x 8 out weights> ] # `q` adapter for layer 3. Note the final layer has a adapter size of 8
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]
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```
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#### LoRA Module id mapping
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See LoraModule::ModuleType for model id mapping
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| module name (as specified in convert_checkpoint.py scripts) | module id | description |
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| --------------------------------------------- | --------- | ----------- |
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| attn_qkv | 0 | compbined qkv adapter |
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| attn_q | 1 | q adapter |
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| attn_k | 2 | k adapter |
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| attn_v | 3 | v adapter |
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| attn_dense | 4 | adapter for the dense layer in attention |
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| mlp_h_to_4h | 5 | for llama2 adapter for gated mlp layer after attention / RMSNorm: up projection |
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| mlp_4h_to_h | 6 | for llama2 adapter for gated mlp layer after attention / RMSNorm: down projection |
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| mlp_gate | 7 | for llama2 adapter for gated mlp later after attention / RMSNorm: gate |
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