mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-10 20:04:52 +00:00
updated to include interally managed kv-cache
This commit is contained in:
rsnm2
parent
79ea7ff02f
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03fda99ee1
@ -1659,6 +1659,442 @@
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"source": []
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Intenally Managed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {},
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"outputs": [],
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"source": [
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"import deepsparse\n",
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"from deepsparse.transformers.utils.helpers import create_causal_mask"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-08-20 11:07:42 deepsparse.transformers WARNING The neuralmagic fork of transformers may not be installed. It can be installed via `pip install nm_transformers`\n",
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"Using pad_token, but it is not set yet.\n",
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"2023-08-20 11:07:54 deepsparse.transformers.pipelines.text_generation INFO Compiling an auxiliary engine to process a prompt with a larger processing length. This improves performance, but may result in additional memory consumption.\n",
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"2023-08-20 11:07:56 deepsparse.transformers.utils.helpers INFO Overwriting in-place the input shapes of the transformer model at /home/robertgshaw/.cache/sparsezoo/neuralmagic/codegen_mono-350m-bigpython_bigquery_thepile-base/model.onnx/model.onnx\n",
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"DeepSparse, Copyright 2021-present / Neuralmagic, Inc. version: 1.6.0.20230815 COMMUNITY | (134dba40) (release) (optimized) (system=avx2, binary=avx2)\n",
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"2023-08-20 11:08:20 deepsparse.transformers.utils.helpers INFO Overwriting in-place the input shapes of the transformer model at /home/robertgshaw/.cache/sparsezoo/neuralmagic/codegen_mono-350m-bigpython_bigquery_thepile-base/model.onnx/model.onnx\n"
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]
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}
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],
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"source": [
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"pipeline = deepsparse.Pipeline.create(\n",
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" task=\"text-generation\", \n",
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" model_path=\"zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none\",\n",
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" use_deepsparse_cache=True,\n",
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" prompt_processing_sequence_length=4,\n",
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" max_generated_tokens=64,\n",
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" sequence_length=128\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"sequence = \"Finish the following function for computing a fibonacci sequence: \\n\\n fib(n):\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"TextGenerationOutput(sequences=['\\n\\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n return fib(n-1) + fib(n-2)\\n\\n# Call the function.\\nprint(fib(5))\\n\\n# This code'], logits=None, session_id=None)"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pipeline(sequences=sequence)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"singletoken_engine = pipeline.engine\n",
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"multitoken_engine = pipeline.multitoken_engine\n",
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"assert singletoken_engine.kv_cache == multitoken_engine.kv_cache\n",
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"kv_cache = singletoken_engine.kv_cache"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"engine_inputs = pipeline.process_inputs(pipeline.parse_inputs(sequences=sequence))[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 271,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"without maintaining\n",
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"Finish the following function for computing a fibonacci sequence: \n",
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"\n",
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" fib(n):\n",
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"\n",
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" if n == 0:\n",
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" return 0\n",
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" elif n == 1:\n",
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" return 1\n",
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" else:\n",
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" return fib(n-1) + fib(n-2)\n",
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"\n",
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"# Call the function.\n",
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"print(fib(5))\n",
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"\n",
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"# This code is contributed by Nikhil Kumar Singh(nickzuck_007)\n",
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"<|endoftext|>\n",
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"\n",
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"\n",
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"maintaining\n",
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"Finish the following function for computing a fibonacci sequence: \n",
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"\n",
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" fib(n):\n",
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"\n",
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" if n == 0:\n",
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" return 0\n",
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" elif n == 1:\n",
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" return 1\n",
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" else:\n",
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" return fib(n-1) + fib(n-2)\n",
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"\n",
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"# Call the function.\n",
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"print(fib(5))\n",
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"\n",
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"# This code is contributed by Nikhil Kumar Singh(nickzuck_007)\n",
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"<|endoftext|>\n"
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]
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}
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],
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"source": [
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"import numpy\n",
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"\n",
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"multitoken_length = pipeline.prompt_processing_sequence_length\n",
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"sequence_length = pipeline.sequence_length\n",
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"\n",
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"def empty_past_key_values(engine):\n",
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" past_key_values = {}\n",
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" for idx, name in enumerate(engine.engine.input_names):\n",
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" if name.startswith(\"past_key_values\"):\n",
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" shape = engine.engine.input_shapes[idx]\n",
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" past_key_values[name] = numpy.zeros(shape, dtype=engine.kv_cache_data_type)\n",
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"\n",
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" return past_key_values\n",
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"\n",
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"def engine_inputs_for_decode(tokens):\n",
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" assert(len(tokens) < sequence_length)\n",
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" \n",
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" engine_inputs = {}\n",
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" engine_inputs[\"input_ids\"] = numpy.array([[tokens[-1]]])\n",
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" engine_inputs[\"attention_mask\"] = numpy.zeros((1, sequence_length), dtype=numpy.int64)\n",
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" engine_inputs[\"attention_mask\"][:, -len(tokens):] = 1\n",
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" \n",
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" engine_inputs[\"causal_mask\"] = create_causal_mask(\n",
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" engine_inputs[\"input_ids\"],\n",
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" engine_inputs[\"attention_mask\"]\n",
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" )\n",
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" engine_inputs[\"positions\"] = numpy.array([[len(tokens) - 1]], dtype=numpy.int64)\n",
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" \n",
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" return engine_inputs\n",
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"\n",
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"def engine_inputs_for_prefill(tokens):\n",
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" num_batches = len(tokens) // multitoken_length\n",
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" token_batches = [tokens[i * multitoken_length : (i+1) * multitoken_length] for i in range(0, num_batches)]\n",
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"\n",
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" for idx, token_batch in enumerate(token_batches):\n",
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" num_processed_tokens = multitoken_length * idx\n",
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" \n",
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" engine_inputs = {}\n",
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" engine_inputs[\"input_ids\"] = numpy.array([token_batch])\n",
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"\n",
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" # make attention mask from the right\n",
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" engine_inputs[\"attention_mask\"] = numpy.zeros((1, sequence_length), dtype=numpy.int64)\n",
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" engine_inputs[\"attention_mask\"][:, -(num_processed_tokens + multitoken_length):] = 1\n",
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"\n",
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" # make positions (building from the right)\n",
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" assert multitoken_length > 1\n",
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" engine_inputs[\"positions\"] = numpy.arange(\n",
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" num_processed_tokens, num_processed_tokens + multitoken_length\n",
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" ).reshape(1, -1).astype(numpy.int64)\n",
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"\n",
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" # make causal mask (building from the right)\n",
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" engine_inputs[\"causal_mask\"] = create_causal_mask(\n",
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" input_ids=engine_inputs[\"input_ids\"], \n",
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" attention_mask=engine_inputs[\"attention_mask\"]\n",
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" )\n",
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"\n",
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" yield engine_inputs\n",
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"\n",
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"def call_engine(engine, engine_inputs, past_key_values):\n",
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" # format inputs as list\n",
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" inputs = [\n",
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" past_key_values[name] if name.startswith(\"past_key_values\") \n",
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" else engine_inputs[name] for name in engine.engine.input_names\n",
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" ]\n",
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"\n",
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" # run inference\n",
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" logits, *kvs = engine.engine._eng_net.execute_list_out(inputs, engine.kv_cache._kv_cache)\n",
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"\n",
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" # format output as dict\n",
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" past_names = [name for name in engine.engine.input_names if name.startswith(\"past_key_values\")]\n",
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" past_key_values = {name: arr for name, arr in zip(past_names, kvs)}\n",
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" \n",
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" return logits, past_key_values\n",
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"\n",
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"# bad -- returns a numpy.insert returns a full copy (update does NOT happen in place)\n",
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"def insert_past_key_values(past_key_values, num_items=1, padding_value=0):\n",
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" dtype = next(iter(past_key_values.values())).dtype\n",
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"\n",
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" for name in past_key_values:\n",
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" padding_value = numpy.array(padding_value, dtype=dtype)\n",
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" past_key_values[name] = numpy.insert(past_key_values[name], [0]*num_items, padding_value, axis=2)\n",
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" return past_key_values\n",
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"\n",
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"# bad --- calls np.ascontiguous\n",
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"def slice_past_key_values(past_key_values, slice_idx):\n",
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" for name in past_key_values:\n",
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" past_key_values[name] = numpy.ascontiguousarray(past_key_values[name][:,:,slice_idx:,:])\n",
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" return past_key_values\n",
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" \n",
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"# maintians the kv cache state at pipeline level\n",
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"def decode_maintain(tokens, past_key_values): \n",
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" engine_inputs = engine_inputs_for_decode(tokens)\n",
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"\n",
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" logits, past_key_values = call_engine(\n",
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" singletoken_engine,\n",
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" engine_inputs=engine_inputs,\n",
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" past_key_values=past_key_values\n",
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" )\n",
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"\n",
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" # cleanup state (this is BAD - calls np.ascontiguous)\n",
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" past_key_values = slice_past_key_values(past_key_values, 1)\n",
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"\n",
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" assert logits.shape[0] == 1 # assert batch 1 right now\n",
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" assert logits.shape[1] == 1 # assert only one element\n",
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" return logits, past_key_values\n",
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"\n",
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"# maintians the kv cache state at pipeline level\n",
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"def prefill_maintain(tokens):\n",
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" tokens_processed = 0\n",
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" past_key_values = empty_past_key_values(multitoken_engine)\n",
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"\n",
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" for engine_inputs in engine_inputs_for_prefill(tokens):\n",
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" logits, past_key_values = call_engine(\n",
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" multitoken_engine, \n",
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" engine_inputs=engine_inputs, \n",
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" past_key_values=past_key_values\n",
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" )\n",
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" tokens_processed += multitoken_length\n",
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"\n",
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" # BAD - calls np.ascontgious - cleans up that engine returns past with prior_seq_len + input_ids_len\n",
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" past_key_values = slice_past_key_values(past_key_values, multitoken_length)\n",
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" \n",
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" # (this is BAD - returns a copy) - expand kv cache for single token engine \n",
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" past_key_values = insert_past_key_values(past_key_values, num_items=(multitoken_length-1))\n",
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"\n",
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" # loop of singletoken engine for anything left over\n",
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" while tokens_processed < len(tokens):\n",
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" logits, past_key_values = decode_maintain(\n",
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" tokens=tokens[:tokens_processed+1],\n",
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" past_key_values=past_key_values\n",
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" )\n",
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" tokens_processed += 1\n",
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" \n",
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" return logits, past_key_values\n",
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"\n",
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"empty_past_key_values_multi = empty_past_key_values(multitoken_engine)\n",
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"empty_past_key_values_single = empty_past_key_values(singletoken_engine)\n",
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"\n",
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"# does not maintian kv cache state at pipeline level\n",
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"def decode(tokens):\n",
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" engine_inputs = engine_inputs_for_decode(tokens)\n",
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"\n",
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" logits, past_key_values = call_engine(\n",
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" singletoken_engine,\n",
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" engine_inputs=engine_inputs,\n",
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" past_key_values=empty_past_key_values_single\n",
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" )\n",
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"\n",
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" return logits\n",
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"\n",
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"# does not maintain the state at pipeline level\n",
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"def prefill(tokens):\n",
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" tokens_processed = 0\n",
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" \n",
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" for engine_inputs in engine_inputs_for_prefill(tokens):\n",
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" logits, _ = call_engine(\n",
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" multitoken_engine, \n",
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" engine_inputs=engine_inputs, \n",
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" past_key_values=empty_past_key_values_multi\n",
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" )\n",
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" tokens_processed += multitoken_length\n",
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"\n",
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" # loop of singletoken engine for anything left over\n",
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" while tokens_processed < len(tokens):\n",
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" logits = decode(tokens[:tokens_processed+1])\n",
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" tokens_processed += 1\n",
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" \n",
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" return logits\n",
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"\n",
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"def sample_token(logits):\n",
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" assert(logits.shape[0] == 1)\n",
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" return numpy.argmax(logits[0,-1,:])\n",
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"\n",
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"eos_token = pipeline.tokenizer.eos_token_id\n",
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"\n",
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"print(\"without maintaining\")\n",
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"pipeline._reset_engines_cache()\n",
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"engine_inputs = pipeline.process_inputs(pipeline.parse_inputs(sequences=sequence))[0]\n",
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"tokens = engine_inputs[0][engine_inputs[1].nonzero()].tolist()\n",
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"\n",
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"logits = prefill(tokens)\n",
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"tokens.append(sample_token(logits))\n",
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||||||
|
"while len(tokens) < sequence_length and tokens[-1] != eos_token:\n",
|
||||||
|
" logits = decode(tokens)\n",
|
||||||
|
" tokens.append(sample_token(logits))\n",
|
||||||
|
"\n",
|
||||||
|
"print(pipeline.tokenizer.decode(tokens))\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\n\\nmaintaining\")\n",
|
||||||
|
"pipeline._reset_engines_cache()\n",
|
||||||
|
"engine_inputs = pipeline.process_inputs(pipeline.parse_inputs(sequences=sequence))[0]\n",
|
||||||
|
"tokens = engine_inputs[0][engine_inputs[1].nonzero()].tolist()\n",
|
||||||
|
"\n",
|
||||||
|
"logits, past_key_values = prefill_maintain(tokens)\n",
|
||||||
|
"tokens.append(sample_token(logits))\n",
|
||||||
|
"while len(tokens) < sequence_length and tokens[-1] != eos_token:\n",
|
||||||
|
" logits, past_key_values = decode_maintain(tokens, past_key_values)\n",
|
||||||
|
" tokens.append(sample_token(logits))\n",
|
||||||
|
" \n",
|
||||||
|
"print(pipeline.tokenizer.decode(tokens))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 278,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Finish the following function for computing a fibonacci sequence: \n",
|
||||||
|
"\n",
|
||||||
|
" fib(n):\n",
|
||||||
|
"\n",
|
||||||
|
" if n == 0:\n",
|
||||||
|
" return 0\n",
|
||||||
|
" elif n == 1:\n",
|
||||||
|
" return 1\n",
|
||||||
|
" else:\n",
|
||||||
|
" return fib(n-1) + fib(n-2)\n",
|
||||||
|
"\n",
|
||||||
|
"# Call the function.\n",
|
||||||
|
"print(fib(5))\n",
|
||||||
|
"\n",
|
||||||
|
"# This code is contributed by Nikhil Kumar Singh(nickzuck_007)\n",
|
||||||
|
"<|endoftext|>\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"def prefill_pipeline(pipeline, tokens):\n",
|
||||||
|
" num_tokens_processed = 0\n",
|
||||||
|
" for engine_inputs in pipeline.engine_inputs_for_prefill(tokens):\n",
|
||||||
|
" _, logits = pipeline.multitoken_engine(engine_inputs)\n",
|
||||||
|
" num_tokens_processed += multitoken_length\n",
|
||||||
|
"\n",
|
||||||
|
" if num_tokens_processed > 0:\n",
|
||||||
|
" pipeline.engine.transfer_cache_state(cache=pipeline.multitoken_engine.kv_cache)\n",
|
||||||
|
"\n",
|
||||||
|
" run_tokens = [] if num_tokens_processed == 0 else tokens[:num_tokens_processed]\n",
|
||||||
|
" for token in tokens[num_tokens_processed:]:\n",
|
||||||
|
" run_tokens.append(token)\n",
|
||||||
|
" new_token, logits = pipeline.autoregressive_inference(run_tokens)\n",
|
||||||
|
" return logits\n",
|
||||||
|
" \n",
|
||||||
|
"pipeline._reset_engines_cache()\n",
|
||||||
|
"engine_inputs = pipeline.process_inputs(pipeline.parse_inputs(sequences=sequence))[0]\n",
|
||||||
|
"tokens = engine_inputs[0][engine_inputs[1].nonzero()].tolist()\n",
|
||||||
|
"\n",
|
||||||
|
"logits = prefill_pipeline(pipeline, tokens)\n",
|
||||||
|
"tokens.append(sample_token(logits))\n",
|
||||||
|
"\n",
|
||||||
|
"while len(tokens) < pipeline.sequence_length and tokens[-1] != eos_token:\n",
|
||||||
|
" _, logits = pipeline.autoregressive_inference(tokens)\n",
|
||||||
|
" tokens.append(sample_token(logits))\n",
|
||||||
|
"\n",
|
||||||
|
"print(pipeline.tokenizer.decode(tokens))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 277,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Finish the following function for computing a fibonacci sequence: \n",
|
||||||
|
"\n",
|
||||||
|
" fib(n):\n",
|
||||||
|
"\n",
|
||||||
|
" if n == 0:\n",
|
||||||
|
" return 0\n",
|
||||||
|
" elif n == 1:\n",
|
||||||
|
" return 1\n",
|
||||||
|
" else:\n",
|
||||||
|
" return fib(n-1) + fib(n-2)\n",
|
||||||
|
"\n",
|
||||||
|
"# Call the function.\n",
|
||||||
|
"print(fib(5))\n",
|
||||||
|
"\n",
|
||||||
|
"# This code\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print(f\"{sequence}{pipeline(sequences=sequence).sequences[0]}\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": null,
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user