[NPU L0] update layernorm & code refactor (#12287)
* update layernorm & code refactor * fix style * add common utils * change to Pool() * remove print
This commit is contained in:
Ruonan Wang
GitHub
parent
4467645088
commit
821b0033ed
3 changed files with 233 additions and 195 deletions
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@ -0,0 +1,54 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from openvino.runtime import Core, serialize
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import os
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def update_names_of_IR_and_export_blob(model, model_name, dir):
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xml_path = os.path.join(dir, model_name + ".xml")
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model.save(xml_path)
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new_ir_path = os.path.join(dir, model_name + "_new.xml")
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blob_path = os.path.join(dir, model_name + ".blob")
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core = Core()
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core.set_property("NPU", {"NPU_COMPILATION_MODE_PARAMS":
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"compute-layers-with-higher-precision=Sqrt,Power,ReduceMean,Add"})
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core.set_property("NPU", {"PERFORMANCE_HINT": "LATENCY"})
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model = core.read_model(xml_path)
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inputs = model.inputs
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for idx, input in enumerate(inputs):
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if len(input.names) == 0:
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model.inputs[idx].set_names({f"input_{idx}"})
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outputs = model.outputs
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for idx, input in enumerate(outputs):
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if len(input.names) == 0:
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model.outputs[idx].set_names({f"output_{idx}"})
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# rewrite this model to a new IR path
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if new_ir_path is not None:
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serialize(model, new_ir_path)
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if blob_path is not None:
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compiledModel = core.compile_model(model, device_name="NPU")
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model_stream = compiledModel.export_model()
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with open(blob_path, 'wb') as f:
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f.write(model_stream)
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os.remove(xml_path)
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os.remove(new_ir_path)
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return blob_path
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@ -15,7 +15,6 @@
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#
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from openvino.runtime import Core, serialize
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import os
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import torch
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from ipex_llm.utils.common import invalidInputError
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@ -31,6 +30,7 @@ from ipex_llm.utils.common import invalidInputError
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import tempfile
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import numpy as np
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from ipex_llm.transformers.npu_models.lm_head import SlicedLMHead
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from multiprocessing import Pool
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def generate(
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@ -188,41 +188,6 @@ def generate(
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return output
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def update_names_of_IR_and_export_blob(model, model_name, dir):
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xml_path = os.path.join(dir, model_name + ".xml")
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model.save(xml_path)
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new_ir_path = os.path.join(dir, model_name + "_new.xml")
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blob_path = os.path.join(dir, model_name + ".blob")
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core = Core()
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core.set_property("NPU", {"NPU_COMPILATION_MODE_PARAMS":
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"compute-layers-with-higher-precision=Sqrt,Power,ReduceMean,Add"})
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core.set_property("NPU", {"PERFORMANCE_HINT": "LATENCY"})
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model = core.read_model(xml_path)
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inputs = model.inputs
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for idx, input in enumerate(inputs):
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if len(input.names) == 0:
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model.inputs[idx].set_names({f"input_{idx}"})
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outputs = model.outputs
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for idx, input in enumerate(outputs):
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if len(input.names) == 0:
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model.outputs[idx].set_names({f"output_{idx}"})
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# rewrite this model to a new IR path
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if new_ir_path is not None:
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serialize(model, new_ir_path)
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if blob_path is not None:
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compiledModel = core.compile_model(model, device_name="NPU")
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model_stream = compiledModel.export_model()
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with open(blob_path, 'wb') as f:
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f.write(model_stream)
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os.remove(xml_path)
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os.remove(new_ir_path)
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return blob_path
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def convert_llm(model: torch.nn.Module,
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kv_len: int,
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max_prompt_len: int,
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@ -235,180 +200,41 @@ def convert_llm(model: torch.nn.Module,
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n_splits_linear = model.config.hidden_size // group_size
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n_splits_down_proj = model.config.intermediate_size // group_size
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if model.config.model_type == "llama":
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from ipex_llm.transformers.npu_models.convert_mp import convert_llama
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convert_llama(model,
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max_output_len=kv_len,
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max_prompt_len=max_prompt_len,
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decoder=False,
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transpose_value_cache=transpose_value_cache)
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from .llama import LowBitLlamaLMHead, LlamaEmbedding
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with tempfile.TemporaryDirectory() as temp_dir:
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# generate lm_head blob
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weight_dir = os.path.join(temp_dir, "model_weights")
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os.mkdir(weight_dir)
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num_heads = model.model.layers[0].self_attn.num_heads
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num_key_value_heads = model.model.layers[0].self_attn.num_key_value_heads
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head_dim = model.model.layers[0].self_attn.head_dim
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intermediate_size = model.config.intermediate_size
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layer_num = len(model.model.layers)
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rms_norm_eps = model.config.rms_norm_eps
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vocab_size = model.config.vocab_size
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model_norm = model.model.norm
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lm_head = model.lm_head
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if n_splits_linear == 1:
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weights = [(lm_head.weight, lm_head.scale)]
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else:
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lm_heads = lm_head.lm_heads
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lm_head_weights = []
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scales = []
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for i in range(n_splits_linear):
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lm_head_weights.append(lm_heads[i].weight)
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scales.append(lm_heads[i].scale)
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weights = [(torch.stack(lm_head_weights, axis=0),
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torch.stack(scales, axis=0))]
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if isinstance(weights[0], tuple):
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np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
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else: # FP16 Linear
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np_dtype = np.float16
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from .llama import convert_llama_layer, convert_lm_head_and_embedding
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first_blob_path, last_blob_path = convert_lm_head_and_embedding(model, n_splits_linear,
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temp_dir, weight_dir)
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new_lm_head = LowBitLlamaLMHead(
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[1, 1, num_heads * head_dim],
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num_heads=num_heads,
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num_key_value_heads=num_key_value_heads,
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max_seq_len=kv_len,
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rms_norm_eps=rms_norm_eps,
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mode="decode",
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transpose_value=False,
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dtype=np_dtype,
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model_norm_weight=model_norm.weight.to(torch.float16),
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vocab_size=vocab_size,
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n_splits=n_splits_linear
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)
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last_blob_path = update_names_of_IR_and_export_blob(new_lm_head, "lm_head", temp_dir)
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# save weights bins files
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if n_splits_linear == 1:
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weight_numpy = [
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lm_head.weight.data.numpy(), lm_head.scale.data.numpy(),
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]
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else:
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weight_numpy = [v.numpy() for v in weights[0]]
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for idx, weight in enumerate(weight_numpy):
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bin_file = os.path.join(weight_dir, f"model_lm_head_input_{1+idx}.bin")
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weight.tofile(bin_file)
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embedding_layer = model.model.embed_tokens
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new_embedding = LlamaEmbedding(
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vocab_size=model.config.vocab_size,
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embedding_dim=model.config.hidden_size,
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embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
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padding_idx=model.config.pad_token_id,
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dtype=np.float16,
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)
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first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
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temp_dir)
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# generate decoder layer blob
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from ipex_llm.transformers.npu_models.llama_mp import LowBitLlamaMultiDecoderlayer
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param_list = []
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for layer_idx in range(0, layer_num):
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curr_layer = model.model.layers[layer_idx]
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attn_layer = curr_layer.self_attn
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mlp_layer = curr_layer.mlp
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param_list.append((model, layer_idx, n_splits_linear, n_splits_down_proj,
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temp_dir, weight_dir, transpose_value_cache, kv_len, group_size))
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with Pool() as pool:
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result = pool.starmap(convert_llama_layer, param_list)
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weights = []
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if n_splits_linear == 1:
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for q, k, v, o, g, u in zip(attn_layer.q_proj_dq_list,
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attn_layer.k_proj_dq_list,
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attn_layer.v_proj_dq_list,
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attn_layer.o_proj_dq_list,
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mlp_layer.gate_proj_dq_list,
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mlp_layer.up_proj_dq_list):
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weights.append((q.weight, q.scale))
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weights.append((k.weight, k.scale))
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weights.append((v.weight, v.scale))
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weights.append((o.weight, o.scale))
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weights.append((g.weight, g.scale))
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weights.append((u.weight, u.scale))
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else:
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for layer_list in [attn_layer.q_proj_dq_list, attn_layer.k_proj_dq_list,
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attn_layer.v_proj_dq_list, attn_layer.o_proj_dq_list,
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mlp_layer.gate_proj_dq_list, mlp_layer.up_proj_dq_list]:
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l_weights = []
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scales = []
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for l in layer_list:
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l_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(l_weights, axis=0),
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torch.stack(scales, axis=0)))
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if n_splits_down_proj == 1:
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for l in mlp_layer.down_proj_dq_list:
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weights.append((l.weight, l.scale))
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else:
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l_weights = []
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scales = []
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for l in mlp_layer.down_proj_dq_list:
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l_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
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cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
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layer_norm_0 = curr_layer.input_layernorm.weight.to(torch.float16)
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layer_norm_1 = curr_layer.post_attention_layernorm.weight.to(torch.float16)
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if isinstance(weights[0], tuple):
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np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
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else: # FP16 Linear
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np_dtype = np.float16
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if layer_idx == 0:
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single_decoder = LowBitLlamaMultiDecoderlayer(
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[1, 1, num_heads * head_dim],
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input_layernorm_weights=None,
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post_attn_layernorm_weights=None,
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cached_cos=cached_cos,
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cached_sin=cached_sin,
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num_heads=num_heads,
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num_key_value_heads=num_key_value_heads,
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num_layers=1,
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max_seq_len=kv_len,
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rms_norm_eps=rms_norm_eps,
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intermediate_size=intermediate_size,
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mode="decode",
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transpose_value=transpose_value_cache,
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dtype=np_dtype,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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)
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rest_blob_path = update_names_of_IR_and_export_blob(single_decoder,
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"decoder_layer",
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temp_dir)
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input_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_3.bin")
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post_lm_bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_4.bin")
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layer_norm_0.data.numpy().tofile(input_lm_bin_file)
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layer_norm_1.data.numpy().tofile(post_lm_bin_file)
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for idx, (weight, scale) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{7+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{7+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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# Prefill Runner
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from ipex_llm.transformers.npu_models.convert_mp import convert_llama
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convert_llama(model,
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max_output_len=kv_len,
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max_prompt_len=max_prompt_len,
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decoder=False,
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transpose_value_cache=transpose_value_cache)
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# patch attrs for generate
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model.kv_len = kv_len
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model.num_head = num_heads
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model.head_dim = head_dim
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model.num_head = model.model.layers[0].self_attn.num_heads
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model.head_dim = model.model.layers[0].self_attn.head_dim
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model.num_layers = layer_num
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model.transpose_value_cache = transpose_value_cache
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try:
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res = InitLLMPipeline(kv_len, num_heads, head_dim, layer_num,
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res = InitLLMPipeline(kv_len, model.num_head, model.head_dim, layer_num,
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model.vocab_size, weight_dir, "model",
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first_blob_path, last_blob_path, rest_blob_path)
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first_blob_path, last_blob_path,
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os.path.join(temp_dir, "decoder_layer"))
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except:
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invalidInputError(False,
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"False to InitLLMPipeline.")
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@ -15,10 +15,13 @@
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#
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import torch
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import numpy as np
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from ipex_llm.transformers.npu_models.mp_models_base import LLMBaseNNFactory
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from typing import Sequence
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from intel_npu_acceleration_library.backend.factory import NNFactory
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import os
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from .common import update_names_of_IR_and_export_blob
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class LowBitLlamaLMHead(LLMBaseNNFactory):
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@ -120,3 +123,158 @@ class LlamaEmbedding(NNFactory):
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print("start compiling")
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self.compile()
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def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir):
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num_heads = model.model.layers[0].self_attn.num_heads
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num_key_value_heads = model.model.layers[0].self_attn.num_key_value_heads
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head_dim = model.model.layers[0].self_attn.head_dim
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rms_norm_eps = model.config.rms_norm_eps
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vocab_size = model.config.vocab_size
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model_norm = model.model.norm
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lm_head = model.lm_head
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if n_splits_linear == 1:
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weights = [(lm_head.weight, lm_head.scale)]
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else:
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lm_heads = lm_head.lm_heads
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lm_head_weights = []
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scales = []
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for i in range(n_splits_linear):
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lm_head_weights.append(lm_heads[i].weight)
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scales.append(lm_heads[i].scale)
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weights = [(torch.stack(lm_head_weights, axis=0),
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torch.stack(scales, axis=0))]
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if isinstance(weights[0], tuple):
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np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
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else: # FP16 Linear
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np_dtype = np.float16
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new_lm_head = LowBitLlamaLMHead(
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[1, 1, num_heads * head_dim],
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num_heads=num_heads,
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num_key_value_heads=num_key_value_heads,
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max_seq_len=1,
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rms_norm_eps=rms_norm_eps,
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mode="decode",
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transpose_value=False,
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dtype=np_dtype,
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model_norm_weight=model_norm.weight.to(torch.float16),
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vocab_size=vocab_size,
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n_splits=n_splits_linear
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)
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last_blob_path = update_names_of_IR_and_export_blob(new_lm_head, "lm_head", temp_dir)
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# save weights bins files
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if n_splits_linear == 1:
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weight_numpy = [
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lm_head.weight.data.numpy(), lm_head.scale.data.numpy(),
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]
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else:
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weight_numpy = [v.numpy() for v in weights[0]]
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for idx, weight in enumerate(weight_numpy):
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bin_file = os.path.join(weight_dir, f"model_lm_head_input_{1+idx}.bin")
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weight.tofile(bin_file)
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embedding_layer = model.model.embed_tokens
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new_embedding = LlamaEmbedding(
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vocab_size=model.config.vocab_size,
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embedding_dim=model.config.hidden_size,
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embedding_weight=embedding_layer.weight.to(torch.float16).detach().numpy(),
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padding_idx=model.config.pad_token_id,
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dtype=np.float16,
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)
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first_blob_path = update_names_of_IR_and_export_blob(new_embedding, "embedding",
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temp_dir)
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return first_blob_path, last_blob_path
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def convert_llama_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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temp_dir, weight_dir, transpose_value_cache, kv_len, group_size):
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num_heads = model.model.layers[0].self_attn.num_heads
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num_key_value_heads = model.model.layers[0].self_attn.num_key_value_heads
|
||||
head_dim = model.model.layers[0].self_attn.head_dim
|
||||
intermediate_size = model.config.intermediate_size
|
||||
rms_norm_eps = model.config.rms_norm_eps
|
||||
|
||||
from ipex_llm.transformers.npu_models.llama_mp import LowBitLlamaMultiDecoderlayer
|
||||
curr_layer = model.model.layers[layer_idx]
|
||||
attn_layer = curr_layer.self_attn
|
||||
mlp_layer = curr_layer.mlp
|
||||
|
||||
weights = []
|
||||
if n_splits_linear == 1:
|
||||
for q, k, v, o, g, u in zip(attn_layer.q_proj_dq_list,
|
||||
attn_layer.k_proj_dq_list,
|
||||
attn_layer.v_proj_dq_list,
|
||||
attn_layer.o_proj_dq_list,
|
||||
mlp_layer.gate_proj_dq_list,
|
||||
mlp_layer.up_proj_dq_list):
|
||||
weights.append((q.weight, q.scale))
|
||||
weights.append((k.weight, k.scale))
|
||||
weights.append((v.weight, v.scale))
|
||||
weights.append((o.weight, o.scale))
|
||||
weights.append((g.weight, g.scale))
|
||||
weights.append((u.weight, u.scale))
|
||||
else:
|
||||
for layer_list in [attn_layer.q_proj_dq_list, attn_layer.k_proj_dq_list,
|
||||
attn_layer.v_proj_dq_list, attn_layer.o_proj_dq_list,
|
||||
mlp_layer.gate_proj_dq_list, mlp_layer.up_proj_dq_list]:
|
||||
l_weights = []
|
||||
scales = []
|
||||
for l in layer_list:
|
||||
l_weights.append(l.weight)
|
||||
scales.append(l.scale)
|
||||
weights.append((torch.stack(l_weights, axis=0),
|
||||
torch.stack(scales, axis=0)))
|
||||
|
||||
if n_splits_down_proj == 1:
|
||||
for l in mlp_layer.down_proj_dq_list:
|
||||
weights.append((l.weight, l.scale))
|
||||
else:
|
||||
l_weights = []
|
||||
scales = []
|
||||
for l in mlp_layer.down_proj_dq_list:
|
||||
l_weights.append(l.weight)
|
||||
scales.append(l.scale)
|
||||
weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
|
||||
|
||||
cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
|
||||
cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
|
||||
layer_norm_0 = curr_layer.input_layernorm.weight.to(torch.float16)
|
||||
layer_norm_1 = curr_layer.post_attention_layernorm.weight.to(torch.float16)
|
||||
|
||||
if isinstance(weights[0], tuple):
|
||||
np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
|
||||
else: # FP16 Linear
|
||||
np_dtype = np.float16
|
||||
|
||||
single_decoder = LowBitLlamaMultiDecoderlayer(
|
||||
[1, 1, num_heads * head_dim],
|
||||
input_layernorm_weights=[layer_norm_0],
|
||||
post_attn_layernorm_weights=[layer_norm_1],
|
||||
cached_cos=cached_cos,
|
||||
cached_sin=cached_sin,
|
||||
num_heads=num_heads,
|
||||
num_key_value_heads=num_key_value_heads,
|
||||
num_layers=1,
|
||||
max_seq_len=kv_len,
|
||||
rms_norm_eps=rms_norm_eps,
|
||||
intermediate_size=intermediate_size,
|
||||
mode="decode",
|
||||
transpose_value=transpose_value_cache,
|
||||
dtype=np_dtype,
|
||||
n_splits_linear=n_splits_linear,
|
||||
n_splits_down_proj=n_splits_down_proj,
|
||||
group_size=group_size
|
||||
)
|
||||
rest_blob_path = update_names_of_IR_and_export_blob(single_decoder,
|
||||
f"decoder_layer_{layer_idx}",
|
||||
temp_dir)
|
||||
|
||||
for idx, (weight, scale) in enumerate(weights):
|
||||
bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{5+idx*2}.bin")
|
||||
weight.numpy().tofile(bin_file)
|
||||
bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{5+idx*2+1}.bin")
|
||||
scale.numpy().tofile(bin_file)
|
||||
del single_decoder
|
||||
|
|
|
|||
Loading…
Reference in a new issue