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[NPU] update fused layers for GW (#12459)

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* update fused layers for GW

* fix

* fix llama condition for glm model

* update
This commit is contained in:
Ruonan Wang 2024-11-28 01:14:30 -08:00 committed by GitHub
parent 1b533a105c
commit 490bb0ca53
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GPG key ID: B5690EEEBB952194
2 changed files with 42 additions and 18 deletions
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6
python/llm/src/ipex_llm/transformers/npu_model.py
View file

@ -136,6 +136,7 @@ class _BaseAutoModelClass:
mock_device = kwargs.pop('device', None) # For mock on CPU mock_device = kwargs.pop('device', None) # For mock on CPU
convert_model = kwargs.pop('convert_model', False) convert_model = kwargs.pop('convert_model', False)
save_directory = kwargs.pop('save_directory', None) save_directory = kwargs.pop('save_directory', None)
fuse_layers = kwargs.pop('fuse_layers', None)
invalidInputError( invalidInputError(
quantization_group_size in [0, 32, 64, 128], quantization_group_size in [0, 32, 64, 128],
@ -204,6 +205,7 @@ class _BaseAutoModelClass:
"transpose_value_cache": transpose_value_cache, "transpose_value_cache": transpose_value_cache,
"convert_model": convert_model, "convert_model": convert_model,
"save_directory": save_directory, "save_directory": save_directory,
"fuse_layers": fuse_layers
} }
model = cls.optimize_npu_model(*args, **optimize_kwargs) model = cls.optimize_npu_model(*args, **optimize_kwargs)
else: else:
@ -243,6 +245,7 @@ class _BaseAutoModelClass:
transpose_value_cache = kwargs.pop("transpose_value_cache", True) transpose_value_cache = kwargs.pop("transpose_value_cache", True)
convert_model = kwargs.pop('convert_model', False) convert_model = kwargs.pop('convert_model', False)
save_directory = kwargs.pop('save_directory', None) save_directory = kwargs.pop('save_directory', None)
fuse_layers = kwargs.pop('fuse_layers', None)
if hasattr(model, "llm"): if hasattr(model, "llm"):
llm = model.llm llm = model.llm
@ -282,7 +285,8 @@ class _BaseAutoModelClass:
group_size=quantization_group_size, group_size=quantization_group_size,
qtype=qtype, qtype=qtype,
convert_model=convert_model, convert_model=convert_model,
save_directory=save_directory) save_directory=save_directory,
fuse_layers=fuse_layers)
model.save_low_bit = types.MethodType(save_low_bit, model) model.save_low_bit = types.MethodType(save_low_bit, model)
return model return model

54
python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
View file

@ -195,7 +195,8 @@ def convert_llm(model: torch.nn.Module,
group_size: int, group_size: int,
qtype: str, qtype: str,
convert_model: bool=False, convert_model: bool=False,
save_directory: str=None): save_directory: str=None,
fuse_layers: int=None):
# whether to set layernorm weight as const # whether to set layernorm weight as const
layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1" layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
if group_size == 0: if group_size == 0:
@ -216,7 +217,8 @@ def convert_llm(model: torch.nn.Module,
n_splits_linear, n_splits_linear,
n_splits_down_proj, n_splits_down_proj,
group_size, group_size,
save_directory) save_directory,
fuse_layers=fuse_layers)
return 0 return 0
if model.config.model_type == "llama": if model.config.model_type == "llama":
with tempfile.TemporaryDirectory() as temp_dir: with tempfile.TemporaryDirectory() as temp_dir:
@ -422,18 +424,22 @@ def convert_llm_for_deploy(model: torch.nn.Module,
n_splits_linear: int, n_splits_linear: int,
n_splits_down_proj: int, n_splits_down_proj: int,
group_size: int, group_size: int,
save_directory: str=None): save_directory: str=None,
fuse_layers: int=None):
os.mkdir(save_directory) os.mkdir(save_directory)
weight_dir = os.path.join(save_directory, "model_weights") weight_dir = os.path.join(save_directory, "model_weights")
os.mkdir(weight_dir) os.mkdir(weight_dir)
layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1" layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
if model.config.model_type == "qwen2": if model.config.model_type == "qwen2":
if model.config.hidden_size == 1536: if group_size == 0:
# Qwen2-1.5B-Instruct if model.config.hidden_size == 1536:
fused_layers = 1 # Qwen2-1.5B-Instruct
fused_layers = 1 if fuse_layers is None else fuse_layers
else:
fused_layers = 2 if fuse_layers is None else fuse_layers
else: else:
fused_layers = 2 fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
update_dict = {"kv_len": kv_len, update_dict = {"kv_len": kv_len,
"num_head": model.model.layers[0].self_attn.num_heads, "num_head": model.model.layers[0].self_attn.num_heads,
"head_dim": model.model.layers[0].self_attn.head_dim, "head_dim": model.model.layers[0].self_attn.head_dim,
@ -469,20 +475,31 @@ def convert_llm_for_deploy(model: torch.nn.Module,
embedding_post = False embedding_post = False
cos_sin_input = False cos_sin_input = False
use_prefill_sdp = False use_prefill_sdp = False
if model.config.vocab_size == 32000: if group_size == 0:
# for Llama2-7B if model.config.intermediate_size == 11008:
fused_layers = 4 # for Llama2-7B
use_prefill_sdp = True fused_layers = 4 if fuse_layers is None else fuse_layers
else: use_prefill_sdp = True
if model.config.intermediate_size == 8192: elif model.config.intermediate_size == 14336:
# for Llama3-8B
fused_layers = 2 if fuse_layers is None else fuse_layers
use_prefill_sdp = True
elif not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
# llama3.2 1B & # llama3.2 3B # llama3.2 1B & # llama3.2 3B
embedding_post = True embedding_post = True
cos_sin_input = True cos_sin_input = True
fused_layers = 2 fused_layers = 2 if fuse_layers is None else fuse_layers
else: else:
# for Llama3-8B fused_layers = 2 if fuse_layers is None else fuse_layers
fused_layers = 2 else:
if model.config.intermediate_size in [11008, 14336]:
# for Llama2-7B & Llama3-8B
use_prefill_sdp = True use_prefill_sdp = True
elif not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
# llama3.2 1B & # llama3.2 3B
embedding_post = True
cos_sin_input = True
fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
update_dict = {"kv_len": kv_len, update_dict = {"kv_len": kv_len,
"num_head": model.model.layers[0].self_attn.num_heads, "num_head": model.model.layers[0].self_attn.num_heads,
"head_dim": model.model.layers[0].self_attn.head_dim, "head_dim": model.model.layers[0].self_attn.head_dim,
@ -518,7 +535,10 @@ def convert_llm_for_deploy(model: torch.nn.Module,
save_directory, weight_dir, transpose_value_cache, max_prompt_len, save_directory, weight_dir, transpose_value_cache, max_prompt_len,
group_size, layernorm_const, "prefill") group_size, layernorm_const, "prefill")
elif model.config.model_type == "minicpm": elif model.config.model_type == "minicpm":
fused_layers = 4 if group_size == 0:
fused_layers = 4 if fuse_layers is None else fuse_layers
else:
fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
update_dict = {"kv_len": kv_len, update_dict = {"kv_len": kv_len,
"num_head": model.model.layers[0].self_attn.num_heads, "num_head": model.model.layers[0].self_attn.num_heads,
"head_dim": model.model.layers[0].self_attn.head_dim, "head_dim": model.model.layers[0].self_attn.head_dim,