[NPU] support asym_int4 for llama (#12556)
* add llama-imatrix * fix bugs in llama.py * style fix
This commit is contained in:
Zijie Li
GitHub
parent
d127a8654c
commit
fcb474820d
2 changed files with 124 additions and 32 deletions
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@ -72,6 +72,7 @@ class LowBitLlamaMultiDecoderlayer(LLMBaseNNFactory):
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group_size: int = 0,
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cos_len: int = 1,
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keep_position_ids=True,
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asym: bool = False,
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):
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super().__init__(max_seq_len=max_seq_len,
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transpose_value=transpose_value,
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@ -80,7 +81,8 @@ class LowBitLlamaMultiDecoderlayer(LLMBaseNNFactory):
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device=device,
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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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group_size=group_size,
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asym=asym)
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self.max_seq_len = max_seq_len
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self.intermediate_size = intermediate_size
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self.dtype = dtype
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@ -278,7 +280,8 @@ class FusedLlamaLowBitMultiDecoderlayer(torch.nn.Module):
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do_print: bool = False,
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n_splits_linear: int = 1,
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n_splits_down_proj: int = 1,
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group_size: int = 0
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group_size: int = 0,
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asym: bool = False,
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):
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super().__init__()
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@ -286,8 +289,10 @@ class FusedLlamaLowBitMultiDecoderlayer(torch.nn.Module):
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op_parameters = []
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for w in parameters:
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if isinstance(w, tuple): # from QuantizedLinear
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if isinstance(w, tuple) and not asym: # from QuantizedLinear
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op_parameters.append((w[0].numpy(), w[1].numpy()))
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elif isinstance(w, tuple) and asym: # from QuantizedLinear
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op_parameters.append((w[0].numpy(), w[1].numpy(), w[2].numpy()))
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elif w.dtype in [torch.int8, torch.uint8]: # QuantizedLinear weight
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op_parameters.append(w.numpy())
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elif isinstance(w, np.ndarray): # scale
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@ -341,7 +346,8 @@ class FusedLlamaLowBitMultiDecoderlayer(torch.nn.Module):
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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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group_size=group_size,
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asym=asym,
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)
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self.backend_decoders.append(decoder)
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@ -427,6 +433,7 @@ class FusedLlamaLowBitDecoderlayer(torch.nn.Module):
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n_splits_down_proj: int = 1,
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group_size: int = 0,
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cos_len: int = 1,
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asym: bool = False,
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):
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super().__init__()
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self.op_parameters = parameters
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@ -460,6 +467,7 @@ class FusedLlamaLowBitDecoderlayer(torch.nn.Module):
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size,
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cos_len=cos_len,
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asym=asym,
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)
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self.layer_norm_0 = layer_norm_0
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self.layer_norm_1 = layer_norm_1
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@ -555,6 +563,7 @@ def run_decode(
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layer_indexs = range(layer_start, layer_end)
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n_splits_linear = len(model.model.layers[0].mlp.gate_proj_dq_list)
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n_splits_down_proj = len(model.model.layers[0].mlp.down_proj_dq_list)
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asym = getattr(model.config, "asym", False)
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for layer_idx in layer_indexs:
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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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@ -567,9 +576,16 @@ def run_decode(
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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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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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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if hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
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@ -603,7 +619,8 @@ def run_decode(
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do_print=False,
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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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group_size=group_size,
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asym=asym,
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)
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dist.barrier()
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@ -814,6 +831,7 @@ def run_prefill(
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layer_indexs = range(layer_start, layer_end)
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n_splits_linear = len(model.model.layers[0].mlp.gate_proj_dq_list)
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n_splits_down_proj = len(model.model.layers[0].mlp.down_proj_dq_list)
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asym = getattr(model.config, "asym", False)
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for layer_idx in layer_indexs:
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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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@ -827,10 +845,18 @@ def run_prefill(
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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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), torch.stack(scales, axis=0)))
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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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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 hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
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cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
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@ -859,6 +885,7 @@ def run_prefill(
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size,
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cos_len=cos_len,
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asym=asym,
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)
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layer_weights.extend(weights)
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@ -130,15 +130,29 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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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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asym = getattr(model.config, "asym", False)
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if n_splits_linear == 1:
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asym = lm_head.qtype == "asym_int4_rtn"
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if asym:
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weights = [(lm_head.weight, lm_head.scale, lm_head.zero)]
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else:
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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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asym = lm_heads[0].qtype == "asym_int4_rtn"
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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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zeros = []
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for l in lm_heads:
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lm_head_weights.append(l.weight)
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scales.append(l.scale)
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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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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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torch.stack(zeros, axis=0))]
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else:
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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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@ -156,16 +170,23 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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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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n_splits=n_splits_linear,
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asym=asym
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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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True, False)
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# save weights bins files
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if n_splits_linear == 1:
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if not asym:
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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 = [
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lm_head.weight.data.numpy(), lm_head.scale.data.numpy(),
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lm_head.zero.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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@ -234,6 +255,7 @@ def convert_llama_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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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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rms_norm_eps = model.config.rms_norm_eps
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asym = getattr(model.config, "asym", False)
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from ipex_llm.transformers.npu_models.llama_mp import LowBitLlamaMultiDecoderlayer
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curr_layer = model.model.layers[layer_idx]
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@ -247,9 +269,16 @@ def convert_llama_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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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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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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if hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
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@ -299,7 +328,8 @@ def convert_llama_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size,
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cos_len=input_len,
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keep_position_ids=keep_position_ids
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keep_position_ids=keep_position_ids,
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asym=asym
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)
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rest_blob_path = update_names_of_IR_and_export_blob(single_decoder,
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@ -329,11 +359,24 @@ def convert_llama_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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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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st_idx = 8
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if not asym:
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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_{st_idx+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_{st_idx+idx*2+1}.bin")
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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else:
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for idx, (weight, scale, zero) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*3}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3+1}.bin")
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scale.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3+2}.bin")
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zero.numpy().tofile(bin_file)
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del single_decoder
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@ -347,6 +390,7 @@ def convert_fused_llama_layer(model, fused_layers, n_splits_linear, n_splits_dow
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rms_norm_eps = model.config.rms_norm_eps
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layer_num = len(model.model.layers)
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fused_layer_num = layer_num // fused_layers
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asym = getattr(model.config, "asym", False)
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from ipex_llm.transformers.npu_models.llama_mp import LowBitLlamaMultiDecoderlayer
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for i in range(fused_layers):
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@ -370,9 +414,16 @@ def convert_fused_llama_layer(model, fused_layers, n_splits_linear, n_splits_dow
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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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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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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if hasattr(curr_layer.self_attn.rotary_emb, "cos_cached"):
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@ -397,12 +448,25 @@ def convert_fused_llama_layer(model, fused_layers, n_splits_linear, n_splits_dow
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layer_norm_1.data.numpy().tofile(post_lm_bin_file)
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st_idx = 5
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# 6, 7 are past k/v
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if not asym:
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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_{st_idx+idx*2}.bin")
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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else:
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for idx, (weight, scale, zero) in enumerate(weights):
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3+1}.bin")
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scale.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3+2}.bin")
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zero.numpy().tofile(bin_file)
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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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@ -426,7 +490,8 @@ def convert_fused_llama_layer(model, fused_layers, n_splits_linear, n_splits_dow
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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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group_size=group_size,
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asym=asym
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)
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update_names_of_IR_and_export_blob(fused_decoder,
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f"decoder_layer_{i}",
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