refactor device check and remove cohere/mixtral support (#12659)
This commit is contained in:
Yishuo Wang
GitHub
parent
ea65e4fecc
commit
ddc0ef3993
9 changed files with 44 additions and 1359 deletions
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@ -1710,31 +1710,6 @@ def _optimize_post(model):
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convert_forward(model, module.VisionAttention, qwen2_vision_attention_forward)
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convert_forward(model, module.Qwen2VLModel, qwen2_vl_model_forward)
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convert_forward(model, module.Qwen2VLAttention, qwen2_vl_attention_forward)
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elif model.config.model_type == "cohere":
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# for CohereForAI/c4ai-command-r-v01
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invalidInputError(version.parse(trans_version) >= version.parse("4.40.0"),
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"Please upgrade transformers to 4.40.0 or higher version "
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"to run Mixtral models.")
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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if version.parse(trans_version) >= version.parse("4.41.0"):
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from ipex_llm.transformers.models.cohere import cohere_model_forward_4_41
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convert_forward(model,
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module.CohereModel,
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cohere_model_forward_4_41)
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else:
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from ipex_llm.transformers.models.cohere import cohere_model_forward
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convert_forward(model,
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module.CohereModel,
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cohere_model_forward)
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from ipex_llm.transformers.models.cohere import cohere_attention_forward
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convert_forward(model,
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module.CohereAttention,
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cohere_attention_forward)
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convert_forward(model,
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module.CohereMLP,
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mlp_silu_forward)
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elif model.config.model_type == "aquila":
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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@ -1746,31 +1721,6 @@ def _optimize_post(model):
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convert_forward(model,
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module.AquilaRMSNorm,
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rms_norm_forward)
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elif model.config.model_type == "mixtral":
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# For mistralai/Mixtral-8x7B-v0.1
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invalidInputError(version.parse(trans_version) >= version.parse("4.36.0"),
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"Please upgrade transformers to 4.36.0 or higher version "
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"to run Mixtral models.")
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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from ipex_llm.transformers.models.mixtral import mixtral_moeblock_forward, \
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mixtral_attention_forward, mixtral_mlp_forward, mixtral_model_forward
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convert_forward(model,
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module.MixtralAttention,
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mixtral_attention_forward)
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convert_forward(model,
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module.MixtralRMSNorm,
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rms_norm_forward)
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convert_forward(model,
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module.MixtralSparseMoeBlock,
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mixtral_moeblock_forward)
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convert_forward(model,
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module.MixtralBLockSparseTop2MLP,
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mixtral_mlp_forward)
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convert_forward(model,
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module.MixtralModel,
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mixtral_model_forward)
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elif model.config.model_type == "phi-msft" and \
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hasattr(model.config, "num_local_experts"):
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# For phixtral, limit the condition to avoid applying on phi-2 hosted by ModelScope
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@ -1785,29 +1735,19 @@ def _optimize_post(model):
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module.MLP,
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phixtral_mlp_forward)
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elif model.config.model_type == "mistral":
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if model.config.architectures is not None and \
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model.config.architectures[0] == "MixtralForCausalLM":
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# For DiscoResearch/mixtral-7b-8expert
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invalidInputError(version.parse(trans_version) >= version.parse("4.36.0"),
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"Please upgrade transformers to 4.36.0 or higher version "
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"to run Mixtral models.")
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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convert_forward(model, module.MistralRMSNorm, rms_norm_forward)
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else:
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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from ipex_llm.transformers.models.mistral import mistral_model_forward
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from ipex_llm.transformers.models.mistral import mistral_attention_forward
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from ipex_llm.transformers.models.common import rms_norm_forward
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from ipex_llm.transformers.models.common import mlp_silu_forward
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from ipex_llm.transformers.models.mistral import mistral_model_forward
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from ipex_llm.transformers.models.mistral import mistral_attention_forward
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from ipex_llm.transformers.models.common import rms_norm_forward
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from ipex_llm.transformers.models.common import mlp_silu_forward
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convert_forward(model, module.MistralModel, mistral_model_forward)
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convert_forward(model, module.MistralAttention, mistral_attention_forward)
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convert_forward(model, module.MistralSdpaAttention, mistral_attention_forward)
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convert_forward(model, module.MistralRMSNorm, rms_norm_forward)
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convert_forward(model, module.MistralMLP, mlp_silu_forward)
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convert_forward(model, module.MistralModel, mistral_model_forward)
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convert_forward(model, module.MistralAttention, mistral_attention_forward)
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convert_forward(model, module.MistralSdpaAttention, mistral_attention_forward)
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convert_forward(model, module.MistralRMSNorm, rms_norm_forward)
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convert_forward(model, module.MistralMLP, mlp_silu_forward)
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elif model.config.model_type == "gemma":
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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@ -33,7 +33,7 @@ from ipex_llm.transformers.speculative import greedy, deepmind_sample, logits_to
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_crop_past_key_values, _prepare_generate_args, _non_cpu_ipex_verify, clear_benchmarks,\
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_prepare_generate_args_4_45
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.utils import get_xpu_device_type
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from ipex_llm.transformers.utils import get_xpu_device_name
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logger = logging.getLogger("ipex_llm.lookup")
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@ -295,7 +295,7 @@ def lookup_generate(self,
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invalidInputError(input_ids.shape[0] == 1,
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"Prompt lookup is currently not supported with batch inference.")
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device_name = get_xpu_device_type(input_ids)
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device_name = get_xpu_device_name(input_ids.device)
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candidates_generator = PromptLookupCandidateGenerator(
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num_output_tokens=num_output_tokens,
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@ -51,7 +51,7 @@ from torch import Tensor, device, dtype, nn
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from operator import mul
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from functools import reduce
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from ipex_llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
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from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, \
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from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_name, \
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get_ipex_version
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from ipex_llm.transformers.convert import is_deepspeed_available, get_use_vllm
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@ -266,7 +266,7 @@ def reshape_lm_head_input(x):
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def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
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device = get_xpu_device_type(x)
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device_name = get_xpu_device_name(x.device)
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batch_size = x.shape[0]
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hard_condition = (
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x.dtype in [torch.float, torch.half]
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@ -286,7 +286,7 @@ def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
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or (
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qtype in [SYM_INT8, FP4, FP6, Q4_K, Q6_K]
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and batch_size <= 48
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and device in ["arc", "flex", "pvc", "mtl"]
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and device_name in ["arc", "pvc", "mtl", "lnl", "arl"]
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and x.shape[1] % 256 == 0
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and output_len % 32 == 0
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)
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@ -295,8 +295,8 @@ def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
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if hard_condition:
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return (
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batch_size > 1
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or (device in ["arc", "flex"] and qtype in [SYM_INT8, FP4])
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or (device in ["arc", "flex", "mtl"] and qtype in [FP8E4])
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or (device in ["arc"] and qtype in [SYM_INT8, FP4])
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or (device in ["arc", "mtl"] and qtype in [FP8E4])
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or (device in ["lnl"] and qtype in [SYM_INT4] and x.shape[1] % 512 == 0)
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or (device in ["bmg"] and qtype in [SYM_INT4, FP8E5])
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)
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@ -603,7 +603,7 @@ class LowBitLinear(nn.Linear):
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# empty cache before and after lm_head at first token when input > 1024
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# on arc or IPEX_LLM_LOW_MEM is set to 1 at inference time.
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if self.device is None:
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self.device = get_xpu_device_type(self.weight.data)
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self.device = get_xpu_device_name(self.weight.data.device)
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self.low_memory_mode = \
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self.low_memory_mode and \
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(self.device == "arc" or os.environ.get("IPEX_LLM_LOW_MEM", None) == "1")
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@ -782,7 +782,7 @@ class FP16Linear(nn.Linear):
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if not self.use_esimd_kernel(x):
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if (
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get_ipex_version() < "2.1.10+xpu"
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or get_xpu_device_type(x) not in ["arc", "flex", "pvc"]
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or get_xpu_device_name(x.device) not in ["arc", "pvc"]
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or self.disable_fp16_opt
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):
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if self.weight_type == 2:
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@ -848,7 +848,7 @@ class FP16Linear(nn.Linear):
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return result.to(x.dtype)
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def use_esimd_kernel(self, x):
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gpu_type = get_xpu_device_type(x)
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gpu_type = get_xpu_device_name(x.device)
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if self.disable_fp16_opt:
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return False
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# esimd kernel can only be used for Arc and Flex
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@ -1,589 +0,0 @@
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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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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/cohere/modeling_cohere.py
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# coding=utf-8
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# Copyright 2024 Cohere team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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# This file is based on the LLama model definition file in transformers
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"""PyTorch Cohere model."""
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import math
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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import torch.utils.checkpoint
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from typing import Optional, Tuple, List
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from ipex_llm.transformers.models.utils import repeat_kv
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from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
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from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import use_decoding_fast_path
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
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from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.kv import DynamicFp8Cache
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from ipex_llm.utils.common import invalidInputError
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try:
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from transformers.cache_utils import Cache, DynamicCache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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def cohere_model_forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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):
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use_cache = use_cache if use_cache is not None \
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else self.config.use_cache
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if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
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if not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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output_attentions = output_attentions if output_attentions is not None \
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else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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invalidInputError(False,
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"You cannot specify both input_ids and inputs_embeds at the same time")
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if self.gradient_checkpointing and self.training and use_cache:
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invalidInputError(False,
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"`use_cache=True` is incompatible "
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"with gradient checkpointing. Setting `use_cache=False`.")
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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past_seen_tokens = 0
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if use_cache: # kept for BC (cache positions)
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if not isinstance(past_key_values, Cache):
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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past_seen_tokens = past_key_values.get_seq_length()
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if cache_position is None:
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if isinstance(past_key_values, Cache):
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invalidInputError(False, "cache_position is a required argument when using Cache.")
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = self._update_causal_mask(attention_mask,
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inputs_embeds, cache_position, past_seen_tokens)
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# embed positions
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hidden_states = inputs_embeds
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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causal_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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cache_position,
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)
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else:
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# ipex-llm changes
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curr_device = decoder_layer.input_layernorm.weight.device
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if causal_mask is not None:
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causal_mask = causal_mask.to(curr_device)
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if position_ids is not None:
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position_ids = position_ids.to(curr_device)
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# ipex-llm changes end
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache,
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all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def cohere_model_forward_4_41(
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self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
):
|
||||
use_cache = use_cache if use_cache is not None \
|
||||
else self.config.use_cache
|
||||
if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
|
||||
if not isinstance(past_key_values, DynamicFp8Cache):
|
||||
past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
|
||||
output_attentions = output_attentions if output_attentions is not None \
|
||||
else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None
|
||||
else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
invalidInputError(False,
|
||||
"You cannot specify both input_ids and inputs_embeds at the same time")
|
||||
|
||||
if self.gradient_checkpointing and self.training and use_cache:
|
||||
invalidInputError(False,
|
||||
"`use_cache=True` is incompatible "
|
||||
"with gradient checkpointing. Setting `use_cache=False`.")
|
||||
use_cache = False
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
past_seen_tokens = 0
|
||||
return_legacy_cache = False
|
||||
# kept for BC (non `Cache` `past_key_values` inputs)
|
||||
if use_cache and not isinstance(past_key_values, Cache):
|
||||
return_legacy_cache = True
|
||||
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = self._update_causal_mask(
|
||||
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||||
)
|
||||
|
||||
# embed positions
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = None
|
||||
|
||||
for decoder_layer in self.layers:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
causal_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position,
|
||||
)
|
||||
else:
|
||||
# ipex-llm changes
|
||||
curr_device = decoder_layer.input_layernorm.weight.device
|
||||
if causal_mask is not None:
|
||||
causal_mask = causal_mask.to(curr_device)
|
||||
if position_ids is not None:
|
||||
position_ids = position_ids.to(curr_device)
|
||||
# ipex-llm changes end
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
if return_legacy_cache:
|
||||
next_cache = next_cache.to_legacy_cache()
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, next_cache,
|
||||
all_hidden_states, all_self_attns] if v is not None)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
|
||||
def cohere_attention_forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
if use_quantize_kv_cache(self.q_proj, hidden_states):
|
||||
forward_function = cohere_attention_forward_quantized
|
||||
else:
|
||||
forward_function = cohere_attention_forward_origin
|
||||
return forward_function(
|
||||
self=self,
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def cohere_attention_forward_quantized(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
|
||||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
|
||||
if self.use_qk_norm:
|
||||
query_states = self.q_norm(query_states)
|
||||
key_states = self.k_norm(key_states)
|
||||
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len,
|
||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
past_key_value = getattr(self, "past_key_value", past_key_value)
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||||
cos, sin = self.rotary_emb(value_states, position_ids)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
# sin and cos are specific to RoPE models; position_ids needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx,
|
||||
cache_kwargs, new_layout=True)
|
||||
if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
|
||||
and not hidden_states.requires_grad:
|
||||
import xe_addons
|
||||
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask)
|
||||
attn_weights = None
|
||||
else:
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states,
|
||||
value_states, query_states.dtype)
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query_states,
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attention_mask is not None: # no matter the length, we just slice it
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
attn_weights = attn_weights + causal_mask
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights,
|
||||
dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights,
|
||||
p=self.attention_dropout, training=self.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
||||
"`attn_output` should be of size "
|
||||
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
||||
f" but is {attn_output.size()}")
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def cohere_attention_forward_origin(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
|
||||
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
|
||||
decoding_fast_path = use_decoding_fast_path(self.q_proj,
|
||||
use_fuse_rope,
|
||||
enough_kv_room,
|
||||
bsz * q_len)
|
||||
if decoding_fast_path:
|
||||
hidden_states = hidden_states.view(1, -1)
|
||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
||||
kv_seq_len = cache_k.shape[-2]
|
||||
import xe_linear
|
||||
query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
|
||||
self.q_proj.weight,
|
||||
self.k_proj.weight,
|
||||
self.v_proj.weight,
|
||||
position_ids,
|
||||
cache_k, cache_v,
|
||||
self.q_proj.weight.qtype,
|
||||
self.v_proj.weight.qtype,
|
||||
kv_seq_len,
|
||||
self.head_dim,
|
||||
self.rotary_emb.base,)
|
||||
kv_seq_len += 1
|
||||
# update past_key_value's seem_tokens and kv caches.
|
||||
if self.layer_idx == 0:
|
||||
past_key_value._seen_tokens = kv_seq_len
|
||||
past_key_value.key_cache[self.layer_idx] = key_states
|
||||
past_key_value.value_cache[self.layer_idx] = value_states
|
||||
else:
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
|
||||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
|
||||
if self.use_qk_norm:
|
||||
query_states = self.q_norm(query_states)
|
||||
key_states = self.k_norm(key_states)
|
||||
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
|
||||
self.head_dim).transpose(1, 2)
|
||||
|
||||
past_key_value = getattr(self, "past_key_value", past_key_value)
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
if self.layer_idx is None:
|
||||
invalidInputError(
|
||||
False,
|
||||
"The cache structure has changed since version v4.36. "
|
||||
f"If you are using {self.__class__.__name__} "
|
||||
"for auto-regressive decoding with k/v caching, "
|
||||
"please make sure to initialize the attention class with a layer index."
|
||||
)
|
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||||
cos, sin = self.rotary_emb(value_states, position_ids)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
if self.layer_idx == 0:
|
||||
past_key_value._seen_tokens += key_states.shape[-2]
|
||||
|
||||
if len(past_key_value.key_cache) <= self.layer_idx:
|
||||
past_key_value.key_cache.append(key_states)
|
||||
past_key_value.value_cache.append(value_states)
|
||||
else:
|
||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
||||
|
||||
if not enough_kv_room:
|
||||
# allocate new
|
||||
new_c_k, new_c_v = extend_kv_cache(bsz,
|
||||
self.num_key_value_heads, # Support GQA
|
||||
self.head_dim,
|
||||
cache_k.size(2),
|
||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
||||
dtype=cache_k.dtype,
|
||||
device=device)
|
||||
|
||||
new_c_k[:] = cache_k
|
||||
new_c_v[:] = cache_v
|
||||
cache_k = new_c_k
|
||||
cache_v = new_c_v
|
||||
|
||||
key_states, value_states = append_kv_cache(cache_k,
|
||||
cache_v,
|
||||
key_states,
|
||||
value_states)
|
||||
|
||||
# update past_key_value
|
||||
past_key_value.key_cache[self.layer_idx] = key_states
|
||||
past_key_value.value_cache[self.layer_idx] = value_states
|
||||
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
if not self.training and not hidden_states.requires_grad and \
|
||||
use_flash_attention(query_states, key_states, attention_mask):
|
||||
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
||||
key_states.to(device, dtype=torch.float16),
|
||||
value_states.to(device, dtype=torch.float16),
|
||||
is_causal=True)
|
||||
attn_weights = None
|
||||
elif not self.training and not hidden_states.requires_grad and \
|
||||
use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
||||
import xe_addons
|
||||
if attention_mask is not None:
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
else:
|
||||
causal_mask = None
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states, causal_mask)
|
||||
attn_output = attn_output.view(query_states.shape)
|
||||
attn_weights = None
|
||||
else:
|
||||
attn_weights = torch.matmul(query_states,
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attention_mask is not None: # no matter the length, we just slice it
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
attn_weights = attn_weights + causal_mask
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights,
|
||||
dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights,
|
||||
p=self.attention_dropout, training=self.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
||||
"`attn_output` should be of size "
|
||||
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
||||
f" but is {attn_output.size()}")
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|
||||
|
|
@ -53,10 +53,10 @@ def siglip_attention_forward(
|
|||
qkv = qkv.transpose(1, 2)
|
||||
query_states, key_states, value_states = qkv.chunk(3, dim=1)
|
||||
|
||||
from ipex_llm.transformers.utils import get_xpu_device_type
|
||||
from ipex_llm.transformers.utils import get_xpu_device_name
|
||||
if (
|
||||
self.head_dim == 72
|
||||
and get_xpu_device_type(query_states) in ["arc", "flex"] and
|
||||
and get_xpu_device_name(query_states.device) == "arc" and
|
||||
query_states.dtype in [torch.float, torch.half]
|
||||
):
|
||||
n_heads, kv_length = query_states.size(1), key_states.size(2)
|
||||
|
|
|
|||
|
|
@ -1,576 +0,0 @@
|
|||
#
|
||||
# Copyright 2016 The BigDL Authors.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
# Some parts of this file is adapted from
|
||||
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/modeling_mixtral.py
|
||||
|
||||
# coding=utf-8
|
||||
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
""" PyTorch Mixtral model."""
|
||||
import math
|
||||
from typing import Optional, Tuple, Union, List
|
||||
from transformers.modeling_outputs import MoeModelOutputWithPast
|
||||
from transformers.cache_utils import Cache, DynamicCache
|
||||
from transformers.modeling_attn_mask_utils import (
|
||||
_prepare_4d_causal_attention_mask,
|
||||
)
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
from ipex_llm.ggml.quantize import ggml_tensor_qtype
|
||||
from ipex_llm.utils.common import invalidInputError
|
||||
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
|
||||
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
|
||||
from ipex_llm.transformers.models.utils import should_use_fuse_rope
|
||||
from ipex_llm.transformers.models.utils import use_decoding_fast_path
|
||||
from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
|
||||
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
|
||||
from ipex_llm.transformers.low_bit_linear import IQ2_XXS
|
||||
|
||||
import os
|
||||
|
||||
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
|
||||
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
|
||||
to (batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
|
||||
n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
def mixtral_moeblock_forward(self,
|
||||
hidden_states: torch.Tensor):
|
||||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
bs = hidden_states.shape[0]
|
||||
# router_logits: (batch * sequence_length, n_experts)
|
||||
router_logits = self.gate(hidden_states)
|
||||
|
||||
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||||
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
||||
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||||
# we cast back to the input dtype
|
||||
routing_weights = routing_weights.to(hidden_states.dtype)
|
||||
|
||||
if bs == 1:
|
||||
selected_experts = selected_experts[0].cpu().tolist()
|
||||
for idx in range(self.top_k):
|
||||
exp_id = selected_experts[idx]
|
||||
expert_layer = self.experts[exp_id]
|
||||
weight = routing_weights[:, idx]
|
||||
if idx == 0:
|
||||
final_hidden_states = expert_layer(hidden_states, weight)
|
||||
else:
|
||||
final_hidden_states = final_hidden_states + expert_layer(hidden_states, weight)
|
||||
elif bs < 256 and hidden_states.device.type == 'xpu':
|
||||
final_hidden_states = torch.zeros((batch_size * sequence_length, hidden_dim),
|
||||
dtype=hidden_states.dtype, device=hidden_states.device)
|
||||
import xe_linear
|
||||
indexes = xe_linear.get_moe_indexes(selected_experts.to(torch.int32).cpu(), 8)
|
||||
for expert_idx in range(self.num_experts):
|
||||
expert_layer = self.experts[expert_idx]
|
||||
idx_list = indexes[0][expert_idx]
|
||||
top_x_list = indexes[1][expert_idx]
|
||||
if len(idx_list) == 0:
|
||||
continue
|
||||
|
||||
top_x = torch.tensor(top_x_list, dtype=torch.long, device=hidden_states.device)
|
||||
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
|
||||
current_hidden_states = expert_layer(current_state,
|
||||
routing_weights[top_x_list, idx_list, None])
|
||||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||||
else:
|
||||
final_hidden_states = torch.zeros(
|
||||
(batch_size * sequence_length, hidden_dim),
|
||||
dtype=hidden_states.dtype,
|
||||
device=hidden_states.device
|
||||
)
|
||||
# One hot encode the selected experts to create an expert mask
|
||||
# this will be used to easily index which expert is going to be sollicitated
|
||||
expert_mask = torch.nn.functional.one_hot(selected_experts,
|
||||
num_classes=self.num_experts).permute(2, 1, 0)
|
||||
|
||||
# Loop over all available experts in the model and perform the computation on each expert
|
||||
for expert_idx in range(self.num_experts):
|
||||
expert_layer = self.experts[expert_idx]
|
||||
idx, top_x = torch.where(expert_mask[expert_idx])
|
||||
|
||||
if top_x.shape[0] == 0:
|
||||
continue
|
||||
|
||||
# in torch it is faster to index using lists than torch tensors
|
||||
top_x_list = top_x.tolist()
|
||||
idx_list = idx.tolist()
|
||||
|
||||
# Index the correct hidden states and compute the expert hidden state for
|
||||
# the current expert. We need to make sure to multiply the output hidden
|
||||
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||||
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
|
||||
current_hidden_states = expert_layer(current_state,
|
||||
routing_weights[top_x_list, idx_list, None])
|
||||
|
||||
# However `index_add_` only support torch tensors for indexing so we'll use
|
||||
# the `top_x` tensor here.
|
||||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||||
|
||||
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||||
return final_hidden_states, router_logits
|
||||
|
||||
|
||||
def mixtral_attention_forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor]=None,
|
||||
position_ids: Optional[torch.LongTensor]=None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]]=None,
|
||||
output_attentions: bool=False,
|
||||
use_cache: bool=False,
|
||||
padding_mask: Optional[torch.Tensor]=None,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
# for flash attention
|
||||
original_dtype = hidden_states.dtype
|
||||
|
||||
use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
|
||||
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
|
||||
decoding_fast_path = use_decoding_fast_path(self.q_proj,
|
||||
use_fuse_rope,
|
||||
enough_kv_room,
|
||||
bsz * q_len)
|
||||
|
||||
if decoding_fast_path:
|
||||
hidden_states = hidden_states.view(1, -1)
|
||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
||||
kv_seq_len = cache_k.shape[-2]
|
||||
import xe_linear
|
||||
query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
|
||||
self.q_proj.weight,
|
||||
self.k_proj.weight,
|
||||
self.v_proj.weight,
|
||||
position_ids,
|
||||
cache_k, cache_v,
|
||||
self.q_proj.weight.qtype,
|
||||
self.v_proj.weight.qtype,
|
||||
kv_seq_len,
|
||||
self.head_dim,
|
||||
self.rotary_emb.base,)
|
||||
kv_seq_len += 1
|
||||
# update past_key_value's seem_tokens and kv caches.
|
||||
if self.layer_idx == 0:
|
||||
past_key_value.seen_tokens = kv_seq_len
|
||||
past_key_value.key_cache[self.layer_idx] = key_states
|
||||
past_key_value.value_cache[self.layer_idx] = value_states
|
||||
# diasble it for now as it will cause output change for unknown reason
|
||||
# elif decoding_fast_path and self.q_proj.qtype == IQ2_XXS:
|
||||
# # this path self.v_proj use q4_0
|
||||
# hidden_states = hidden_states.view(1, -1)
|
||||
# cache_k = past_key_value.key_cache[self.layer_idx]
|
||||
# cache_v = past_key_value.value_cache[self.layer_idx]
|
||||
# kv_seq_len = cache_k.shape[-2]
|
||||
# import xe_linear
|
||||
# query_states, key_states = xe_linear.forward_qk(hidden_states,
|
||||
# self.q_proj.weight,
|
||||
# self.k_proj.weight,
|
||||
# position_ids,
|
||||
# cache_k,
|
||||
# self.q_proj.weight.qtype,
|
||||
# kv_seq_len,
|
||||
# self.head_dim,
|
||||
# 10000)
|
||||
# kv_seq_len += 1
|
||||
# # update past_key_value's seem_tokens and kv caches.
|
||||
# if self.layer_idx == 0:
|
||||
# past_key_value.seen_tokens = kv_seq_len
|
||||
# # update value_states
|
||||
# value_states = self.v_proj(hidden_states)
|
||||
# value_states = value_states.view(bsz, q_len,
|
||||
# self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
# new_size = (cache_v.size(0),
|
||||
# cache_v.size(1),
|
||||
# cache_v.size(2) + value_states.size(2),
|
||||
# cache_v.size(3))
|
||||
# new_cache_v = cache_v.as_strided(new_size, cache_v.stride(), storage_offset=0)
|
||||
# new_cache_v[:, :, cache_v.size(2):cache_v.size(2)+value_states.size(2), :] = value_states
|
||||
|
||||
# past_key_value.key_cache[self.layer_idx] = key_states
|
||||
# past_key_value.value_cache[self.layer_idx] = new_cache_v
|
||||
else:
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len,
|
||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len,
|
||||
self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
if self.layer_idx is None:
|
||||
invalidInputError(False,
|
||||
"The cache structure has changed since version v4.36. "
|
||||
f"If you are using {self.__class__.__name__} for "
|
||||
"auto-regressive decodingwith k/v caching, please make sure "
|
||||
"to initialize the attention class with a layer index.")
|
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||||
|
||||
if use_fuse_rope:
|
||||
import xe_addons
|
||||
xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
|
||||
query_states, key_states)
|
||||
else:
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
||||
cos, sin, position_ids, "mixtral")
|
||||
|
||||
if past_key_value is not None:
|
||||
# update the number of seen tokens
|
||||
if self.layer_idx == 0:
|
||||
past_key_value.seen_tokens += key_states.shape[-2]
|
||||
|
||||
# reuse k, v, self_attention
|
||||
# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
|
||||
if len(past_key_value.key_cache) <= self.layer_idx:
|
||||
past_key_value.key_cache.append(key_states)
|
||||
past_key_value.value_cache.append(value_states)
|
||||
else:
|
||||
cache_k = past_key_value.key_cache[self.layer_idx]
|
||||
cache_v = past_key_value.value_cache[self.layer_idx]
|
||||
|
||||
if not enough_kv_room:
|
||||
# allocate new
|
||||
new_c_k, new_c_v = extend_kv_cache(bsz,
|
||||
self.num_key_value_heads, # Support GQA
|
||||
self.head_dim,
|
||||
cache_k.size(2),
|
||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
||||
dtype=cache_k.dtype,
|
||||
device=device)
|
||||
|
||||
new_c_k[:] = cache_k
|
||||
new_c_v[:] = cache_v
|
||||
cache_k = new_c_k
|
||||
cache_v = new_c_v
|
||||
|
||||
key_states, value_states = append_kv_cache(cache_k,
|
||||
cache_v,
|
||||
key_states,
|
||||
value_states)
|
||||
|
||||
# update past_key_value
|
||||
past_key_value.key_cache[self.layer_idx] = key_states
|
||||
past_key_value.value_cache[self.layer_idx] = value_states
|
||||
|
||||
if not self.training and not hidden_states.requires_grad:
|
||||
fsdp_flag = use_flash_attention(query_states, key_states)
|
||||
else:
|
||||
fsdp_flag = False
|
||||
if fsdp_flag:
|
||||
attention_dtype = torch.float16 # use fp16 for flash attention
|
||||
else:
|
||||
attention_dtype = original_dtype
|
||||
|
||||
# repeat k/v heads if n_kv_heads < n_heads
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
|
||||
dtype=attention_dtype)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
|
||||
dtype=attention_dtype)
|
||||
|
||||
if fsdp_flag:
|
||||
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype),
|
||||
key_states,
|
||||
value_states,
|
||||
is_causal=True)
|
||||
attn_weights = None
|
||||
elif use_sdp(query_states.shape[2], key_states.shape[2], self.head_dim, query_states):
|
||||
import xe_addons
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask)
|
||||
attn_output = attn_output.view(query_states.shape)
|
||||
attn_weights = None
|
||||
else:
|
||||
attn_weights = torch.matmul(
|
||||
query_states.to(key_states.dtype),
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||||
invalidInputError(
|
||||
False,
|
||||
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
|
||||
f" but is {attn_weights.size()}"
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||||
invalidInputError(
|
||||
False,
|
||||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
||||
f" but is {attention_mask.size()}"
|
||||
)
|
||||
|
||||
attn_weights = attn_weights + attention_mask
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.\
|
||||
softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||||
invalidInputError(
|
||||
False,
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
|
||||
f" but is {attn_output.size()}"
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def mixtral_mlp_forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
routing_weights
|
||||
) -> torch.Tensor:
|
||||
qtype = getattr(self.w1, "qtype", None)
|
||||
if mlp_fusion_check(x, qtype, self.training):
|
||||
import xe_linear
|
||||
return self.w2(xe_linear.mlp_forward_xpu(
|
||||
x, self.w1.weight.data, self.w3.weight.data,
|
||||
x.shape[0], x.shape[1], self.w1.out_len,
|
||||
SILU, qtype,
|
||||
)) * routing_weights
|
||||
else:
|
||||
current_hidden_states = self.act_fn(self.w1(x)) * self.w3(x)
|
||||
current_hidden_states = self.w2(current_hidden_states)
|
||||
return routing_weights * current_hidden_states
|
||||
|
||||
|
||||
def mixtral_model_forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
output_router_logits: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, MoeModelOutputWithPast]:
|
||||
# to be compatible with transformers>=4.37.0
|
||||
self._use_flash_attention_2 = self.config._attn_implementation == "flash_attention_2"
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None \
|
||||
else self.config.output_attentions
|
||||
output_router_logits = (
|
||||
output_router_logits if output_router_logits is not None
|
||||
else self.config.output_router_logits
|
||||
)
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else
|
||||
self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# retrieve input_ids and inputs_embeds
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
invalidInputError(False, "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") # noqa
|
||||
elif input_ids is not None:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
invalidInputError(False, "You have to specify either decoder_input_ids or decoder_inputs_embeds") # noqa
|
||||
|
||||
past_key_values_length = 0
|
||||
|
||||
if use_cache:
|
||||
use_legacy_cache = not isinstance(past_key_values, Cache)
|
||||
if use_legacy_cache:
|
||||
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
||||
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
||||
|
||||
if position_ids is None:
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length, seq_length + past_key_values_length,
|
||||
dtype=torch.long, device=device
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||||
else:
|
||||
position_ids = position_ids.view(-1, seq_length).long()
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
if attention_mask is not None and self._use_flash_attention_2 and use_cache:
|
||||
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
|
||||
if is_padding_right:
|
||||
invalidInputError(
|
||||
False,
|
||||
"You are attempting to perform batched generation with padding_side='right'"
|
||||
" this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to " # noqa
|
||||
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
||||
)
|
||||
|
||||
if self._use_flash_attention_2:
|
||||
# 2d mask is passed through the layers
|
||||
attention_mask = attention_mask \
|
||||
if (attention_mask is not None and 0 in attention_mask) else None
|
||||
else:
|
||||
# 4d mask is passed through the layers
|
||||
attention_mask = _prepare_4d_causal_attention_mask(
|
||||
attention_mask,
|
||||
(batch_size, seq_length),
|
||||
inputs_embeds,
|
||||
past_key_values_length,
|
||||
sliding_window=self.config.sliding_window,
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." # noqa
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
all_router_logits = () if output_router_logits else None
|
||||
next_decoder_cache = None
|
||||
|
||||
for decoder_layer in self.layers:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
output_router_logits,
|
||||
use_cache,
|
||||
)
|
||||
else:
|
||||
# bigdl-llm changes:
|
||||
#
|
||||
# Avoid moving `attention_mask`` and `position_ids`` to other devices multiple times.
|
||||
#
|
||||
# When the model is partitioned on two different devices using
|
||||
# `accelerate`'s `dispatch``, a hook to move inputs to the correct device is
|
||||
# added to each layer's `forward``, which will result in moving `attention_mask`
|
||||
# and `position_ids`, which allocated on device:0, to other devices for each
|
||||
# decoder layer not in device:0.
|
||||
#
|
||||
# To avoid this, we move `attention_mask` and `position_ids` to the device of
|
||||
# the current layer before the forward call, so that the moving is only done once
|
||||
# for each devices other than devie:0.
|
||||
#
|
||||
curr_device = decoder_layer.input_layernorm.weight.device
|
||||
if attention_mask is not None:
|
||||
attention_mask = attention_mask.to(curr_device)
|
||||
if position_ids is not None:
|
||||
position_ids = position_ids.to(curr_device)
|
||||
# bigdl-llm changes end
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
output_router_logits=output_router_logits,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
if output_router_logits:
|
||||
all_router_logits += (layer_outputs[-1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = None
|
||||
if use_cache:
|
||||
next_cache = next_decoder_cache.to_legacy_cache() \
|
||||
if use_legacy_cache else next_decoder_cache
|
||||
|
||||
if not return_dict:
|
||||
return tuple(
|
||||
v
|
||||
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] # noqa
|
||||
if v is not None
|
||||
)
|
||||
return MoeModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
router_logits=all_router_logits,
|
||||
)
|
||||
|
|
@ -36,7 +36,7 @@ import math
|
|||
import torch
|
||||
from typing import Optional
|
||||
|
||||
from ipex_llm.transformers.utils import get_xpu_device_type
|
||||
from ipex_llm.transformers.utils import get_xpu_device_name
|
||||
from ipex_llm.transformers.models.common import padding_qkv_hd
|
||||
from ipex_llm.transformers.models.common import scaled_dot_product_attention
|
||||
from diffusers.models.attention_processor import Attention
|
||||
|
|
@ -144,7 +144,7 @@ class AttnProcessor2_0:
|
|||
|
||||
def upcast_vae(self):
|
||||
# workaround overflow and ipex's bugs
|
||||
if get_xpu_device_type(self.vae.post_quant_conv.weight) in ["arc", "flex", "pvc"]:
|
||||
if get_xpu_device_name(self.vae.post_quant_conv.weight.device) == "arc":
|
||||
self.vae.to(torch.bfloat16)
|
||||
else:
|
||||
self.vae.decoder.up_blocks.to(torch.bfloat16)
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ import torch
|
|||
import warnings
|
||||
from ipex_llm.utils.common import invalidInputError
|
||||
from ipex_llm.ggml.quantize import ggml_tensor_qtype
|
||||
from ipex_llm.transformers.utils import get_ipex_version, get_xpu_device_type
|
||||
from ipex_llm.transformers.utils import get_ipex_version, get_xpu_device_name
|
||||
from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_XXS, FP4, FP8E4,\
|
||||
FP6, ASYM_INT4
|
||||
|
||||
|
|
@ -85,16 +85,14 @@ def use_quantize_kv_cache(linear: torch.nn.Module, x: torch.Tensor, kv_group: in
|
|||
return os.environ["IPEX_LLM_QUANTIZE_KV_CACHE"] == "1"
|
||||
elif os.environ.get("IPEX_LLM_LOW_MEM", None) is not None:
|
||||
return os.environ["IPEX_LLM_LOW_MEM"] == "1"
|
||||
elif linear.qtype in [ggml_tensor_qtype["fp16"], ggml_tensor_qtype["bf16"]]:
|
||||
return False
|
||||
else:
|
||||
return x.device.type == 'xpu' and kv_cache_device_check(x, kv_group) \
|
||||
and hasattr(linear, "qtype") and \
|
||||
linear.qtype != ggml_tensor_qtype["fp16"] and linear.qtype != ggml_tensor_qtype["bf16"]
|
||||
|
||||
|
||||
def kv_cache_device_check(x: torch.Tensor, kv_group: int) -> bool:
|
||||
return (get_xpu_device_type(x) in ["mtl", "lnl"] and kv_group <= 1) or \
|
||||
((get_xpu_device_type(x) == "arc" or get_xpu_device_type(x) == "flex") and
|
||||
1 < x.size(0) and x.size(0) <= 8)
|
||||
device_name = get_xpu_device_name(x.device)
|
||||
return (
|
||||
device_name in ["mtl", "lnl", "arl"] and kv_group == 1
|
||||
or device_name in ["arc", "bmg"] and x.size(0) > 1
|
||||
)
|
||||
|
||||
|
||||
def init_fp8_kv_cache(batch_size, num_heads, current_length, head_dim, device):
|
||||
|
|
@ -226,57 +224,6 @@ def is_enough_kv_cache_room_4_31(past_key_value, seq_len=1):
|
|||
(past_key_value[0].size(2) + seq_len) * past_key_value[0].size(3)
|
||||
|
||||
|
||||
def use_flash_attention(query, key, attention_mask=None):
|
||||
# here we support query's shape is always [batch_size, head_num, q_len, head_dim],
|
||||
# key's shape is always [batch_size, head_num, k_len, head_dim]
|
||||
invalidInputError(query.dim() == 4,
|
||||
"Here query input of use_flash_attention should be [batch_size, "
|
||||
"head_num, q_len, head_dim]")
|
||||
invalidInputError(key.dim() == 4,
|
||||
"Here key input of use_flash_attention should be [batch_size, "
|
||||
"head_num, k_len, head_dim]")
|
||||
bsz, _, q_len, _ = query.size()
|
||||
k_len = key.size()[2]
|
||||
# check whether ipex flash attention can be used
|
||||
if q_len != k_len:
|
||||
# now only use flash attention for first token
|
||||
# as it seems have no performance benifit for rest token now
|
||||
return False
|
||||
if query.device.type != "xpu":
|
||||
# ipex flash attention only support for xpu
|
||||
return False
|
||||
ipex_version = get_ipex_version()
|
||||
if ipex_version <= "2.0.110+xpu":
|
||||
# ipex flash attention is supported from ipex 2.1
|
||||
return False
|
||||
if not torch.xpu.has_xetla():
|
||||
# ipex flash attention is only supported for xetla
|
||||
# may update this later
|
||||
return False
|
||||
elif get_xpu_device_type(query) != "pvc":
|
||||
return False
|
||||
if query.dtype not in [torch.float32, torch.float16]:
|
||||
# only use flash attention for fp32/fp16 input
|
||||
return False
|
||||
if bsz > 1:
|
||||
# as flash attention doesn't support attn_mask in ipex 2.1,
|
||||
# so it will cause output error for padded batch input
|
||||
if attention_mask is None:
|
||||
return True
|
||||
else:
|
||||
# TODO: below logic may change for different model
|
||||
# attention mask shape : [bsz, 1, q_len, k_len]
|
||||
if attention_mask[0].squeeze()[0, 0].item() != 0:
|
||||
# first batch contains padding
|
||||
# otherwise we suppose it should be a upper triangular matrix
|
||||
# at the same time, the diagonal is also 0
|
||||
return False
|
||||
elif not attention_mask.equal(attention_mask[0].repeat(bsz, 1, 1, 1)):
|
||||
# check whether mask of every batch is the same
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def use_sdp(q_len, kv_len, head_dim, query_states):
|
||||
return (
|
||||
query_states.device.type == "xpu"
|
||||
|
|
@ -315,38 +262,16 @@ def mlp_fusion_check(x, qtype, training):
|
|||
if training or x.requires_grad:
|
||||
return False
|
||||
if qtype == FP6:
|
||||
device = get_xpu_device_type(x)
|
||||
if device in ["mtl", "lnl"]:
|
||||
device = get_xpu_device_name(x.device)
|
||||
if device in ["mtl", "lnl", "arl"]:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def use_decoding_fast_path(proj,
|
||||
use_fuse_rope,
|
||||
enough_kv_room,
|
||||
bs,
|
||||
qtype_check=decoding_fast_path_qtype_check):
|
||||
if proj is None:
|
||||
return False
|
||||
device = get_xpu_device_type(proj.weight)
|
||||
if not qtype_check(proj):
|
||||
return False
|
||||
if not use_fuse_rope:
|
||||
return False
|
||||
if not enough_kv_room:
|
||||
return False
|
||||
if bs != 1:
|
||||
return False
|
||||
|
||||
if device in ["uhd"]:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def use_xmx(x: torch.Tensor, qtype: int):
|
||||
device = get_xpu_device_type(x)
|
||||
device = get_xpu_device_name(x.device)
|
||||
return (
|
||||
device in ["arc", "flex", "pvc"]
|
||||
device in ["arc", "pvc"]
|
||||
and qtype in [SYM_INT4, SYM_INT8, FP8E4, FP8E5]
|
||||
and (
|
||||
(device == "pvc" and 1 < x.size(0) <= 16)
|
||||
|
|
@ -370,7 +295,7 @@ def fp16_fusion_check(proj, x, training):
|
|||
return False
|
||||
if x.requires_grad:
|
||||
return False
|
||||
device_type = get_xpu_device_type(x)
|
||||
device_type = get_xpu_device_name(x.device)
|
||||
if device_type != "pvc":
|
||||
return False
|
||||
return True
|
||||
|
|
@ -439,7 +364,7 @@ def should_use_compresskv(x: torch.Tensor, prompt_len: int):
|
|||
else:
|
||||
if use_compress_kv is None:
|
||||
return (
|
||||
get_xpu_device_type(x) in ["mtl", "lnl"]
|
||||
get_xpu_device_name(x.device) in ["mtl", "lnl", "arl"]
|
||||
and prompt_len >= 1800
|
||||
and prompt_len <= 4500
|
||||
)
|
||||
|
|
|
|||
|
|
@ -168,27 +168,12 @@ def get_ipex_version():
|
|||
return _ipex_version
|
||||
|
||||
|
||||
def get_xpu_device_type(x):
|
||||
if x.device.type != "xpu":
|
||||
return x.device.type
|
||||
name = torch.xpu.get_device_name(x.device.index)
|
||||
if name.startswith("Intel(R) Arc(TM) A"):
|
||||
return "arc"
|
||||
elif name.startswith("Intel(R) Graphics [0xe20b]"):
|
||||
return "bmg"
|
||||
elif name.startswith("Intel(R) Arc(TM)"):
|
||||
if 'V' in name:
|
||||
return "lnl"
|
||||
else:
|
||||
return "mtl"
|
||||
elif name.startswith("Intel(R) Data Center GPU Flex"):
|
||||
return "flex"
|
||||
elif name.startswith("Intel(R) Data Center GPU Max"):
|
||||
return "pvc"
|
||||
elif name.startswith("Intel(R) UHD"):
|
||||
return "uhd"
|
||||
def get_xpu_device_name(device: torch.device):
|
||||
if device.type != "xpu":
|
||||
return device.type
|
||||
else:
|
||||
return "others"
|
||||
import xe_linear
|
||||
return xe_linear.get_xpu_device_name(device)
|
||||
|
||||
|
||||
def load_imatrix_data(imatrix_file):
|
||||
|
|
|
|||
Loading…
Reference in a new issue