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import torch |
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import torch.nn as nn |
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from tqdm import tqdm |
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from qmllm.quantization.quant_funcs import pseudo_quantize_tensor |
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from qmllm.quantization.qlinear import WALinear |
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from transformers.models.bloom.modeling_bloom import BloomForCausalLM |
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from transformers.models.opt.modeling_opt import OPTForCausalLM |
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def get_named_linears(module): |
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return {name: m for name, m in module.named_modules() if isinstance(m, nn.Linear)} |
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def get_blocks(model): |
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if model.__class__.__name__ == "LlamaForCausalLM": |
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layers = model.model.layers |
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elif model.__class__.__name__ == "LlavaLlamaForCausalLM": |
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layers = model.model.layers |
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elif model.__class__.__name__ == "LlavaQwenForCausalLM": |
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layers = model.model.layers |
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elif model.__class__.__name__ == "InternLM2ForCausalLM": |
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layers = model.model.layers |
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elif model.__class__.__name__ == "InternVLChatModel": |
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layers = model.language_model.model.layers |
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elif model.__class__.__name__ == "Qwen2VLForConditionalGeneration": |
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layers = model.model.layers |
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elif model.__class__.__name__ == "LlavaLlamaModel": |
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layers = model.llm.model.layers |
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elif isinstance(model, OPTForCausalLM): |
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layers = model.model.decoder.layers |
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elif isinstance(model, BloomForCausalLM): |
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layers = model.transformer.h |
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elif "mpt" in str(model.__class__).lower(): |
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layers = model.transformer.blocks |
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elif "falcon" in str(model.__class__).lower(): |
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layers = model.transformer.h |
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elif "bigcode" in str(model.__class__).lower(): |
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layers = model.transformer.h |
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elif "neox" in str(model.__class__).lower(): |
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layers = model.gpt_neox.layers |
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else: |
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raise NotImplementedError(type(model)) |
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return layers |
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@torch.no_grad() |
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def pseudo_quantize_model_weight( |
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model, |
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w_bit, |
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q_config, |
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): |
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layers = get_blocks(model) |
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for i in tqdm(range(len(layers)), desc="pseudo weight quantization..."): |
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named_linears = get_named_linears(layers[i]) |
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for n, m in named_linears.items(): |
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m.weight.data = pseudo_quantize_tensor( |
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m.weight.data, n_bits=w_bit, **q_config |
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) |
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def get_module_by_name_suffix(model, module_name: str): |
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for name, module in model.named_modules(): |
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if name.endswith(module_name): |
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return module |
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@torch.no_grad() |
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def pseudo_quantize_model_weight_act( |
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model, |
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w_bit, |
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a_bit, |
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): |
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layers = get_blocks(model) |
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for i in tqdm(range(len(layers)), desc="pseudo weight activation quantization..."): |
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named_linears = get_named_linears(layers[i]) |
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for n, m in named_linears.items(): |
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new_linear = WALinear.from_float(m, weight_quant="per_channel", act_quant="per_token", w_bit=w_bit, a_bit=a_bit) |
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father_module = get_module_by_name_suffix(layers[i], '.'.join(n.split(".")[:-1])) |
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setattr(father_module, n.split('.')[-1], new_linear) |
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del new_linear, m |
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torch.cuda.empty_cache() |
