GPTQv2 support
GPTQv2 support. 1. Adds dependency on `triton` 2. Refactors autograd_4bit to include both GPTQv1 and GPTQv2 3. Introduces new environment variable GPTQ_VERSION to select autograd_4bit version 4. Fixes triton kernels 5. Matrix multiplications are in fp16
This commit is contained in:
Andrey Glushenkov
GitHub
parent
86387a0a35
commit
f20570343f
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@ -95,5 +95,6 @@ class Finetune4bConfig:
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f"{self.warmup_steps=}\n{self.save_steps=}\n{self.save_total_limit=}\n" +\
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f"{self.logging_steps=}\n" +\
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f"{self.checkpoint=}\n{self.skip=}\n" +\
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f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}"
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f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}\n" +\
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f"{self.groupsize=}\n"
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return s.replace("self.", "")
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10
README.md
10
README.md
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@ -34,10 +34,20 @@ pip install -r requirements.txt
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~The same finetune script from https://github.com/tloen/alpaca-lora can be used.~<br>
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After installation, this script can be used:
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GPTQv1:
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```
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python finetune.py
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```
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or
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```
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GPTQ_VERSION=1 python finetune.py
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```
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GPTQv2:
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```
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GPTQ_VERSION=2 python finetune.py
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```
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# Inference
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@ -0,0 +1,21 @@
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import os
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from colorama import init, Fore, Back, Style
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init(autoreset=True)
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try:
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GPTQ_VERSION = int(os.environ["GPTQ_VERSION"])
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except:
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print(Style.BRIGHT + Fore.YELLOW + "GPTQ_VERSION environment not provided. Fallback to GPTQv1")
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GPTQ_VERSION = 1 # Fallback version
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loader = None
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if GPTQ_VERSION == 1:
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from .autograd_4bit_v1 import Autograd4bitQuantLinear, load_llama_model_4bit_low_ram
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print(Style.BRIGHT + Fore.GREEN + "GPTQv1 set")
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elif GPTQ_VERSION == 2:
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from .autograd_4bit_v2 import Autograd4bitQuantLinear, load_llama_model_4bit_low_ram
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print(Style.BRIGHT + Fore.GREEN + "GPTQv2 set")
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else:
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raise ValueError("GPTQ_VERSION not set or invalid")
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@ -0,0 +1,208 @@
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import matmul_utils_4bit as mm4b
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import torch
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import torch.nn as nn
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import time
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class AutogradMatmul4bit(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, qweight, scales, zeros, groupsize=-1):
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ctx.save_for_backward(qweight, scales, zeros)
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ctx.groupsize = groupsize
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if groupsize == -1:
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output = mm4b._matmul4bit_v1_recons(x, qweight, scales, zeros)
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else:
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output = mm4b._matmul4bit_v2_recons(x, qweight, scales, zeros, groupsize)
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output = output.clone()
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return output
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@staticmethod
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def backward(ctx, grad_output):
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qweight, scales, zeros = ctx.saved_tensors
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groupsize = ctx.groupsize
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if groupsize == -1:
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grad = mm4b._matmul4bit_v1_recons(grad_output, qweight, scales, zeros, transpose=True)
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else:
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grad = mm4b._matmul4bit_v2_recons(grad_output, qweight, scales, zeros, groupsize=groupsize, transpose=True)
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return grad, None, None, None, None
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# Assumes layer is perfectly divisible into 256 * 256 blocks
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class Autograd4bitQuantLinear(nn.Module):
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def __init__(self, in_features, out_features, groupsize=None):
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super().__init__()
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bits = 4
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self.in_features = in_features
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self.out_features = out_features
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self.bits = bits
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self.register_buffer('zeros', torch.empty((out_features, 1)))
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self.register_buffer('scales', torch.empty((out_features, 1)))
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self.bias = nn.Parameter(torch.empty(out_features))
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self.register_buffer(
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'qweight', torch.empty((in_features // 256 * (bits * 8), out_features), dtype=torch.int)
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)
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def forward(self, x):
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if torch.is_grad_enabled():
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out = AutogradMatmul4bit.apply(x, self.qweight, self.scales,
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self.qzeros if self.groupsize != -1 else self.zeros, self.groupsize)
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out.add_(self.bias)
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else:
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out = mm4b.matmul4bit(x, self.qweight, self.scales,
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self.qzeros if self.groupsize != -1 else self.zeros, self.groupsize)
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out.add_(self.bias)
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return out
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def make_quant_for_4bit_autograd(module, names, name='', groupsize=-1):
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if isinstance(module, Autograd4bitQuantLinear):
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return
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for attr in dir(module):
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tmp = getattr(module, attr)
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name1 = name + '.' + attr if name != '' else attr
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if name1 in names:
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setattr(
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module, attr, Autograd4bitQuantLinear(tmp.in_features, tmp.out_features, groupsize=groupsize)
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)
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for name1, child in module.named_children():
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make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1, groupsize=groupsize)
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def model_to_half(model):
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model.half()
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear):
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m.zeros = m.zeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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print('Converted as Half.')
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def model_to_float(model):
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model.float()
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear):
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m.zeros = m.zeros.float()
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m.scales = m.scales.float()
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m.bias = m.bias.float()
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print('Converted as Float.')
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def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
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if type(module) in layers:
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return {name: module}
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res = {}
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for name1, child in module.named_children():
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res.update(find_layers(
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child, layers=layers, name=name + '.' + name1 if name != '' else name1
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))
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return res
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def load_llama_model_4bit_low_ram(config_path, model_path, groupsize=-1, half=False, device_map="auto", seqlen=2048):
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import accelerate
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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print("Loading Model ...")
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t0 = time.time()
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with accelerate.init_empty_weights():
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config = LlamaConfig.from_pretrained(config_path)
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model = LlamaForCausalLM(config)
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model = model.eval()
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layers = find_layers(model)
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for name in ['lm_head']:
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if name in layers:
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del layers[name]
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make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
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model = accelerate.load_checkpoint_and_dispatch(
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model=model,
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checkpoint=model_path,
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device_map=device_map,
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no_split_module_classes=["LlamaDecoderLayer"]
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)
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model.seqlen = seqlen
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if half:
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model_to_half(model)
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tokenizer = LlamaTokenizer.from_pretrained(config_path)
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tokenizer.truncation_side = 'left'
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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def load_llama_model_4bit_low_ram_and_offload_to_cpu(config_path, model_path, lora_path=None, groupsize=-1, seqlen=2048, max_memory=None):
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import accelerate
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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if max_memory is None:
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max_memory = {0: '24Gib', 'cpu': '48Gib'}
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print("Loading Model ...")
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t0 = time.time()
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with accelerate.init_empty_weights():
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config = LlamaConfig.from_pretrained(config_path)
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model = LlamaForCausalLM(config)
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model = model.eval()
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layers = find_layers(model)
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for name in ['lm_head']:
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if name in layers:
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del layers[name]
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make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
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accelerate.load_checkpoint_in_model(model, checkpoint=model_path, device_map={'': 'cpu'})
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# rotary_emb fix
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for n, m in model.named_modules():
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if 'rotary_emb' in n:
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cos_cached = m.cos_cached.clone().cpu()
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sin_cached = m.sin_cached.clone().cpu()
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break
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if lora_path is not None:
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from peft import PeftModel
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from peft.tuners.lora import Linear4bitLt
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model = PeftModel.from_pretrained(model, lora_path, device_map={'': 'cpu'}, torch_dtype=torch.float32)
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print('{} Lora Applied.'.format(lora_path))
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model.seqlen = seqlen
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print('Apply half ...')
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear) or ((lora_path is not None) and isinstance(m, Linear4bitLt)):
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m.zeros = m.zeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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print('Dispatching model ...')
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device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
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model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True, main_device=0)
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torch.cuda.empty_cache()
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print('Total {:.2f} Gib VRAM used.'.format(torch.cuda.memory_allocated() / 1024 / 1024))
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# rotary_emb fix
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for n, m in model.named_modules():
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if 'rotary_emb' in n:
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if getattr(m, '_hf_hook', None):
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if isinstance(m._hf_hook, accelerate.hooks.SequentialHook):
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hooks = m._hf_hook.hooks
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else:
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hooks = [m._hf_hook]
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for hook in hooks:
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if hook.offload:
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if n + '.sin_cached' not in hook.weights_map.dataset.state_dict.keys():
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hook.weights_map.dataset.state_dict[n + '.sin_cached'] = sin_cached.clone().cpu()
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hook.weights_map.dataset.state_dict[n + '.cos_cached'] = cos_cached.clone().cpu()
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tokenizer = LlamaTokenizer.from_pretrained(config_path)
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tokenizer.truncation_side = 'left'
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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@ -0,0 +1,221 @@
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from colorama import init, Fore, Back, Style
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import torch
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import torch.nn as nn
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import time
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import math
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import triton
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from triton_utils import matmul_248_kernel, trans_matmul_248_kernel
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class AutogradMatmul4bit(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
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grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
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matmul_248_kernel[grid](input, qweight, output,
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scales, qzeros, g_idx,
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input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0))
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ctx.save_for_backward(qweight, scales, qzeros, g_idx)
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ctx.input_shape, ctx.bits,ctx.maxq = input.shape,bits, maxq
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return output
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@staticmethod
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def backward(ctx, grad_output):
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input_shape, bits, maxq = ctx.input_shape, ctx.bits, ctx.maxq
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qweight, scales, qzeros, g_idx = ctx.saved_tensors
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grade_input = None
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if ctx.needs_input_grad[0]:
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grade_input = torch.empty((input_shape[0], input_shape[1]), device='cuda', dtype=torch.float32)
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grid = lambda META: (triton.cdiv(input_shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(input_shape[1], META['BLOCK_SIZE_K']),)
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trans_matmul_248_kernel[grid](grad_output, qweight, grade_input,
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scales, qzeros, g_idx,
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input_shape[0], qweight.shape[1], input_shape[1], bits, maxq,
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grad_output.stride(0), grad_output.stride(1),
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qweight.stride(0), qweight.stride(1),
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grade_input.stride(0), grade_input.stride(1),
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scales.stride(0), qzeros.stride(0))
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return grade_input, None, None, None, None, None, None
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class Autograd4bitQuantLinear(nn.Module):
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def __init__(self, in_features, out_features, groupsize, bias=True):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.bits = 4 # Hardcoded 4-bits quantizations
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self.maxq = 2 ** self.bits - 1
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self.groupsize = groupsize if groupsize != -1 else in_features
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self.register_buffer('qweight', torch.zeros((in_features // 32 * self.bits, out_features), dtype=torch.int32))
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self.register_buffer('qzeros', torch.zeros((math.ceil(in_features / self.groupsize), out_features // 32 * self.bits), dtype=torch.int32))
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self.register_buffer('scales', torch.zeros((math.ceil(in_features / self.groupsize), out_features), dtype=torch.float16))
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self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(in_features)], dtype = torch.int32))
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if bias:
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self.register_buffer('bias', torch.zeros(out_features,dtype=torch.float16))
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else:
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self.bias = None
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.out_features, )
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out = AutogradMatmul4bit.apply(x.reshape(-1,x.shape[-1]), self.qweight, self.scales,
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self.qzeros, self.g_idx, self.bits, self.maxq)
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out = out + self.bias if self.bias is not None else out
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return out.reshape(out_shape)
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def make_quant_for_4bit_autograd(module, names, name='', groupsize=-1):
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if isinstance(module, Autograd4bitQuantLinear):
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return
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for attr in dir(module):
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tmp = getattr(module, attr)
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name1 = name + '.' + attr if name != '' else attr
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if name1 in names:
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setattr(
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module, attr, Autograd4bitQuantLinear(tmp.in_features, tmp.out_features, groupsize=groupsize)
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)
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for name1, child in module.named_children():
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make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1, groupsize=groupsize)
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def model_to_half(model):
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model.half()
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear):
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m.qzeros = m.qzeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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print(Style.BRIGHT + Fore.YELLOW + 'Converted as Half.')
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def model_to_float(model):
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model.float()
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear):
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m.qzeros = m.qzeros.float()
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m.scales = m.scales.float()
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m.bias = m.bias.float()
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print(Style.BRIGHT + Fore.YELLOW + 'Converted as Float.')
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def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
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if type(module) in layers:
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return {name: module}
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res = {}
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for name1, child in module.named_children():
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res.update(find_layers(
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child, layers=layers, name=name + '.' + name1 if name != '' else name1
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))
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return res
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def load_llama_model_4bit_low_ram(config_path, model_path, groupsize=-1, half=False, device_map="auto", seqlen=2048):
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import accelerate
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
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t0 = time.time()
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with accelerate.init_empty_weights():
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config = LlamaConfig.from_pretrained(config_path)
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model = LlamaForCausalLM(config)
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model = model.eval()
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layers = find_layers(model)
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for name in ['lm_head']:
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if name in layers:
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del layers[name]
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make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
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model = accelerate.load_checkpoint_and_dispatch(
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model=model,
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checkpoint=model_path,
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device_map=device_map,
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no_split_module_classes=["LlamaDecoderLayer"]
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)
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model.seqlen = seqlen
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if half:
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model_to_half(model)
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tokenizer = LlamaTokenizer.from_pretrained(config_path)
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tokenizer.truncation_side = 'left'
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print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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def load_llama_model_4bit_low_ram_and_offload_to_cpu(config_path, model_path, lora_path=None, groupsize=-1, seqlen=2048, max_memory=None):
|
||||
import accelerate
|
||||
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
|
||||
|
||||
if max_memory is None:
|
||||
max_memory = {0: '24Gib', 'cpu': '48Gib'}
|
||||
|
||||
print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
|
||||
t0 = time.time()
|
||||
|
||||
with accelerate.init_empty_weights():
|
||||
config = LlamaConfig.from_pretrained(config_path)
|
||||
model = LlamaForCausalLM(config)
|
||||
model = model.eval()
|
||||
layers = find_layers(model)
|
||||
for name in ['lm_head']:
|
||||
if name in layers:
|
||||
del layers[name]
|
||||
make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
|
||||
accelerate.load_checkpoint_in_model(model, checkpoint=model_path, device_map={'': 'cpu'})
|
||||
|
||||
# rotary_emb fix
|
||||
for n, m in model.named_modules():
|
||||
if 'rotary_emb' in n:
|
||||
cos_cached = m.cos_cached.clone().cpu()
|
||||
sin_cached = m.sin_cached.clone().cpu()
|
||||
break
|
||||
|
||||
if lora_path is not None:
|
||||
from peft import PeftModel
|
||||
from peft.tuners.lora import Linear4bitLt
|
||||
model = PeftModel.from_pretrained(model, lora_path, device_map={'': 'cpu'}, torch_dtype=torch.float32)
|
||||
print(Style.BRIGHT + Fore.GREEN + '{} Lora Applied.'.format(lora_path))
|
||||
|
||||
model.seqlen = seqlen
|
||||
|
||||
print('Apply half ...')
|
||||
for n, m in model.named_modules():
|
||||
if isinstance(m, Autograd4bitQuantLinear) or ((lora_path is not None) and isinstance(m, Linear4bitLt)):
|
||||
m.qzeros = m.qzeros.half()
|
||||
m.scales = m.scales.half()
|
||||
m.bias = m.bias.half()
|
||||
|
||||
print('Dispatching model ...')
|
||||
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
|
||||
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True, main_device=0)
|
||||
torch.cuda.empty_cache()
|
||||
print(Style.BRIGHT + Fore.YELLOW + 'Total {:.2f} Gib VRAM used.'.format(torch.cuda.memory_allocated() / 1024 / 1024))
|
||||
|
||||
# rotary_emb fix
|
||||
for n, m in model.named_modules():
|
||||
if 'rotary_emb' in n:
|
||||
if getattr(m, '_hf_hook', None):
|
||||
if isinstance(m._hf_hook, accelerate.hooks.SequentialHook):
|
||||
hooks = m._hf_hook.hooks
|
||||
else:
|
||||
hooks = [m._hf_hook]
|
||||
for hook in hooks:
|
||||
if hook.offload:
|
||||
if n + '.sin_cached' not in hook.weights_map.dataset.state_dict.keys():
|
||||
hook.weights_map.dataset.state_dict[n + '.sin_cached'] = sin_cached.clone().cpu()
|
||||
hook.weights_map.dataset.state_dict[n + '.cos_cached'] = cos_cached.clone().cpu()
|
||||
|
||||
tokenizer = LlamaTokenizer.from_pretrained(config_path)
|
||||
tokenizer.truncation_side = 'left'
|
||||
|
||||
print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
||||
|
||||
return model, tokenizer
|
||||
|
|
@ -109,6 +109,7 @@ if not ft_config.skip:
|
|||
per_device_train_batch_size=ft_config.mbatch_size,
|
||||
gradient_accumulation_steps=ft_config.gradient_accumulation_steps,
|
||||
warmup_steps=ft_config.warmup_steps,
|
||||
optim="adamw_torch",
|
||||
num_train_epochs=ft_config.epochs,
|
||||
learning_rate=ft_config.lr,
|
||||
fp16=True,
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ bitsandbytes
|
|||
datasets
|
||||
sentencepiece
|
||||
safetensors
|
||||
triton
|
||||
git+https://github.com/huggingface/transformers.git
|
||||
git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
|
||||
git+https://github.com/sterlind/peft.git
|
||||
|
|
|
|||
|
|
@ -0,0 +1,154 @@
|
|||
import torch
|
||||
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
# %
|
||||
# :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`
|
||||
# decorator, which consumes:
|
||||
# - A list of :code:`triton.Config` objects that define different configurations of
|
||||
# meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try
|
||||
# - An autotuning *key* whose change in values will trigger evaluation of all the
|
||||
# provided configs
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
|
||||
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
|
||||
],
|
||||
key=['M', 'N', 'K'],
|
||||
)
|
||||
@triton.jit
|
||||
def matmul_kernel(
|
||||
# Pointers to matrices
|
||||
a_ptr, b_ptr, c_ptr,
|
||||
# Matrix dimensions
|
||||
M, N, K,
|
||||
# The stride variables represent how much to increase the ptr by when moving by 1
|
||||
# element in a particular dimension. E.g. stride_am is how much to increase a_ptr
|
||||
# by to get the element one row down (A has M rows)
|
||||
stride_am, stride_ak,
|
||||
stride_bk, stride_bn,
|
||||
stride_cm, stride_cn,
|
||||
# Meta-parameters
|
||||
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
|
||||
GROUP_SIZE_M: tl.constexpr,
|
||||
ACTIVATION: tl.constexpr,
|
||||
):
|
||||
"""Kernel for computing the matmul C = A x B.
|
||||
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
|
||||
"""
|
||||
# -----------------------------------------------------------
|
||||
# Map program ids `pid` to the block of C it should compute.
|
||||
# This is done in a grouped ordering to promote L2 data reuse
|
||||
# See above `L2 Cache Optimizations` section for details
|
||||
pid = tl.program_id(axis=0)
|
||||
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
|
||||
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
|
||||
num_pid_in_group = GROUP_SIZE_M * num_pid_n
|
||||
group_id = pid // num_pid_in_group
|
||||
first_pid_m = group_id * GROUP_SIZE_M
|
||||
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
|
||||
pid_m = first_pid_m + (pid % group_size_m)
|
||||
pid_n = (pid % num_pid_in_group) // group_size_m
|
||||
|
||||
# ----------------------------------------------------------
|
||||
# Create pointers for the first blocks of A and B.
|
||||
# We will advance this pointer as we move in the K direction
|
||||
# and accumulate
|
||||
# a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
|
||||
# b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers
|
||||
# see above `Pointer Arithmetics` section for details
|
||||
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
|
||||
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
|
||||
offs_k = tl.arange(0, BLOCK_SIZE_K)
|
||||
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
|
||||
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
|
||||
|
||||
# -----------------------------------------------------------
|
||||
# Iterate to compute a block of the C matrix
|
||||
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
|
||||
# of fp32 values for higher accuracy.
|
||||
# `accumulator` will be converted back to fp16 after the loop
|
||||
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
|
||||
for k in range(0, K, BLOCK_SIZE_K):
|
||||
# Note that for simplicity, we don't apply a mask here.
|
||||
# This means that if K is not a multiple of BLOCK_SIZE_K,
|
||||
# this will access out-of-bounds memory and produce an
|
||||
# error or (worse!) incorrect results.
|
||||
a = tl.load(a_ptrs)
|
||||
b = tl.load(b_ptrs)
|
||||
# We accumulate along the K dimension
|
||||
accumulator += tl.dot(a, b)
|
||||
# Advance the ptrs to the next K block
|
||||
a_ptrs += BLOCK_SIZE_K * stride_ak
|
||||
b_ptrs += BLOCK_SIZE_K * stride_bk
|
||||
# you can fuse arbitrary activation functions here
|
||||
# while the accumulator is still in FP32!
|
||||
if ACTIVATION == "leaky_relu":
|
||||
accumulator = leaky_relu(accumulator)
|
||||
c = accumulator.to(tl.float16)
|
||||
|
||||
# -----------------------------------------------------------
|
||||
# Write back the block of the output matrix C
|
||||
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
|
||||
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
|
||||
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
|
||||
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
|
||||
tl.store(c_ptrs, c, mask=c_mask)
|
||||
|
||||
|
||||
# we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`
|
||||
@triton.jit
|
||||
def leaky_relu(x):
|
||||
x = x + 1
|
||||
return tl.where(x >= 0, x, 0.01 * x)
|
||||
|
||||
def matmul(a, b, activation=""):
|
||||
# checks constraints
|
||||
assert a.shape[1] == b.shape[0], "incompatible dimensions"
|
||||
assert a.is_contiguous(), "matrix A must be contiguous"
|
||||
assert b.is_contiguous(), "matrix B must be contiguous"
|
||||
M, K = a.shape
|
||||
K, N = b.shape
|
||||
assert (
|
||||
K % 32 == 0
|
||||
), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"
|
||||
# allocates output
|
||||
c = torch.empty((M, N), device=a.device, dtype=a.dtype)
|
||||
# 1D launch kernel where each block gets its own program.
|
||||
grid = lambda META: (
|
||||
triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
|
||||
)
|
||||
matmul_kernel[grid](
|
||||
a, b, c,
|
||||
M, N, K,
|
||||
a.stride(0), a.stride(1),
|
||||
b.stride(0), b.stride(1),
|
||||
c.stride(0), c.stride(1),
|
||||
ACTIVATION=activation,
|
||||
)
|
||||
return c
|
||||
|
||||
|
||||
|
||||
torch.manual_seed(0)
|
||||
a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
|
||||
b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
|
||||
triton_output = matmul(a, b)
|
||||
torch_output = torch.matmul(a, b)
|
||||
print(f"triton_output={triton_output}")
|
||||
print(f"torch_output={torch_output}")
|
||||
if triton.testing.allclose(triton_output, torch_output):
|
||||
print("✅ Triton and Torch match")
|
||||
else:
|
||||
print("❌ Triton and Torch differ")
|
||||
Loading…
Reference in New Issue