HQQ FSDP

.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

hqq/core/quantize.py

@@ -28,8 +28,14 @@ class Quantizer:
 			  '2bit_u8':BitPack.unpack_2bit_u8,
 			  '1bit_u8':BitPack.unpack_1bit_u8}
 
+	unpack_view_dtype = {'8bit_u8':torch.uint8,
+						 '4bit_u8':torch.uint8,
+						 '3bit_32':torch.int32,
+						 '2bit_u8':torch.uint8,
+						 '1bit_u8':torch.uint8}
+
 	@classmethod
-	def quantize(cls, tensor, nbits=4, channel_wise=True, group_size=64, optimize=False, round_zero=False, axis=0, bitpack=True):
+	def quantize(cls, tensor, nbits=4, channel_wise=True, group_size=64, optimize=False, round_zero=False, axis=0, bitpack=True, compute_dtype=None, view_as_float=False):
 		assert nbits in Quantizer.SUPPORTED_BITS, "nbits=" + str(nbits) + " not supported."
 		assert axis in [0, 1], "axis should be either 0 or 1"
 		if(group_size is not None):
@@ -70,10 +76,13 @@ def quantize(cls, tensor, nbits=4, channel_wise=True, group_size=64, optimize=Fa
 
 		#Store meta-data (we invert the scale for dequantization)
 		meta = {'nbits':nbits, 'group_size':group_size, 'shape':shape, 'scale':1./scale, 'zero':zero, 'axis':axis, 'packing':Quantizer.bit_to_packing[nbits]}
-
+		meta['unpack_view_dtype'] = Quantizer.unpack_view_dtype[meta['packing']]
+
 		#Pack bits
+		meta['view_as_float'] = view_as_float
 		if(bitpack):
 			W_q = Quantizer.pack[meta['packing']](W_q)
+			if view_as_float: W_q = W_q.view(torch.float32 if compute_dtype is None else compute_dtype) # store quantized weights as compute_dtype
 		else:
 			W_q = W_q.to(tensor.dtype) 
 			meta['packing'] = None
@@ -89,6 +98,7 @@ def quantize(cls, tensor, nbits=4, channel_wise=True, group_size=64, optimize=Fa
 	def dequantize(cls, W_q, meta):
 		compute_dtype = meta['compute_dtype'] if ('compute_dtype' in meta) else torch.float16
 		if(meta['packing']):
+			if meta['view_as_float']: W_q = W_q.view(meta['unpack_view_dtype'])
 			W_r = Quantizer.unpack[meta['packing']](W_q).to(compute_dtype)
 			if((meta['group_size'] is not None) and (meta['nbits']==3)):
 				W_r = W_r[:meta['group_size']] if (meta['axis']==0) else W_r[:,:meta['group_size']]
@@ -248,7 +258,7 @@ def backward(ctx, grad_output):
 class HQQLinear(torch.nn.Module):
 	backend = HQQBackend.PYTORCH #Default
 
-	def __init__(self, linear_layer, quant_config, del_orig=True, compute_dtype=torch.float16, device_n=0):
+	def __init__(self, linear_layer, quant_config, del_orig=True, compute_dtype=torch.float16, device_n=0, initialize=True):
 		super().__init__()
 		self.ready         = False
 		self.in_gpu        = False
@@ -257,17 +267,23 @@ def __init__(self, linear_layer, quant_config, del_orig=True, compute_dtype=torc
 		self.device_n      = device_n
 		self.compute_dtype = compute_dtype
 		self.quant_config  = copy.deepcopy(quant_config)
+		self.del_orig      = del_orig
 		self.offload_meta  = self.quant_config.pop('offload_meta') if (self.quant_config is not None) else None
 
 		self.set_backend(HQQLinear.backend) #Default backend
 
-		if(linear_layer is not None):
-			self.bias = None if (linear_layer.bias==None) else linear_layer.bias.to(self.compute_dtype).cuda()
-			self.quantize(linear_layer.weight.data, **self.quant_config)
+		self.linear_layer = linear_layer
 
-		if(del_orig): del linear_layer
-		torch.cuda.empty_cache()
+		if(initialize): self.initialize()
+
+	def initialize(self):
+		if(self.linear_layer is not None):
+			self.quantize(self.linear_layer.weight.data, **self.quant_config)
+			self.bias = None if (self.linear_layer.bias==None) else self.linear_layer.bias.to(self.compute_dtype).cuda()
 
+		if(self.del_orig): del self.linear_layer
+		torch.cuda.empty_cache()
+
 	#Set backends
 	@classmethod
 	def set_backend(cls, backend: HQQBackend):
@@ -312,13 +328,30 @@ def to(self, *args, **kwargs):
 	def half(self, *args, **kwargs):
 		return self 
 
-	def state_dict(self):
+	def state_dict(self, *args, **kwargs):
 		return {'W_q':self.W_q, 'meta':self.meta, 'bias':self.bias}
 
 	def load_state_dict(self, state_dict):
 		self.W_q    = state_dict['W_q']
 		self.meta   = state_dict['meta']
 		self.bias   = state_dict['bias'] if ('bias' in state_dict) else None
+
+		#Float view settings
+		if('unpack_view_dtype' not in self.meta):
+			self.meta['unpack_view_dtype'] = Quantizer.unpack_view_dtype[self.meta['packing']]
+
+		if('view_as_float' not in self.meta):
+			self.meta['view_as_float'] = False
+
+		if('meta_scale' in self.meta):
+			if('view_as_float' not in self.meta['meta_scale']):
+				self.meta['meta_scale']['view_as_float'] = False
+
+		if('meta_zero' in self.meta):
+			if('view_as_float' not in self.meta['meta_zero']):
+				self.meta['meta_zero']['view_as_float'] = False
+
+		#Check GPU
 		self.in_gpu = self.W_q.device.type == 'cuda'
 		if(self.in_gpu): 
 			if('zero' in self.meta):
@@ -340,15 +373,15 @@ def quantize(self, W, weight_quant_params, scale_quant_params, zero_quant_params
 		self.in_features, self.out_features = W.t().shape
 
 		#Quantize
-		W_q , meta = Quantizer.quantize(W, **weight_quant_params) 
+		W_q , meta = Quantizer.quantize(W, compute_dtype=self.compute_dtype, **weight_quant_params)
 		meta.update({'quant_scale':quant_scale, 'quant_zero':quant_zero})
 
 		if(meta['quant_zero']):
-			meta['zero_q'], meta['meta_zero']    = Quantizer.quantize(meta['zero'],  **zero_quant_params);  del meta['zero']
+			meta['zero_q'], meta['meta_zero']    = Quantizer.quantize(meta['zero'], view_as_float=False, **zero_quant_params);  del meta['zero']
 			meta['meta_zero']['compute_dtype']   = self.compute_dtype
 
 		if(meta['quant_scale']):
-			meta['scale_q'] , meta['meta_scale'] = Quantizer.quantize(meta['scale'], **scale_quant_params); del meta['scale']
+			meta['scale_q'] , meta['meta_scale'] = Quantizer.quantize(meta['scale'], view_as_float=False, **scale_quant_params); del meta['scale']
 			meta['meta_scale']['compute_dtype']  = self.compute_dtype
 
 		self.W_q   = W_q
@@ -414,6 +447,7 @@ def forward_pytorch_compile(self, x):
 	#Requires building the aten backend
 	@torch.jit.ignore
 	def dequantize_Wq_aten(self, W_q, meta):
+		if meta['view_as_float']: W_q = W_q.view(meta['unpack_view_dtype'])
 		return hqq_aten.dequantize(W_q, meta['scale'], meta['zero'], meta['shape'], meta['group_size'] if (meta['group_size']) else -1, meta['nbits'], meta['axis'], meta['packing'])
 
 	def dequantize_aten(self):
@@ -444,7 +478,7 @@ def dequantize_aten(self):
 				meta['zero'] = self.dequantize_Wq_aten(meta['zero_q'], meta['meta_zero']); del_keys.append('zero')
 			else:
 				meta['zero']  = Quantizer.dequantize(meta['zero_q'],  meta['meta_zero']);  del_keys.append('zero')
-
+		  
 		W_est = self.dequantize_Wq_aten(W_q, meta)
 
 		#Cleanup
@@ -503,11 +537,11 @@ def forward_aten_backprop(self, x):
 	# 	return hqq_aten.forward_with_quant(*args)
 
 
-def hqq_base_quant_config(nbits=4, group_size=64, quant_zero=True, quant_scale=False, offload_meta=False):
+def hqq_base_quant_config(nbits=4, group_size=64, quant_zero=True, quant_scale=False, offload_meta=False, view_as_float=False):
 	assert nbits in Quantizer.SUPPORTED_BITS, "nbits value not supported. Check Quantizer.SUPPORTED_BITS."
 	if(group_size is not None):
 		assert is_divisible(group_size, 8), "Invalid group_size param: the value should be a multiple of 8." 
-	weight_quant_params = {'nbits':nbits,'channel_wise':True,  'group_size':group_size, 'optimize':True, 'round_zero':True if nbits==4 else False} 
+	weight_quant_params = {'nbits':nbits,'channel_wise':True,  'group_size':group_size, 'optimize':True, 'round_zero':True if nbits==4 else False, 'view_as_float':view_as_float} 
 
 	if(offload_meta):
 		if((quant_scale!=quant_zero)):
@@ -521,7 +555,6 @@ def hqq_base_quant_config(nbits=4, group_size=64, quant_zero=True, quant_scale=F
 		scale_quant_params  = {'nbits':8, 'channel_wise':True,  'group_size':128,  'optimize':False} if (quant_scale) else None
 		zero_quant_params   = {'nbits':8, 'channel_wise':False, 'group_size':None, 'optimize':False} if (quant_zero)  else None
 
-
 	return {'weight_quant_params':weight_quant_params, 'scale_quant_params':scale_quant_params, 'zero_quant_params':zero_quant_params, 'offload_meta':offload_meta}
 
 #Alias: follow similar Auto-GPTQ naming