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Add N:M sparsity #15

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Dec 9, 2024
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Add N:M sparsity #15

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2 changes: 2 additions & 0 deletions fasterai/_modidx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -198,6 +198,8 @@
'fasterai/sparse/sparsifier.py'),
'fasterai.sparse.sparsifier.Sparsifier._save_weights': ( 'sparse.sparsifier.html#sparsifier._save_weights',
'fasterai/sparse/sparsifier.py'),
'fasterai.sparse.sparsifier.Sparsifier.apply_nm_sparsity': ( 'sparse.sparsifier.html#sparsifier.apply_nm_sparsity',
'fasterai/sparse/sparsifier.py'),
'fasterai.sparse.sparsifier.Sparsifier.print_sparsity': ( 'sparse.sparsifier.html#sparsifier.print_sparsity',
'fasterai/sparse/sparsifier.py'),
'fasterai.sparse.sparsifier.Sparsifier.save_model': ( 'sparse.sparsifier.html#sparsifier.save_model',
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21 changes: 20 additions & 1 deletion fasterai/sparse/sparsifier.py
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Expand Up @@ -15,7 +15,8 @@
# %% ../../nbs/01_sparse.sparsifier.ipynb 5
class Sparsifier():
"Class providing sparsifying capabilities"
def __init__(self, model, granularity, context, criteria, layer_type=nn.Conv2d):
def __init__(self, model, granularity, context, criteria, nm=False, layer_type=nn.Conv2d):
if nm == True: print('Sparsity automatically set to 50%')
store_attr()
self._save_weights() # Save the original weights
self._reset_threshold()
Expand Down Expand Up @@ -116,10 +117,28 @@ def _compute_threshold(self, scores, sparsity, round_to):
return self.threshold

def _compute_mask(self, scores, threshold):
if self.nm == True: return self.apply_nm_sparsity(scores)
if threshold > scores.max(): threshold = scores.max() # Make sure we don't remove every weight of a given layer
return scores.ge(threshold).to(dtype=scores.dtype)

def print_sparsity(self):
for k,m in enumerate(self.model.modules()):
if isinstance(m, self.layer_type):
print(f"Sparsity in {m.__class__.__name__} {k}: {100. * float(torch.sum(m.weight == 0))/ float(m.weight.nelement()):.2f}%")

def apply_nm_sparsity(self, scores):
out_channels, in_channels, kernel_height, kernel_width = scores.shape
sparse_mask = torch.ones_like(scores)
if in_channels * kernel_height * kernel_width % 16 != 0:
print(f"Skipping 2:4 sparsity, Cin * Kh * Kw is not a multiple of 16")
return sparse_mask # Return weights unchanged if condition is not met
for out_ch in range(out_channels):
for h in range(kernel_height):
for w in range(kernel_width):
kernel_weights = scores[out_ch, :, h, w]
blocks = kernel_weights.view(-1, 4) # Flatten into blocks of 4
_, indices = blocks.topk(2, dim=1, largest=True, sorted=False) # Retain top-2 absolute values in each block
mask = torch.zeros_like(blocks)
mask.scatter_(1, indices, 1)
sparse_mask[out_ch, :, h, w] = mask.view(-1) # Reshape and place the mask in the appropriate location
return sparse_mask
4 changes: 2 additions & 2 deletions fasterai/sparse/sparsify_callback.py
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Expand Up @@ -17,15 +17,15 @@
# %% ../../nbs/02_sparse.sparsify_callback.ipynb 4
class SparsifyCallback(Callback):
"Sparsify model during training"
def __init__(self, sparsity, granularity, context, criteria, schedule, lth=False, rewind_epoch=0, reset_end=False, save_tickets=False, model=None, round_to=None, layer_type=nn.Conv2d):
def __init__(self, sparsity, granularity, context, criteria, schedule, lth=False, rewind_epoch=0, reset_end=False, save_tickets=False, model=None, round_to=None, nm=False, layer_type=nn.Conv2d):
store_attr()
self.sparsity = listify(self.sparsity)

def before_fit(self):
print(f'Pruning of {self.granularity} until a sparsity of {self.sparsity}%')
assert self.schedule.start_pct*self.n_epoch>=self.rewind_epoch, 'You must rewind to an epoch before the start of the pruning process'
model = self.model or self.learn.model
self.sparsifier = Sparsifier(model, self.granularity, self.context, self.criteria, self.layer_type)
self.sparsifier = Sparsifier(model, self.granularity, self.context, self.criteria, self.nm, self.layer_type)

def before_epoch(self):
if self.epoch == self.rewind_epoch:
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51 changes: 41 additions & 10 deletions nbs/01_sparse.sparsifier.ipynb
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Expand Up @@ -30,8 +30,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/home/HubensN/miniconda3/envs/prune/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory\n",
" warn(f\"Failed to load image Python extension: {e}\")\n"
"/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
Expand Down Expand Up @@ -75,7 +75,8 @@
"#| export\n",
"class Sparsifier():\n",
" \"Class providing sparsifying capabilities\"\n",
" def __init__(self, model, granularity, context, criteria, layer_type=nn.Conv2d):\n",
" def __init__(self, model, granularity, context, criteria, nm=False, layer_type=nn.Conv2d):\n",
" if nm == True: print('Sparsity automatically set to 50%')\n",
" store_attr()\n",
" self._save_weights() # Save the original weights\n",
" self._reset_threshold()\n",
Expand Down Expand Up @@ -176,13 +177,31 @@
" return self.threshold\n",
" \n",
" def _compute_mask(self, scores, threshold):\n",
" if self.nm == True: return self.apply_nm_sparsity(scores)\n",
" if threshold > scores.max(): threshold = scores.max() # Make sure we don't remove every weight of a given layer\n",
" return scores.ge(threshold).to(dtype=scores.dtype)\n",
" \n",
" def print_sparsity(self):\n",
" for k,m in enumerate(self.model.modules()):\n",
" if isinstance(m, self.layer_type):\n",
" print(f\"Sparsity in {m.__class__.__name__} {k}: {100. * float(torch.sum(m.weight == 0))/ float(m.weight.nelement()):.2f}%\")"
" print(f\"Sparsity in {m.__class__.__name__} {k}: {100. * float(torch.sum(m.weight == 0))/ float(m.weight.nelement()):.2f}%\")\n",
"\n",
" def apply_nm_sparsity(self, scores):\n",
" out_channels, in_channels, kernel_height, kernel_width = scores.shape\n",
" sparse_mask = torch.ones_like(scores)\n",
" if in_channels * kernel_height * kernel_width % 16 != 0:\n",
" print(f\"Skipping 2:4 sparsity, Cin * Kh * Kw is not a multiple of 16\")\n",
" return sparse_mask # Return weights unchanged if condition is not met\n",
" for out_ch in range(out_channels):\n",
" for h in range(kernel_height):\n",
" for w in range(kernel_width):\n",
" kernel_weights = scores[out_ch, :, h, w]\n",
" blocks = kernel_weights.view(-1, 4) # Flatten into blocks of 4\n",
" _, indices = blocks.topk(2, dim=1, largest=True, sorted=False) # Retain top-2 absolute values in each block\n",
" mask = torch.zeros_like(blocks)\n",
" mask.scatter_(1, indices, 1)\n",
" sparse_mask[out_ch, :, h, w] = mask.view(-1) # Reshape and place the mask in the appropriate location\n",
" return sparse_mask"
]
},
{
Expand All @@ -195,22 +214,26 @@
"text/markdown": [
"---\n",
"\n",
"[source](https://github.com/nathanhubens/fasterai/tree/master/blob/master/fasterai/sparse/sparsifier.py#L16){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"### Sparsifier\n",
"\n",
"> Sparsifier (model, granularity, context, criteria, layer_type=<class\n",
"> 'torch.nn.modules.conv.Conv2d'>)\n",
"> Sparsifier (model, granularity, context, criteria, nm=False,\n",
"> layer_type=<class 'torch.nn.modules.conv.Conv2d'>)\n",
"\n",
"Class providing sparsifying capabilities"
"*Class providing sparsifying capabilities*"
],
"text/plain": [
"---\n",
"\n",
"[source](https://github.com/nathanhubens/fasterai/tree/master/blob/master/fasterai/sparse/sparsifier.py#L16){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"### Sparsifier\n",
"\n",
"> Sparsifier (model, granularity, context, criteria, layer_type=<class\n",
"> 'torch.nn.modules.conv.Conv2d'>)\n",
"> Sparsifier (model, granularity, context, criteria, nm=False,\n",
"> layer_type=<class 'torch.nn.modules.conv.Conv2d'>)\n",
"\n",
"Class providing sparsifying capabilities"
"*Class providing sparsifying capabilities*"
]
},
"execution_count": null,
Expand Down Expand Up @@ -250,13 +273,17 @@
"text/markdown": [
"---\n",
"\n",
"[source](https://github.com/nathanhubens/fasterai/tree/master/blob/master/fasterai/sparse/sparsifier.py#L23){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"### Sparsifier.sparsify_layer\n",
"\n",
"> Sparsifier.sparsify_layer (m, sparsity, round_to=None)"
],
"text/plain": [
"---\n",
"\n",
"[source](https://github.com/nathanhubens/fasterai/tree/master/blob/master/fasterai/sparse/sparsifier.py#L23){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"### Sparsifier.sparsify_layer\n",
"\n",
"> Sparsifier.sparsify_layer (m, sparsity, round_to=None)"
Expand Down Expand Up @@ -288,13 +315,17 @@
"text/markdown": [
"---\n",
"\n",
"[source](https://github.com/nathanhubens/fasterai/tree/master/blob/master/fasterai/sparse/sparsifier.py#L31){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"### Sparsifier.sparsify_model\n",
"\n",
"> Sparsifier.sparsify_model (sparsity, round_to=None)"
],
"text/plain": [
"---\n",
"\n",
"[source](https://github.com/nathanhubens/fasterai/tree/master/blob/master/fasterai/sparse/sparsifier.py#L31){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"### Sparsifier.sparsify_model\n",
"\n",
"> Sparsifier.sparsify_model (sparsity, round_to=None)"
Expand Down
4 changes: 2 additions & 2 deletions nbs/02_sparse.sparsify_callback.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -73,15 +73,15 @@
"#| export\n",
"class SparsifyCallback(Callback):\n",
" \"Sparsify model during training\"\n",
" def __init__(self, sparsity, granularity, context, criteria, schedule, lth=False, rewind_epoch=0, reset_end=False, save_tickets=False, model=None, round_to=None, layer_type=nn.Conv2d):\n",
" def __init__(self, sparsity, granularity, context, criteria, schedule, lth=False, rewind_epoch=0, reset_end=False, save_tickets=False, model=None, round_to=None, nm=False, layer_type=nn.Conv2d):\n",
" store_attr()\n",
" self.sparsity = listify(self.sparsity)\n",
"\n",
" def before_fit(self):\n",
" print(f'Pruning of {self.granularity} until a sparsity of {self.sparsity}%')\n",
" assert self.schedule.start_pct*self.n_epoch>=self.rewind_epoch, 'You must rewind to an epoch before the start of the pruning process'\n",
" model = self.model or self.learn.model\n",
" self.sparsifier = Sparsifier(model, self.granularity, self.context, self.criteria, self.layer_type)\n",
" self.sparsifier = Sparsifier(model, self.granularity, self.context, self.criteria, self.nm, self.layer_type)\n",
"\n",
" def before_epoch(self):\n",
" if self.epoch == self.rewind_epoch:\n",
Expand Down
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