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chore(sync): merge dev into main (#1379)
* Optimize latency (#1259) * add attribute: configs/config.py Optimize latency: tools/rvc_for_realtime.py * new file: assets/Synthesizer_inputs.pth * fix: configs/config.py fix: tools/rvc_for_realtime.py * fix bug: infer/lib/infer_pack/models.py * new file: assets/hubert_inputs.pth new file: assets/rmvpe_inputs.pth modified: configs/config.py new features: infer/lib/rmvpe.py new features: tools/jit_export/__init__.py new features: tools/jit_export/get_hubert.py new features: tools/jit_export/get_rmvpe.py new features: tools/jit_export/get_synthesizer.py optimize: tools/rvc_for_realtime.py * optimize: tools/jit_export/get_synthesizer.py fix bug: tools/jit_export/__init__.py * Fixed a bug caused by using half on the CPU: infer/lib/rmvpe.py Fixed a bug caused by using half on the CPU: tools/jit_export/__init__.py Fixed CIRCULAR IMPORT: tools/jit_export/get_rmvpe.py Fixed CIRCULAR IMPORT: tools/jit_export/get_synthesizer.py Fixed a bug caused by using half on the CPU: tools/rvc_for_realtime.py * Remove useless code: infer/lib/rmvpe.py * Delete gui_v1 copy.py * Delete .vscode/launch.json * Delete jit_export_test.py * Delete tools/rvc_for_realtime copy.py * Delete configs/config.json * Delete .gitignore * Fix exceptions caused by switching inference devices: infer/lib/rmvpe.py Fix exceptions caused by switching inference devices: tools/jit_export/__init__.py Fix exceptions caused by switching inference devices: tools/rvc_for_realtime.py * restore * replace(you can undo this commit) * remove debug_print --------- Co-authored-by: Ftps <ftpsflandre@gmail.com> * Fixed some bugs when exporting ONNX model (#1254) * fix import (#1280) * fix import * lint * 🎨 同步 locale (#1242) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Fix jit load and import issue (#1282) * fix jit model loading : infer/lib/rmvpe.py * modified: assets/hubert/.gitignore move file: assets/hubert_inputs.pth -> assets/hubert/hubert_inputs.pth modified: assets/rmvpe/.gitignore move file: assets/rmvpe_inputs.pth -> assets/rmvpe/rmvpe_inputs.pth fix import: gui_v1.py * feat(workflow): trigger on dev * feat(workflow): add close-pr on non-dev branch * Add input wav and delay time monitor for real-time gui (#1293) * feat(workflow): trigger on dev * feat(workflow): add close-pr on non-dev branch * 🎨 同步 locale (#1289) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: edit PR template * add input wav and delay time monitor --------- Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com> * Optimize latency using scripted jit (#1291) * feat(workflow): trigger on dev * feat(workflow): add close-pr on non-dev branch * 🎨 同步 locale (#1289) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: edit PR template * Optimize-latency-using-scripted: configs/config.py Optimize-latency-using-scripted: infer/lib/infer_pack/attentions.py Optimize-latency-using-scripted: infer/lib/infer_pack/commons.py Optimize-latency-using-scripted: infer/lib/infer_pack/models.py Optimize-latency-using-scripted: infer/lib/infer_pack/modules.py Optimize-latency-using-scripted: infer/lib/jit/__init__.py Optimize-latency-using-scripted: infer/lib/jit/get_hubert.py Optimize-latency-using-scripted: infer/lib/jit/get_rmvpe.py Optimize-latency-using-scripted: infer/lib/jit/get_synthesizer.py Optimize-latency-using-scripted: infer/lib/rmvpe.py Optimize-latency-using-scripted: tools/rvc_for_realtime.py * modified: infer/lib/infer_pack/models.py * fix some bug: configs/config.py fix some bug: infer/lib/infer_pack/models.py fix some bug: infer/lib/rmvpe.py * Fixed abnormal reference of logger in multiprocessing: infer/modules/train/train.py --------- Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Format code (#1298) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * 🎨 同步 locale (#1299) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: optimize actions * feat(workflow): add sync dev * feat: optimize actions * feat: optimize actions * feat: optimize actions * feat: optimize actions * feat: add jit options (#1303) Delete useless code: infer/lib/jit/get_synthesizer.py Optimized code: tools/rvc_for_realtime.py * Code refactor + re-design inference ui (#1304) * Code refacor + re-design inference ui * Fix tabname * i18n jp --------- Co-authored-by: Ftps <ftpsflandre@gmail.com> * feat: optimize actions * feat: optimize actions * Update README & en_US locale file (#1309) * critical: some bug fixes (#1322) * JIT acceleration switch does not support hot update * fix padding bug of rmvpe in torch-directml * fix padding bug of rmvpe in torch-directml * Fix STFT under torch_directml (#1330) * chore(format): run black on dev (#1318) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * chore(i18n): sync locale on dev (#1317) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: allow for tta to be passed to uvr (#1361) * chore(format): run black on dev (#1373) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Added script for automatically download all needed models at install (#1366) * Delete modules.py * Add files via upload * Add files via upload * Add files via upload * Add files via upload * chore(i18n): sync locale on dev (#1377) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * chore(format): run black on dev (#1376) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Update IPEX library (#1362) * Update IPEX library * Update ipex index * chore(format): run black on dev (#1378) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> --------- Co-authored-by: Chengjia Jiang <46401978+ChasonJiang@users.noreply.github.com> Co-authored-by: Ftps <ftpsflandre@gmail.com> Co-authored-by: shizuku_nia <102004222+ShizukuNia@users.noreply.github.com> Co-authored-by: Ftps <63702646+Tps-F@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com> Co-authored-by: yxlllc <33565655+yxlllc@users.noreply.github.com> Co-authored-by: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com> Co-authored-by: Blaise <133521603+blaise-tk@users.noreply.github.com> Co-authored-by: Rice Cake <gak141808@gmail.com> Co-authored-by: AWAS666 <33494149+AWAS666@users.noreply.github.com> Co-authored-by: Dmitry <nda2911@yandex.ru> Co-authored-by: Disty0 <47277141+Disty0@users.noreply.github.com>
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@@ -17,7 +17,6 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.device = torch.xpu.device
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torch.cuda.device_count = torch.xpu.device_count
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torch.cuda.device_of = torch.xpu.device_of
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torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
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torch.cuda.get_device_name = torch.xpu.get_device_name
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torch.cuda.get_device_properties = torch.xpu.get_device_properties
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torch.cuda.init = torch.xpu.init
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@@ -169,9 +168,23 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.get_device_properties.minor = 7
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torch.cuda.ipc_collect = lambda *args, **kwargs: None
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torch.cuda.utilization = lambda *args, **kwargs: 0
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if hasattr(torch.xpu, "getDeviceIdListForCard"):
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torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
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torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard
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else:
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torch.cuda.getDeviceIdListForCard = torch.xpu.get_device_id_list_per_card
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torch.cuda.get_device_id_list_per_card = (
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torch.xpu.get_device_id_list_per_card
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)
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ipex_hijacks()
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attention_init()
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try:
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from .diffusers import ipex_diffusers
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ipex_diffusers()
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except Exception: # pylint: disable=broad-exception-caught
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pass
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except Exception as e:
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return False, e
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return True, None
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@@ -16,17 +16,15 @@ def torch_bmm(input, mat2, *, out=None):
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input.shape[1],
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mat2.shape[2],
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)
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block_multiply = 2.4 if input.dtype == torch.float32 else 1.2
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block_size = (
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(batch_size_attention * input_tokens * mat2_shape) / 1024 * block_multiply
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) # MB
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block_multiply = input.element_size()
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slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
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block_size = batch_size_attention * slice_block_size
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split_slice_size = batch_size_attention
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if block_size >= 4000:
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if block_size > 4:
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do_split = True
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# Find something divisible with the input_tokens
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while (
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(split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply
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) > 4000:
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while (split_slice_size * slice_block_size) > 4:
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split_slice_size = split_slice_size // 2
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if split_slice_size <= 1:
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split_slice_size = 1
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@@ -34,16 +32,12 @@ def torch_bmm(input, mat2, *, out=None):
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else:
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do_split = False
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split_block_size = (
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(split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply
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) # MB
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split_2_slice_size = input_tokens
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if split_block_size >= 4000:
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if split_slice_size * slice_block_size > 4:
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slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply
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do_split_2 = True
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# Find something divisible with the input_tokens
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while (
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(split_slice_size * split_2_slice_size * mat2_shape) / 1024 * block_multiply
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) > 4000:
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while (split_2_slice_size * slice_block_size2) > 4:
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split_2_slice_size = split_2_slice_size // 2
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if split_2_slice_size <= 1:
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split_2_slice_size = 1
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@@ -91,22 +85,25 @@ def scaled_dot_product_attention(
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query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False
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):
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# ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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shape_one, batch_size_attention, query_tokens, shape_four = query.shape
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block_multiply = 2.4 if query.dtype == torch.float32 else 1.2
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block_size = (
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(shape_one * batch_size_attention * query_tokens * shape_four)
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/ 1024
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* block_multiply
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) # MB
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if len(query.shape) == 3:
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batch_size_attention, query_tokens, shape_four = query.shape
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shape_one = 1
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no_shape_one = True
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else:
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shape_one, batch_size_attention, query_tokens, shape_four = query.shape
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no_shape_one = False
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block_multiply = query.element_size()
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slice_block_size = (
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shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply
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)
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block_size = batch_size_attention * slice_block_size
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split_slice_size = batch_size_attention
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if block_size >= 4000:
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if block_size > 4:
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do_split = True
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# Find something divisible with the shape_one
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while (
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(shape_one * split_slice_size * query_tokens * shape_four)
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/ 1024
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* block_multiply
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) > 4000:
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while (split_slice_size * slice_block_size) > 4:
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split_slice_size = split_slice_size // 2
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if split_slice_size <= 1:
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split_slice_size = 1
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@@ -114,20 +111,14 @@ def scaled_dot_product_attention(
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else:
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do_split = False
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split_block_size = (
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(shape_one * split_slice_size * query_tokens * shape_four)
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/ 1024
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* block_multiply
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) # MB
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split_2_slice_size = query_tokens
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if split_block_size >= 4000:
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if split_slice_size * slice_block_size > 4:
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slice_block_size2 = (
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shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply
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)
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do_split_2 = True
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# Find something divisible with the batch_size_attention
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while (
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(shape_one * split_slice_size * split_2_slice_size * shape_four)
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/ 1024
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* block_multiply
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) > 4000:
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while (split_2_slice_size * slice_block_size2) > 4:
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split_2_slice_size = split_2_slice_size // 2
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if split_2_slice_size <= 1:
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split_2_slice_size = 1
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@@ -146,31 +137,63 @@ def scaled_dot_product_attention(
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): # pylint: disable=invalid-name
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start_idx_2 = i2 * split_2_slice_size
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end_idx_2 = (i2 + 1) * split_2_slice_size
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if no_shape_one:
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hidden_states[
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start_idx:end_idx, start_idx_2:end_idx_2
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] = original_scaled_dot_product_attention(
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query[start_idx:end_idx, start_idx_2:end_idx_2],
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key[start_idx:end_idx, start_idx_2:end_idx_2],
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value[start_idx:end_idx, start_idx_2:end_idx_2],
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attn_mask=attn_mask[
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start_idx:end_idx, start_idx_2:end_idx_2
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]
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if attn_mask is not None
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else attn_mask,
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dropout_p=dropout_p,
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is_causal=is_causal,
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)
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else:
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hidden_states[
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:, start_idx:end_idx, start_idx_2:end_idx_2
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] = original_scaled_dot_product_attention(
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query[:, start_idx:end_idx, start_idx_2:end_idx_2],
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key[:, start_idx:end_idx, start_idx_2:end_idx_2],
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value[:, start_idx:end_idx, start_idx_2:end_idx_2],
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attn_mask=attn_mask[
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:, start_idx:end_idx, start_idx_2:end_idx_2
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]
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if attn_mask is not None
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else attn_mask,
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dropout_p=dropout_p,
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is_causal=is_causal,
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)
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else:
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if no_shape_one:
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hidden_states[
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:, start_idx:end_idx, start_idx_2:end_idx_2
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start_idx:end_idx
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] = original_scaled_dot_product_attention(
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query[:, start_idx:end_idx, start_idx_2:end_idx_2],
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key[:, start_idx:end_idx, start_idx_2:end_idx_2],
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value[:, start_idx:end_idx, start_idx_2:end_idx_2],
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attn_mask=attn_mask[:, start_idx:end_idx, start_idx_2:end_idx_2]
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query[start_idx:end_idx],
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key[start_idx:end_idx],
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value[start_idx:end_idx],
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attn_mask=attn_mask[start_idx:end_idx]
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if attn_mask is not None
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else attn_mask,
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dropout_p=dropout_p,
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is_causal=is_causal,
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)
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else:
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hidden_states[
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:, start_idx:end_idx
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] = original_scaled_dot_product_attention(
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query[:, start_idx:end_idx],
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key[:, start_idx:end_idx],
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value[:, start_idx:end_idx],
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attn_mask=attn_mask[:, start_idx:end_idx]
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if attn_mask is not None
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else attn_mask,
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dropout_p=dropout_p,
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is_causal=is_causal,
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)
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else:
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hidden_states[
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:, start_idx:end_idx
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] = original_scaled_dot_product_attention(
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query[:, start_idx:end_idx],
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key[:, start_idx:end_idx],
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value[:, start_idx:end_idx],
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attn_mask=attn_mask[:, start_idx:end_idx]
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if attn_mask is not None
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else attn_mask,
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dropout_p=dropout_p,
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is_causal=is_causal,
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)
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else:
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return original_scaled_dot_product_attention(
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query,
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