425 lines
15 KiB
Python
425 lines
15 KiB
Python
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import triton
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MAX_FUSED_SIZE : int = 65536
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next_power_of_2 = triton.next_power_of_2
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import functools
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# torch.cuda.amp.custom_fwd is deprecated >= 2.4
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import torch
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from packaging.version import Version
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if Version(torch.__version__) < Version("2.4.0"):
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torch_amp_custom_fwd = torch.cuda.amp.custom_fwd
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torch_amp_custom_bwd = torch.cuda.amp.custom_bwd
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else:
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torch_amp_custom_fwd = torch.amp.custom_fwd(device_type = "cuda")
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torch_amp_custom_bwd = torch.amp.custom_bwd(device_type = "cuda")
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pass
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# tl.math.tanh now is libdevice.tanh
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from packaging.version import Version
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import triton
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import triton.language as tl
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if Version(triton.__version__) >= Version("3.0.0"):
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from triton.language.extra import libdevice
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triton_tanh = libdevice.tanh
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triton_cast = tl.cast
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else:
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triton_tanh = tl.math.tanh
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# No casting in old Triton versions
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@triton.jit
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def triton_cast(x, dtype):
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return x.to(dtype)
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pass
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pass
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def calculate_settings(n : int) -> (int, int,):
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BLOCK_SIZE : int = next_power_of_2(n)
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if BLOCK_SIZE > MAX_FUSED_SIZE:
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raise RuntimeError(f"Cannot launch Triton kernel since n = {n} exceeds "\
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f"the maximum CUDA blocksize = {MAX_FUSED_SIZE}.")
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num_warps : int = 4
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if BLOCK_SIZE >= 32768: num_warps = 32
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elif BLOCK_SIZE >= 8192: num_warps = 16
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elif BLOCK_SIZE >= 2048: num_warps = 8
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return BLOCK_SIZE, num_warps
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pass
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import bitsandbytes as bnb
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# https://github.com/bitsandbytes-foundation/bitsandbytes/pull/1330/files
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HAS_CUDA_STREAM = Version(bnb.__version__) > Version("0.43.3")
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global CUDA_STREAM
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CUDA_STREAM = None
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get_ptr = bnb.functional.get_ptr
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import ctypes
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cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
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cdequantize_blockwise_fp16_nf4 = bnb.functional.lib.cdequantize_blockwise_fp16_nf4
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cdequantize_blockwise_bf16_nf4 = bnb.functional.lib.cdequantize_blockwise_bf16_nf4
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cgemm_4bit_inference_naive_fp16 = bnb.functional.lib.cgemm_4bit_inference_naive_fp16
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cgemm_4bit_inference_naive_bf16 = bnb.functional.lib.cgemm_4bit_inference_naive_bf16
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def QUANT_STATE(W):
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return getattr(W, "quant_state", None)
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pass
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def get_lora_parameters(proj):
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# For DPO or disabled adapters
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base_layer = (proj.base_layer if hasattr(proj, "base_layer") else proj)
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W = base_layer.weight
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if not hasattr(proj, "disable_adapters") or proj.disable_adapters or proj.merged:
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return W, QUANT_STATE(W), None, None, None
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pass
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active_adapter = proj.active_adapters[0] if \
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hasattr(proj, "active_adapters") else proj.active_adapter
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A = proj.lora_A [active_adapter].weight
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B = proj.lora_B [active_adapter].weight
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s = proj.scaling[active_adapter]
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return W, QUANT_STATE(W), A, B, s
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pass
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@functools.cache
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def get_lora_parameters_bias(proj):
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# For DPO or disabled adapters
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base_layer = getattr(proj, "base_layer", proj) # (proj.base_layer if hasattr(proj, "base_layer") else proj)
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W = base_layer.weight
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bias = base_layer.bias
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# if not hasattr(proj, "disable_adapters") or proj.disable_adapters or proj.merged:
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if getattr(proj, "disable_adapters", True) or proj.merged:
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return W, QUANT_STATE(W), None, None, None, bias
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pass
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active_adapter = proj.active_adapters[0] if \
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getattr(proj, "active_adapters", ) else proj.active_adapter
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A = proj.lora_A [active_adapter].weight
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B = proj.lora_B [active_adapter].weight
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s = proj.scaling[active_adapter]
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return W, QUANT_STATE(W), A, B, s, bias
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pass
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if HAS_CUDA_STREAM:
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def fast_dequantize(W, quant_state = None, out = None):
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if quant_state is None: return W
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if type(quant_state) is not list:
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# New quant_state as a class
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# https://github.com/TimDettmers/bitsandbytes/pull/763/files
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absmax = quant_state.absmax
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shape = quant_state.shape
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dtype = quant_state.dtype
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blocksize = quant_state.blocksize
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offset = quant_state.offset
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state2 = quant_state.state2
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absmax2 = state2.absmax
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code2 = state2.code
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blocksize2 = state2.blocksize
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else:
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# Old quant_state as a list of lists
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absmax, shape, dtype, blocksize, compressed_stats, _, _ = quant_state
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offset, state2 = compressed_stats
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absmax2, code2, blocksize2, _, _, _, _ = state2
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pass
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global CUDA_STREAM
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if CUDA_STREAM is None: CUDA_STREAM = torch.cuda.current_stream("cuda:0")
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# Create weight matrix
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if out is None:
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out = torch.empty(shape, dtype = dtype, device = "cuda:0")
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else:
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assert(out.shape == shape)
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assert(out.dtype == dtype)
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# NF4 dequantization of statistics
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n_elements_absmax = absmax.numel()
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out_absmax = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0")
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# Do dequantization
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ptr_out_absmax = get_ptr(out_absmax)
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cdequantize_blockwise_fp32(
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get_ptr(code2), get_ptr(absmax), get_ptr(absmax2), ptr_out_absmax,
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ctypes.c_int(blocksize2), ctypes.c_int(n_elements_absmax), CUDA_STREAM,
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)
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out_absmax += offset
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fx = cdequantize_blockwise_fp16_nf4 if dtype == torch.float16 else \
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cdequantize_blockwise_bf16_nf4
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fx(get_ptr(None), get_ptr(W), ptr_out_absmax, get_ptr(out),
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ctypes.c_int(blocksize), ctypes.c_int(out.numel()), CUDA_STREAM,)
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# Careful returning transposed data
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is_transposed = (True if W.shape[0] == 1 else False)
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return out.t() if is_transposed else out
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pass
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else:
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def fast_dequantize(W, quant_state = None, out = None):
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if quant_state is None: return W
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if type(quant_state) is not list:
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# New quant_state as a class
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# https://github.com/TimDettmers/bitsandbytes/pull/763/files
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absmax = quant_state.absmax
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shape = quant_state.shape
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dtype = quant_state.dtype
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blocksize = quant_state.blocksize
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offset = quant_state.offset
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state2 = quant_state.state2
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absmax2 = state2.absmax
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code2 = state2.code
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blocksize2 = state2.blocksize
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else:
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# Old quant_state as a list of lists
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absmax, shape, dtype, blocksize, compressed_stats, _, _ = quant_state
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offset, state2 = compressed_stats
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absmax2, code2, blocksize2, _, _, _, _ = state2
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pass
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# Create weight matrix
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if out is None:
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out = torch.empty(shape, dtype = dtype, device = "cuda:0")
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else:
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assert(out.shape == shape)
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assert(out.dtype == dtype)
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# NF4 dequantization of statistics
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n_elements_absmax = absmax.numel()
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out_absmax = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0")
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# Do dequantization
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ptr_out_absmax = get_ptr(out_absmax)
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cdequantize_blockwise_fp32(
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get_ptr(code2), get_ptr(absmax), get_ptr(absmax2), ptr_out_absmax,
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ctypes.c_int(blocksize2), ctypes.c_int(n_elements_absmax),
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)
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out_absmax += offset
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fx = cdequantize_blockwise_fp16_nf4 if dtype == torch.float16 else \
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cdequantize_blockwise_bf16_nf4
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fx(get_ptr(None), get_ptr(W), ptr_out_absmax, get_ptr(out),
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ctypes.c_int(blocksize), ctypes.c_int(out.numel()),)
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# Careful returning transposed data
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is_transposed = (True if W.shape[0] == 1 else False)
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return out.t() if is_transposed else out
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pass
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pass
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if HAS_CUDA_STREAM:
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def fast_gemv(X, W, quant_state, out = None):
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if quant_state is None: return torch.matmul(X, W, out = out)
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# For fast X @ W where seq_len == 1
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# From https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L1469
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_, q_len, hd = X.shape
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# assert(q_len == 1)
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if type(quant_state) is not list:
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# https://github.com/TimDettmers/bitsandbytes/pull/763/files
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absmax = quant_state.absmax
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shape = quant_state.shape
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dtype = quant_state.dtype
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blocksize = quant_state.blocksize
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stats = quant_state.code
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offset = quant_state.offset
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state2 = quant_state.state2
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absmax2 = state2.absmax
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code2 = state2.code
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blocksize2 = state2.blocksize
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else:
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absmax, shape, dtype, blocksize, compressed_stats, quant_type, stats = quant_state
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offset, state2 = compressed_stats
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absmax2, code2, blocksize2, _, _, _, _ = state2
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pass
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global CUDA_STREAM
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if CUDA_STREAM is None: CUDA_STREAM = torch.cuda.current_stream("cuda:0")
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# assert(dtype == X.dtype)
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bout = shape[0]
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if out is None:
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out = torch.empty((1, 1, bout,), dtype = dtype, device = "cuda:0")
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# else:
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# assert(out.shape == (1, 1, bout,))
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# pass
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n = 1
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m = shape[0]
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k = shape[1]
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lda = shape[0]
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ldc = shape[0]
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ldb = (hd+1)//2
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m = ctypes.c_int32(m)
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n = ctypes.c_int32(n)
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k = ctypes.c_int32(k)
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lda = ctypes.c_int32(lda)
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ldb = ctypes.c_int32(ldb)
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ldc = ctypes.c_int32(ldc)
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df = torch.empty(absmax.shape, dtype = torch.float32, device = "cuda:0")
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cdequantize_blockwise_fp32(
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get_ptr(code2), get_ptr(absmax), get_ptr(absmax2), get_ptr(df),
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ctypes.c_int(blocksize2), ctypes.c_int(df.numel()), CUDA_STREAM,
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)
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df += offset
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absmax = df
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fx = cgemm_4bit_inference_naive_fp16 if dtype == torch.float16 else \
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cgemm_4bit_inference_naive_bf16
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blocksize = ctypes.c_int32(blocksize)
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fx(m, n, k, get_ptr(X), get_ptr(W), get_ptr(absmax), get_ptr(stats), get_ptr(out),
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lda, ldb, ldc, blocksize, CUDA_STREAM,)
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return out
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pass
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else:
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def fast_gemv(X, W, quant_state, out = None):
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if quant_state is None: return torch.matmul(X, W, out = out)
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# For fast X @ W where seq_len == 1
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# From https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L1469
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_, q_len, hd = X.shape
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# assert(q_len == 1)
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if type(quant_state) is not list:
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# https://github.com/TimDettmers/bitsandbytes/pull/763/files
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absmax = quant_state.absmax
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shape = quant_state.shape
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dtype = quant_state.dtype
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blocksize = quant_state.blocksize
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stats = quant_state.code
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offset = quant_state.offset
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state2 = quant_state.state2
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absmax2 = state2.absmax
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code2 = state2.code
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blocksize2 = state2.blocksize
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else:
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absmax, shape, dtype, blocksize, compressed_stats, quant_type, stats = quant_state
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offset, state2 = compressed_stats
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absmax2, code2, blocksize2, _, _, _, _ = state2
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pass
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# assert(dtype == X.dtype)
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bout = shape[0]
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if out is None:
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out = torch.empty((1, 1, bout,), dtype = dtype, device = "cuda:0")
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# else:
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# assert(out.shape == (1, 1, bout,))
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# pass
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n = 1
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m = shape[0]
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k = shape[1]
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lda = shape[0]
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ldc = shape[0]
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ldb = (hd+1)//2
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m = ctypes.c_int32(m)
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n = ctypes.c_int32(n)
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k = ctypes.c_int32(k)
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lda = ctypes.c_int32(lda)
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ldb = ctypes.c_int32(ldb)
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ldc = ctypes.c_int32(ldc)
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df = torch.empty(absmax.shape, dtype = torch.float32, device = "cuda:0")
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cdequantize_blockwise_fp32(
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get_ptr(code2), get_ptr(absmax), get_ptr(absmax2), get_ptr(df),
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ctypes.c_int(blocksize2), ctypes.c_int(df.numel()),
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)
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df += offset
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absmax = df
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fx = cgemm_4bit_inference_naive_fp16 if dtype == torch.float16 else \
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cgemm_4bit_inference_naive_bf16
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blocksize = ctypes.c_int32(blocksize)
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fx(m, n, k, get_ptr(X), get_ptr(W), get_ptr(absmax), get_ptr(stats), get_ptr(out),
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lda, ldb, ldc, blocksize,)
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return out
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pass
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pass
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def fast_linear_forward(proj, X, temp_lora = None, out = None):
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W, W_quant, lora_A, lora_B, lora_S, bias = get_lora_parameters_bias(proj)
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bsz, q_len, in_dim = X.shape
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if q_len != 1: return matmul_lora(X, W, W_quant, lora_A, lora_B, lora_S)
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if W_quant is None:
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out = torch.matmul(X, W.t(), out = out)
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elif bsz == 1 and q_len == 1:
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out = fast_gemv(X, W, W_quant, out = out)
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else:
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W = fast_dequantize(W.t(), W_quant)
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out = torch.matmul(X, W, out = out)
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pass
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# Add in LoRA weights
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if lora_A is not None:
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out_dim = out.shape[2]
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dtype = X.dtype
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if not hasattr(lora_A, "_fast_lora"):
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lora_A._fast_lora = lora_A.to(dtype)
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lora_B._fast_lora = lora_B.to(dtype)
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pass
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if bsz == 1:
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out = out.view(out_dim)
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temp_lora = torch.mv(lora_A._fast_lora, X.ravel(), out = temp_lora)
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out.addmv_(lora_B._fast_lora, temp_lora, alpha = lora_S)
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else:
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out = out.view(bsz, out_dim)
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temp_lora = torch.mm(X.view(bsz, in_dim), lora_A._fast_lora.t(), out = temp_lora)
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out.addmm_(temp_lora, lora_B._fast_lora.t(), alpha = lora_S)
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pass
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out = out.view(bsz, 1, out_dim)
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pass
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if bias is not None: out += bias
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return out
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pass
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def matmul_lora(X, W, W_quant, A, B, s, out = None):
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dtype = X.dtype
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W = fast_dequantize(W.t(), W_quant)
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if X.dim() == 3:
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batch, seq_len, d = X.shape
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X = X.view(-1, X.shape[-1])
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reshape = True
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else:
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reshape = False
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pass
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out = torch.matmul(X, W, out = out)
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if W_quant is not None: del W
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if A is not None:
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# LoRA is enabled
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A, B = A.t(), B.t()
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out += (X @ A.to(dtype)) @ (s * B.to(dtype))
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pass
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return out.view(batch, seq_len, -1) if reshape else out
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pass
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