221 lines
7.3 KiB
Python
221 lines
7.3 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 = 65536
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next_power_of_2 = triton.next_power_of_2
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def calculate_settings(n):
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BLOCK_SIZE = 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 = 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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get_ptr = bnb.functional.get_ptr
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import ctypes
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import torch
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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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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")
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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")
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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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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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bsz, 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((bsz, 1, bout,), dtype = dtype, device = "cuda")
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else:
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assert(out.shape == (bsz, 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 = (X.shape[-1]+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")
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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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ptr_W = get_ptr(W)
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ptr_absmax = get_ptr(absmax)
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ptr_stats = get_ptr(stats)
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blocksize = ctypes.c_int32(blocksize)
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for row in range(bsz):
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fx(m, n, k, get_ptr(X[row]), ptr_W, ptr_absmax, ptr_stats, get_ptr(out[row]),
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lda, ldb, ldc, blocksize)
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pass
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return out
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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 = get_lora_parameters(proj)
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bsz, _, in_dim = X.shape
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if W_quant is None:
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out = torch.matmul(X, W.t())
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elif bsz <= 4:
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# Only batches of 4 are faster with Gemv
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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 bsz == 1:
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out = out.view(out_dim)
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temp_lora = torch.mv(lora_A.to(dtype), X.ravel(), out = temp_lora)
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out.addmv_(lora_B.to(dtype), 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.to(dtype).t(), out = temp_lora)
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out.addmm_(temp_lora, lora_B.to(dtype).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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return out
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pass
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