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import Optional +import warnings +from warnings import warn + +import torch + +import bitsandbytes.functional as F + +# The inverse transformation for the colTuring and colAmpere format were contributed by Alex Borzunov: +# https://github.com/bigscience-workshop/petals/blob/main/src/petals/utils/linear8bitlt_patch.py + + +""" + This class pools outlier dimensions across layers. + This is particularly important for small models where outlier features + are less systematic and occur with low frequency. +""" + + +class GlobalOutlierPooler: + _instance = None + + def __init__(self): + raise RuntimeError("Call get_instance() instead") + + def initialize(self): + self.outliers = set() + self.model_dim = None + + @classmethod + def get_instance(cls): + if cls._instance is None: + cls._instance = cls.__new__(cls) + cls._instance.initialize() + return cls._instance + + def add_outliers(self, outlier_idx, feature_dim): + if self.model_dim is None: + self.model_dim = feature_dim + if feature_dim != self.model_dim: + return # we do not encode outliers for the 2nd FFN layer + + self.outliers.update(outlier_idx.tolist()) + + def get_current_outlier_idx(self): + return torch.Tensor(list(self.outliers)).to(torch.int64) + + +_is_compiling = torch.compiler.is_compiling + + +@dataclass +class MatmulLtState: + _tile_indices: Optional[torch.Tensor] = None # TODO: remove + + force_no_igemmlt: bool = False + + CB: Optional[torch.Tensor] = None + CxB: Optional[torch.Tensor] = None # TODO: Deprecate/remove + SB: Optional[torch.Tensor] = None + SCB: Optional[torch.Tensor] = None + + CxBt: Optional[torch.Tensor] = None # TODO: Deprecate/remove + SBt: Optional[torch.Tensor] = None + CBt: Optional[torch.Tensor] = None + + subB: Optional[torch.Tensor] = None + + outlier_pool: Optional[GlobalOutlierPooler] = None + has_accumulated_gradients = False + threshold = 0.0 + idx: Optional[torch.Tensor] = None + is_training = True + has_fp16_weights = True + use_pool = False + formatB = "row" # TODO: Deprecate/remove + + def reset_grads(self): + self.CB = None + self.CxB = None + self.SB = None + self.SCB = None + + self.CxBt = None + self.SBt = None + self.CBt = None + + @property + def tile_indices(self): + raise ValueError("tile_indices is no longer supported.") + + +class MatMul8bitLt(torch.autograd.Function): + @staticmethod + def forward( + ctx: torch.autograd.function.FunctionCtx, + A: torch.Tensor, + B: torch.Tensor, + out: Optional[torch.Tensor] = None, + bias: Optional[torch.Tensor] = None, + state: Optional[MatmulLtState] = None, + ): + state = state or MatmulLtState() + + # default of pytorch behavior if inputs are empty + ctx.is_empty = False + if prod(A.shape) == 0: + ctx.is_empty = True + ctx.A = A + ctx.B = B + ctx.bias = bias + if A.shape[-1] == B.shape[0]: + return torch.empty(A.shape[:-1] + B.shape[1:], dtype=A.dtype, device=A.device) + else: + return torch.empty(A.shape[:-1] + B.shape[:1], dtype=A.dtype, device=A.device) + + input_shape = A.shape + + # Cast A to fp16 + if A.dtype != torch.float16 and not _is_compiling(): + warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization") + + if len(A.shape) == 3: + A = A.reshape(-1, A.shape[-1]) + + # 1. Quantize A. Note that as a side-effect, outliers are suppressed in CA/CAt. + if ctx.needs_input_grad[1]: + # Slower path + CA, CAt, SCA, SCAt, outlier_cols = F.int8_double_quant(A.to(torch.float16), threshold=state.threshold) + else: + # Fast path + CA, SCA, outlier_cols = F.int8_vectorwise_quant(A.to(torch.float16), threshold=state.threshold) + CAt = SCAt = None + + has_grad = False + + if state.has_fp16_weights or state.CB is None: + has_grad = getattr(B, "grad", None) is not None + is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1) + if is_transposed: + B = B.contiguous() + + if (state.is_training and not has_grad) or state.CB is None or state.SCB is None: + state.reset_grads() + + # 2. Quantize B + state.CB, state.SCB, _ = F.int8_vectorwise_quant(B.to(torch.float16)) + + # Handle sparse decomposition + if state.threshold > 0.0: + state.idx = outlier_cols + + # Mixed Int8 Matmul + Dequant + Bias + output, subA = torch.ops.bitsandbytes.int8_mixed_scaled_mm( + A, + CA, + state.CB, + SCA, + state.SCB, + outlier_cols, + bias, + ) + + else: + # Int8 Matmul + Dequant + Bias + output = torch.ops.bitsandbytes.int8_scaled_mm.default( + CA, state.CB, SCA, state.SCB, bias=bias, dtype=A.dtype + ) + subA = None + + # 5. Save state + ctx.state = state + + ctx.grad_shape = input_shape + ctx.dtype_A = A.dtype + ctx.dtype_bias = None if bias is None else bias.dtype + + if any(ctx.needs_input_grad[:2]): + ctx.tensors = (CAt, subA, A) + ctx.tensor_states = (SCAt, state.idx) + else: + ctx.tensors = [None, None, None] + ctx.tensor_states = (None, None) + ctx.save_for_backward(None, None) + + output_shape = (*input_shape[:-1], state.CB.shape[0]) + + if len(input_shape) == 3: + return output.reshape(output_shape) + + return output + + @staticmethod + def backward(ctx: torch.autograd.function.FunctionCtx, grad_output: torch.Tensor): + if ctx.is_empty: + bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias) + return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None + + req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad + CAt, subA, _A = ctx.tensors + SCAt, idx = ctx.tensor_states + state: MatmulLtState = ctx.state + grad_A = grad_B = grad_bias = None + + if req_gradBias: + # compute grad_bias first before changing grad_output dtype + grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias) + + # Cast grad_output to fp16 + if len(grad_output.shape) == 3: + grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous() + + if req_gradB: + Cgrad, _, _, SCgradt, _ = F.int8_double_quant(grad_output.to(torch.float16)) + + grad_B = torch.ops.bitsandbytes.int8_scaled_mm.default( + Cgrad.t().contiguous(), + CAt.t(), + SCgradt, + SCAt, + dtype=torch.float16, + ) + + if state.threshold > 0.0 and subA is not None and subA.numel() > 0: + grad_B[:, idx] += torch.matmul(grad_output.t(), subA) + + if req_gradA: + if state.CB is not None: + CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0)) + grad_A = torch.matmul(grad_output.to(ctx.dtype_A), CB).view(ctx.grad_shape) + else: + raise Exception("State must contain CB matrix for backward") + + return grad_A, grad_B, None, grad_bias, None + + +class MatMul8bitFp(torch.autograd.Function): + # For Intel CPU and XPU MatMul8bitFp is much faster (~3x) than MatMul8bitLt in finetune. + # Because the MatMul8bitLt has more mechanisms in computing grad. + # We don't have fast kernel for quant/dequant 8bit in CPU/XPU, so it's very slow. + # We'd like to use dequant + matmul to run finetune with good performance. + + @staticmethod + def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState): + if state.has_fp16_weights or state.CB is None: + has_grad = getattr(B, "grad", None) is not None + is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1) + if is_transposed: + B = B.contiguous() + + if (state.is_training and not has_grad) or state.CB is None or state.SCB is None: + state.reset_grads() + state.CB, state.SCB, _ = F.int8_vectorwise_quant(B.to(torch.float16)) + B = state.CB + + CB = state.CB.data.to(A.dtype).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0)) + output = torch.nn.functional.linear(A, CB, bias) + ctx.state = state + ctx.dtype_A = A.dtype + ctx.grad_shape = A.shape + ctx.A = A + ctx.dtype_bias = None if bias is None else bias.dtype + return output + + @staticmethod + def backward(ctx, grad_output): + req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad + A = ctx.A + state = ctx.state + grad_A = grad_B = grad_bias = None + if req_gradBias: + # compute grad_bias first before changing grad_output dtype + grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias) + + # Cast grad_output to fp16 + if len(grad_output.shape) == 3: + grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous() + + if req_gradB: + grad_B = torch.matmul(A.t(), grad_output).t() + + if req_gradA: + if state.CB is not None: + CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0)) + grad_A = torch.matmul(grad_output.to(ctx.dtype_A), CB).view(ctx.grad_shape) + else: + raise Exception("State must contain CB matrix for backward") + + return grad_A, grad_B, None, grad_bias, None + + +class MatMul4Bit(torch.autograd.Function): + # forward is the same, but we added the fallback for pre-turing GPUs + # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None") + + @staticmethod + def forward(ctx, A, B, out=None, bias=None, quant_state: Optional[F.QuantState] = None): + # default of pytorch behavior if inputs are empty + ctx.is_empty = False + if prod(A.shape) == 0: + ctx.is_empty = True + ctx.A = A + ctx.B = B + ctx.bias = bias + B_shape = quant_state.shape + if A.shape[-1] == B_shape[0]: + return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device) + else: + return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device) + + # 1. Dequantize + # 2. MatmulnN + output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias) + + # 3. Save state + ctx.state = quant_state + ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype + + if any(ctx.needs_input_grad[:2]): + ctx.tensors = (None, B) + else: + ctx.tensors = (None, None) + + return output + + @staticmethod + def backward(ctx, grad_output): + if ctx.is_empty: + bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias) + return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None + + req_gradA, _, _, req_gradBias, _ = ctx.needs_input_grad + _, B = ctx.tensors + + grad_A, grad_B, grad_bias = None, None, None + + if req_gradBias: + # compute grad_bias first before changing grad_output dtype + grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias) + + # not supported by PyTorch. TODO: create work-around + # if req_gradB: grad_B = torch.matmul(grad_output.t(), A) + if req_gradA: + grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t()) + + return grad_A, grad_B, None, grad_bias, None + + +def matmul( + A: torch.Tensor, + B: torch.Tensor, + out: Optional[torch.Tensor] = None, + state: Optional[MatmulLtState] = None, + threshold=0.0, + bias: Optional[torch.Tensor] = None, +): + state = state or MatmulLtState() + if threshold > 0.0: + state.threshold = threshold + # MatMul8bitLt is slower because no fast kernel for quant/dequant 8bit in CPU/XPU + if state.is_training: + if A.device.type in ("cpu", "xpu"): + return MatMul8bitFp.apply(A, B, out, bias, state) + return MatMul8bitLt.apply(A, B, out, bias, state) + + +def matmul_4bit( + A: torch.Tensor, + B: torch.Tensor, + quant_state: F.QuantState, + out: Optional[torch.Tensor] = None, + bias: Optional[torch.Tensor] = None, +): + assert quant_state is not None + # Change dtype to input dtype on CPU + if A.device.type == "cpu": + quant_state.dtype = A.dtype + + if getattr(quant_state, "packing_format_for_cpu", False): + out = F.gemv_4bit(A, B, out, state=quant_state) + if bias is not None: + out += bias + return out + else: + return MatMul4Bit.apply(A, B, out, bias, quant_state) + + if A.numel() == A.shape[-1] and A.requires_grad == False and A.device.type != "hpu": + if A.shape[-1] % quant_state.blocksize != 0: + warn( + f"Some matrices hidden dimension is not a multiple of {quant_state.blocksize} and efficient inference kernels are not supported for these (slow). Matrix input size found: {A.shape}", + ) + return MatMul4Bit.apply(A, B, out, bias, quant_state) + else: + out = F.gemv_4bit(A, B.t(), out, state=quant_state) + if bias is not None: + out += bias + return out + else: + return MatMul4Bit.apply(A, B, out, bias, quant_state) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c84d5194d50d6a341d9a0977184d2b92d2d32283 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__pycache__/utils.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c77695abfefadd049dfd00a383b15f4c2bed4748 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/__pycache__/utils.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a8abb59865e01e28143b28ea271c560b6a6ae33c Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__pycache__/ops.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__pycache__/ops.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..313b798f2843108365fecd0ad6c79ff1b63397ce Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/__pycache__/ops.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/ops.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..107f26c84d3dd94afd4df513b8056603c365d9df --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cpu/ops.py @@ -0,0 +1,301 @@ +from collections.abc import Sequence +import ctypes as ct +import logging +from math import prod + +import torch + +from bitsandbytes.functional import get_ptr, has_avx512bf16 + +from ..._ops import register_kernel +from ...cextension import ErrorHandlerMockBNBNativeLibrary, lib + +logger = logging.getLogger(__name__) + +_has_avx512 = torch.backends.cpu.get_cpu_capability() == "AVX512" + +# torch._int_mm for s8@s8->s32 is supported on CPU from torch 2.4+. +# However, we can overflow if we use this without AVX512_VNNI support. +# This is fixed in torch 2.6+, so we set this as the minimum to be safe. +# For more information: https://github.com/pytorch/pytorch/pull/136942 +# TODO(matthewdouglas): aarch64? +if torch.__version__ >= (2, 6): + + @register_kernel("bitsandbytes::int8_linear_matmul", "cpu") + def _(A: torch.Tensor, B: torch.Tensor): + return torch._int_mm( + A.reshape(-1, A.shape[-1]), + B.t(), + ).reshape(*A.shape[:-1], B.shape[0]) + + +if not isinstance(lib, ErrorHandlerMockBNBNativeLibrary): + + @register_kernel("bitsandbytes::quantize_blockwise", "cpu") + def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]: + torch._check_is_size(blocksize) + + n = A.numel() + + # Only FP32 has c++ kernrl + if A.dtype == torch.float32: + blocks = -(n // -blocksize) + + absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32) + out = torch.empty_like(A, dtype=torch.uint8) + + lib.cquantize_blockwise_cpu_fp32( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(n), + ) + else: + rem = n % blocksize + has_rem = rem > 0 + blocks = n // blocksize + has_rem + absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32) + A_reshaped = A.reshape(n) + A_com = A_reshaped[: n - rem] + A_com_reshaped = A_com.reshape(n // blocksize, blocksize) + absmax[: blocks - has_rem] = torch.abs(A_com_reshaped).max(dim=-1)[0] + scaled_A = torch.clamp(A_com_reshaped * (1 / absmax[: blocks - has_rem].view(-1, 1)), -1, 1) + scaled_A = scaled_A.reshape(-1) + if has_rem: + absmax[-1] = torch.abs(A_reshaped[n - rem :]).max() + scaled_A_rem = torch.clamp(A_reshaped[n - rem :] * (1 / absmax[-1]), -1, 1) + scaled_A = torch.cat([scaled_A, scaled_A_rem], dim=0) + + diff = torch.abs(scaled_A.unsqueeze(-1) - code.to(scaled_A.device)) + out = torch.argmin(diff, dim=-1).to(torch.uint8).to(scaled_A.device).reshape(A.shape) + + return out, absmax + + @register_kernel("bitsandbytes::dequantize_blockwise", "cpu") + def _( + A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype + ) -> torch.Tensor: + torch._check_is_size(blocksize) + torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}") + + out = torch.empty_like(A, dtype=dtype) + if dtype == torch.float32: + lib.cdequantize_blockwise_cpu_fp32( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(A.numel()), + ) + elif dtype == torch.bfloat16: + lib.cdequantize_blockwise_cpu_bf16( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(A.numel()), + ) + elif dtype == torch.float16: + lib.cdequantize_blockwise_cpu_fp16( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(A.numel()), + ) + else: + out = code[A.reshape(-1).int()] + blocks = out.shape[-1] // blocksize + res = out.shape[-1] % blocksize + if res != 0: + out = torch.nn.functional.pad(out, (0, blocksize - res), mode="constant", value=0) + out = (out.view(-1, blocksize) * absmax.view(-1, 1)).to(dtype).reshape(-1) + out = out[: blocks * blocksize + res] + out = out.reshape(A.shape) + + return out + + @register_kernel("bitsandbytes::dequantize_4bit", "cpu") + def _( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, + ) -> torch.Tensor: + torch._check_is_size(blocksize) + torch._check(quant_type in ("nf4", "fp4"), lambda: f"quant_type must be nf4 or fp4, got {quant_type}") + torch._check( + dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit dequantization only supports 16/32-bit floats, but got {dtype}", + ) + + # Fallback as AVX512 implementation has accuracy issues with fp16/fp32 and blocksize >= 2048 + # Note: this is not a common use case. + avx512_fallback = _has_avx512 and blocksize >= 2048 and dtype != torch.bfloat16 + + # Odd shape is not supported by this kernel; fallback to generic implementation + shape_fallback = shape[-1] % 2 != 0 + + if avx512_fallback or shape_fallback: + from ..default.ops import _dequantize_4bit_impl + + return _dequantize_4bit_impl(A, absmax, blocksize, quant_type, shape, dtype) + + # Enable non uint8 dtype + if A.dtype != torch.uint8: + A = A.view(torch.uint8) + + # TODO: support half precision absmax + if absmax.dtype != torch.float32: + absmax = absmax.float() + + if len(shape) == 1: + shape = (1, shape[0]) + + m = prod(shape[:-1]) + n = shape[-1] + + A = A.reshape(m, n // 2) + out = torch.empty(shape, dtype=dtype, device=A.device) + + if quant_type == "fp4": + if dtype == torch.float32: + lib.cdequantize_blockwise_cpu_fp4_fp32( + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(m), + ct.c_longlong(n), + ) + elif dtype == torch.bfloat16: + lib.cdequantize_blockwise_cpu_fp4_bf16( + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(m), + ct.c_longlong(n), + ) + elif dtype == torch.float16: + lib.cdequantize_blockwise_cpu_fp4_fp16( + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(m), + ct.c_longlong(n), + ) + elif quant_type == "nf4": + if dtype == torch.float32: + lib.cdequantize_blockwise_cpu_nf4_fp32( + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(m), + ct.c_longlong(n), + ) + elif dtype == torch.bfloat16: + lib.cdequantize_blockwise_cpu_nf4_bf16( + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(m), + ct.c_longlong(n), + ) + elif dtype == torch.float16: + lib.cdequantize_blockwise_cpu_nf4_fp16( + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_longlong(blocksize), + ct.c_longlong(m), + ct.c_longlong(n), + ) + else: + raise ValueError + + return out + + if has_avx512bf16(): + gemm_4bit_forward_kernel = None + try: + from kernels import get_kernel + + gemm_4bit_forward_kernel = get_kernel("kernels-community/quantization_bitsandbytes").gemm_4bit_forward + except Exception as exc: # pragma: no cover - best effort fallback + gemm_4bit_forward_kernel = None + logger.warning( + "Failed to load CPU gemm_4bit_forward from kernels-community: %s. Please make sure you already `pip install kernels` and the kernels >= 0.11.1", + exc, + ) + + @register_kernel("bitsandbytes::gemv_4bit", "cpu") + def _( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + ) -> torch.Tensor: + assert B.dtype == torch.uint8, "Only support uint8 qweight" + dtype = A.dtype + quant_type = "fp4" if code[1] > 0 else "nf4" + # cpu fused op only support bf16 for now. + if dtype != torch.bfloat16: + A = A.to(torch.bfloat16) + + final_out_shape = (*A.shape[:-1], shapeB[0]) + A = A.reshape(-1, A.shape[-1]) + out_shape = (*A.shape[:-1], shapeB[0]) + if gemm_4bit_forward_kernel is not None: + quant_type_num = 1 if quant_type == "fp4" else 0 + out = gemm_4bit_forward_kernel(A, B, absmax, blocksize, quant_type_num) + else: + out = torch.empty(out_shape, dtype=A.dtype, device=A.device) + M = A.shape[0] + N = shapeB[0] + K = A.shape[1] + x_strideM = A.stride(0) + out_strideM = out.stride(0) + if quant_type == "fp4": + lib.gemv_4bit_inference_cpu_fp4_bf16( + ct.c_int64(M), + ct.c_int64(N), + ct.c_int64(K), + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(out), + ct.c_int64(blocksize), + ct.c_int64(x_strideM), + ct.c_int64(out_strideM), + ) + elif quant_type == "nf4": + lib.gemv_4bit_inference_cpu_nf4_bf16( + ct.c_int64(M), + ct.c_int64(N), + ct.c_int64(K), + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(out), + ct.c_int64(blocksize), + ct.c_int64(x_strideM), + ct.c_int64(out_strideM), + ) + + if dtype != torch.bfloat16: + out = out.to(dtype) + + return out.reshape(final_out_shape) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dfc3d4f169ca0f068c0aacf829404fdbce3b66b0 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__pycache__/ops.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__pycache__/ops.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..394889bfbe16ffc14259eb28f01a7059a0832f8b Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/__pycache__/ops.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/ops.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..ccbe3549f359f081c9310351afb8a3f27a2818b3 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/cuda/ops.py @@ -0,0 +1,770 @@ +from collections.abc import Sequence +import ctypes as ct +from math import prod +from typing import Optional + +import torch + +from bitsandbytes.functional import CUBLAS_Context, _cuda_device_of, _get_tensor_stream, get_ptr + +from ..._ops import register_kernel +from ...cextension import ROCM_WARP_SIZE_64, lib + + +@register_kernel("bitsandbytes::int8_linear_matmul", "cuda") +def _(A: torch.Tensor, B: torch.Tensor): + out = torch.empty((*A.shape[:-1], B.shape[0]), device=A.device, dtype=torch.int32) + return _int8_linear_matmul_impl(A, B, out) + + +@register_kernel("bitsandbytes::int8_linear_matmul.out", "cuda") +def _(A: torch.Tensor, B: torch.Tensor, out: torch.Tensor): + _int8_linear_matmul_impl(A, B, out) + + +def _int8_linear_matmul_impl(A: torch.Tensor, B: torch.Tensor, out: torch.Tensor): + A, B = B, A + + shapeA = A.shape + shapeB = B.shape + + torch._check(A.dtype == torch.int8, lambda: "B must be int8") + torch._check(B.dtype == torch.int8, lambda: "A must be int8") + torch._check(A.ndim == 2, lambda: "Only two dimensional matrices are supported for argument B") + torch._check(B.ndim in [2, 3], lambda: "Only two or three dimensional matrices are supported for argument A") + torch._check(prod(shapeB) > 0, lambda: f"Input tensor dimensions need to be > 0: {shapeB}") + torch._check(out.dtype == torch.int32) + + shapeC = (*shapeB[:-1], shapeA[0]) + torch._check(out.shape == shapeC, lambda: f"Output shape {out.shape} does not match expected shape {shapeC}") + + k, m = shapeA + n = prod(shapeB[:-1]) + lda = shapeA[-1] # Weights (outputs, inputs) + ldb = shapeB[-1] # Activations (batch, tokens, inputs) + ldc = shapeC[-1] # Output (batch, tokens, outputs) + + torch._check( + lda == ldb, + lambda: f"int8_linear_matmul only supports B^T @ A. Inner dimensions do not match: B @ A = {shapeB} @ {shapeA}", + ) + + # cuBLASLt does not support int8 matmul with inner dimensions that are not divisible by 4. + # We'll fall back to a slower fp32 calculation in this circumstance. + # Fortunately, this should not be very common. + if lda % 4 != 0: + result = torch.matmul(B.float(), A.float().t()).to(torch.int32) + return out.copy_(result) + + with _cuda_device_of(A): + ctx = CUBLAS_Context.get_instance().get_context(A.device) + ptrA = get_ptr(A) + ptrB = get_ptr(B) + ptrC = get_ptr(out) + ptrRowScale = None + m = ct.c_int32(m) + n = ct.c_int32(n) + k = ct.c_int32(k) + lda = ct.c_int32(lda) + ldb = ct.c_int32(ldb) + ldc = ct.c_int32(ldc) + stream = _get_tensor_stream(A) + + has_error = lib.cigemmlt_32(ctx, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc, stream) + + if has_error: + if has_error == 100: + # `ERR_NOT_IMPLEMENTED` is defined as 100 in `ops.cu` + # TODO: Warn and implement a fallback to fp32 compute? + raise NotImplementedError("int8_linear_matmul not implemented!") + else: + raise RuntimeError( + f"cublasLt ran into an error!\n\t{shapeA=}, {shapeB=}, {shapeC=}\n\t{(lda, ldb, ldc)=}\n\t{(m, n, k)=}" + ) + + return out + + +@register_kernel("bitsandbytes::int8_mm_dequant", "cuda") +def _( + A: torch.Tensor, + row_stats: torch.Tensor, + col_stats: torch.Tensor, + dtype: Optional[torch.dtype] = None, + bias: Optional[torch.Tensor] = None, +) -> torch.Tensor: + torch._check(A.dtype == torch.int32, lambda: f"A must be int32, got {A.dtype}") + torch._check(row_stats.dtype == torch.float32, lambda: f"row_stats must be float32, got {row_stats.dtype}") + torch._check(col_stats.dtype == torch.float32, lambda: f"col_stats must be float32, got {col_stats.dtype}") + + # Note: cuda kernel only currently supports fp16 output. + # We'll later cast to desired dtype if needed. + out = torch.empty_like(A, dtype=torch.float16) + + ptrA = get_ptr(A) + ptrOut = get_ptr(out) + ptrRowStats = get_ptr(row_stats) + ptrColStats = get_ptr(col_stats) + numRows = ct.c_int32(prod(A.shape[:-1])) + numCols = ct.c_int32(A.shape[-1]) + + # Note: fused bias in the kernel is only supported for fp16 + # TODO(matthewdouglas): Consider supporting bf16 fused bias + ptrBias = get_ptr(bias) if bias is not None and bias.dtype == torch.float16 else None + + with _cuda_device_of(A): + lib.cdequant_mm_int32_fp16( + ptrA, ptrRowStats, ptrColStats, ptrOut, ptrBias, numRows, numCols, _get_tensor_stream(A) + ) + + # Add bias separately if not fused in kernel + if bias is not None and bias.dtype != torch.float16: + out.add_(bias) + + return out.to(dtype or torch.float16) + + +@register_kernel("bitsandbytes::int8_vectorwise_quant", "cuda") +def _(A: torch.Tensor, threshold=0.0): + torch._check(A.dtype == torch.float16, lambda: f"A must be float16, got {A.dtype}") + torch._check(threshold >= 0.0, lambda: "threshold must be non-negative") + + rows = prod(A.shape[:-1]) + cols = A.shape[-1] + + row_stats = torch.empty(rows, device=A.device, dtype=torch.float32) + out_row = torch.empty(A.shape, device=A.device, dtype=torch.int8) + + outlier_cols = None + + if threshold > 0.0: + # TODO we could improve perf of this + outliers = A.abs() >= threshold + + if outliers.any(): + outlier_cols = torch.argwhere(outliers.any(dim=0)).view(-1) + else: + # Needed for torch.compile support. + outlier_cols = torch.empty(0, device=A.device, dtype=torch.int64) + + with _cuda_device_of(A): + lib.cint8_vector_quant( + get_ptr(A), + get_ptr(out_row), + get_ptr(row_stats), + ct.c_float(threshold), + ct.c_int32(rows), + ct.c_int32(cols), + _get_tensor_stream(A), + ) + + # Zero out values from outlier columns across all rows. + # The kernel will handle this for outliers themselves, so we can optimize for rows=1. + if rows > 1 and outlier_cols is not None: + out_row[:, outlier_cols] = 0 + + return out_row, row_stats, outlier_cols + + +@register_kernel("bitsandbytes::int8_double_quant", "cuda") +def _( + A: torch.Tensor, + threshold=0.0, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + # Use CUDA kernel for rowwise and COO tensor + quant_row, row_stats, outlier_cols = torch.ops.bitsandbytes.int8_vectorwise_quant.default( + A, + threshold=threshold, + ) + + # PyTorch impl for colwise + col_stats, outlier_mask = _get_col_absmax(A, threshold=threshold) + if threshold > 0.0 and outlier_mask is not None: + A = A.masked_fill(outlier_mask, 0.0) + quant_col = torch.round(A.mul(127.0) / col_stats.unsqueeze(0)).to(torch.int8) + + return quant_row, quant_col, row_stats, col_stats.flatten().float(), outlier_cols + + +def _get_col_absmax( + A: torch.Tensor, + threshold=0.0, +) -> tuple[torch.Tensor, Optional[torch.Tensor]]: + torch._check(A.is_floating_point()) + + outlier_mask = None + + absA = A.abs().view(-1, A.shape[-1]) + + if threshold > 0.0: + # Filter outliers from stats when enabled + outlier_mask = absA >= threshold + absA.masked_fill_(outlier_mask, 0.0) + + # shape [cols]; unsqueeze(0) gives [1,cols] + col_stats = absA.amax(dim=0, keepdim=False).float() + + return col_stats, outlier_mask + + +@register_kernel("bitsandbytes::quantize_blockwise", "cuda") +def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]: + A = A.contiguous() + torch._check_is_size(blocksize) + + if ROCM_WARP_SIZE_64: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64]) + else: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32]) + + torch._check(code.dtype == torch.float32, lambda: f"code must be float32, got {code.dtype}") + + n = A.numel() + blocks = -(n // -blocksize) + absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32) + out = torch.empty_like(A, dtype=torch.uint8) + + with _cuda_device_of(A): + args = ( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_int32(blocksize), + ct.c_int(A.numel()), + ) + + if A.dtype == torch.float16: + lib.cquantize_blockwise_fp16(*args) + elif A.dtype == torch.bfloat16: + lib.cquantize_blockwise_bf16(*args) + elif A.dtype == torch.float32: + lib.cquantize_blockwise_fp32(*args) + else: + raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}") + + return out, absmax + + +@register_kernel("bitsandbytes::dequantize_blockwise", "cuda") +def _(A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype) -> torch.Tensor: + out = torch.empty_like(A, dtype=dtype) + _dequantize_blockwise_impl(A, absmax, code, blocksize, dtype, out=out) + return out + + +@register_kernel("bitsandbytes::dequantize_blockwise.out", "cuda") +def _( + A: torch.Tensor, + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}") + torch._check(out.shape == A.shape, lambda: f"Expected out.shape == {A.shape}, got {out.shape}") + _dequantize_blockwise_impl(A, absmax, code, blocksize, dtype, out=out) + + +def _dequantize_blockwise_impl( + A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype, out: torch.Tensor +) -> None: + A = A.contiguous() + if ROCM_WARP_SIZE_64: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64]) + else: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32]) + + torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}") + torch._check( + dtype in [torch.float16, torch.bfloat16, torch.float32], + lambda: f"Blockwise dequantization only supports 16bit/32bit floating types, got {dtype}", + ) + + with _cuda_device_of(A): + args = ( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_int(blocksize), + ct.c_int(A.numel()), + _get_tensor_stream(A), + ) + + if dtype == torch.float16: + lib.cdequantize_blockwise_fp16(*args) + elif dtype == torch.bfloat16: + lib.cdequantize_blockwise_bf16(*args) + elif dtype == torch.float32: + lib.cdequantize_blockwise_fp32(*args) + + +@register_kernel("bitsandbytes::quantize_4bit", "cuda") +def _( + A: torch.Tensor, blocksize: int, quant_type: str, quant_storage: torch.dtype +) -> tuple[torch.Tensor, torch.Tensor]: + A = A.contiguous() + if ROCM_WARP_SIZE_64: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64]) + else: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32]) + + torch._check(quant_type in ["fp4", "nf4"]) + torch._check( + A.dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit quantization only supports 16/32-bit floats, but got {A.dtype}", + ) + + n = A.numel() + blocks = -(n // -blocksize) + absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32) + out = torch.empty(((n + 1) // (quant_storage.itemsize * 2), 1), device=A.device, dtype=quant_storage) + + with _cuda_device_of(A): + args = ( + None, + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_int32(blocksize), + ct.c_int32(n), + ) + + if A.dtype == torch.bfloat16: + if quant_type == "fp4": + lib.cquantize_blockwise_bf16_fp4(*args) + else: + lib.cquantize_blockwise_bf16_nf4(*args) + elif A.dtype == torch.float16: + if quant_type == "fp4": + lib.cquantize_blockwise_fp16_fp4(*args) + else: + lib.cquantize_blockwise_fp16_nf4(*args) + elif A.dtype == torch.float32: + if quant_type == "fp4": + lib.cquantize_blockwise_fp32_fp4(*args) + else: + lib.cquantize_blockwise_fp32_nf4(*args) + + return out, absmax + + +@register_kernel("bitsandbytes::dequantize_4bit", "cuda") +def _( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, +) -> torch.Tensor: + out = torch.empty(shape, dtype=dtype, device=A.device) + _dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out) + return out + + +@register_kernel("bitsandbytes::dequantize_4bit.out", "cuda") +def _( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + torch._check(out.shape == shape, lambda: f"Expected out.shape == {shape}, got {out.shape}") + torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}") + _dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out) + + +def _dequantize_4bit_impl( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + A = A.contiguous() + if ROCM_WARP_SIZE_64: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64]) + else: + torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32]) + + torch._check(quant_type in ["fp4", "nf4"]) + torch._check( + dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit dequantization only supports 16/32-bit floats, but got {dtype}", + ) + + with _cuda_device_of(A): + args = ( + None, + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_int(blocksize), + ct.c_int32(out.numel()), + _get_tensor_stream(A), + ) + + if out.dtype == torch.bfloat16: + if quant_type == "fp4": + lib.cdequantize_blockwise_bf16_fp4(*args) + else: + lib.cdequantize_blockwise_bf16_nf4(*args) + elif out.dtype == torch.float16: + if quant_type == "fp4": + lib.cdequantize_blockwise_fp16_fp4(*args) + else: + lib.cdequantize_blockwise_fp16_nf4(*args) + elif out.dtype == torch.float32: + if quant_type == "fp4": + lib.cdequantize_blockwise_fp32_fp4(*args) + else: + lib.cdequantize_blockwise_fp32_nf4(*args) + + +@register_kernel("bitsandbytes::gemv_4bit", "cuda") +def _( + A: torch.Tensor, B: torch.Tensor, shapeB: Sequence[int], absmax: torch.Tensor, code: torch.Tensor, blocksize: int +) -> torch.Tensor: + shape = (*A.shape[:-1], shapeB[0]) + out = torch.empty(shape, device=A.device, dtype=A.dtype) + _gemv_4bit_impl(A, B, shapeB, absmax, code, blocksize, out=out) + return out + + +@register_kernel("bitsandbytes::gemv_4bit.out", "cuda") +def _( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + out: torch.Tensor, +) -> None: + torch._check( + out.shape == (*A.shape[:-1], shapeB[0]), + lambda: f"Expected out.shape == {(*A.shape[:-1], shapeB[0])}, got {out.shape}", + ) + torch._check(out.dtype == A.dtype, lambda: f"Expected out.dtype == {A.dtype}, got {out.dtype}") + _gemv_4bit_impl(A, B, shapeB, absmax, code, blocksize, out=out) + + +def _gemv_4bit_impl( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + out: torch.Tensor, +) -> None: + torch._check_is_size(blocksize) + + # Note: these checks are not strictly necessary, and cost more than they are worth, so they are commented out for now. + # torch._check( + # A.numel() == A.size(-1), + # lambda: f"A must be a vector with leading dimensions of 1, got {A.shape}", + # ) + # torch._check( + # A.dtype in [torch.float16, torch.bfloat16, torch.float32], + # lambda: f"A must be float16, bfloat16, or float32, got {A.dtype}", + # ) + # torch._check( + # B.dtype in [torch.uint8, torch.bfloat16, torch.float16, torch.float32], + # lambda: f"B must be backed by storage of type uint8, bfloat16, float16, or float32, got {B.dtype}", + # ) + # torch._check(absmax.dtype == torch.float32, lambda: f"absmax must be float32, got {absmax.dtype}") + # torch._check(code.dtype == torch.float32, lambda: f"code must be float32, got {code.dtype}") + + m = ct.c_int32(shapeB[0]) + n = ct.c_int32(1) + k = ct.c_int32(shapeB[1]) + + lda = m + ldb = ct.c_int32((A.shape[-1] + 1) // 2) + ldc = m + + stream = _get_tensor_stream(A) + + with _cuda_device_of(A): + if A.dtype == torch.float16: + lib.cgemm_4bit_inference_naive_fp16( + m, + n, + k, + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(code), + get_ptr(out), + lda, + ldb, + ldc, + ct.c_int32(blocksize), + stream, + ) + elif A.dtype == torch.bfloat16: + lib.cgemm_4bit_inference_naive_bf16( + m, + n, + k, + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(code), + get_ptr(out), + lda, + ldb, + ldc, + ct.c_int32(blocksize), + stream, + ) + elif A.dtype == torch.float32: + lib.cgemm_4bit_inference_naive_fp32( + m, + n, + k, + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(code), + get_ptr(out), + lda, + ldb, + ldc, + ct.c_int32(blocksize), + stream, + ) + + +"""C FUNCTIONS FOR OPTIMIZERS""" +str2optimizer32bit = { + "adam": ( + lib.cadam32bit_grad_fp32, + lib.cadam32bit_grad_fp16, + lib.cadam32bit_grad_bf16, + ), + "momentum": ( + lib.cmomentum32bit_grad_32, + lib.cmomentum32bit_grad_16, + ), + "rmsprop": ( + lib.crmsprop32bit_grad_32, + lib.crmsprop32bit_grad_16, + ), + "lion": ( + lib.clion32bit_grad_fp32, + lib.clion32bit_grad_fp16, + lib.clion32bit_grad_bf16, + ), + "adagrad": ( + lib.cadagrad32bit_grad_32, + lib.cadagrad32bit_grad_16, + ), + "lamb": ( + lib.cadam32bit_grad_fp32, + lib.cadam32bit_grad_fp16, + lib.cadam32bit_grad_bf16, + ), + "ademamix": ( + lib.cademamix32bit_grad_fp32, + lib.cademamix32bit_grad_fp16, + lib.cademamix32bit_grad_bf16, + ), +} + +str2optimizer8bit_blockwise = { + "adam": ( + lib.cadam_8bit_blockwise_grad_fp32, + lib.cadam_8bit_blockwise_grad_fp16, + lib.cadam_8bit_blockwise_grad_bf16, + ), + "momentum": ( + lib.cmomentum_8bit_blockwise_grad_fp32, + lib.cmomentum_8bit_blockwise_grad_fp16, + lib.cmomentum_8bit_blockwise_grad_bf16, + ), + "rmsprop": ( + lib.crmsprop_8bit_blockwise_grad_fp32, + lib.crmsprop_8bit_blockwise_grad_fp16, + lib.crmsprop_8bit_blockwise_grad_bf16, + ), + "lion": ( + lib.clion_8bit_blockwise_grad_fp32, + lib.clion_8bit_blockwise_grad_fp16, + lib.clion_8bit_blockwise_grad_bf16, + ), + "adagrad": ( + lib.cadagrad_8bit_blockwise_grad_fp32, + lib.cadagrad_8bit_blockwise_grad_fp16, + lib.cadagrad_8bit_blockwise_grad_bf16, + ), + "ademamix": ( + lib.cademamix_8bit_blockwise_grad_fp32, + lib.cademamix_8bit_blockwise_grad_fp16, + lib.cademamix_8bit_blockwise_grad_bf16, + ), +} + + +def _optimizer_update_32bit_impl( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + unorm_vec: Optional[torch.Tensor], + max_unorm: float, + param_norm: float, + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + weight_decay: float, + step: int, + lr: float, + gnorm_scale: float, + skip_zeros=False, +) -> None: + optim_fns = str2optimizer32bit.get(optimizer_name, None) + if optim_fns is None: + raise ValueError( + f"Unsupported optimizer name: {optimizer_name}. Supported optimizers: {list(str2optimizer8bit_blockwise.keys())}" + ) + if g.dtype == torch.float32: + optim_func = optim_fns[0] + elif g.dtype == torch.float16: + optim_func = optim_fns[1] + elif g.dtype == torch.bfloat16 and len(optim_fns) == 3: + optim_func = optim_fns[2] + else: + raise ValueError( + f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}", + ) + + with _cuda_device_of(g): + optim_func( + get_ptr(g), + get_ptr(p), + get_ptr(state1), + get_ptr(state2), + get_ptr(unorm_vec), + ct.c_float(max_unorm), + ct.c_float(param_norm), + ct.c_float(beta1), + ct.c_float(beta2), + ct.c_float(beta3), + ct.c_float(alpha), + ct.c_float(eps), + ct.c_float(weight_decay), + ct.c_int32(step), + ct.c_float(lr), + ct.c_float(gnorm_scale), + ct.c_bool(skip_zeros), + ct.c_int32(g.numel()), + ) + + +def _optimizer_update_8bit_blockwise_impl( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + step: int, + lr: float, + qmap1: torch.Tensor, + qmap2: Optional[torch.Tensor], + absmax1: torch.Tensor, + absmax2: Optional[torch.Tensor], + weight_decay: float, + gnorm_scale: float, + skip_zeros=False, +) -> None: + # torch._check( + # g.numel() == p.numel(), + # lambda: f"g and p must have the same number of elements, got {g.numel()} and {p.numel()}", + # ) + # compute_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + # torch._check( + # g.dtype in compute_dtypes, + # lambda: f"g must be bfloat16, float16, or float32, got {g.dtype}", + # ) + # torch._check( + # g.dtype == p.dtype, + # lambda: f"Expected all tensors to have the same dtype, got g.dtype={g.dtype}, p.dtype={p.dtype}", + # ) + # torch._check( + # state1.dtype == torch.uint8, + # lambda: f"state1 must be uint8, got {state1.dtype}", + # ) + # torch._check( + # qmap1.dtype == absmax1.dtype == torch.float32, + # lambda: f"Expected qmap1 and absmax1 to be float32, got qmap1.dtype={qmap1.dtype}, absmax1.dtype={absmax1.dtype}", + # ) + # if state2 is not None: + # torch._check( + # state2.dtype == torch.uint8, + # lambda: f"state2 must be uint8, got {state2.dtype}", + # ) + # torch._check( + # qmap2.dtype == absmax2.dtype == torch.float32, + # lambda: f"Expected qmap2 and absmax2 to be float32, got qmap2.dtype={qmap2.dtype}, absmax2.dtype={absmax2.dtype}", + # ) + optimizer_fns = str2optimizer8bit_blockwise.get(optimizer_name) + if optimizer_fns is None: + raise ValueError( + f"Unsupported optimizer name: {optimizer_name}. Supported optimizers: {list(str2optimizer8bit_blockwise.keys())}" + ) + + if g.dtype == torch.float32: + optimizer_fn = optimizer_fns[0] + elif g.dtype == torch.float16: + optimizer_fn = optimizer_fns[1] + elif g.dtype == torch.bfloat16: + optimizer_fn = optimizer_fns[2] + else: + raise ValueError( + f"Unsupported gradient dtype: {g.dtype}. Supported dtypes: torch.float32, torch.float16, torch.bfloat16" + ) + + with _cuda_device_of(g): + optimizer_fn( + get_ptr(p), + get_ptr(g), + get_ptr(state1), + get_ptr(state2), + ct.c_float(beta1), + ct.c_float(beta2), + ct.c_float(beta3), + ct.c_float(alpha), + ct.c_float(eps), + ct.c_int32(step), + ct.c_float(lr), + get_ptr(qmap1), + get_ptr(qmap2), + get_ptr(absmax1), + get_ptr(absmax2), + ct.c_float(weight_decay), + ct.c_float(gnorm_scale), + ct.c_bool(skip_zeros), + ct.c_int32(g.numel()), + ) + + +register_kernel("bitsandbytes::optimizer_update_8bit_blockwise", "cuda")(_optimizer_update_8bit_blockwise_impl) +register_kernel("bitsandbytes::optimizer_update_32bit", "cuda")(_optimizer_update_32bit_impl) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..da86ebf9a2d0978fbcf7e58b79db5058f5017b24 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__pycache__/ops.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__pycache__/ops.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9dfa22a4b7116cf79f6e767119942ac348f3f369 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/__pycache__/ops.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/ops.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..707aeb3c3b3e58fb089377e37f3dd6a5cbbaaaf7 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/default/ops.py @@ -0,0 +1,616 @@ +from collections.abc import Sequence +from functools import wraps +from math import prod, sqrt +from typing import Optional + +import torch + +from ..._ops import register_kernel +from ..utils import CODE + + +def _try_torch_compile(func=None, **compile_kwargs): + """ + Wrapper around torch.compile that falls back to the original function if compilation fails. + """ + + def decorator(fn): + try: + compiled_fn = torch.compile(fn, **compile_kwargs) + + @wraps(fn) + def wrapper(*args, **kwargs): + try: + return compiled_fn(*args, **kwargs) + except Exception: + return fn(*args, **kwargs) + + return wrapper + except Exception: + return fn + + if func is None: + return decorator + else: + return decorator(func) + + +@register_kernel("bitsandbytes::int8_mm_dequant", "default") +def _( + A: torch.Tensor, + row_stats: torch.Tensor, + col_stats: torch.Tensor, + dtype: Optional[torch.dtype] = None, + bias: Optional[torch.Tensor] = None, +) -> torch.Tensor: + torch._check(A.dtype == torch.int32, lambda: f"A must be int32, got {A.dtype}") + torch._check(row_stats.dtype == torch.float32, lambda: f"row_stats must be float32, got {row_stats.dtype}") + torch._check(col_stats.dtype == torch.float32, lambda: f"col_stats must be float32, got {col_stats.dtype}") + + A_calc = A.view(-1, A.shape[-1]) + row_stats = row_stats.reshape(-1).unsqueeze(-1) + col_stats = col_stats.reshape(-1).unsqueeze(0) + + out = A_calc * (row_stats * col_stats) * 6.200124e-05 + if bias is not None: + out += bias + + return out.to(dtype or torch.float16) + + +@register_kernel("bitsandbytes::int8_mixed_scaled_mm", "default") +def _( + A: torch.Tensor, + CA: torch.Tensor, + CB: torch.Tensor, + SCA: torch.Tensor, + SCB: torch.Tensor, + outlier_cols: Optional[torch.Tensor] = None, + bias: Optional[torch.Tensor] = None, +) -> tuple[torch.Tensor, Optional[torch.Tensor]]: + subB = None + + if outlier_cols is not None and outlier_cols.numel(): + # Extract the inputs with outliers in original precision + subA = A[:, outlier_cols].contiguous() + + # Dequantize the corresponding weight columns + subB = ( + torch.ops.bitsandbytes.int8_vectorwise_dequant.default(CB[:, outlier_cols].contiguous(), SCB) + .to(A.dtype) + .t() + ) + + # TODO: if state.has_fp16_weights: subB = B[:, outlier_cols].t() + + else: + # Needed for torch.compile when there are no outliers. + subA = torch.empty(0, device=A.device, dtype=A.dtype) + + # Int8 Matmul + Dequant + Bias + output = torch.ops.bitsandbytes.int8_scaled_mm.default(CA, CB, SCA, SCB, bias=bias, dtype=A.dtype) + + if subB is not None: + # Add the outlier columns back to the output + output = output.addmm(subA, subB) + + return output, subA + + +@register_kernel("bitsandbytes::int8_scaled_mm", "default") +def _( + A: torch.Tensor, + B: torch.Tensor, + row_stats: torch.Tensor, + col_stats: torch.Tensor, + bias: Optional[torch.Tensor] = None, + dtype: Optional[torch.dtype] = None, +) -> torch.Tensor: + out_i32 = torch.ops.bitsandbytes.int8_linear_matmul.default(A, B) + return torch.ops.bitsandbytes.int8_mm_dequant.default( + out_i32, + row_stats, + col_stats, + dtype=dtype or torch.float16, + bias=bias, + ) + + +@register_kernel("bitsandbytes::int8_linear_matmul", "default") +def _(A: torch.Tensor, B: torch.Tensor): + return _int8_linear_matmul_impl(A, B) + + +@register_kernel("bitsandbytes::int8_linear_matmul.out", "default") +def _(A: torch.Tensor, B: torch.Tensor, out: torch.Tensor): + torch._check(out.dtype == torch.int32) + _int8_linear_matmul_impl(A, B, out) + + +def _int8_linear_matmul_impl(A: torch.Tensor, B: torch.Tensor, out: Optional[torch.Tensor] = None): + # Naive implementation: perform matmul in fp32 + result = torch.matmul(A.float(), B.float().t()).to(torch.int32) + if out is not None: + result = out.copy_(result) + return result + + +@register_kernel("bitsandbytes::int8_vectorwise_quant", "default") +def _(A: torch.Tensor, threshold=0.0): + rows = prod(A.shape[:-1]) + outlier_cols = None + + outlier_restore = None + + if threshold > 0.0: + outliers = A.abs() >= threshold + + if outliers.any(): + # Determine which columns contain outliers, and zero out the + # outliers ahead of quantization. We need to keep a backup of these + # outliers to restore them after quantization. + outlier_cols = torch.argwhere(outliers.any(dim=0)).view(-1) + outlier_restore = A[outliers].clone() + A[outliers] = 0 + else: + # Needed for torch.compile support. + outlier_cols = torch.empty(0, device=A.device, dtype=torch.int64) + + # Get absmax for each row. + row_stats = torch.max(A.abs(), dim=1).values.float() + + # Quantize row-wise to int8. + out_row = torch.round(A * (127.0 / row_stats.unsqueeze(-1))).to(torch.int8) + + # Zero out values from outlier columns across all rows. + if rows > 1 and outlier_cols is not None: + out_row[:, outlier_cols] = 0 + + # Restore outliers. + if outlier_restore is not None: + A[outliers] = outlier_restore + + return out_row, row_stats, outlier_cols + + +@register_kernel("bitsandbytes::quantize_blockwise", "default") +def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]: + torch._check_is_size(blocksize) + + n = A.numel() + rem = n % blocksize + has_rem = rem > 0 + blocks = n // blocksize + has_rem + absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32) + A_reshaped = A.reshape(n) + A_com = A_reshaped[: n - rem] + A_com_reshaped = A_com.reshape(n // blocksize, blocksize) + absmax[: blocks - has_rem] = torch.abs(A_com_reshaped).max(dim=-1)[0] + scaled_A = torch.clamp(A_com_reshaped * (1 / absmax[: blocks - has_rem].view(-1, 1)), -1, 1) + scaled_A = scaled_A.reshape(-1) + if has_rem: + absmax[-1] = torch.abs(A_reshaped[n - rem :]).max() + scaled_A_rem = torch.clamp(A_reshaped[n - rem :] * (1 / absmax[-1]), -1, 1) + scaled_A = torch.cat([scaled_A, scaled_A_rem], dim=0) + + diff = torch.abs(scaled_A.unsqueeze(-1) - code.to(scaled_A.device)) + out = torch.argmin(diff, dim=-1).to(torch.uint8).to(scaled_A.device).reshape(A.shape) + + return out, absmax + + +@register_kernel("bitsandbytes::dequantize_blockwise", "default") +def _(A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype) -> torch.Tensor: + torch._check_is_size(blocksize) + torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}") + + out = code[A.reshape(-1).int()] + blocks = out.shape[-1] // blocksize + res = out.shape[-1] % blocksize + if res != 0: + out = torch.nn.functional.pad(out, (0, blocksize - res), mode="constant", value=0) + out = (out.view(-1, blocksize) * absmax.view(-1, 1)).to(dtype).reshape(-1) + out = out[: blocks * blocksize + res] + out = out.reshape(A.shape) + + return out + + +@register_kernel("bitsandbytes::quantize_4bit", "default") +def _( + A: torch.Tensor, blocksize: int, quant_type: str, quant_storage: torch.dtype +) -> tuple[torch.Tensor, torch.Tensor]: + torch._check_is_size(blocksize) + torch._check(quant_type in ("nf4", "fp4"), lambda: f"quant_type must be nf4 or fp4, got {quant_type}") + torch._check( + A.dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit quantization only supports 16/32-bit floats, but got {A.dtype}", + ) + + n = A.numel() + full_blocks = n // blocksize + rem = n % blocksize + blocks = full_blocks + 1 if rem else full_blocks + absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32) + A_flattened = A.reshape(n) + + # Scale full blocks of the tensor to [-1, 1] + A_full_blocks = A_flattened[: n - rem].reshape(n // blocksize, blocksize) + absmax[:full_blocks] = torch.abs(A_full_blocks).max(dim=-1)[0] + scaled = torch.clamp(A_full_blocks * (1 / absmax[:full_blocks].view(-1, 1)), -1, 1).reshape(-1) + + # Scale any partial block + if rem: + A_rem = A_flattened[-rem:] + absmax[-1] = torch.abs(A_rem).max() + scaled_rem = torch.clamp(A_rem * (1 / absmax[-1]), -1, 1) + scaled = torch.cat([scaled, scaled_rem], dim=0) + + # Quantize with the lookup table + code = CODE[quant_type].to(scaled.device).to(scaled.dtype) + quantized = torch.argmin(torch.abs(scaled.view(-1, 1) - code), dim=-1, keepdim=True).to(torch.uint8) + + # Pack two quantized values per byte + packed = quantized[::2] << 4 | quantized[1::2] + + if quant_storage != torch.uint8: + packed = packed.squeeze().view(quant_storage).unsqueeze(1) + + return packed, absmax.float() + + +def _dequantize_4bit_impl( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, +) -> torch.Tensor: + # Enable non uint8 dtype + if A.dtype != torch.uint8: + A = A.view(torch.uint8) + + A = A.reshape(-1) + # Map nf4 to [-1, 1] + out_dq = torch.empty(A.size(0) * 2, dtype=torch.int32, device=A.device) + n = out_dq.numel() + out_dq[1::2] = A & 0xF + out_dq[::2] = A >> 4 + # code is fp32, cast to dtype to avoid the mismatch issue + code = CODE[quant_type].to(dtype).to(A.device) + out_dq = code[out_dq] + + # Apply scales + if out_dq.numel() != n: + assert out_dq.numel() == n + 1 + out_dq = torch.narrow(out_dq, 0, 0, n) + blocks = n // blocksize + blocks += 1 if n % blocksize > 0 else 0 + rem = n % blocksize + has_rem = rem > 0 + + out = torch.empty(shape, dtype=dtype, device=A.device).reshape(-1) + if has_rem: + out[: n - rem] = (out_dq[: n - rem].view(-1, blocksize) * absmax[: blocks - has_rem].view(-1, 1)).reshape(-1) + out[n - rem :] = out_dq[n - rem :] * absmax[-1] + else: + out = out_dq.view(-1, blocksize) * absmax.view(-1, 1) + + out = out.reshape(-1, *shape[1:]).to(dtype) + + return out + + +@register_kernel("bitsandbytes::dequantize_4bit", "default") +def _( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, +) -> torch.Tensor: + torch._check_is_size(blocksize) + torch._check(quant_type in ("nf4", "fp4"), lambda: f"quant_type must be nf4 or fp4, got {quant_type}") + torch._check( + dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit dequantization only supports 16/32-bit floats, but got {dtype}", + ) + + return _dequantize_4bit_impl(A, absmax, blocksize, quant_type, shape, dtype) + + +@register_kernel("bitsandbytes::gemv_4bit", "default") +def _( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, +) -> torch.Tensor: + # Applied from dequantize_4bit + quant_type = "fp4" if code[1] > 0 else "nf4" + B_dq = torch.ops.bitsandbytes.dequantize_4bit.default(B, absmax, blocksize, quant_type, shapeB, A.dtype) + + return torch.nn.functional.linear( + A, + B_dq, + bias=None, + ) + + +MOMENTUM = 0 +RMSPROP = 1 +ADAGRAD = 2 +ADAM = 3 +# LION should be larger than MOMENTUM, RMSPROP, ADAGRAD due to comparison in kernels +LION = 4 +ADEMAMIX = 5 + +name2optimizer_id = { + "momentum": MOMENTUM, + "rmsprop": RMSPROP, + "adagrad": ADAGRAD, + "adam": ADAM, + "lion": LION, + "ademamix": ADEMAMIX, +} + + +@_try_torch_compile +def _optimizer_precondition_32bit( + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + unorm_vec: torch.Tensor, + beta1: float, + beta2: float, + eps: float, + weight_decay: float, + step: int, + lr: float, + gnorm_scale: float, + optimizer_id: int, +): + """Preprocessing optimizer, computing update norm""" + + g_vals = gnorm_scale * g + + if optimizer_id == 3: # ADAM + correction1 = 1.0 / (1.0 - beta1**step) + correction2 = 1.0 / (1.0 - beta2**step) + + s1_vals = state1 * beta1 + (1.0 - beta1) * g_vals + s2_vals = state2 * beta2 + (1.0 - beta2) * g_vals * g_vals + + s1_vals = s1_vals * correction1 + s2_vals = s2_vals * correction2 + + update_vals = s1_vals / (torch.sqrt(s2_vals) + eps) + update_norm = update_vals * update_vals + + elif optimizer_id == 5: # ADEMAMIX + update_norm = state1 + + elif optimizer_id == 0: # MOMENTUM + if step == 1: + s1_vals = g_vals + else: + s1_vals = state1 * beta1 + g_vals + update_norm = s1_vals * s1_vals + + elif optimizer_id == 4: # LION + s1_vals = state1 * beta2 + (1.0 - beta2) * g_vals + update_norm = s1_vals + + elif optimizer_id == 1: # RMSPROP + s1_vals = state1 * beta1 + (1.0 - beta1) * g_vals * g_vals + update_vals = g_vals / (torch.sqrt(s1_vals) + eps) + update_norm = update_vals * update_vals + + elif optimizer_id == 2: # ADAGRAD + s1_vals = state1 + g_vals * g_vals + update_vals = g_vals / (torch.sqrt(s1_vals) + eps) + update_norm = update_vals * update_vals + + total_norm = torch.sum(update_norm) + unorm_vec.add_(total_norm) + + +@_try_torch_compile +def _optimizer_update_32bit( + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + unorm_vec: Optional[torch.Tensor], + max_unorm: float, + param_norm: float, + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + weight_decay: float, + step: int, + lr: float, + gnorm_scale: float, + optimizer_id: int, +): + """Unified optimizer update kernel""" + + p_vals = p.float() + g_vals = (gnorm_scale * g).float() + if optimizer_id in [0, 1, 2, 4] and weight_decay > 0.0: + g_vals = g_vals + p_vals * weight_decay + + update_scale = 1.0 + if max_unorm > 0.0: + current_unorm = torch.sqrt(unorm_vec) + if optimizer_id in [0, 1, 2, 4]: # 1-state optimizers + if current_unorm > max_unorm * param_norm + eps: + update_scale = (max_unorm * param_norm + eps) / current_unorm + else: # 2-state optimizers + if current_unorm > max_unorm * param_norm: + update_scale = (max_unorm * param_norm) / current_unorm + + if optimizer_id == 3: # ADAM + s1_vals = state1 * beta1 + (1.0 - beta1) * g_vals + s2_vals = state2 * beta2 + (1.0 - beta2) * g_vals * g_vals + + correction1 = 1.0 - beta1**step + correction2 = sqrt(1.0 - beta2**step) + step_size = -lr * correction2 / correction1 + + if weight_decay > 0.0: + p_vals = p_vals * (1.0 - lr * weight_decay) + + update_val = update_scale * step_size * (s1_vals / (torch.sqrt(s2_vals) + eps * correction2)) + p_vals = p_vals + update_val + + state1.copy_(s1_vals) + state2.copy_(s2_vals) + + elif optimizer_id == 5: # ADEMAMIX + s1_vals = state1[0] + s3_vals = state1[1] + s2_vals = state2 + + m1 = s1_vals * beta1 + (1.0 - beta1) * g_vals + m2 = s3_vals * beta3 + (1.0 - beta3) * g_vals + nu = s2_vals * beta2 + (1.0 - beta2) * g_vals * g_vals + + correction1 = 1.0 - beta1**step + correction2 = sqrt(1.0 - beta2**step) + + if weight_decay > 0.0: + p_vals = p_vals * (1.0 - lr * weight_decay) + + mixed_momentum = (m1 / correction1) + (alpha * m2) + adaptive_term = (torch.sqrt(nu) / correction2) + eps + p_vals = p_vals - lr * (mixed_momentum / adaptive_term) + + state1[0].copy_(m1) + state1[1].copy_(m2) + state2.copy_(nu) + + elif optimizer_id == 0: # MOMENTUM + if step == 1: + s1_vals = g_vals + else: + s1_vals = state1 * beta1 + g_vals + + update_val = update_scale * (-lr * s1_vals) + p_vals = p_vals + update_val + + state1.copy_(s1_vals) + + elif optimizer_id == 4: # LION + momentum_update = state1 * beta1 + (1.0 - beta1) * g_vals + update_val = update_scale * lr * torch.sign(momentum_update) + p_vals = p_vals - update_val + + s1_vals = state1 * beta2 + (1.0 - beta2) * g_vals + state1.copy_(s1_vals) + + elif optimizer_id == 1: # RMSPROP + s1_vals = state1 * beta1 + (1.0 - beta1) * g_vals * g_vals + update_val = update_scale * lr * g_vals / (torch.sqrt(s1_vals) + eps) + p_vals = p_vals - update_val + + state1.copy_(s1_vals) + + elif optimizer_id == 2: # ADAGRAD + s1_vals = state1 + g_vals * g_vals + update_val = lr * g_vals / (torch.sqrt(s1_vals) + eps) + p_vals = p_vals - update_val + + state1.copy_(s1_vals) + + p.copy_(p_vals) + + +@register_kernel("bitsandbytes::optimizer_update_32bit", "default") +def _( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + unorm_vec: Optional[torch.Tensor], + max_unorm: float, + param_norm: float, + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + weight_decay: float, + step: int, + lr: float, + gnorm_scale: float = 1.0, + skip_zeros=False, +) -> None: + """ + 32-bit optimizer implemented by PyTorch with @torch.compile + """ + if skip_zeros: + raise NotImplementedError("skip_zeros is not supported yet") + + optimizer_id = name2optimizer_id[optimizer_name] + + if optimizer_name == "lion": + _optimizer_update_32bit( + g, + p, + state1, + state2, + unorm_vec, + max_unorm, + param_norm, + beta1, + beta2, + beta3, + alpha, + eps, + weight_decay, + step, + lr, + gnorm_scale, + optimizer_id, + ) + + if max_unorm > 0.0: + unorm_vec.zero_() + _optimizer_precondition_32bit( + g, p, state1, state2, unorm_vec, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, optimizer_id + ) + else: + if max_unorm > 0.0: + unorm_vec.zero_() + _optimizer_precondition_32bit( + g, p, state1, state2, unorm_vec, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, optimizer_id + ) + + _optimizer_update_32bit( + g, + p, + state1, + state2, + unorm_vec, + max_unorm, + param_norm, + beta1, + beta2, + beta3, + alpha, + eps, + weight_decay, + step, + lr, + gnorm_scale, + optimizer_id, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..af49cf4f93cfabdcc7725f25e334a013d8150b8c Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__pycache__/ops.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__pycache__/ops.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..634162a97d6cde991fa16e0c3f30cf11b6ed3a20 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/__pycache__/ops.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/ops.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..9ecd63e0b34f18eaf64fa54cf156ff4faf9742a1 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/hpu/ops.py @@ -0,0 +1,55 @@ +from collections.abc import Sequence +import math + +import torch + +from ..._ops import register_kernel +from ..utils import GAUDI_SW_VER + + +# convert btw standard 4-bit compression format and ipex compression format +# needed for backward compatibility with older versions of gaudi sw +def _reverse_4bit_compress_format(weight: torch.Tensor): + out_1 = (weight & 0xF0) >> 4 + out_2 = (weight & 0xF) << 4 + out = out_1 | out_2 + return out + + +@register_kernel("bitsandbytes::dequantize_4bit", "hpu") +def _( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, +) -> torch.Tensor: + torch._check_is_size(blocksize) + torch._check(quant_type == "nf4", lambda: f"quant_type must be nf4, got {quant_type}") + torch._check( + A.dtype in [torch.bfloat16, torch.uint8], + lambda: f"quant_storage supports uint8 or bfloat16, but got {A.dtype}", + ) + + # Enable non uint8 dtype + if A.dtype != torch.uint8: + A = A.view(torch.uint8) + + A = A.reshape(-1) + + if GAUDI_SW_VER and (GAUDI_SW_VER.major < 1 or GAUDI_SW_VER.minor < 22): + A = _reverse_4bit_compress_format(A) + + # HPU dequantization function for NF4 quantized tensors. + out_dq = torch.ops.hpu.dequantize_nf4( + A, + absmax.to(dtype), + blocksize, + out_shape=(math.prod(shape),), + out_dtype=dtype, + ) + + output = out_dq.reshape(shape) + + return output diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..528900df307992c75d79a294331fb1e4b2cd2820 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_4bit.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_4bit.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bc6593454f86bab5ad886feaded4dbd58637e777 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_4bit.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_8bit_quant.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_8bit_quant.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e3875526d41c969a38da043b35a58a5b66424243 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_8bit_quant.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_optim.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_optim.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ff403c5d9e074d5a0de6192a60bd3229e462e85b Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/kernels_optim.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/ops.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/ops.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..febe2612155d4831ae662974d2bf4f8ddb81805e Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/__pycache__/ops.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_4bit.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_4bit.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd59fad2eaf5d991f39d85627133964dbcaefa9 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_4bit.py @@ -0,0 +1,577 @@ +import torch + +import triton +import triton.language as tl + + +# Triton implementation of similar CUDA kernel to avoid loading code from csrc/kernels.cu::dQuantizeFP4 +# @triton.autotune( +# configs=[ +# triton.Config({"SPLIT_NUM_BLOCKS": 1, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_NUM_BLOCKS": 1}), +# triton.Config({"SPLIT_NUM_BLOCKS": 2}), +# triton.Config({"SPLIT_NUM_BLOCKS": 4}), +# triton.Config({"SPLIT_NUM_BLOCKS": 8}), +# ], +# key=["n_elements"], +# ) +@triton.jit +def quantize_fp4_blockwise_kernel( + A_ptr, + absmax_ptr, + out_ptr, + n_elements, + BLOCK_SIZE: tl.constexpr, + SPLIT_NUM_BLOCKS: tl.constexpr, +): + PAIRED_SPLIT_NUM_BLOCKS: tl.constexpr = SPLIT_NUM_BLOCKS * 2 + block_start_idx = tl.program_id(0) * PAIRED_SPLIT_NUM_BLOCKS + thread_idx = tl.arange(0, PAIRED_SPLIT_NUM_BLOCKS * BLOCK_SIZE) + + offsets = block_start_idx * BLOCK_SIZE + thread_idx + mask = offsets < n_elements + + A = tl.load(A_ptr + offsets, mask=mask, other=0.0) + + # To be able process several blocks -> (PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE) + A_reshaped = tl.reshape(A, (PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE)) + + # Calculating absamax for each block + absmax = tl.max(tl.abs(A_reshaped), axis=1) + tl.store(absmax_ptr + block_start_idx + tl.arange(0, PAIRED_SPLIT_NUM_BLOCKS), absmax) + + A_normalized = A_reshaped / absmax[:, None] + A_normalized = tl.clamp(A_normalized, -1.0, 1.0) + + sign = tl.where(A_normalized < 0, 0b1000, 0b0000) + A_absf = tl.abs(A_normalized) + + result = tl.where( + A_absf > 0.29166667, + tl.where( + A_absf > 0.583333, tl.where(A_absf > 0.8333333, 0b011, 0b010), tl.where(A_absf > 0.4166667, 0b101, 0b100) + ), + tl.where( + A_absf > 0.0859375, + tl.where(A_absf > 0.20833333, 0b0111, 0b0110), + tl.where(A_absf > 0.00260417, 0b0001, 0b0000), + ), + ) + quantized = (result ^ sign).to(tl.uint8) + + quantized = quantized.reshape((PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE // 2, 2)) + left, right = quantized.split() + packed = left << 4 | (right & 0xF) + + packed_flat = tl.reshape(packed, (BLOCK_SIZE * SPLIT_NUM_BLOCKS,)) + out_offsets = block_start_idx * BLOCK_SIZE // 2 + tl.arange(0, SPLIT_NUM_BLOCKS * BLOCK_SIZE) + # Use n - n//2 instead of (n+1)//2 to avoid integer overflow for large n + out_mask = out_offsets < (n_elements - n_elements // 2) + tl.store(out_ptr + out_offsets, packed_flat, mask=out_mask) + + +# Triton implementation of similar CUDA kernel to avoid loading code from csrc/kernels.cu::dQuantizeNF4 +# @triton.autotune( +# configs=[ +# triton.Config({"SPLIT_NUM_BLOCKS": 1, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_NUM_BLOCKS": 1}), +# triton.Config({"SPLIT_NUM_BLOCKS": 2}), +# triton.Config({"SPLIT_NUM_BLOCKS": 4}), +# triton.Config({"SPLIT_NUM_BLOCKS": 8}), +# ], +# key=["n_elements"], +# ) +@triton.jit +def quantize_nf4_blockwise_kernel( + A_ptr, + absmax_ptr, + out_ptr, + n_elements, + BLOCK_SIZE: tl.constexpr, + SPLIT_NUM_BLOCKS: tl.constexpr, +): + PAIRED_SPLIT_NUM_BLOCKS: tl.constexpr = SPLIT_NUM_BLOCKS * 2 + block_start_idx = tl.program_id(0) * PAIRED_SPLIT_NUM_BLOCKS + thread_idx = tl.arange(0, PAIRED_SPLIT_NUM_BLOCKS * BLOCK_SIZE) + + offsets = block_start_idx * BLOCK_SIZE + thread_idx + mask = offsets < n_elements + + A = tl.load(A_ptr + offsets, mask=mask, other=0.0) + + # To be able process several blocks -> (PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE) + A_reshaped = tl.reshape(A, (PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE)) + + # Calculating absamax for each block + absmax = tl.max(tl.abs(A_reshaped), axis=1) + tl.store(absmax_ptr + block_start_idx + tl.arange(0, PAIRED_SPLIT_NUM_BLOCKS), absmax) + + A_normalized = A_reshaped / absmax[:, None] + A_normalized = tl.clamp(A_normalized, -1.0, 1.0) + + result = tl.where( + A_normalized > 0.03979014977812767, + tl.where( + A_normalized > 0.3893125355243683, + tl.where( + A_normalized > 0.6427869200706482, + tl.where(A_normalized > 0.8614784181118011, 0b1111, 0b1110), + tl.where(A_normalized > 0.5016634166240692, 0b1101, 0b1100), + ), + tl.where( + A_normalized > 0.2035212516784668, + tl.where(A_normalized > 0.2920137718319893, 0b1011, 0b1010), + tl.where(A_normalized > 0.1202552504837513, 0b1001, 0b1000), + ), + ), + tl.where( + A_normalized > -0.33967943489551544, + tl.where( + A_normalized > -0.13791173323988914, + tl.where(A_normalized > -0.045525018125772476, 0b0111, 0b0110), + tl.where(A_normalized > -0.23460740596055984, 0b0101, 0b0100), + ), + tl.where( + A_normalized > -0.6106329262256622, + tl.where(A_normalized > -0.4599952697753906, 0b0011, 0b0010), + tl.where(A_normalized > -0.8480964004993439, 0b0001, 0b0000), + ), + ), + ) + quantized = result.to(tl.uint8) + + quantized = quantized.reshape((PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE // 2, 2)) + + left, right = quantized.split() + packed = left << 4 | (right & 0xF) + + packed_flat = tl.reshape(packed, (BLOCK_SIZE * SPLIT_NUM_BLOCKS,)) + out_offsets = block_start_idx * BLOCK_SIZE // 2 + tl.arange(0, SPLIT_NUM_BLOCKS * BLOCK_SIZE) + # Use n - n//2 instead of (n+1)//2 to avoid integer overflow for large n + out_mask = out_offsets < (n_elements - n_elements // 2) + tl.store(out_ptr + out_offsets, packed_flat, mask=out_mask) + + +def quantize_4bit_blockwise_triton(A, blocksize, quant_type, blocks, absmax, num_elements, quantized_out): + # grid = lambda META: (triton.cdiv(blocks, META["SPLIT_NUM_BLOCKS"]),) + split_num_blocks = 4 + grid = (triton.cdiv(blocks, split_num_blocks),) + if quant_type == "fp4": + quantize_fp4_blockwise_kernel[grid]( + A_ptr=A, + absmax_ptr=absmax, + out_ptr=quantized_out, + n_elements=num_elements, + BLOCK_SIZE=blocksize, + SPLIT_NUM_BLOCKS=split_num_blocks, + ) + else: + quantize_nf4_blockwise_kernel[grid]( + A_ptr=A, + absmax_ptr=absmax, + out_ptr=quantized_out, + n_elements=num_elements, + BLOCK_SIZE=blocksize, + SPLIT_NUM_BLOCKS=split_num_blocks, + ) + return quantized_out, absmax + + +@triton.jit +def dequant_4bit_body_util(a, offsets, quant_ptr, absmax_ptr, n_elems, QUANT_BLOCK: tl.constexpr): + PAIRED_QUANT_BLOCK: tl.constexpr = QUANT_BLOCK // 2 + mask = offsets < n_elems + higher = a & 0xF + # lower 4bits + lower = a >> 4 + + abs_offsets = offsets // PAIRED_QUANT_BLOCK + absmax = tl.load(absmax_ptr + abs_offsets, mask=mask, other=1.0, eviction_policy="evict_last") + + # apply conversion + lower_4 = tl.load(quant_ptr + lower, eviction_policy="evict_last") + higher_4 = tl.load(quant_ptr + higher, eviction_policy="evict_last") + + mul_high = higher_4 * absmax + mul_low = lower_4 * absmax + out_dq = tl.interleave(mul_low, mul_high) + return out_dq + + +# Triton implementation of similar CUDA kernel to avoid loading code from csrc/kernels.cu::dDequantizeFP4Tree +@triton.jit +def dequantize_fp4_tree(val, absmax): + # val: tl.tensor (uint8) + # absmax: tl.tensor (float32/float16) + # 00001100 00001011 00001001 00001111 + sign = tl.where((val & 0b1000) == 0b1000, -1.0, 1.0) # -1 + third_bit = (val & 0b0100) == 0b0100 # True + second_bit = (val & 0b0010) == 0b0010 # False + first_bit = (val & 0b0001) == 0b0001 # False + + branch1 = tl.where( + second_bit, + tl.where(first_bit, 0.25, 0.16666667), # 1111, 1110 + tl.where(first_bit, 0.5, 0.33333333), # 1101, 1100 + ) + branch2 = tl.where( + second_bit, + tl.where(first_bit, 1.0, 0.66666667), # 1011, 1010 + tl.where(first_bit, 0.00520833, 0.0), # 1001, 1000 + ) + out = tl.where(third_bit, branch1, branch2) + return out * sign * absmax + + +@triton.jit +def dequant_fp4_body_util(a, offsets, absmax_ptr, n_elems, QUANT_BLOCK: tl.constexpr): + PAIRED_QUANT_BLOCK: tl.constexpr = QUANT_BLOCK // 2 + mask = offsets < n_elems + higher = a & 0xF + lower = a >> 4 + + abs_offsets = offsets // PAIRED_QUANT_BLOCK + absmax = tl.load(absmax_ptr + abs_offsets, mask=mask, other=1.0, eviction_policy="evict_last") + mul_high = dequantize_fp4_tree(higher, absmax) + mul_low = dequantize_fp4_tree(lower, absmax) + out_dq = tl.interleave(mul_low, mul_high) + return out_dq + + +# Triton implementation of similar CUDA kernel to avoid loading code from csrc/kernels.cu::dDequantizeNF4 +@triton.jit +def dequantize_nf4_tree(val): + # val: tl.tensor (uint8) + cond0 = (val & 0b1000) == 0b1000 + cond1 = (val & 0b0100) == 0b0100 + cond2 = (val & 0b0010) == 0b0010 + cond3 = (val & 0b0001) == 0b0001 + + # Positive branch (val & 0b1000) == 8 + branch_pos = tl.where( + cond1, + tl.where( + cond2, + tl.where(cond3, 1.0, 0.7229568362236023), # 1111, 1110 + tl.where(cond3, 0.5626170039176941, 0.44070982933044434), # 1101, 1100 + ), + tl.where( + cond2, + tl.where(cond3, 0.33791524171829224, 0.24611230194568634), # 1011, 1010 + tl.where(cond3, 0.16093020141124725, 0.07958029955625534), # 1001, 1000 + ), + ) + + # Negative branch (val & 0b1000) == 0 + branch_neg = tl.where( + cond1, + tl.where( + cond2, + tl.where(cond3, 0.0, -0.09105003625154495), # 0111, 0110 + tl.where(cond3, -0.18477343022823334, -0.28444138169288635), # 0101, 0100 + ), + tl.where( + cond2, + tl.where(cond3, -0.39491748809814453, -0.5250730514526367), # 0011, 0010 + tl.where(cond3, -0.6961928009986877, -1.0), # 0001, 0000 + ), + ) + return tl.where(cond0, branch_pos, branch_neg) + + +@triton.jit +def dequant_nf4_body_util(a, offsets, absmax_ptr, n_elems, QUANT_BLOCK: tl.constexpr): + PAIRED_QUANT_BLOCK: tl.constexpr = QUANT_BLOCK // 2 + mask = offsets < n_elems + higher = a & 0xF + # lower 4bits + lower = a >> 4 + + abs_offsets = offsets // PAIRED_QUANT_BLOCK + absmax = tl.load(absmax_ptr + abs_offsets, mask=mask, other=1.0, eviction_policy="evict_last") + mul_high = dequantize_nf4_tree(higher) * absmax + mul_low = dequantize_nf4_tree(lower) * absmax + out_dq = tl.interleave(mul_low, mul_high) + return out_dq + + +# All such kernels are similar, so maybe code can be generalised. +# @triton.autotune( +# configs=[ +# # # triton.Config({'SPLIT_SIZE': 64}), +# # # # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'auto'}, num_stages=4, num_warps=32), +# triton.Config({'SPLIT_SIZE': 128}), +# triton.Config({'SPLIT_SIZE': 128}, num_warps = 32, num_stages = 2), +# # # triton.Config({'SPLIT_SIZE': 128}, num_warps = 4, num_stages = 4), +# # # # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'auto'}, num_stages=4, num_warps=32), +# triton.Config({'SPLIT_SIZE': 256}), +# triton.Config({'SPLIT_SIZE': 256}, num_warps = 32, num_stages = 2), +# # triton.Config({'SPLIT_SIZE': 256}, num_warps = 4, num_stages = 4), +# triton.Config({'SPLIT_SIZE': 512}), +# triton.Config({'SPLIT_SIZE': 512}, num_warps = 32, num_stages = 2), +# # triton.Config({'SPLIT_SIZE': 512}, num_warps = 4, num_stages = 4), +# # # # triton.Config({'SPLIT_SIZE': 512, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 512, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 512, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # # # triton.Config({'SPLIT_SIZE': 512, 'grf_mode': 'auto'}, num_stages=4, num_warps=32), +# # # triton.Config({'SPLIT_SIZE': 1024}), +# # # # triton.Config({'SPLIT_SIZE': 2048}), +# # # # triton.Config({'SPLIT_SIZE': 4096}), +# # # # triton.Config({'SPLIT_SIZE': 8192}), +# # # # triton.Config({'SPLIT_SIZE': 16384}), +# ], +# key=['num_paired_elements'], +# ) +@triton.jit +def dequant_4bit_kernel( + a_ptr, + c_ptr, + quant_ptr, + absmax_ptr, + num_paired_elements, + num_output_elements, + QUANT_BLOCK: tl.constexpr, + SPLIT_SIZE: tl.constexpr, +): + pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0. + block_start = pid * SPLIT_SIZE + offsets = block_start + tl.arange(0, SPLIT_SIZE) + mask = offsets < num_paired_elements + + a = tl.load(a_ptr + offsets, mask, eviction_policy="evict_first") + + out_dq = dequant_4bit_body_util( + a=a, + offsets=offsets, + quant_ptr=quant_ptr, + absmax_ptr=absmax_ptr, + n_elems=num_paired_elements, + QUANT_BLOCK=QUANT_BLOCK, + ) + + out_block_start = pid * SPLIT_SIZE * 2 + offs = out_block_start + tl.arange(0, SPLIT_SIZE * 2) + mask = offs < num_output_elements + tl.store(c_ptr + offs, out_dq, mask) + + +# @triton.autotune( +# configs=[ +# triton.Config({'SPLIT_SIZE': 128}, num_warps = 32, num_stages = 2), +# triton.Config({'SPLIT_SIZE': 256}), +# triton.Config({'SPLIT_SIZE': 256}, num_warps = 32, num_stages = 2), +# triton.Config({'SPLIT_SIZE': 512}), +# triton.Config({'SPLIT_SIZE': 512}, num_warps = 32, num_stages = 2), +# triton.Config({'SPLIT_SIZE': 1024}, num_warps = 32, num_stages = 2), +# ], +# key=['num_paired_elements'], +# ) +@triton.jit +def dequant_fp4_kernel( + a_ptr, + c_ptr, + absmax_ptr, + num_paired_elements, + num_output_elements, + QUANT_BLOCK: tl.constexpr, + SPLIT_SIZE: tl.constexpr, +): + pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0. + block_start = pid * SPLIT_SIZE + offsets = block_start + tl.arange(0, SPLIT_SIZE) + mask = offsets < num_paired_elements + + a = tl.load(a_ptr + offsets, mask, eviction_policy="evict_first") + + out_dq = dequant_fp4_body_util( + a=a, + offsets=offsets, + absmax_ptr=absmax_ptr, + n_elems=num_paired_elements, + QUANT_BLOCK=QUANT_BLOCK, + ) + + out_block_start = pid * SPLIT_SIZE * 2 + offs = out_block_start + tl.arange(0, SPLIT_SIZE * 2) + mask = offs < num_output_elements + tl.store(c_ptr + offs, out_dq, mask) + + +# @triton.autotune( +# configs=[ +# triton.Config({'SPLIT_SIZE': 128}, num_warps = 32, num_stages = 2), +# triton.Config({'SPLIT_SIZE': 256}), +# triton.Config({'SPLIT_SIZE': 256}, num_warps = 32, num_stages = 2), +# triton.Config({'SPLIT_SIZE': 512}), +# triton.Config({'SPLIT_SIZE': 512}, num_warps = 32, num_stages = 2), +# triton.Config({'SPLIT_SIZE': 1024}, num_warps = 32, num_stages = 2), +# ], +# key=['num_paired_elements'], +# ) +@triton.jit +def dequant_nf4_kernel( + a_ptr, + c_ptr, + absmax_ptr, + num_paired_elements, + num_output_elements, + QUANT_BLOCK: tl.constexpr, + SPLIT_SIZE: tl.constexpr, +): + pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0. + block_start = pid * SPLIT_SIZE + offsets = block_start + tl.arange(0, SPLIT_SIZE) + mask = offsets < num_paired_elements + + a = tl.load(a_ptr + offsets, mask, eviction_policy="evict_first") + + out_dq = dequant_nf4_body_util( + a=a, + offsets=offsets, + absmax_ptr=absmax_ptr, + n_elems=num_paired_elements, + QUANT_BLOCK=QUANT_BLOCK, + ) + + out_block_start = pid * SPLIT_SIZE * 2 + offs = out_block_start + tl.arange(0, SPLIT_SIZE * 2) + mask = offs < num_output_elements + tl.store(c_ptr + offs, out_dq, mask) + + +def dequantize_4bit_impl( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + # It's will be processed as an array, so + # actual length is row * col + # Elements are in uint8 format, so interleaved + # so total amount of data is 2 * elem_count + number_of_paired_elements = A.numel() + num_output_elements = out.numel() + # we assume that split_size > quant_blocksize + + SPLIT_SIZE = 256 + # grid = lambda META: (triton.cdiv(number_of_paired_elements, META['SPLIT_SIZE']), ) + grid = (triton.cdiv(number_of_paired_elements, SPLIT_SIZE),) + if quant_type == "fp4": + dequant_fp4_kernel[grid](A, out, absmax, number_of_paired_elements, num_output_elements, blocksize, SPLIT_SIZE) + else: + dequant_nf4_kernel[grid](A, out, absmax, number_of_paired_elements, num_output_elements, blocksize, SPLIT_SIZE) + + +def dequantize_4bit_impl_passing_code( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + code: torch.Tensor, + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + number_of_paired_elements = A.numel() + num_output_elements = out.numel() + # we assume that split_size > quant_blocksize + + SPLIT_SIZE = 256 + # grid = lambda META: (triton.cdiv(number_of_paired_elements, META['SPLIT_SIZE']), ) + grid = (triton.cdiv(number_of_paired_elements, SPLIT_SIZE),) + dequant_4bit_kernel[grid]( + A, out, code, absmax, number_of_paired_elements, num_output_elements, blocksize, SPLIT_SIZE + ) + + +######################### Fallback dequantization functions ######################### +## for debug ## + + +# @triton.autotune( +# configs=[ +# # triton.Config({'SPLIT_NUM_BLOCKS': 1, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_NUM_BLOCKS': 1, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_NUM_BLOCKS': 1, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # # +# # triton.Config({"SPLIT_NUM_BLOCKS": 1, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# # +# triton.Config({"SPLIT_NUM_BLOCKS": 2}), +# # triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "large"}, num_stages=2, num_warps=32), +# # # triton.Config({'SPLIT_NUM_BLOCKS': 2, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "auto"}, num_stages=2, num_warps=32), +# # triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# # triton.Config({"SPLIT_NUM_BLOCKS": 4, "grf_mode": "large"}, num_stages=2, num_warps=32), +# # triton.Config({"SPLIT_NUM_BLOCKS": 4, "grf_mode": "large"}, num_stages=4, num_warps=32), +# # triton.Config({'SPLIT_NUM_BLOCKS': 8, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# ], +# key=["n_elements", "BLOCK_SIZE"], +# ) +@triton.jit +def quantize_4bit_blockwise_kernel( + A_ptr, + code_ptr, + absmax_ptr, + out_ptr, + n_elements, + BLOCK_SIZE: tl.constexpr, + CODE_SIZE: tl.constexpr, + SPLIT_NUM_BLOCKS: tl.constexpr, +): + PAIRED_SPLIT_NUM_BLOCKS: tl.constexpr = SPLIT_NUM_BLOCKS * 2 + block_start_idx = tl.program_id(0) * PAIRED_SPLIT_NUM_BLOCKS + thread_idx = tl.arange(0, PAIRED_SPLIT_NUM_BLOCKS * BLOCK_SIZE) + + offsets = block_start_idx * BLOCK_SIZE + thread_idx + mask = offsets < n_elements + + A = tl.load(A_ptr + offsets, mask=mask, other=0.0) + + # To be able process several blocks -> (PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE) + A_reshaped = tl.reshape(A, (PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE)) + + # Calculating absamax for each block + absmax = tl.max(tl.abs(A_reshaped), axis=1) + tl.store(absmax_ptr + block_start_idx + tl.arange(0, PAIRED_SPLIT_NUM_BLOCKS), absmax) + + A_normalized = A_reshaped / absmax[:, None] + A_normalized = tl.clamp(A_normalized, -1.0, 1.0) + + lower_pivot = tl.zeros((PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE), dtype=tl.int32) + upper_pivot = tl.full((PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE), CODE_SIZE - 1, dtype=tl.int32) + + for _ in range(4): # ceil(log2(code_size)) = 4, actually, in general case should be input parameter + pivot = (lower_pivot + upper_pivot) // 2 + val = tl.load(code_ptr + pivot) + is_higher = A_normalized > val # code[pivot] + lower_pivot = tl.where(is_higher, pivot, lower_pivot) + upper_pivot = tl.where(is_higher, upper_pivot, pivot) + + # Choose closest level + lower_val = tl.load(code_ptr + lower_pivot) + upper_val = tl.load(code_ptr + upper_pivot) + lower_dist = tl.abs(A_normalized - lower_val) + upper_dist = tl.abs(A_normalized - upper_val) + quantized = tl.where(lower_dist <= upper_dist, lower_pivot, upper_pivot).to(tl.uint8) + + quantized = quantized.reshape((PAIRED_SPLIT_NUM_BLOCKS, BLOCK_SIZE // 2, 2)) + quantized = quantized.to(tl.uint8, bitcast=True) + left, right = quantized.split() + packed = left << 4 | (right & 0xF) + + # Reduce don't guarantee the order of the elements passed to unite_2_int4 + # packed = tl.reduce(quantized, axis=2, combine_fn=unite_2_int4) + # packed = packed.to(tl.uint8, bitcast=True) + + packed_flat = tl.reshape(packed, (BLOCK_SIZE * SPLIT_NUM_BLOCKS,)) + out_offsets = block_start_idx * BLOCK_SIZE // 2 + tl.arange(0, SPLIT_NUM_BLOCKS * BLOCK_SIZE) + out_mask = out_offsets < n_elements // 2 + tl.store(out_ptr + out_offsets, packed_flat, mask=out_mask) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_8bit_quant.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_8bit_quant.py new file mode 100644 index 0000000000000000000000000000000000000000..c0a5a21efff7507e1409fc2d021c9814bed22c1e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_8bit_quant.py @@ -0,0 +1,195 @@ +import torch + +import triton +import triton.language as tl + + +# @triton.autotune( +# configs=[ +# # triton.Config({'SPLIT_SIZE': 64}), +# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'auto'}, num_stages=4, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 128}), +# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'large'}, num_stages=4, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'auto'}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_SIZE": 256}), +# # triton.Config({'SPLIT_SIZE': 256, 'grf_mode': 'large'}, num_stages=2, num_warps=32), +# # triton.Config({'SPLIT_SIZE': 256, 'grf_mode': 'auto'}, num_stages=2, num_warps=32), +# triton.Config({"SPLIT_SIZE": 512}), +# # triton.Config({'SPLIT_SIZE': 1024}), +# ], +# key=["num_paired_elements", "QUANT_BLOCK"], +# ) +@triton.jit +def dequant_8bit_kernel( + a_ptr, + out_ptr, + code_ptr, + absmax_ptr, + n, + QUANT_BLOCK: tl.constexpr, + SPLIT_SIZE: tl.constexpr, +): + pid = tl.program_id(axis=0) + block_start = pid * SPLIT_SIZE + offsets = block_start + tl.arange(0, SPLIT_SIZE) + mask = offsets < n + out_dq = dequant_8bit_blockwise_kernel_util(a_ptr, offsets, code_ptr, absmax_ptr, mask, QUANT_BLOCK) + tl.store(out_ptr + offsets, out_dq, mask) + + +def dequant_8bit_blockwise( + a: torch.Tensor, + absmax: torch.Tensor, + quant_state_code: torch.Tensor, + quant_blocksize: int = 64, + dtype: torch.dtype = None, + out: torch.Tensor = None, +): + n = a.numel() + if out is None: + if dtype is None: + raise ValueError("If out is None, dtype must be specified") + out = torch.empty_like(a, dtype=dtype, device=a.device) + + SPLIT_SIZE = 256 + # grid = lambda META: (triton.cdiv(number_of_paired_elements, META["SPLIT_SIZE"]),) + grid = (triton.cdiv(n, SPLIT_SIZE),) + dequant_8bit_kernel[grid]( + a, + out, + quant_state_code, + absmax, + n, + quant_blocksize, + SPLIT_SIZE, + ) + return out + + +# @triton.autotune( +# configs=[ +# triton.Config({"SPLIT_NUM_BLOCKS": 1, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "auto"}, num_stages=4, num_warps=32), +# triton.Config({"SPLIT_NUM_BLOCKS": 1}), +# triton.Config({"SPLIT_NUM_BLOCKS": 2}), +# ], +# key=["n_elements"], +# ) +@triton.jit +def quantize_8bit_blockwise_kernel( + A_ptr, + code_ptr, + absmax_ptr, + out_ptr, + n_elements, + BLOCK_SIZE: tl.constexpr, + CODE_SIZE: tl.constexpr, + SPLIT_NUM_BLOCKS: tl.constexpr, +): + block_start_idx = tl.program_id(0) * SPLIT_NUM_BLOCKS + thread_idx = tl.arange(0, SPLIT_NUM_BLOCKS * BLOCK_SIZE) + + offsets = block_start_idx * BLOCK_SIZE + thread_idx + mask = offsets < n_elements + + A = tl.load(A_ptr + offsets, mask=mask, other=0.0) + + quantized, absmax = quantize_8bit_blockwise_kernel_util(A, code_ptr, CODE_SIZE, BLOCK_SIZE, SPLIT_NUM_BLOCKS) + tl.store(absmax_ptr + block_start_idx + tl.arange(0, SPLIT_NUM_BLOCKS), absmax) + tl.store(out_ptr + offsets, quantized, mask=mask) + + +def quantize_blockwise_triton(A, code, blocksize, absmax=None, out=None): + n = A.numel() + blocks = -(n // -blocksize) + + if absmax is None: + absmax = torch.empty((blocks,), device=A.device, dtype=A.dtype) + if out is None: + out = torch.empty_like(A.flatten(), dtype=torch.uint8) + + split_num_blocks = 1 + grid = (triton.cdiv(blocks, split_num_blocks),) + # grid = lambda META: (triton.cdiv(blocks, META["SPLIT_NUM_BLOCKS"]),) + quantize_8bit_blockwise_kernel[grid]( + A_ptr=A, + code_ptr=code, + absmax_ptr=absmax, + out_ptr=out, + n_elements=n, + BLOCK_SIZE=blocksize, + CODE_SIZE=code.numel(), + SPLIT_NUM_BLOCKS=split_num_blocks, + # num_warps=1, + # num_stages=2, + ) + out = out.reshape(A.shape) + + return out, absmax + + +@triton.jit +def quantize_8bit_blockwise_kernel_util( + a, + code_ptr, + CODE_SIZE: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + N_PER_TH: tl.constexpr, +): + # To be able process several blocks -> (BLOCK_SIZE, SPLIT_NUM_BLOCKS) + a_reshaped = tl.reshape(a, (N_PER_TH, BLOCK_SIZE)) + + # Calculating absmax for each block + absmax = tl.max(tl.abs(a_reshaped), axis=1) + + a_normalized = a_reshaped / absmax[:, None] + a_normalized = tl.clamp(a_normalized, -1.0, 1.0) + + lower_pivot = tl.zeros((N_PER_TH, BLOCK_SIZE), dtype=tl.int32) + upper_pivot = tl.full((N_PER_TH, BLOCK_SIZE), CODE_SIZE - 1, dtype=tl.int32) + + # ceil(log2(code_size)) = 8, actually, in general case should be input parameter + for _ in range(8): + pivot = (lower_pivot + upper_pivot) // 2 + val = tl.load(code_ptr + pivot) + is_higher = a_normalized > val # code[pivot] + lower_pivot = tl.where(is_higher, pivot, lower_pivot) + upper_pivot = tl.where(is_higher, upper_pivot, pivot) + + # Choose closest level + lower_val = tl.load(code_ptr + lower_pivot) + upper_val = tl.load(code_ptr + upper_pivot) + lower_dist = tl.abs(a_normalized - lower_val) + upper_dist = tl.abs(a_normalized - upper_val) + quantized = tl.where(lower_dist <= upper_dist, lower_pivot, upper_pivot).to(tl.uint8) + + # too slow approach + # diff = tl.abs(A_normalized[:, :, None] - code[None, None, :]) + # quantized = tl.argmin(diff, axis=2).to(tl.uint8) + + quantized_flat = tl.reshape(quantized, (BLOCK_SIZE * N_PER_TH,)) + return quantized_flat, absmax + + +@triton.jit +def dequant_8bit_blockwise_kernel_util( + a_ptr, + offsets, + code_ptr, + absmax_ptr, + mask, + BLOCK_SIZE: tl.constexpr, +): + a = tl.load(a_ptr + offsets, mask, other=0).to(tl.uint8) + scaled_int8 = tl.load(code_ptr + a, mask) + # Load scales + absmax_offsets = offsets // BLOCK_SIZE + absmax = tl.load(absmax_ptr + absmax_offsets, mask=mask, other=0.0, eviction_policy="evict_last") + # Apply scales + out_dq = scaled_int8 * absmax + return out_dq diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_optim.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_optim.py new file mode 100644 index 0000000000000000000000000000000000000000..2cd6d8c93c02b1679ae3728004e4d122507d048c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/kernels_optim.py @@ -0,0 +1,1154 @@ +import math +from typing import Optional + +import torch + +import triton +import triton.language as tl + +# from triton.language.extra import libdevice +from .kernels_8bit_quant import ( + dequant_8bit_blockwise, + dequant_8bit_blockwise_kernel_util, + quantize_8bit_blockwise_kernel_util, + quantize_blockwise_triton, +) + +MOMENTUM = 0 +RMSPROP = 1 +ADAGRAD = 2 +ADAM = 3 +# LION should be larger than MOMENTUM, RMSPROP, ADAGRAD due to comparison in kernels +LION = 4 +ADEMAMIX = 5 + +name2optimizer_id = { + "momentum": MOMENTUM, + "rmsprop": RMSPROP, + "adagrad": ADAGRAD, + "adam": ADAM, + "lion": LION, + "ademamix": ADEMAMIX, +} + + +@triton.jit +def _optimizer_precondition_2state_32bit( + g_ptr, + p_ptr, + state1_ptr, + state2_ptr, + unorm_ptr, + beta1: tl.constexpr, + beta2: tl.constexpr, + eps: tl.constexpr, + weight_decay: tl.constexpr, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale: tl.constexpr, + n_elements, + OPTIMIZER_ID: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + N_PER_TH: tl.constexpr, +): + """Preprocessing optimizer, computing update norm (2-state optimizer)""" + pid = tl.program_id(axis=0) + block_start_idx = pid * N_PER_TH + offsets = block_start_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE * N_PER_TH) + mask = offsets < n_elements + + g_vals = tl.load(g_ptr + offsets, mask=mask, other=0.0) + s1_vals = tl.load(state1_ptr + offsets, mask=mask, other=0.0) + s2_vals = tl.load(state2_ptr + offsets, mask=mask, other=0.0) + + g_vals = gnorm_scale * g_vals + + correction1 = 1.0 / (1.0 - beta1_step) + correction2 = 1.0 / (1.0 - beta2_step) + + if OPTIMIZER_ID == 3: # ADAM + s1_vals = s1_vals * beta1 + (1.0 - beta1) * g_vals + s2_vals = s2_vals * beta2 + (1.0 - beta2) * g_vals * g_vals + + s1_vals = s1_vals * correction1 + s2_vals = s2_vals * correction2 + + update_vals = s1_vals / (tl.sqrt(s2_vals) + eps) + + update_norm = update_vals * update_vals + + elif OPTIMIZER_ID == 5: # ADEMAMIX + update_norm = s1_vals + + total_norm = tl.sum(tl.where(mask, update_norm, 0.0)) + + tl.atomic_add(unorm_ptr, total_norm) + + +@triton.jit +def _optimizer_precondition_1state_32bit( + g_ptr, + p_ptr, + state1_ptr, + state2_ptr, + unorm_ptr, + beta1: tl.constexpr, + beta2: tl.constexpr, + eps: tl.constexpr, + weight_decay, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale: tl.constexpr, + n_elements, + OPTIMIZER_ID: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + N_PER_TH: tl.constexpr, +): + """Preprocessing optimizer, computing update norm (1-state optimizer)""" + pid = tl.program_id(axis=0) + block_start_idx = pid * N_PER_TH + offsets = block_start_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE * N_PER_TH) + mask = offsets < n_elements + + g_vals = tl.load(g_ptr + offsets, mask=mask, other=0.0) + s1_vals = tl.load(state1_ptr + offsets, mask=mask, other=0.0) + + g_vals = gnorm_scale * g_vals + + if OPTIMIZER_ID == 0: # MOMENTUM + if step == 1: + s1_vals = g_vals + else: + s1_vals = s1_vals * beta1 + g_vals + update_norm = s1_vals * s1_vals + + elif OPTIMIZER_ID == 4: # LION + s1_vals = s1_vals * beta2 + (1.0 - beta2) * g_vals + update_norm = s1_vals + + elif OPTIMIZER_ID == 1: # RMSPROP + s1_vals = s1_vals * beta1 + (1.0 - beta1) * g_vals * g_vals + update_vals = g_vals / (tl.sqrt(s1_vals) + eps) + update_norm = update_vals * update_vals + + elif OPTIMIZER_ID == 2: # ADAGRAD + s1_vals = s1_vals + g_vals * g_vals + update_vals = g_vals / (tl.sqrt(s1_vals) + eps) + update_norm = update_vals * update_vals + + total_norm = tl.sum(tl.where(mask, update_norm, 0.0)) + + tl.atomic_add(unorm_ptr, total_norm) + + +@triton.jit +def _optimizer_update_2state_32bit_triton_kernel( + g_ptr, + p_ptr, + state1_ptr, + state2_ptr, + unorm_ptr, + max_unorm: tl.constexpr, + param_norm, + beta1: tl.constexpr, + beta2: tl.constexpr, + beta3, + alpha, + eps: tl.constexpr, + weight_decay: tl.constexpr, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale: tl.constexpr, + skip_zeros, + n_elements, + OPTIMIZER_ID: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + N_PER_TH: tl.constexpr, +): + """2-state optimizer kernel""" + pid = tl.program_id(axis=0) + block_start_idx = pid * N_PER_TH + offsets = block_start_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE * N_PER_TH) + mask = offsets < n_elements + + g_vals = tl.load(g_ptr + offsets, mask=mask, other=0.0).to(tl.float32) + p_vals = tl.load(p_ptr + offsets, mask=mask, other=0.0).to(tl.float32) + s1_vals = tl.load(state1_ptr + offsets, mask=mask, other=0.0) + s2_vals = tl.load(state2_ptr + offsets, mask=mask, other=0.0) + + if OPTIMIZER_ID == 5: # ADEMAMIX + s3_vals = tl.load(state1_ptr + n_elements + offsets, mask=mask, other=0.0) + + g_vals = gnorm_scale * g_vals + + update_scale = 1.0 + if max_unorm > 0.0: + current_unorm = tl.sqrt(tl.load(unorm_ptr)) + if current_unorm > max_unorm * param_norm: + update_scale = (max_unorm * param_norm) / current_unorm + + if OPTIMIZER_ID == 3: # ADAM + s1_vals = s1_vals * beta1 + (1.0 - beta1) * g_vals + s2_vals = s2_vals * beta2 + (1.0 - beta2) * g_vals * g_vals + + correction1 = 1.0 - beta1_step + correction2 = tl.sqrt(1.0 - beta2_step) + step_size = -lr * correction2 / correction1 + + if weight_decay > 0.0: + p_vals = p_vals * (1.0 - lr * weight_decay) + + update_val = update_scale * step_size * (s1_vals / (tl.sqrt(s2_vals) + eps * correction2)) + p_vals = p_vals + update_val + + elif OPTIMIZER_ID == 5: # ADEMAMIX + s1_vals = s1_vals * beta1 + (1.0 - beta1) * g_vals # m1 + s3_vals = s3_vals * beta3 + (1.0 - beta3) * g_vals # m2 + s2_vals = s2_vals * beta2 + (1.0 - beta2) * g_vals * g_vals # nu + + correction1 = 1.0 - beta1_step + correction2 = tl.sqrt(1.0 - beta2_step) + + if weight_decay > 0.0: + p_vals = p_vals * (1.0 - lr * weight_decay) + + mixed_momentum = (s1_vals / correction1) + (alpha * s3_vals) + adaptive_term = (tl.sqrt(s2_vals) / correction2) + eps + p_vals = p_vals - lr * (mixed_momentum / adaptive_term) + + tl.store(p_ptr + offsets, p_vals, mask=mask) + tl.store(state1_ptr + offsets, s1_vals, mask=mask) + tl.store(state2_ptr + offsets, s2_vals, mask=mask) + + if OPTIMIZER_ID == 5: # ADEMAMIX + tl.store(state1_ptr + n_elements + offsets, s3_vals, mask=mask) + + +@triton.jit +def _optimizer_update_1state_32bit_triton_kernel( + g_ptr, + p_ptr, + state1_ptr, + state2_ptr, + unorm_ptr, + max_unorm: tl.constexpr, + param_norm, + beta1: tl.constexpr, + beta2: tl.constexpr, + beta3, + alpha, + eps: tl.constexpr, + weight_decay: tl.constexpr, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale: tl.constexpr, + skip_zeros, + n_elements, + OPTIMIZER_ID: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + N_PER_TH: tl.constexpr, +): + """1-state optimizer kernel""" + pid = tl.program_id(axis=0) + block_start_idx = pid * N_PER_TH + offsets = block_start_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE * N_PER_TH) + mask = offsets < n_elements + + g_vals = tl.load(g_ptr + offsets, mask=mask, other=0.0).to(tl.float32) + p_vals = tl.load(p_ptr + offsets, mask=mask, other=0.0).to(tl.float32) + s1_vals = tl.load(state1_ptr + offsets, mask=mask, other=0.0) + + g_vals = gnorm_scale * g_vals + if weight_decay > 0.0: + g_vals = g_vals + p_vals * weight_decay + + update_scale = 1.0 + if max_unorm > 0.0: + current_unorm = tl.sqrt(tl.load(unorm_ptr)) + if current_unorm > max_unorm * param_norm + eps: + update_scale = (max_unorm * param_norm + eps) / current_unorm + + if OPTIMIZER_ID == 0: # MOMENTUM + if step == 1: + s1_vals = g_vals + else: + s1_vals = s1_vals * beta1 + g_vals + + update_val = update_scale * (-lr * s1_vals) + p_vals = p_vals + update_val + + elif OPTIMIZER_ID == 4: # LION + momentum_update = s1_vals * beta1 + (1.0 - beta1) * g_vals + update_val = update_scale * lr * tl.where(momentum_update > 0, 1.0, tl.where(momentum_update < 0, -1.0, 0.0)) + p_vals = p_vals - update_val + + s1_vals = s1_vals * beta2 + (1.0 - beta2) * g_vals + + elif OPTIMIZER_ID == 1: # RMSPROP + s1_vals = s1_vals * beta1 + (1.0 - beta1) * g_vals * g_vals + + update_val = update_scale * lr * g_vals / (tl.sqrt(s1_vals) + eps) + p_vals = p_vals - update_val + + elif OPTIMIZER_ID == 2: # ADAGRAD + s1_vals = s1_vals + g_vals * g_vals + + update_val = lr * g_vals / (tl.sqrt(s1_vals) + eps) + p_vals = p_vals - update_val + + tl.store(p_ptr + offsets, p_vals, mask=mask) + tl.store(state1_ptr + offsets, s1_vals, mask=mask) + + +name2optimizer_32bit_fn = { + "adam": { + "preprocess": _optimizer_precondition_2state_32bit, + "update": _optimizer_update_2state_32bit_triton_kernel, + }, + "ademamix": { + "preprocess": _optimizer_precondition_2state_32bit, + "update": _optimizer_update_2state_32bit_triton_kernel, + }, + "momentum": { + "preprocess": _optimizer_precondition_1state_32bit, + "update": _optimizer_update_1state_32bit_triton_kernel, + }, + "rmsprop": { + "preprocess": _optimizer_precondition_1state_32bit, + "update": _optimizer_update_1state_32bit_triton_kernel, + }, + "adagrad": { + "preprocess": _optimizer_precondition_1state_32bit, + "update": _optimizer_update_1state_32bit_triton_kernel, + }, + "lion": { + "preprocess": _optimizer_precondition_1state_32bit, + "update": _optimizer_update_1state_32bit_triton_kernel, + }, +} + + +def optimizer_update_32bit_impl( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + unorm_vec: Optional[torch.Tensor], + max_unorm: float, + param_norm: float, + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + weight_decay: float, + step: int, + lr: float, + gnorm_scale: float = 1.0, + skip_zeros=False, +) -> None: + """ + 32-bit optimizer implemented by Triton + """ + if skip_zeros: + raise NotImplementedError("skip_zeros is not supported on XPU yet") + + BLOCK_SIZE = 256 + N_PER_TH = 1 # Number of blocks processed per thread. + grid = (triton.cdiv(p.numel(), BLOCK_SIZE * N_PER_TH),) + optimizer_id = name2optimizer_id[optimizer_name] + fn_preprocess = name2optimizer_32bit_fn[optimizer_name]["preprocess"] + fn_update = name2optimizer_32bit_fn[optimizer_name]["update"] + + # In torch=2.7 on XPU there is an issue with libdevice.pow, leading to an error. + # For backwards compatibility we precompute the bias correction factors. + beta1_step = beta1**step + beta2_step = beta2**step + + if optimizer_name == "lion": + fn_update[grid]( + g, + p, + state1, + state2, + unorm_vec, + max_unorm, + param_norm, + beta1, + beta2, + beta3, + alpha, + eps, + weight_decay, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale, + skip_zeros, + p.numel(), + optimizer_id, + BLOCK_SIZE, + N_PER_TH, + num_warps=2, + ) + + if max_unorm > 0.0: + unorm_vec.zero_() + fn_preprocess[grid]( + g, + p, + state1, + state2, + unorm_vec, + beta1, + beta2, + eps, + weight_decay, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale, + p.numel(), + optimizer_id, + BLOCK_SIZE, + N_PER_TH, + num_warps=2, + ) + + else: + if max_unorm > 0.0: + unorm_vec.zero_() + fn_preprocess[grid]( + g, + p, + state1, + state2, + unorm_vec, + beta1, + beta2, + eps, + weight_decay, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale, + p.numel(), + optimizer_id, + BLOCK_SIZE, + N_PER_TH, + num_warps=2, + ) + + fn_update[grid]( + g, + p, + state1, + state2, + unorm_vec, + max_unorm, + param_norm, + beta1, + beta2, + beta3, + alpha, + eps, + weight_decay, + step, + beta1_step, + beta2_step, + lr, + gnorm_scale, + skip_zeros, + p.numel(), + optimizer_id, + BLOCK_SIZE, + N_PER_TH, + num_warps=2, + ) + + +########################################### +# Pure torch implementation for reference # +########################################### + + +@torch.compile +def _dequantize_blockwise_pytorch( + A: torch.Tensor, + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + dtype: torch.dtype, +) -> torch.Tensor: + """ + Pure PyTorch reference implementation for block-wise dequantization. + """ + if A.numel() == 0: + return torch.empty_like(A, dtype=dtype) + + A_flat = A.flatten() + num_elements = A_flat.numel() + + dequantized_flat = code.to(A.device)[A_flat.long()].to(dtype) + + num_blocks = math.ceil(num_elements / blocksize) + pad_len = num_blocks * blocksize - num_elements + if pad_len > 0: + dequantized_flat = torch.nn.functional.pad(dequantized_flat, (0, pad_len)) + + dequantized_blocks = dequantized_flat.reshape(num_blocks, blocksize) + + rescaled_blocks = dequantized_blocks * absmax.unsqueeze(1).to(dtype) + + rescaled_flat = rescaled_blocks.flatten() + if pad_len > 0: + rescaled_flat = rescaled_flat[:-pad_len] + + return rescaled_flat.reshape(A.shape) + + +@torch.compile +def _quantize_blockwise_pytorch( + A: torch.Tensor, + code: torch.Tensor, + blocksize: int, +) -> tuple[torch.Tensor, torch.Tensor]: + """ + Pure PyTorch reference implementation for block-wise quantization. + """ + if A.numel() == 0: + return torch.empty_like(A, dtype=torch.uint8), torch.empty(0, dtype=torch.float32, device=A.device) + + A_flat = A.flatten() + num_elements = A_flat.numel() + + num_blocks = math.ceil(num_elements / blocksize) + + pad_len = num_blocks * blocksize - num_elements + if pad_len > 0: + A_flat = torch.nn.functional.pad(A_flat, (0, pad_len)) + + A_blocks = A_flat.reshape(num_blocks, blocksize) + + absmax = torch.max(torch.abs(A_blocks), dim=1, keepdim=True)[0] + absmax[absmax == 0] = 1.0 + + scaled_blocks = A_blocks / absmax + + # Inefficient but straightforward quantization, takes a lot of memory + diff = torch.abs(scaled_blocks.unsqueeze(2) - code.to(A.device)) + quantized_indices = torch.argmin(diff, dim=2).to(torch.uint8) + + quantized_flat = quantized_indices.flatten() + if pad_len > 0: + quantized_flat = quantized_flat[:-pad_len] + + return quantized_flat.reshape(A.shape), absmax.flatten() + + +# Main updated function +def optimizer_update_8bit_blockwise_pytorch( + p: torch.Tensor, + g: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + beta1: float, + beta2: float, + beta3: float, # ADEMIX + alpha: float, # ADEMIX + eps: float, + step: int, + lr: float, + qmap1: torch.Tensor, + qmap2: Optional[torch.Tensor], + absmax1: torch.Tensor, + absmax2: Optional[torch.Tensor], + weight_decay: float, + gnorm_scale: float, + skip_zeros: bool, + # ADEMIX + *, + optimizer_name: str, +) -> None: + """ + Pure PyTorch implementation of the 8-bit block-wise optimizer update step. + This version ensures high-precision updates for float16 parameters. + """ + if skip_zeros: + raise ValueError("skip_zeros is not supported on XPU yet.") + + blocksize = 256 + + with torch.no_grad(): + # Dequantize states to perform updates in 32-bit precision + if optimizer_name == "ademamix" and absmax1.ndim == 2: + # For AdEMAMix, state1 holds two EMAs, so absmax1 is stacked. + s1_1_fp32 = _dequantize_blockwise_pytorch(state1[0], absmax1[0], qmap1, blocksize, torch.float32) + s1_2_fp32 = _dequantize_blockwise_pytorch(state1[1], absmax1[1], qmap1, blocksize, torch.float32) + state1_fp32 = torch.stack([s1_1_fp32, s1_2_fp32]) + else: + state1_fp32 = _dequantize_blockwise_pytorch(state1, absmax1, qmap1, blocksize, torch.float32) + + state2_fp32 = None + if state2 is not None: + state2_fp32 = _dequantize_blockwise_pytorch(state2, absmax2, qmap2, blocksize, torch.float32) + + grad = g.float() * gnorm_scale + + # Create a 32-bit copy of the parameter for high-precision updates + p_fp32 = p.data.float() + + if optimizer_name == "adam": + state1_fp32.mul_(beta1).add_(grad, alpha=1.0 - beta1) + state2_fp32.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2) + + bias_correction1 = 1.0 - beta1**step + bias_correction2 = 1.0 - beta2**step + + denom = (state2_fp32.sqrt() / math.sqrt(bias_correction2)).add_(eps) + + if weight_decay > 0.0: + p_fp32.mul_(1.0 - lr * weight_decay) + p_fp32.addcdiv_(state1_fp32, denom, value=-lr / bias_correction1) + + elif optimizer_name == "ademamix": + m1_fp32, m2_fp32 = state1_fp32[0], state1_fp32[1] + nu_fp32 = state2_fp32 + + m1_fp32.mul_(beta1).add_(grad, alpha=1.0 - beta1) + m2_fp32.mul_(beta3).add_(grad, alpha=1.0 - beta3) + nu_fp32.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2) + + bias_correction1 = 1.0 - beta1**step + bias_correction2 = math.sqrt(1.0 - beta2**step) + + update = (m1_fp32 / bias_correction1 + alpha * m2_fp32) / (nu_fp32.sqrt() / bias_correction2 + eps) + + if weight_decay > 0.0: + p_fp32.mul_(1.0 - lr * weight_decay) + + p_fp32.add_(update, alpha=-lr) + state1_fp32 = torch.stack([m1_fp32, m2_fp32]) + + elif optimizer_name == "momentum": + grad.add_(p_fp32, alpha=weight_decay) + if step == 1: + state1_fp32.copy_(grad) + else: + state1_fp32.mul_(beta1).add_(grad) + p_fp32.add_(state1_fp32, alpha=-lr) + + elif optimizer_name == "rmsprop": + grad.add_(p_fp32, alpha=weight_decay) + state1_fp32.mul_(beta1).addcmul_(grad, grad, value=1.0 - beta1) + p_fp32.addcdiv_(grad, state1_fp32.sqrt().add_(eps), value=-lr) + + elif optimizer_name == "lion": + if weight_decay > 0.0: + p_fp32.mul_(1.0 - lr * weight_decay) + + update_dir = torch.sign(state1_fp32.mul(beta1) + grad.mul(1.0 - beta1)) + p_fp32.add_(update_dir, alpha=-lr) + + state1_fp32.mul_(beta2).add_(grad, alpha=1.0 - beta2) + + elif optimizer_name == "adagrad": + grad.add_(p_fp32, alpha=weight_decay) + state1_fp32.addcmul_(grad, grad, value=1.0) + p_fp32.addcdiv_(grad, state1_fp32.sqrt().add_(eps), value=-lr) + + else: + raise NotImplementedError( + f"Pure PyTorch implementation for optimizer '{optimizer_name}' is not available." + ) + + # Copy the updated 32-bit parameter back to the original tensor + p.data.copy_(p_fp32) + + # Re-quantize states and update state tensors in-place + if optimizer_name == "ademamix": + new_m1_8bit, new_absmax_m1 = _quantize_blockwise_pytorch(state1_fp32[0], qmap1, blocksize) + new_m2_8bit, new_absmax_m2 = _quantize_blockwise_pytorch(state1_fp32[1], qmap1, blocksize) + state1[0].copy_(new_m1_8bit) + state1[1].copy_(new_m2_8bit) + absmax1[0].copy_(new_absmax_m1) + absmax1[1].copy_(new_absmax_m2) + + new_state2_8bit, new_absmax2 = _quantize_blockwise_pytorch(state2_fp32, qmap2, blocksize) + state2.copy_(new_state2_8bit) + absmax2.copy_(new_absmax2) + else: + new_state1_8bit, new_absmax1 = _quantize_blockwise_pytorch(state1_fp32, qmap1, blocksize) + state1.copy_(new_state1_8bit) + absmax1.copy_(new_absmax1) + + if state2_fp32 is not None: + new_state2_8bit, new_absmax2 = _quantize_blockwise_pytorch(state2_fp32, qmap2, blocksize) + state2.copy_(new_state2_8bit) + absmax2.copy_(new_absmax2) + + +####################################### +# Mixed torch + triton implementation # +####################################### + + +# Much more memory efficient due to using triton for quantization/dequantization +def optimizer_update_8bit_blockwise_triton_quant( + p: torch.Tensor, + g: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + beta1: float, + beta2: float, + beta3: float, # ADEMIX + alpha: float, # ADEMIX + eps: float, + step: int, + lr: float, + qmap1: torch.Tensor, + qmap2: Optional[torch.Tensor], + absmax1: torch.Tensor, + absmax2: Optional[torch.Tensor], + weight_decay: float, + gnorm_scale: float, + skip_zeros: bool, + # ADEMIX + *, + optimizer_name: str, +) -> None: + """ + Pure PyTorch implementation of the 8-bit block-wise optimizer update step. + This version ensures high-precision updates for float16 parameters. + """ + if skip_zeros and not torch.any(g): + return + + blocksize = 256 + grad = g.float() * gnorm_scale + + with torch.no_grad(): + # Create a 32-bit copy of the parameter for high-precision updates + p_fp32 = p.data.float() + + # Dequantize states to perform updates in 32-bit precision + if optimizer_name == "ademamix" and absmax1.ndim == 2: + # For AdEMAMix, state1 holds two EMAs, so absmax1 is stacked. + s1_1_fp32 = dequant_8bit_blockwise(state1[0], absmax1[0], qmap1, blocksize, dtype=torch.float32) + s1_2_fp32 = dequant_8bit_blockwise(state1[1], absmax1[1], qmap1, blocksize, dtype=torch.float32) + state1_fp32 = torch.stack([s1_1_fp32, s1_2_fp32]) + else: + state1_fp32 = dequant_8bit_blockwise(state1, absmax1, qmap1, blocksize, dtype=torch.float32) + + state2_fp32 = None + if state2 is not None: + state2_fp32 = dequant_8bit_blockwise(state2, absmax2, qmap2, blocksize, dtype=torch.float32) + + # Apply optimizer-specific update logic + if optimizer_name == "adam": + if weight_decay > 0.0: + p_fp32.mul_(1.0 - lr * weight_decay) + + state1_fp32.mul_(beta1).add_(grad, alpha=1.0 - beta1) + state2_fp32.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2) + + bias_correction1 = 1.0 - beta1**step + bias_correction2 = 1.0 - beta2**step + + denom = (state2_fp32.sqrt() / math.sqrt(bias_correction2)).add_(eps) + p_fp32.addcdiv_(state1_fp32, denom, value=-lr / bias_correction1) + + elif optimizer_name == "ademamix": + m1_fp32, m2_fp32 = state1_fp32[0], state1_fp32[1] + nu_fp32 = state2_fp32 + + m1_fp32.mul_(beta1).add_(grad, alpha=1.0 - beta1) + m2_fp32.mul_(beta3).add_(grad, alpha=1.0 - beta3) + nu_fp32.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2) + + bias_correction1 = 1.0 - beta1**step + bias_correction2 = math.sqrt(1.0 - beta2**step) + + update = (m1_fp32 / bias_correction1 + alpha * m2_fp32) / (nu_fp32.sqrt() / bias_correction2 + eps) + + if weight_decay > 0.0: + p_fp32.mul_(1.0 - lr * weight_decay) + + p_fp32.add_(update, alpha=-lr) + state1_fp32 = torch.stack([m1_fp32, m2_fp32]) + + elif optimizer_name == "momentum": + grad.add_(p_fp32, alpha=weight_decay) + if step == 1: + state1_fp32.copy_(grad) + else: + state1_fp32.mul_(beta1).add_(grad) + p_fp32.add_(state1_fp32, alpha=-lr) + + elif optimizer_name == "rmsprop": + grad.add_(p_fp32, alpha=weight_decay) + state1_fp32.mul_(beta1).addcmul_(grad, grad, value=1.0 - beta1) + p_fp32.addcdiv_(grad, state1_fp32.sqrt().add_(eps), value=-lr) + + elif optimizer_name == "lion": + if weight_decay > 0.0: + p_fp32.mul_(1.0 - lr * weight_decay) + + update_dir = torch.sign(state1_fp32.mul(beta1) + grad.mul(1.0 - beta1)) + p_fp32.add_(update_dir, alpha=-lr) + + state1_fp32.mul_(beta2).add_(grad, alpha=1.0 - beta2) + + elif optimizer_name == "adagrad": + grad.add_(p_fp32, alpha=weight_decay) + state1_fp32.addcmul_(grad, grad, value=1.0) + p_fp32.addcdiv_(grad, state1_fp32.sqrt().add_(eps), value=-lr) + + else: + raise NotImplementedError( + f"Pure PyTorch implementation for optimizer '{optimizer_name}' is not available." + ) + + # Copy the updated 32-bit parameter back to the original tensor + p.data.copy_(p_fp32) + + # Re-quantize states and update state tensors in-place + if optimizer_name == "ademamix": + new_m1_8bit, new_absmax_m1 = quantize_blockwise_triton(state1_fp32[0], qmap1, blocksize) + new_m2_8bit, new_absmax_m2 = quantize_blockwise_triton(state1_fp32[1], qmap1, blocksize) + state1[0].copy_(new_m1_8bit) + state1[1].copy_(new_m2_8bit) + absmax1[0].copy_(new_absmax_m1) + absmax1[1].copy_(new_absmax_m2) + + new_state2_8bit, new_absmax2 = quantize_blockwise_triton(state2_fp32, qmap2, blocksize) + state2.copy_(new_state2_8bit) + absmax2.copy_(new_absmax2) + else: + new_state1_8bit, new_absmax1 = quantize_blockwise_triton(state1_fp32, qmap1, blocksize) + state1.copy_(new_state1_8bit) + absmax1.copy_(new_absmax1) + + if state2_fp32 is not None: + new_state2_8bit, new_absmax2 = quantize_blockwise_triton(state2_fp32, qmap2, blocksize) + state2.copy_(new_state2_8bit) + absmax2.copy_(new_absmax2) + + +######################### +# Triton implementation # +######################### + + +@triton.jit +def _optimizer_update_1state_8bit_blockwise_triton_kernel( + # Tensors + p_ptr, + g_ptr, + state1_ptr, + state2_ptr, + beta1: tl.constexpr, + beta2: tl.constexpr, + beta3, + alpha, + eps: tl.constexpr, + step, + beta1_step, + beta2_step, + lr, + qmap1_ptr, + qmap2_ptr, + absmax1_ptr, + absmax2_ptr, + weight_decay, + gnorm_scale, + # Meta-parameters + n_elements, + BLOCK_SIZE_N: tl.constexpr, + N_PER_TH: tl.constexpr, + OPTIMIZER_ID: tl.constexpr, +): + """ + Triton kernel for 8-bit optimizers that use one momentum state. + Supports: Momentum, RMSprop, Adagrad, Lion. + """ + # 1. Boilerplate: pid, offsets, mask + pid = tl.program_id(axis=0) + block_start_idx = pid * N_PER_TH + offsets = block_start_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N * N_PER_TH) + mask = offsets < n_elements + + # 2. Load and dequantize tensors + g = tl.load(g_ptr + offsets, mask=mask, other=0.0).to(tl.float32) * gnorm_scale + p = tl.load(p_ptr + offsets, mask=mask, other=0.0).to(tl.float32) + s1 = dequant_8bit_blockwise_kernel_util(state1_ptr, offsets, qmap1_ptr, absmax1_ptr, mask, BLOCK_SIZE_N) + + # 3. Optimizer-specific updates + # LION + if weight_decay > 0.0 and OPTIMIZER_ID == 2: + p *= 1.0 - lr * weight_decay + # Apply weight decay for momentum, rmsprop, adagrad + elif weight_decay > 0.0: + g += p * weight_decay + + # Momentum update + if OPTIMIZER_ID == 0: # MOMENTUM + if step == 1: + s1 = g + else: + s1 = s1 * beta1 + g + p -= lr * s1 + + # RMSprop update + elif OPTIMIZER_ID == 1: # RMSPROP + s1 = s1 * beta1 + (1.0 - beta1) * g * g + p -= lr * (g / (tl.sqrt(s1) + eps)) + + # Adagrad update + elif OPTIMIZER_ID == 2: # ADAGRAD + s1 += g * g + p -= lr * (g / (tl.sqrt(s1) + eps)) + + # Lion update + elif OPTIMIZER_ID == 4: # LION + val = s1 * beta1 + (1.0 - beta1) * g + update = tl.where(val > 0.0, 1.0, tl.where(val < 0.0, -1.0, 0.0)) + p -= lr * update + s1 = s1 * beta2 + (1.0 - beta2) * g + + # 4. Store updated parameter and requantized state + tl.store(p_ptr + offsets, p.to(p_ptr.dtype.element_ty), mask=mask) + s1_codes, new_absmax1 = quantize_8bit_blockwise_kernel_util(s1, qmap1_ptr, 256, BLOCK_SIZE_N, N_PER_TH) + tl.store(state1_ptr + offsets, s1_codes, mask=mask) + tl.store(absmax1_ptr + block_start_idx + tl.arange(0, N_PER_TH), new_absmax1) + + +@triton.jit +def _optimizer_update_2state_8bit_blockwise_triton_kernel( + # Tensors + p_ptr, + g_ptr, + state1_ptr, + state2_ptr, + beta1: tl.constexpr, + beta2: tl.constexpr, + # ademamix changes alpha and beta3 + beta3, + # ademamix changes alpha and beta3 + alpha, + eps: tl.constexpr, + step, + beta1_step, + beta2_step, + lr, + qmap1_ptr, + qmap2_ptr, + absmax1_ptr, + absmax2_ptr, + weight_decay: tl.constexpr, + gnorm_scale: tl.constexpr, + # Meta-parameters + n_elements, + BLOCK_SIZE_N: tl.constexpr, + N_PER_TH: tl.constexpr, + OPTIMIZER_ID: tl.constexpr, +): + """ + Triton kernel for 8-bit optimizers that use two momentum states. + Supports: Adam, AdEMAMix. + """ + # 1. Boilerplate: pid, offsets, mask + pid = tl.program_id(axis=0) + block_start_idx = pid * N_PER_TH + offsets = block_start_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N * N_PER_TH) + mask = offsets < n_elements + + # 2. Load and dequantize tensors + g = tl.load(g_ptr + offsets, mask=mask, other=0.0).to(tl.float32) * gnorm_scale + p = tl.load(p_ptr + offsets, mask=mask, other=0.0).to(tl.float32) + + # 3. Optimizer-specific updates + if OPTIMIZER_ID == 3: # ADAM + s1 = dequant_8bit_blockwise_kernel_util(state1_ptr, offsets, qmap1_ptr, absmax1_ptr, mask, BLOCK_SIZE_N) + s2 = dequant_8bit_blockwise_kernel_util(state2_ptr, offsets, qmap2_ptr, absmax2_ptr, mask, BLOCK_SIZE_N) + + s1 = s1 * beta1 + (1.0 - beta1) * g + s2 = s2 * beta2 + (1.0 - beta2) * g * g + + # In torch=2.7 on XPU there is an issue with libdevice.pow, leading to an error. + # For backwards compatibility we precompute the bias correction factors. + # bias_correction1 = 1.0 - libdevice.pow(beta1, step) + # bias_correction2 = 1.0 - libdevice.pow(beta2, step) + bias_correction1 = 1.0 - beta1_step + bias_correction2 = 1.0 - beta2_step + + if weight_decay > 0.0: + p *= 1.0 - lr * weight_decay + + denom = tl.sqrt(s2) / tl.sqrt(bias_correction2) + eps + p -= (lr / bias_correction1) * (s1 / denom) + + # Store updated parameter + tl.store(p_ptr + offsets, p.to(p_ptr.dtype.element_ty), mask=mask) + + # Requantize and store states + s1_codes, new_absmax1 = quantize_8bit_blockwise_kernel_util(s1, qmap1_ptr, 256, BLOCK_SIZE_N, N_PER_TH) + tl.store(state1_ptr + offsets, s1_codes, mask=mask) + tl.store(absmax1_ptr + block_start_idx + tl.arange(0, N_PER_TH), new_absmax1) + + s2_codes, new_absmax2 = quantize_8bit_blockwise_kernel_util(s2, qmap2_ptr, 256, BLOCK_SIZE_N, N_PER_TH) + tl.store(state2_ptr + offsets, s2_codes, mask=mask) + tl.store(absmax2_ptr + block_start_idx + tl.arange(0, N_PER_TH), new_absmax2) + + elif OPTIMIZER_ID == 5: # ADEMAMIX + # AdEMAMix has a stacked state1 (m1, m2) and state2 (nu) + m1 = dequant_8bit_blockwise_kernel_util(state1_ptr, offsets, qmap1_ptr, absmax1_ptr, mask, BLOCK_SIZE_N) + m2 = dequant_8bit_blockwise_kernel_util( + state1_ptr + n_elements, + offsets, + qmap1_ptr, + absmax1_ptr + n_elements // BLOCK_SIZE_N, + mask, + BLOCK_SIZE_N, + ) + nu = dequant_8bit_blockwise_kernel_util(state2_ptr, offsets, qmap2_ptr, absmax2_ptr, mask, BLOCK_SIZE_N) + + m1 = m1 * beta1 + (1.0 - beta1) * g + m2 = m2 * beta3 + (1.0 - beta3) * g + nu = nu * beta2 + (1.0 - beta2) * g * g + + # In torch=2.7 on XPU there is an issue with libdevice.pow, leading to an error. + # For backwards compatibility we precompute the bias correction factors. + # bias_correction1 = 1.0 - libdevice.pow(beta1, step) + # bias_correction2 = tl.sqrt(1.0 - libdevice.pow(beta2, step)) + bias_correction1 = 1.0 - beta1_step + bias_correction2 = tl.sqrt(1.0 - beta2_step) + + update = (m1 / bias_correction1 + alpha * m2) / (tl.sqrt(nu) / bias_correction2 + eps) + + if weight_decay > 0.0: + p *= 1.0 - lr * weight_decay + + p -= lr * update + + # Store updated parameter + tl.store(p_ptr + offsets, p.to(p_ptr.dtype.element_ty), mask=mask) + + # Requantize and store all three states + m1_codes, new_absmax_m1 = quantize_8bit_blockwise_kernel_util(m1, qmap1_ptr, 256, BLOCK_SIZE_N, N_PER_TH) + tl.store(state1_ptr + offsets, m1_codes, mask=mask) + tl.store(absmax1_ptr + block_start_idx + tl.arange(0, N_PER_TH), new_absmax_m1) + + m2_codes, new_absmax_m2 = quantize_8bit_blockwise_kernel_util(m2, qmap1_ptr, 256, BLOCK_SIZE_N, N_PER_TH) + tl.store(state1_ptr + n_elements + offsets, m2_codes, mask=mask) + tl.store( + absmax1_ptr + block_start_idx + tl.arange(0, N_PER_TH) + n_elements // BLOCK_SIZE_N, + new_absmax_m2, + ) + + nu_codes, new_absmax_nu = quantize_8bit_blockwise_kernel_util(nu, qmap2_ptr, 256, BLOCK_SIZE_N, N_PER_TH) + tl.store(state2_ptr + offsets, nu_codes, mask=mask) + tl.store(absmax2_ptr + block_start_idx + tl.arange(0, N_PER_TH), new_absmax_nu) + + +name2optimizer_fn = { + "momentum": _optimizer_update_1state_8bit_blockwise_triton_kernel, + "rmsprop": _optimizer_update_1state_8bit_blockwise_triton_kernel, + "adagrad": _optimizer_update_1state_8bit_blockwise_triton_kernel, + "adam": _optimizer_update_2state_8bit_blockwise_triton_kernel, + "lion": _optimizer_update_1state_8bit_blockwise_triton_kernel, + "ademamix": _optimizer_update_2state_8bit_blockwise_triton_kernel, +} + + +def optimizer_update_8bit_blockwise_impl( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + step: int, + lr: float, + qmap1: torch.Tensor, + qmap2: Optional[torch.Tensor], + absmax1: torch.Tensor, + absmax2: Optional[torch.Tensor], + weight_decay: float = 0.0, + gnorm_scale: float = 1.0, + skip_zeros=False, +) -> None: + if skip_zeros: + raise NotImplementedError("skip_zeros is not supported on XPU yet") + + if optimizer_name == "ademamix": + # Handle AdEMAMIX's stacked state tensors + if state1.dim() < 2 or state1.shape[0] != 2: + raise ValueError( + f"For ademamix, state1 must be a stacked tensor of shape (2, ...), but got {state1.shape}" + ) + if absmax1.dim() < 2 or absmax1.shape[0] != 2: + raise ValueError( + f"For ademamix, absmax1 must be a stacked tensor of shape (2, ...), but got {absmax1.shape}" + ) + + BLOCK_SIZE = 256 + N_PER_TH = 1 # Number of blocks processed per thread. + grid = (triton.cdiv(p.numel(), BLOCK_SIZE * N_PER_TH),) + fn = name2optimizer_fn[optimizer_name] + optimizer_id = name2optimizer_id[optimizer_name] + + # In torch=2.7 on XPU there is an issue with libdevice.pow, leading to an error. + # For backwards compatibility we precompute the bias correction factors. + beta1_step = beta1**step + beta2_step = beta2**step + + fn[grid]( + p, + g, + state1, + state2, + beta1, + beta2, + beta3, + alpha, + eps, + step, + beta1_step, + beta2_step, + lr, + qmap1, + qmap2, + absmax1, + absmax2, + weight_decay, + gnorm_scale, + p.numel(), + BLOCK_SIZE_N=BLOCK_SIZE, + N_PER_TH=N_PER_TH, + OPTIMIZER_ID=optimizer_id, + num_warps=2, + ) + + +# optimizer_update_8bit_blockwise_impl = optimizer_update_8bit_blockwise_pytorch +# optimizer_update_8bit_blockwise_impl = torch.compile(optimizer_update_8bit_blockwise_pytorch_impl) +# optimizer_update_8bit_blockwise_impl = optimizer_update_8bit_blockwise_triton_quant +# optimizer_update_8bit_blockwise_impl = torch.compile(optimizer_update_8bit_blockwise_triton_quant) +optimizer_update_8bit_blockwise_impl = optimizer_update_8bit_blockwise_impl diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/ops.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..3a16961fa21a9075732896d28c780770cd42150b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/triton/ops.py @@ -0,0 +1,298 @@ +from collections.abc import Sequence +from typing import Optional + +import torch + +from . import kernels_4bit, kernels_8bit_quant, kernels_optim + +# currently codes unused, kept for reference +# Should be the same for quant/dequant +# from bitsandbytes.functional import get_4bit_type +# _FP4_QUANT_TABLE = get_4bit_type("fp4", device="xpu") +# _NF4_QUANT_TABLE = get_4bit_type("nf4", device="xpu") +device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda" +torch_accelerator_module = getattr(torch, device_type, torch.cuda) + + +def quantize_blockwise(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]: + torch._check_is_size(blocksize) + # torch._check(A.dtype == torch.float32, lambda: f"A must be float32 on xpu, got {A.dtype}") + with torch_accelerator_module.device(A.device): + out, absmax = kernels_8bit_quant.quantize_blockwise_triton(A, code, blocksize) + return out, absmax.float() + + +def dequantize_blockwise( + A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype +) -> torch.Tensor: + torch._check_is_size(blocksize) + torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}") + # torch._check(dtype == torch.float32, lambda: f"dtype must be float32 on xpu, got {dtype}") + with torch_accelerator_module.device(A.device): + out = kernels_8bit_quant.dequant_8bit_blockwise( + A, + absmax, + code, + blocksize, + dtype=dtype, + ) + return out + + +def dequantize_blockwise_inplace( + A: torch.Tensor, + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + torch._check_is_size(blocksize) + torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}") + torch._check(out.shape == A.shape, lambda: f"Expected out.shape == {A.shape}, got {out.shape}") + torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}") + torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}") + + with torch_accelerator_module.device(A.device): + kernels_8bit_quant.dequant_8bit_blockwise( + A, + absmax, + code, + blocksize, + dtype=dtype, + out=out, + ) + + +def quantize_4bit( + A: torch.Tensor, blocksize: int, quant_type: str, quant_storage: torch.dtype +) -> tuple[torch.Tensor, torch.Tensor]: + torch._check_is_size(blocksize) + # torch._check(quant_type == "nf4", lambda: f"quant_type must be nf4 on CPU, got {quant_type}") + torch._check( + A.dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit quantization only supports 16/32-bit floats, but got {A.dtype}", + ) + + n = A.numel() + + # TODO: Support when weight matrix is not divisible by blocksize + # torch._check(n % blocksize == 0, lambda: f"n must be divisible by blocksize, got {n} and {blocksize}") + + blocks = -(n // -(blocksize * 2)) + + absmax = torch.empty((blocks * 2,), device=A.device, dtype=A.dtype) + # Use n - n//2 instead of (n+1)//2 to avoid integer overflow for large n + out = torch.empty((n - n // 2, 1), device=A.device, dtype=torch.uint8) + + with torch_accelerator_module.device(A.device): + kernels_4bit.quantize_4bit_blockwise_triton( + A, blocksize, quant_type, blocks, absmax, num_elements=n, quantized_out=out + ) + packed = out + + if quant_storage != torch.uint8: + packed = out.squeeze().view(quant_storage).unsqueeze(1) + + return packed, absmax.float() + + +def dequantize_4bit( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, +) -> torch.Tensor: + torch._check_is_size(blocksize) + # torch._check(quant_type == "nf4", lambda: f"quant_type must be nf4 on XPU, got {quant_type}") + torch._check( + dtype in [torch.bfloat16, torch.float16, torch.float32], + lambda: f"Blockwise 4bit dequantization only supports 16/32-bit floats, but got {dtype}", + ) + # torch._check( + # A.dtype == torch.uint8, + # lambda: f"Blockwise 4bit dequantization on XPU only supports uint8 storage, got {A.dtype}", + # ) + # Check if this is fine and fast + if A.dtype != torch.uint8: + A = A.squeeze().view(torch.uint8).unsqueeze(1) + + out = torch.empty(shape, dtype=dtype, device=A.device) + with torch_accelerator_module.device(A.device): + kernels_4bit.dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out) + + return out + + +def dequantize_4bit_inplace( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + torch._check(out.shape == shape, lambda: f"Expected out.shape == {shape}, got {out.shape}") + torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}") + with torch_accelerator_module.device(A.device): + kernels_4bit.dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out) + + +def gemv_4bit( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, +) -> torch.Tensor: + if B.dtype != torch.uint8: + B = B.squeeze().view(torch.uint8).unsqueeze(1) + + B_dq_triton = torch.empty(shapeB, dtype=A.dtype, device=A.device) + + with torch_accelerator_module.device(A.device): + kernels_4bit.dequantize_4bit_impl_passing_code( + B, + absmax, + blocksize, + code, + dtype=A.dtype, + out=B_dq_triton, + ) + + return torch.nn.functional.linear( + A, + B_dq_triton, + bias=None, + ) + + +# optimizer_update_8bit_blockwise_impl = kernels_optim.optimizer_update_8bit_blockwise_pytorch +# optimizer_update_8bit_blockwise_impl = torch.compile(kernels_optim.optimizer_update_8bit_blockwise_pytorch) # 60ms +# optimizer_update_8bit_blockwise_impl = kernels_optim.optimizer_update_8bit_blockwise_triton_quant #2.8ms +# optimizer_update_8bit_blockwise_impl = torch.compile(kernels_optim.optimizer_update_8bit_blockwise_triton_quant) # 2.3ms +optimizer_update_8bit_blockwise_impl = kernels_optim.optimizer_update_8bit_blockwise_impl # ~0.95ms for adam + + +def optimizer_update_8bit_blockwise( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + step: int, + lr: float, + qmap1: torch.Tensor, + qmap2: Optional[torch.Tensor], + absmax1: torch.Tensor, + absmax2: Optional[torch.Tensor], + weight_decay: float = 0.0, + gnorm_scale: float = 1.0, + skip_zeros=False, +) -> None: + # torch._check( + # g.numel() == p.numel(), + # lambda: f"g and p must have the same number of elements, got {g.numel()} and {p.numel()}", + # ) + # compute_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + # torch._check( + # g.dtype in compute_dtypes, + # lambda: f"g must be bfloat16, float16, or float32, got {g.dtype}", + # ) + # torch._check( + # g.dtype == p.dtype, + # lambda: f"Expected all tensors to have the same dtype, got g.dtype={g.dtype}, p.dtype={p.dtype}", + # ) + # torch._check( + # state1.dtype == torch.uint8, + # lambda: f"state1 must be uint8, got {state1.dtype}", + # ) + # torch._check( + # qmap1.dtype == absmax1.dtype == torch.float32, + # lambda: f"Expected qmap1 and absmax1 to be float32, got qmap1.dtype={qmap1.dtype}, absmax1.dtype={absmax1.dtype}", + # ) + # if state2 is not None: + # torch._check( + # state2.dtype == torch.uint8, + # lambda: f"state2 must be uint8, got {state2.dtype}", + # ) + # torch._check( + # qmap2.dtype == absmax2.dtype == torch.float32, + # lambda: f"Expected qmap2 and absmax2 to be float32, got qmap2.dtype={qmap2.dtype}, absmax2.dtype={absmax2.dtype}", + # ) + + with torch_accelerator_module.device(state1.device): + optimizer_update_8bit_blockwise_impl( + optimizer_name=optimizer_name, + g=g, + p=p, + state1=state1, + state2=state2, + beta1=beta1, + beta2=beta2, + beta3=beta3, + alpha=alpha, + eps=eps, + step=step, + lr=lr, + qmap1=qmap1, + qmap2=qmap2, + absmax1=absmax1, + absmax2=absmax2, + weight_decay=weight_decay, + gnorm_scale=gnorm_scale, + skip_zeros=skip_zeros, + ) + + +def optimizer_update_32bit( + optimizer_name: str, + g: torch.Tensor, + p: torch.Tensor, + state1: torch.Tensor, + state2: Optional[torch.Tensor], + unorm_vec: Optional[torch.Tensor], + max_unorm: float, + param_norm: float, + beta1: float, + beta2: float, + beta3: float, + alpha: float, + eps: float, + weight_decay: float, + step: int, + lr: float, + gnorm_scale: float, + skip_zeros=False, +) -> None: + with torch_accelerator_module.device(state1.device): + kernels_optim.optimizer_update_32bit_impl( + optimizer_name=optimizer_name, + g=g, + p=p, + state1=state1, + state2=state2, + unorm_vec=unorm_vec, + max_unorm=max_unorm, + param_norm=param_norm, + beta1=beta1, + beta2=beta2, + beta3=beta3, + alpha=alpha, + eps=eps, + weight_decay=weight_decay, + step=step, + lr=lr, + gnorm_scale=gnorm_scale, + skip_zeros=skip_zeros, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/utils.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ec96a440c15e6917267a97e4850e8e6f2d37c2ec --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/utils.py @@ -0,0 +1,84 @@ +import subprocess + +from packaging import version +import torch + +try: + import triton.language as tl # noqa: F401 + + import triton # noqa: F401 + + triton_available = True +except ImportError: + triton_available = False + + +_NF4_QUANT_TABLE = torch.tensor( + [ + -1.0, + -0.6961928009986877, + -0.5250730514526367, + -0.39491748809814453, + -0.28444138169288635, + -0.18477343022823334, + -0.09105003625154495, + 0.0, + 0.07958029955625534, + 0.16093020141124725, + 0.24611230194568634, + 0.33791524171829224, + 0.44070982933044434, + 0.5626170039176941, + 0.7229568362236023, + 1.0, + ], + dtype=torch.float32, + device="xpu" + if hasattr(torch, "xpu") and torch.xpu.is_available() + else "cpu", # Only cpu/xpu use this table for now. +) +_FP4_QUANT_TABLE = torch.tensor( + [ + 0.0000, + 0.0052, + 0.6667, + 1.0000, + 0.3333, + 0.5000, + 0.1667, + 0.2500, + 0.0000, + -0.0052, + -0.6667, + -1.0000, + -0.3333, + -0.5000, + -0.1667, + -0.2500, + ], + dtype=torch.float32, + device="xpu" + if hasattr(torch, "xpu") and torch.xpu.is_available() + else "cpu", # Only cpu/xpu use this table for now. +) +CODE = {"nf4": _NF4_QUANT_TABLE, "fp4": _FP4_QUANT_TABLE} + + +def get_gaudi_sw_version(): + """ + Returns the installed version of Gaudi SW. + """ + output = subprocess.run( + "pip list | grep habana-torch-plugin", + shell=True, + text=True, + capture_output=True, + ) + # If grep return nothing + if not output.stdout.strip(): + return None + + return version.parse(output.stdout.split("\n")[0].split()[-1]) + + +GAUDI_SW_VER = get_gaudi_sw_version() diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/xpu/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/xpu/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/backends/xpu/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/xpu/__pycache__/__init__.cpython-312.pyc new file 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b/.venv/lib/python3.12/site-packages/bitsandbytes/backends/xpu/ops.py @@ -0,0 +1,242 @@ +from collections.abc import Sequence +import ctypes as ct +import logging + +from packaging import version +import torch + +from bitsandbytes.functional import _get_tensor_stream, get_ptr + +from ..._ops import register_kernel +from ...cextension import ErrorHandlerMockBNBNativeLibrary, lib +from ..utils import triton_available + +logger = logging.getLogger(__name__) + +# _int_mm is available in torch starting from 2.9 version +if version.parse(torch.__version__).release >= version.parse("2.9").release: + + @register_kernel("bitsandbytes::int8_linear_matmul", "xpu") + def _(A: torch.Tensor, B: torch.Tensor): + return torch._int_mm( + A.reshape(-1, A.shape[-1]), + B.t(), + ).reshape(*A.shape[:-1], B.shape[0]) + + +def _dequantize_4bit_impl( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + dtype: torch.dtype, + out: torch.Tensor, +) -> None: + args = ( + None, + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_int(blocksize), + ct.c_int(out.numel()), + _get_tensor_stream(A), + ) + if dtype == torch.bfloat16: + if quant_type == "fp4": + lib.cdequantize_blockwise_bf16_fp4(*args) + else: + lib.cdequantize_blockwise_bf16_nf4(*args) + elif dtype == torch.float16: + if quant_type == "fp4": + lib.cdequantize_blockwise_fp16_fp4(*args) + else: + lib.cdequantize_blockwise_fp16_nf4(*args) + elif dtype == torch.float32: + if quant_type == "fp4": + lib.cdequantize_blockwise_fp32_fp4(*args) + else: + lib.cdequantize_blockwise_fp32_nf4(*args) + + +def _dequantize_blockwise_impl( + A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype, out: torch.Tensor +) -> None: + args = ( + get_ptr(code), + get_ptr(A), + get_ptr(absmax), + get_ptr(out), + ct.c_int(blocksize), + ct.c_int(A.numel()), + _get_tensor_stream(A), + ) + if dtype == torch.float16: + lib.cdequantize_blockwise_fp16(*args) + elif dtype == torch.bfloat16: + lib.cdequantize_blockwise_bf16(*args) + elif dtype == torch.float32: + lib.cdequantize_blockwise_fp32(*args) + + +def _gemv_4bit_impl( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + out: torch.Tensor, +) -> None: + m = ct.c_int32(1) + n = ct.c_int32(shapeB[0]) + k = ct.c_int32(shapeB[1]) + + lda = m + ldb = ct.c_int32((A.shape[-1] + 1) // 2) + ldc = m + + stream = _get_tensor_stream(A) + if A.dtype == torch.float16: + lib.cgemv_4bit_inference_fp16( + m, + n, + k, + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(code), + get_ptr(out), + lda, + ldb, + ldc, + ct.c_int32(blocksize), + stream, + ) + elif A.dtype == torch.bfloat16: + lib.cgemv_4bit_inference_bf16( + m, + n, + k, + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(code), + get_ptr(out), + lda, + ldb, + ldc, + ct.c_int32(blocksize), + stream, + ) + elif A.dtype == torch.float32: + lib.cgemv_4bit_inference_fp32( + m, + n, + k, + get_ptr(A), + get_ptr(B), + get_ptr(absmax), + get_ptr(code), + get_ptr(out), + lda, + ldb, + ldc, + ct.c_int32(blocksize), + stream, + ) + + +# SYCL should be faster for xpu, so at first checking if it is available. +if not isinstance(lib, ErrorHandlerMockBNBNativeLibrary): + logger.info("Register sycl bitsandbytes kernels for XPU") + + # TODO: Remove the triton register when quantization sycl kernel is ready. + if triton_available: + from ..triton import ops as triton_ops + + register_kernel("bitsandbytes::quantize_blockwise", "xpu")(triton_ops.quantize_blockwise) + register_kernel("bitsandbytes::quantize_4bit", "xpu")(triton_ops.quantize_4bit) + register_kernel("bitsandbytes::optimizer_update_8bit_blockwise", "xpu")( + triton_ops.optimizer_update_8bit_blockwise + ) + register_kernel("bitsandbytes::optimizer_update_32bit", "xpu")(triton_ops.optimizer_update_32bit) + + @register_kernel("bitsandbytes::dequantize_4bit", "xpu") + def _( + A: torch.Tensor, + absmax: torch.Tensor, + blocksize: int, + quant_type: str, + shape: Sequence[int], + dtype: torch.dtype, + ) -> torch.Tensor: + out = torch.empty(shape, dtype=dtype, device=A.device) + _dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out) + return out + + @register_kernel("bitsandbytes::dequantize_blockwise", "xpu") + def _( + A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype + ) -> torch.Tensor: + out = torch.empty_like(A, dtype=dtype) + _dequantize_blockwise_impl(A, absmax, code, blocksize, dtype, out=out) + return out + + @register_kernel("bitsandbytes::dequantize_blockwise.out", "xpu") + def _( + A: torch.Tensor, + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + dtype: torch.dtype, + out: torch.Tensor, + ) -> None: + torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}") + torch._check(out.shape == A.shape, lambda: f"Expected out.shape == {A.shape}, got {out.shape}") + _dequantize_blockwise_impl(A, absmax, code, blocksize, dtype, out=out) + + @register_kernel("bitsandbytes::gemv_4bit", "xpu") + def _( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + ) -> torch.Tensor: + shape = (*A.shape[:-1], shapeB[0]) + out = torch.empty(shape, device=A.device, dtype=A.dtype) + _gemv_4bit_impl(A, B, shapeB, absmax, code, blocksize, out=out) + return out + + @register_kernel("bitsandbytes::gemv_4bit.out", "xpu") + def _( + A: torch.Tensor, + B: torch.Tensor, + shapeB: Sequence[int], + absmax: torch.Tensor, + code: torch.Tensor, + blocksize: int, + out: torch.Tensor, + ) -> None: + torch._check( + out.shape == (*A.shape[:-1], shapeB[0]), + lambda: f"Expected out.shape == {(*A.shape[:-1], shapeB[0])}, got {out.shape}", + ) + torch._check(out.dtype == A.dtype, lambda: f"Expected out.dtype == {A.dtype}, got {out.dtype}") + _gemv_4bit_impl(A, B, shapeB, absmax, code, blocksize, out=out) +elif triton_available: + logger.info("Register triton bitsandbytes kernels for XPU") + from ..triton import ops as triton_ops + + register_kernel("bitsandbytes::quantize_blockwise", "xpu")(triton_ops.quantize_blockwise) + register_kernel("bitsandbytes::dequantize_blockwise.out", "xpu")(triton_ops.dequantize_blockwise_inplace) + register_kernel("bitsandbytes::dequantize_blockwise", "xpu")(triton_ops.dequantize_blockwise) + register_kernel("bitsandbytes::quantize_4bit", "xpu")(triton_ops.quantize_4bit) + register_kernel("bitsandbytes::dequantize_4bit.out", "xpu")(triton_ops.dequantize_4bit_inplace) + register_kernel("bitsandbytes::dequantize_4bit", "xpu")(triton_ops.dequantize_4bit) + register_kernel("bitsandbytes::gemv_4bit", "xpu")(triton_ops.gemv_4bit) + register_kernel("bitsandbytes::optimizer_update_8bit_blockwise", "xpu")(triton_ops.optimizer_update_8bit_blockwise) + register_kernel("bitsandbytes::optimizer_update_32bit", "xpu")(triton_ops.optimizer_update_32bit) +else: + logger.warning("Register pytorch bitsandbytes kernels for XPU because no native library or triton packages found.") diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..df652b083de6e100404effeae6d285bdd42419e9 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__pycache__/cuda.cpython-312.pyc 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index 0000000000000000000000000000000000000000..0528a5c9a9aaeb00c6c6ee65dab7630e1addc8f3 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/__pycache__/utils.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/cuda.py b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..29a9a66e1dd7a0a53b3d0ceb5c6e07f8934d783c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/cuda.py @@ -0,0 +1,219 @@ +from collections.abc import Iterable, Iterator +import logging +import os +from pathlib import Path + +import torch + +from bitsandbytes.cextension import HIP_ENVIRONMENT, get_cuda_bnb_library_path +from bitsandbytes.cuda_specs import CUDASpecs +from bitsandbytes.diagnostics.utils import print_dedented + +CUDART_PATH_PREFERRED_ENVVARS = ("CONDA_PREFIX", "LD_LIBRARY_PATH") + +CUDART_PATH_IGNORED_ENVVARS = { + "DBUS_SESSION_BUS_ADDRESS", # hardware related + "GOOGLE_VM_CONFIG_LOCK_FILE", # GCP: requires elevated permissions, causing problems in VMs and Jupyter notebooks + "HOME", # Linux shell default + "LESSCLOSE", + "LESSOPEN", # related to the `less` command + "MAIL", # something related to emails + "OLDPWD", + "PATH", # this is for finding binaries, not libraries + "PWD", # PWD: this is how the shell keeps track of the current working dir + "SHELL", # binary for currently invoked shell + "SSH_AUTH_SOCK", # SSH stuff, therefore unrelated + "SSH_TTY", + "TMUX", # Terminal Multiplexer + "XDG_DATA_DIRS", # XDG: Desktop environment stuff + "XDG_GREETER_DATA_DIR", # XDG: Desktop environment stuff + "XDG_RUNTIME_DIR", + "_", # current Python interpreter +} + +CUDA_RUNTIME_LIB_PATTERNS = ( + ("libamdhip64.so*",) + if HIP_ENVIRONMENT + else ( + "cudart64*.dll", # Windows + "libcudart*.so*", # libcudart.so, libcudart.so.11.0, libcudart.so.12.0, libcudart.so.12.1, libcudart.so.12.2 etc. + "nvcuda*.dll", # Windows + ) +) + +logger = logging.getLogger(__name__) + + +def find_cuda_libraries_in_path_list(paths_list_candidate: str) -> Iterable[Path]: + for dir_string in paths_list_candidate.split(os.pathsep): + if not dir_string: + continue + if os.sep not in dir_string: + continue + try: + dir = Path(dir_string) + try: + if not dir.exists(): + logger.warning(f"The directory listed in your path is found to be non-existent: {dir}") + continue + except OSError: # Assume an esoteric error trying to poke at the directory + pass + for lib_pattern in CUDA_RUNTIME_LIB_PATTERNS: + for pth in dir.glob(lib_pattern): + if pth.is_file() and not pth.is_symlink(): + yield pth + except (OSError, PermissionError): + pass + + +def is_relevant_candidate_env_var(env_var: str, value: str) -> bool: + return ( + env_var in CUDART_PATH_PREFERRED_ENVVARS # is a preferred location + or ( + os.sep in value # might contain a path + and env_var not in CUDART_PATH_IGNORED_ENVVARS # not ignored + and "CONDA" not in env_var # not another conda envvar + and "BASH_FUNC" not in env_var # not a bash function defined via envvar + and "\n" not in value # likely e.g. a script or something? + ) + ) + + +def get_potentially_lib_path_containing_env_vars() -> dict[str, str]: + return {env_var: value for env_var, value in os.environ.items() if is_relevant_candidate_env_var(env_var, value)} + + +def find_cudart_libraries() -> Iterator[Path]: + """ + Searches for a cuda installations, in the following order of priority: + 1. active conda env + 2. LD_LIBRARY_PATH + 3. any other env vars, while ignoring those that + - are known to be unrelated + - don't contain the path separator `/` + + If multiple libraries are found in part 3, we optimistically try one, + while giving a warning message. + """ + candidate_env_vars = get_potentially_lib_path_containing_env_vars() + + for envvar in CUDART_PATH_PREFERRED_ENVVARS: + if envvar in candidate_env_vars: + directory = candidate_env_vars[envvar] + yield from find_cuda_libraries_in_path_list(directory) + candidate_env_vars.pop(envvar) + + for env_var, value in candidate_env_vars.items(): + yield from find_cuda_libraries_in_path_list(value) + + +def _print_cuda_diagnostics(cuda_specs: CUDASpecs) -> None: + print( + f"PyTorch settings found: CUDA_VERSION={cuda_specs.cuda_version_string}, " + f"Highest Compute Capability: {cuda_specs.highest_compute_capability}.", + ) + + binary_path = get_cuda_bnb_library_path(cuda_specs) + if not binary_path.exists(): + print_dedented( + f""" + Library not found: {binary_path}. Maybe you need to compile it from source? + """, + ) + + # 7.5 is the minimum CC for int8 tensor cores + if not cuda_specs.has_imma: + print_dedented( + """ + WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU! + If you run into issues with 8-bit matmul, you can try 4-bit quantization: + https://huggingface.co/blog/4bit-transformers-bitsandbytes + """, + ) + + +def _print_hip_diagnostics(cuda_specs: CUDASpecs) -> None: + print(f"PyTorch settings found: ROCM_VERSION={cuda_specs.cuda_version_string}") + + binary_path = get_cuda_bnb_library_path(cuda_specs) + if not binary_path.exists(): + print_dedented( + f""" + Library not found: {binary_path}. + Maybe you need to compile it from source? If you compiled from source, check that ROCm version + in PyTorch Settings matches your ROCm install. If not, reinstall PyTorch for your ROCm version + and rebuild bitsandbytes. + """, + ) + + hip_major, hip_minor = cuda_specs.cuda_version_tuple + if (hip_major, hip_minor) < (6, 1): + print_dedented( + """ + WARNING: bitsandbytes is fully supported only from ROCm 6.1. + """, + ) + + +def print_diagnostics(cuda_specs: CUDASpecs) -> None: + if HIP_ENVIRONMENT: + _print_hip_diagnostics(cuda_specs) + else: + _print_cuda_diagnostics(cuda_specs) + + +def _print_cuda_runtime_diagnostics() -> None: + cudart_paths = list(find_cudart_libraries()) + if not cudart_paths: + print("CUDA SETUP: WARNING! CUDA runtime files not found in any environmental path.") + elif len(cudart_paths) > 1: + print_dedented( + f""" + Found duplicate CUDA runtime files (see below). + + We select the PyTorch default CUDA runtime, which is {torch.version.cuda}, + but this might mismatch with the CUDA version that is needed for bitsandbytes. + To override this behavior set the `BNB_CUDA_VERSION=` environmental variable. + + For example, if you want to use the CUDA version 122, + BNB_CUDA_VERSION=122 python ... + + OR set the environmental variable in your .bashrc: + export BNB_CUDA_VERSION=122 + + In the case of a manual override, make sure you set LD_LIBRARY_PATH, e.g. + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2, + """, + ) + for pth in cudart_paths: + print(f"* Found CUDA runtime at: {pth}") + + +def _print_hip_runtime_diagnostics() -> None: + cudart_paths = list(find_cudart_libraries()) + if not cudart_paths: + print("WARNING! ROCm runtime files not found in any environmental path.") + elif len(cudart_paths) > 1: + print_dedented( + f""" + Found duplicate ROCm runtime files (see below). + + We select the PyTorch default ROCm runtime, which is {torch.version.hip}, + but this might mismatch with the ROCm version that is needed for bitsandbytes. + + To resolve it, install PyTorch built for the ROCm version you want to use + + and set LD_LIBRARY_PATH to your ROCm install path, e.g. + export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm-6.1.2/lib, + """, + ) + + for pth in cudart_paths: + print(f"* Found ROCm runtime at: {pth}") + + +def print_runtime_diagnostics() -> None: + if HIP_ENVIRONMENT: + _print_hip_runtime_diagnostics() + else: + _print_cuda_runtime_diagnostics() diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/main.py b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/main.py new file mode 100644 index 0000000000000000000000000000000000000000..74da662b6c92a984cb3fe7e6eb4554d5f00dbf5d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/main.py @@ -0,0 +1,118 @@ +import importlib +import platform +import sys +import traceback + +import torch + +from bitsandbytes import __version__ as bnb_version +from bitsandbytes.cextension import BNB_BACKEND +from bitsandbytes.consts import PACKAGE_GITHUB_URL +from bitsandbytes.cuda_specs import get_cuda_specs +from bitsandbytes.diagnostics.cuda import ( + print_diagnostics, +) +from bitsandbytes.diagnostics.utils import print_dedented, print_header + +_RELATED_PACKAGES = [ + "accelerate", + "diffusers", + "numpy", + "pip", + "peft", + "safetensors", + "transformers", + "triton", + "trl", +] + + +def sanity_check(): + from bitsandbytes.optim import Adam + + p = torch.nn.Parameter(torch.rand(10, 10).cuda()) + a = torch.rand(10, 10).cuda() + p1 = p.data.sum().item() + adam = Adam([p]) + out = a * p + loss = out.sum() + loss.backward() + adam.step() + p2 = p.data.sum().item() + assert p1 != p2 + + +def get_package_version(name: str) -> str: + try: + version = importlib.metadata.version(name) + except importlib.metadata.PackageNotFoundError: + version = "not found" + return version + + +def show_environment(): + """Simple utility to print out environment information.""" + + print(f"Platform: {platform.platform()}") + if platform.system() == "Linux": + print(f" libc: {'-'.join(platform.libc_ver())}") + + print(f"Python: {platform.python_version()}") + + print(f"PyTorch: {torch.__version__}") + print(f" CUDA: {torch.version.cuda or 'N/A'}") + print(f" HIP: {torch.version.hip or 'N/A'}") + print(f" XPU: {getattr(torch.version, 'xpu', 'N/A') or 'N/A'}") + + print("Related packages:") + for pkg in _RELATED_PACKAGES: + version = get_package_version(pkg) + print(f" {pkg}: {version}") + + +def main(): + print_header(f"bitsandbytes v{bnb_version}") + show_environment() + print_header("") + + cuda_specs = get_cuda_specs() + + if cuda_specs: + print_diagnostics(cuda_specs) + + # TODO: There's a lot of noise in this; needs improvement. + # print_cuda_runtime_diagnostics() + + if not torch.cuda.is_available(): + print(f"PyTorch says {BNB_BACKEND} is not available. Possible reasons:") + print(f"1. {BNB_BACKEND} driver not installed") + print("2. Using a CPU-only PyTorch build") + print("3. No GPU detected") + + else: + print(f"Checking that the library is importable and {BNB_BACKEND} is callable...") + + try: + sanity_check() + print("SUCCESS!") + return + except RuntimeError as e: + if "not available in CPU-only" in str(e): + print( + f"WARNING: {__package__} is currently running as CPU-only!\n" + "Therefore, 8-bit optimizers and GPU quantization are unavailable.\n\n" + f"If you think that this is so erroneously,\nplease report an issue!", + ) + else: + raise e + except Exception: + traceback.print_exc() + + print_dedented( + f""" + Above we output some debug information. + Please provide this info when creating an issue via {PACKAGE_GITHUB_URL}/issues/new/choose + WARNING: Please be sure to sanitize sensitive info from the output before posting it. + """, + ) + sys.exit(1) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/utils.py b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..facc58b30af8f3eb3a7895e712588dcf092721ee --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/diagnostics/utils.py @@ -0,0 +1,12 @@ +import textwrap + +HEADER_WIDTH = 60 + + +def print_header(txt: str, width: int = HEADER_WIDTH, filler: str = "=") -> None: + txt = f" {txt} " if txt else "" + print(txt.center(width, filler)) + + +def print_dedented(text): + print("\n".join(textwrap.dedent(text).strip().split("\n"))) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..20aff67a33d3d3f2f323379378fae99bd39ba725 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from .modules import ( + Embedding, + Embedding4bit, + Embedding8bit, + EmbeddingFP4, + EmbeddingNF4, + Int8Params, + Linear4bit, + Linear8bitLt, + LinearFP4, + LinearNF4, + OutlierAwareLinear, + Params4bit, + StableEmbedding, + SwitchBackLinearBnb, +) +from .triton_based_modules import ( + StandardLinear, + SwitchBackLinear, + SwitchBackLinearGlobal, + SwitchBackLinearVectorwise, +) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..186df5e7dc2b3908942533ab6b78f1065a1d5d17 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__pycache__/modules.cpython-312.pyc 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0000000000000000000000000000000000000000..784028e465a7d0a15cc46447a6014471c0ad683d Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/__pycache__/triton_based_modules.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/nn/modules.py b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..6f705ab19e8c76d0b28d5b1820cf9876cfed5bb6 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/modules.py @@ -0,0 +1,1177 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import copy +from typing import Any, Optional, TypeVar, Union, overload +import warnings + +import torch +from torch import Tensor, device, dtype, nn +import torch.nn.functional as F + +import bitsandbytes as bnb +from bitsandbytes.functional import ( + QuantState, + _convert_weight_packed_for_cpu, + _convert_weight_packed_for_cpu_inverse, + has_avx512bf16, +) +from bitsandbytes.optim import GlobalOptimManager +from bitsandbytes.utils import INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING, OutlierTracer + +T = TypeVar("T", bound="torch.nn.Module") + + +class StableEmbedding(torch.nn.Embedding): + """ + Custom embedding layer designed to improve stability during training for NLP tasks by using 32-bit optimizer states. It is designed to reduce gradient variations that can result from quantization. This embedding layer is initialized with Xavier uniform initialization followed by layer normalization. + + Example: + + ``` + # Initialize StableEmbedding layer with vocabulary size 1000, embedding dimension 300 + embedding_layer = StableEmbedding(num_embeddings=1000, embedding_dim=300) + + # Reset embedding parameters + embedding_layer.reset_parameters() + + # Perform a forward pass with input tensor + input_tensor = torch.tensor([1, 2, 3]) + output_embedding = embedding_layer(input_tensor) + ``` + + Attributes: + norm (`torch.nn.LayerNorm`): Layer normalization applied after the embedding. + + Methods: + reset_parameters(): Reset embedding parameters using Xavier uniform initialization. + forward(input: Tensor) -> Tensor: Forward pass through the stable embedding layer. + """ + + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + max_norm: Optional[float] = None, + norm_type: float = 2.0, + scale_grad_by_freq: bool = False, + sparse: bool = False, + _weight: Optional[Tensor] = None, + device=None, + dtype=None, + ) -> None: + """ + Args: + num_embeddings (`int`): + The number of unique embeddings (vocabulary size). + embedding_dim (`int`): + The dimensionality of the embedding. + padding_idx (`Optional[int]`): + Pads the output with zeros at the given index. + max_norm (`Optional[float]`): + Renormalizes embeddings to have a maximum L2 norm. + norm_type (`float`, defaults to `2.0`): + The p-norm to compute for the `max_norm` option. + scale_grad_by_freq (`bool`, defaults to `False`): + Scale gradient by frequency during backpropagation. + sparse (`bool`, defaults to `False`): + Computes dense gradients. Set to `True` to compute sparse gradients instead. + _weight (`Optional[Tensor]`): + Pretrained embeddings. + """ + super().__init__( + num_embeddings, + embedding_dim, + padding_idx, + max_norm, + norm_type, + scale_grad_by_freq, + sparse, + _weight, + device, + dtype, + ) + self.norm = torch.nn.LayerNorm(embedding_dim, device=device) + GlobalOptimManager.get_instance().register_module_override(self, "weight", {"optim_bits": 32}) + + def reset_parameters(self) -> None: + torch.nn.init.xavier_uniform_(self.weight) + self._fill_padding_idx_with_zero() + + """ !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding + to make the Layer compatible with Pytorch < 1.9. + This means that if this changes in future PyTorch releases this need to change too + which is cumbersome. However, with this we can ensure compatibility with previous + PyTorch releases. + """ + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def forward(self, input: Tensor) -> Tensor: + emb = F.embedding( + input, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) + + # always apply layer norm in full precision + emb = emb.to(torch.get_default_dtype()) + + return self.norm(emb).to(self.weight.dtype) + + +class Embedding(torch.nn.Embedding): + """ + Embedding class to store and retrieve word embeddings from their indices. + """ + + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + max_norm: Optional[float] = None, + norm_type: float = 2.0, + scale_grad_by_freq: bool = False, + sparse: bool = False, + _weight: Optional[Tensor] = None, + device: Optional[device] = None, + ) -> None: + """ + Args: + num_embeddings (`int`): + The number of unique embeddings (vocabulary size). + embedding_dim (`int`): + The dimensionality of the embedding. + padding_idx (`Optional[int]`): + Pads the output with zeros at the given index. + max_norm (`Optional[float]`): + Renormalizes embeddings to have a maximum L2 norm. + norm_type (`float`, defaults to `2.0`): + The p-norm to compute for the `max_norm` option. + scale_grad_by_freq (`bool`, defaults to `False`): + Scale gradient by frequency during backpropagation. + sparse (`bool`, defaults to `False`): + Computes dense gradients. Set to `True` to compute sparse gradients instead. + _weight (`Optional[Tensor]`): + Pretrained embeddings. + """ + super().__init__( + num_embeddings, + embedding_dim, + padding_idx, + max_norm, + norm_type, + scale_grad_by_freq, + sparse, + _weight, + device=device, + ) + GlobalOptimManager.get_instance().register_module_override(self, "weight", {"optim_bits": 32}) + + def reset_parameters(self) -> None: + torch.nn.init.xavier_uniform_(self.weight) + self._fill_padding_idx_with_zero() + + """ !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding + to make the Layer compatible with Pytorch < 1.9. + This means that if this changes in future PyTorch releases this need to change too + which is cumbersome. However, with this we can ensure compatibility with previous + PyTorch releases. + """ + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def forward(self, input: Tensor) -> Tensor: + emb = F.embedding( + input, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) + + return emb + + +class Params4bit(torch.nn.Parameter): + def __new__( + cls, + data: Optional[torch.Tensor] = None, + requires_grad=False, # quantized weights should be frozen by default + quant_state: Optional[QuantState] = None, + blocksize: Optional[int] = None, + compress_statistics: bool = True, + quant_type: str = "fp4", + quant_storage: torch.dtype = torch.uint8, + module: Optional["Linear4bit"] = None, + bnb_quantized: bool = False, + ) -> "Params4bit": + if data is None: + data = torch.empty(0) + + if blocksize is None: + blocksize = 64 + + self = torch.Tensor._make_subclass(cls, data, requires_grad) + self.blocksize = blocksize + self.compress_statistics = compress_statistics + self.quant_type = quant_type + self.quant_state = quant_state + self.quant_storage = quant_storage + self.bnb_quantized = bnb_quantized + self.data = data + self.module = module + return self + + def __getstate__(self): + state = self.__dict__.copy() + state["data"] = self.data + state["requires_grad"] = self.requires_grad + return state + + def __setstate__(self, state): + self.requires_grad = state["requires_grad"] + self.blocksize = state["blocksize"] + self.compress_statistics = state["compress_statistics"] + self.quant_type = state["quant_type"] + self.quant_state = state["quant_state"] + self.data = state["data"] + self.quant_storage = state["quant_storage"] + self.bnb_quantized = state["bnb_quantized"] + self.module = state["module"] + + def __deepcopy__(self, memo): + new_instance = type(self).__new__(type(self)) + state = self.__getstate__() + new_instance.__setstate__(state) + new_instance.quant_state = copy.deepcopy(state["quant_state"]) + new_instance.data = copy.deepcopy(state["data"]) + return new_instance + + def __copy__(self): + new_instance = type(self).__new__(type(self)) + state = self.__getstate__() + new_instance.__setstate__(state) + return new_instance + + @classmethod + def from_prequantized( + cls, + data: torch.Tensor, + quantized_stats: dict[str, Any], + requires_grad: bool = False, + device="cuda", + module: Optional["Linear4bit"] = None, + **kwargs, + ) -> "Params4bit": + self = torch.Tensor._make_subclass(cls, data.to(device)) + self.requires_grad = requires_grad + self.quant_state = QuantState.from_dict(qs_dict=quantized_stats, device=device) + self.blocksize = self.quant_state.blocksize + self.compress_statistics = self.quant_state.nested + self.quant_type = self.quant_state.quant_type + self.bnb_quantized = True + + self.quant_storage = data.dtype + self.module = module + + if self.module is not None: + self.module.quant_state = self.quant_state + + return self + + def _quantize(self, device): + w = self.data.contiguous().to(device) + w_4bit, quant_state = bnb.functional.quantize_4bit( + w, + blocksize=self.blocksize, + compress_statistics=self.compress_statistics, + quant_type=self.quant_type, + quant_storage=self.quant_storage, + ) + self.data = w_4bit + self.quant_state = quant_state + if self.module is not None: + self.module.quant_state = quant_state + self.bnb_quantized = True + return self + + def cpu(self): + return self.to(device="cpu") + + def cuda(self, device: Optional[int | device | str] = None, non_blocking: bool = False): + if getattr(self.quant_state, "packing_format_for_cpu", False): + self.data, self.quant_state = _convert_weight_packed_for_cpu_inverse(self.data, self.quant_state) + return self.to(device="cuda" if device is None else device, non_blocking=non_blocking) + + def xpu(self, device: Optional[int | device | str] = None, non_blocking: bool = False): + if getattr(self.quant_state, "packing_format_for_cpu", False): + self.data, self.quant_state = _convert_weight_packed_for_cpu_inverse(self.data, self.quant_state) + return self.to(device="xpu" if device is None else device, non_blocking=non_blocking) + + @overload + def to( + self: T, + device: Optional[int | device] = ..., + dtype: Optional[dtype | str] = ..., + non_blocking: bool = ..., + ) -> T: ... + + @overload + def to(self: T, dtype: dtype | str, non_blocking: bool = ...) -> T: ... + + @overload + def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T: ... + + def to(self, *args, **kwargs): + device, dtype, non_blocking, _ = torch._C._nn._parse_to(*args, **kwargs) + + if device is not None and device.type != "meta" and not self.bnb_quantized: + return self._quantize(device) + else: + if self.quant_state is not None: + self.quant_state.to(device) + + new_param = Params4bit( + super().to(device=device, dtype=dtype, non_blocking=non_blocking), + requires_grad=self.requires_grad, + quant_state=self.quant_state, + blocksize=self.blocksize, + compress_statistics=self.compress_statistics, + quant_type=self.quant_type, + quant_storage=self.quant_storage, + bnb_quantized=self.bnb_quantized, + ) + + return new_param + + @classmethod + def __torch_function__(cls, func, types, args=(), kwargs=None): + if kwargs is None: + kwargs = {} + + if func in [torch.chunk, torch.split]: + tensor = args[0] + + result = super().__torch_function__(func, types, args, kwargs) + + if isinstance(result, tuple): + return tuple( + cls( + data=chunk, + requires_grad=tensor.requires_grad, + quant_state=tensor.quant_state, + blocksize=tensor.blocksize, + compress_statistics=tensor.compress_statistics, + quant_type=tensor.quant_type, + quant_storage=tensor.quant_storage, + module=tensor.module, + bnb_quantized=tensor.bnb_quantized, + ) + for chunk in result + ) + else: + return cls( + data=result, + requires_grad=tensor.requires_grad, + quant_state=tensor.quant_state, + blocksize=tensor.blocksize, + compress_statistics=tensor.compress_statistics, + quant_type=tensor.quant_type, + quant_storage=tensor.quant_storage, + module=tensor.module, + bnb_quantized=tensor.bnb_quantized, + ) + + return super().__torch_function__(func, types, args, kwargs) + + +def fix_4bit_weight_quant_state_from_module(module: Union["Embedding4bit", "Linear4bit"]): + if getattr(module.weight, "quant_state", None) is not None: + return + + if getattr(module, "quant_state", None) is None: + warnings.warn( + "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first.", + ) + + # the quant state got lost when the parameter got converted. This happens for example for fsdp + # since we registered the module, we can recover the state here + assert module.weight.shape[1] == 1 + if not isinstance(module.weight, Params4bit): + module.weight = Params4bit(module.weight, quant_storage=module.quant_storage, bnb_quantized=True) + module.weight.quant_state = module.quant_state + + +class Linear4bit(nn.Linear): + """ + This class is the base module for the 4-bit quantization algorithm presented in [QLoRA](https://arxiv.org/abs/2305.14314). + QLoRA 4-bit linear layers uses blockwise k-bit quantization under the hood, with the possibility of selecting various + compute datatypes such as FP4 and NF4. + + In order to quantize a linear layer one should first load the original fp16 / bf16 weights into + the Linear4bit module, then call `quantized_module.to("cuda")` to quantize the fp16 / bf16 weights. + + Example: + + ```python + import torch + import torch.nn as nn + + import bitsandbytes as bnb + from bnb.nn import Linear4bit + + fp16_model = nn.Sequential( + nn.Linear(64, 64), + nn.Linear(64, 64) + ) + + quantized_model = nn.Sequential( + Linear4bit(64, 64), + Linear4bit(64, 64) + ) + + quantized_model.load_state_dict(fp16_model.state_dict()) + quantized_model = quantized_model.to(0) # Quantization happens here + ``` + """ + + def __init__( + self, + input_features, + output_features, + bias=True, + compute_dtype=None, + compress_statistics=True, + quant_type="fp4", + quant_storage=torch.uint8, + device=None, + ): + """ + Initialize Linear4bit class. + + Args: + input_features (`str`): + Number of input features of the linear layer. + output_features (`str`): + Number of output features of the linear layer. + bias (`bool`, defaults to `True`): + Whether the linear class uses the bias term as well. + """ + super().__init__(input_features, output_features, bias, device) + self.weight = Params4bit( + self.weight.data, + requires_grad=False, + compress_statistics=compress_statistics, + quant_type=quant_type, + quant_storage=quant_storage, + module=self, + ) + # self.persistent_buffers = [] # TODO consider as way to save quant state + self.compute_dtype = compute_dtype + self.compute_type_is_set = compute_dtype is not None + self.quant_state = None + self.quant_storage = quant_storage + self.support_avx512bf16_for_cpu = has_avx512bf16() + + def set_compute_type(self, x): + if x.dtype in [torch.float32, torch.bfloat16]: + # the input is in a dtype that is safe to compute in, we switch + # to this type for speed and stability + self.compute_dtype = x.dtype + elif x.dtype == torch.float16: + # we take the compoute dtype passed into the layer + if self.compute_dtype in [None, torch.float32] and (x.numel() == x.shape[-1]): + # single batch inference with input torch.float16 and compute_dtype float32 -> slow inference when it could be fast + # warn the user about this + warnings.warn( + "Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference.", + ) + warnings.filterwarnings("ignore", message=".*inference.") + if self.compute_dtype in [None, torch.float32] and (x.numel() != x.shape[-1]): + warnings.warn( + "Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.", + ) + warnings.filterwarnings("ignore", message=".*inference or training") + + def _save_to_state_dict(self, destination, prefix, keep_vars): + """ + save weight and bias, + then fill state_dict with components of quant_state + """ + if getattr(self.weight, "quant_state", None) is not None and getattr( + self.weight.quant_state, "packing_format_for_cpu", False + ): + self.weight.data, self.weight.quant_state = _convert_weight_packed_for_cpu_inverse( + self.weight.data, self.weight.quant_state + ) + super()._save_to_state_dict(destination, prefix, keep_vars) # saving weight and bias + if getattr(self.weight, "quant_state", None) is not None: + for k, v in self.weight.quant_state.as_dict(packed=True).items(): + destination[prefix + "weight." + k] = v if keep_vars else v.detach() + + def forward(self, x: torch.Tensor): + fix_4bit_weight_quant_state_from_module(self) + quant_state = self.weight.quant_state + + if ( + not getattr(quant_state, "packing_format_for_cpu", False) + and x.device.type == "cpu" + and self.support_avx512bf16_for_cpu + and not self.training + and x.requires_grad == False + ): + self.weight.data, quant_state = _convert_weight_packed_for_cpu(self.weight.data, quant_state) + + # weights are cast automatically as Int8Params, but the bias has to be cast manually + if self.bias is not None and self.bias.dtype != x.dtype: + self.bias.data = self.bias.data.to(x.dtype) + + if not self.compute_type_is_set: + self.set_compute_type(x) + self.compute_type_is_set = True + + inp_dtype = x.dtype + if self.compute_dtype is not None: + x = x.to(self.compute_dtype) + + bias = None if self.bias is None else self.bias.to(self.compute_dtype) + weight = self.weight if getattr(quant_state, "packing_format_for_cpu", False) else self.weight.t() + + return bnb.matmul_4bit(x, weight, bias=bias, quant_state=quant_state).to(inp_dtype) + + +class LinearFP4(Linear4bit): + """ + Implements the FP4 data type. + """ + + def __init__( + self, + input_features, + output_features, + bias=True, + compute_dtype=None, + compress_statistics=True, + quant_storage=torch.uint8, + device=None, + ): + """ + Args: + input_features (`str`): + Number of input features of the linear layer. + output_features (`str`): + Number of output features of the linear layer. + bias (`bool`, defaults to `True`): + Whether the linear class uses the bias term as well. + """ + super().__init__( + input_features, + output_features, + bias, + compute_dtype, + compress_statistics, + "fp4", + quant_storage, + device, + ) + + +class LinearNF4(Linear4bit): + """Implements the NF4 data type. + + Constructs a quantization data type where each bin has equal area under a standard normal distribution N(0, 1) that + is normalized into the range [-1, 1]. + + For more information read the paper: QLoRA: Efficient Finetuning of Quantized LLMs (https://arxiv.org/abs/2305.14314) + + Implementation of the NF4 data type in bitsandbytes can be found in the `create_normal_map` function in + the `functional.py` file: https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236. + """ + + def __init__( + self, + input_features, + output_features, + bias=True, + compute_dtype=None, + compress_statistics=True, + quant_storage=torch.uint8, + device=None, + ): + """ + Args: + input_features (`str`): + Number of input features of the linear layer. + output_features (`str`): + Number of output features of the linear layer. + bias (`bool`, defaults to `True`): + Whether the linear class uses the bias term as well. + """ + super().__init__( + input_features, + output_features, + bias, + compute_dtype, + compress_statistics, + "nf4", + quant_storage, + device, + ) + + +class Int8Params(torch.nn.Parameter): + def __new__( + cls, + data: Optional[torch.Tensor] = None, + requires_grad=True, + has_fp16_weights=False, + CB: Optional[torch.Tensor] = None, + SCB: Optional[torch.Tensor] = None, + ): + if data is None: + data = torch.empty(0) + obj = torch.Tensor._make_subclass(cls, data, requires_grad) + obj.CB = CB + obj.SCB = SCB + obj.has_fp16_weights = has_fp16_weights + return obj + + def _quantize(self, device): + if self.has_fp16_weights: + return super().to(device) + + # We quantize the weight and store in 8bit row-major + B = self.data.contiguous().to(device=device, dtype=torch.float16) + CB, SCB, _ = bnb.functional.int8_vectorwise_quant(B) + self.data = CB + self.CB = CB + self.SCB = SCB + + return self + + def cpu(self): + return self.to(device="cpu") + + def cuda(self, device: Optional[int | device | str] = None, non_blocking: bool = False): + return self.to(device="cuda" if device is None else device, non_blocking=non_blocking) + + def xpu(self, device: Optional[int | device | str] = None, non_blocking: bool = False): + return self.to(device="xpu" if device is None else device, non_blocking=non_blocking) + + def __deepcopy__(self, memo): + # adjust this if new arguments are added to the constructor + new_instance = type(self).__new__( + type(self), + data=copy.deepcopy(self.data, memo), + requires_grad=self.requires_grad, + has_fp16_weights=self.has_fp16_weights, + CB=copy.deepcopy(self.CB, memo), + SCB=copy.deepcopy(self.SCB, memo), + ) + return new_instance + + @overload + def to( + self: T, + device: Optional[int | device] = ..., + dtype: Optional[dtype | str] = ..., + non_blocking: bool = ..., + ) -> T: ... + + @overload + def to(self: T, dtype: dtype | str, non_blocking: bool = ...) -> T: ... + + @overload + def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T: ... + + def to(self, *args, **kwargs): + device, dtype, non_blocking, _ = torch._C._nn._parse_to(*args, **kwargs) + + is_quantized = self.data.dtype == torch.int8 + + if not is_quantized and device is not None and device.type != "meta" and self.data.device.type == "cpu": + # We're moving from a CPU device to a non-meta device. + # In this circumstance, we want to quantize if we haven't already. + return self._quantize(device) + + # Create a new parameter on the target device. + new_param = Int8Params( + super().to(device=device, dtype=dtype, non_blocking=non_blocking), + requires_grad=self.requires_grad, + has_fp16_weights=self.has_fp16_weights, + ) + + # If we had already quantized, move the statistics appropriately. + if is_quantized: + new_param.CB = new_param.data + + if device is not None and self.SCB is not None and self.SCB.device.type != "meta": + new_param.SCB = self.SCB.to(device) + + return new_param + + +def maybe_rearrange_weight(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): + weight = state_dict.get(f"{prefix}weight") + if weight is None: + # if the state dict has no weights for this layer (e.g., LoRA finetuning), do nothing + return + weight_format = state_dict.pop(f"{prefix}weight_format", "row") + + if isinstance(weight_format, torch.Tensor): + weight_format = weight_format.item() + + # For new weights format storage type, we explicitly check + # if weights_format is on the mapping + if isinstance(weight_format, int) and weight_format not in INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING: + raise ValueError(f"Expected supported weight format - got {weight_format}") + elif isinstance(weight_format, int) and weight_format in INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING: + weight_format = INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING[weight_format] + + if weight_format != "row": + raise ValueError(f"Only 'row' weight format is supported, got {weight_format}") + + +class Embedding8bit(nn.Embedding): + """ + This class implements [LLM.int8()](https://arxiv.org/abs/2208.07339) algorithm for embedding layer + + Quantization API is similar to Linear8bitLt: + ```python + import torch + import torch.nn as nn + + from bitsandbytes.nn import Embedding8bit + + fp16_module = nn.Embedding(128, 64) + int8_module = Embedding8bit(128, 64) + + int8_module.load_state_dict(fp16_module.state_dict()) + + int8_module = int8_module.to(0) # Quantization happens here + ``` + """ + + def __init__(self, num_embeddings, embedding_dim, device=None, dtype=None): + super().__init__(num_embeddings, embedding_dim, device=device, dtype=dtype) + self.dtype = self.weight.data.dtype + + self.weight = Int8Params(self.weight.data, has_fp16_weights=False, requires_grad=False) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + raise NotImplementedError("Saving Embedding8bit module is not implemented") + + def forward(self, input: Tensor) -> Tensor: + if not hasattr(self.weight, "SCB"): + raise RuntimeError("Embedding layer is not quantized. Please call .cuda() or .to(device) first.") + + rows = self.weight.data + row_stats = self.weight.SCB + + assert rows.shape == (self.num_embeddings, self.embedding_dim) + assert row_stats.shape == (self.num_embeddings,) + + compressed_output = F.embedding(input, rows) + compressed_output_stats = F.embedding(input, row_stats.view(self.num_embeddings, 1)) + + output = compressed_output * (compressed_output_stats / 127.0) + + return output.to(self.dtype) + + +class Embedding4bit(nn.Embedding): + """ + This is the base class similar to Linear4bit. It implements the 4-bit quantization algorithm presented in + [QLoRA](https://arxiv.org/abs/2305.14314) for embeddings. + + Quantization API is similar to Linear4bit: + ```python + import torch + import torch.nn as nn + + from bitsandbytes.nn import Embedding4bit + + fp16_module = nn.Embedding(128, 64) + quantized_module = Embedding4bit(128, 64) + + quantized_module.load_state_dict(fp16_module.state_dict()) + + quantized_module = quantized_module.to(0) # Quantization happens here + ``` + """ + + def __init__( + self, + num_embeddings, + embedding_dim, + dtype=None, + quant_type="fp4", + quant_storage=torch.uint8, + device=None, + ): + super().__init__(num_embeddings, embedding_dim, device=device, dtype=dtype) + self.dtype = self.weight.data.dtype + + self.weight = Params4bit( + self.weight.data, + requires_grad=False, + compress_statistics=None, + quant_type=quant_type, + quant_storage=quant_storage, + module=self, + ) + + blocksize = self.weight.blocksize + + if embedding_dim % blocksize != 0: + warnings.warn( + f"Embedding size {embedding_dim} is not divisible by block size {blocksize}. " + "This will lead to slow inference.", + ) + + def _forward_with_partial_dequantize(self, input: Tensor): + assert self.embedding_dim % self.weight.quant_state.blocksize == 0 + + w_4bit_uint8 = self.weight.data.view(torch.uint8).view(self.num_embeddings * self.embedding_dim // 2, 1) + + output_4bit = torch.nn.functional.embedding( + weight=w_4bit_uint8.view(self.num_embeddings, self.embedding_dim // 2), + input=input, + ).view(-1, 1) + assert output_4bit.shape == (input.numel() * self.embedding_dim // 2, 1) + + blocks_per_emb = self.embedding_dim // self.weight.blocksize + + absmax = self.weight.quant_state.absmax + assert absmax.shape == (self.num_embeddings * blocks_per_emb,) + + output_absmax = torch.nn.functional.embedding( + weight=absmax.view(self.num_embeddings, blocks_per_emb), + input=input, + ).view( + -1, + ) + assert output_absmax.shape == (input.numel() * blocks_per_emb,) + + output_quant_state = copy.deepcopy(self.weight.quant_state) + output_quant_state.absmax = output_absmax + output_quant_state.shape = torch.Size((*input.shape, self.embedding_dim)) + + output = bnb.functional.dequantize_4bit(output_4bit, output_quant_state) + assert output.shape == (*input.shape, self.embedding_dim) + + return output.to(self.dtype) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + raise NotImplementedError("Saving Embedding4bit module is not implemented") + + def forward(self, input: Tensor) -> Tensor: + fix_4bit_weight_quant_state_from_module(self) + + if self.embedding_dim % self.weight.quant_state.blocksize == 0: + return self._forward_with_partial_dequantize(input) + + dequantized_weight = bnb.functional.dequantize_4bit(self.weight.data, self.weight.quant_state) + + return torch.nn.functional.embedding( + weight=dequantized_weight, + input=input, + ).to(self.dtype) + + +class EmbeddingFP4(Embedding4bit): + def __init__( + self, + num_embeddings, + embedding_dim, + dtype=None, + quant_storage=torch.uint8, + device=None, + ): + super().__init__( + num_embeddings, + embedding_dim, + dtype=dtype, + quant_type="fp4", + quant_storage=quant_storage, + device=device, + ) + + +class EmbeddingNF4(Embedding4bit): + def __init__( + self, + num_embeddings, + embedding_dim, + dtype=None, + quant_storage=torch.uint8, + device=None, + ): + super().__init__( + num_embeddings, + embedding_dim, + dtype=dtype, + quant_type="nf4", + quant_storage=quant_storage, + device=device, + ) + + +class Linear8bitLt(nn.Linear): + """ + This class is the base module for the [LLM.int8()](https://arxiv.org/abs/2208.07339) algorithm. + To read more about it, have a look at the paper. + + In order to quantize a linear layer one should first load the original fp16 / bf16 weights into + the Linear8bitLt module, then call `int8_module.to("cuda")` to quantize the fp16 weights. + + Example: + + ```python + import torch + import torch.nn as nn + + import bitsandbytes as bnb + from bnb.nn import Linear8bitLt + + fp16_model = nn.Sequential( + nn.Linear(64, 64), + nn.Linear(64, 64) + ) + + int8_model = nn.Sequential( + Linear8bitLt(64, 64, has_fp16_weights=False), + Linear8bitLt(64, 64, has_fp16_weights=False) + ) + + int8_model.load_state_dict(fp16_model.state_dict()) + int8_model = int8_model.to(0) # Quantization happens here + ``` + """ + + def __init__( + self, + input_features: int, + output_features: int, + bias=True, + has_fp16_weights=True, + threshold=0.0, + index=None, + device=None, + ): + """ + Initialize Linear8bitLt class. + + Args: + input_features (`int`): + Number of input features of the linear layer. + output_features (`int`): + Number of output features of the linear layer. + bias (`bool`, defaults to `True`): + Whether the linear class uses the bias term as well. + has_fp16_weights (`bool`, defaults to `True`): + If False, weights are quantized to int8 on ``.to(device)``. If True, + weights remain in fp16 and are quantized on-the-fly during each forward pass. + threshold (`float`, defaults to `0.0`): + Outlier threshold for mixed-precision decomposition (LLM.int8()). During the + forward pass, activation columns where any value exceeds this threshold are + computed in fp16, while the remaining columns use int8. This operates on + **activations** (inputs), not on weight values. Set to 0.0 to disable + mixed-precision decomposition and quantize all columns to int8. + index: Indices for weight reordering (used internally). + device: Device to initialize the layer on. + """ + super().__init__(input_features, output_features, bias, device) + self.state = bnb.MatmulLtState() + self.index = index + + self.state.threshold = threshold + self.state.has_fp16_weights = has_fp16_weights + + if threshold > 0.0 and not has_fp16_weights: + self.state.use_pool = True + + self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights) + self._register_load_state_dict_pre_hook(maybe_rearrange_weight) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + super()._save_to_state_dict(destination, prefix, keep_vars) + + # we only need to save SCB as extra data, because CB for quantized weights is already stored in weight.data + scb_name = "SCB" + + # case 1: .cuda was called, SCB is in self.weight + param_from_weight = getattr(self.weight, scb_name) + # case 2: self.init_8bit_state was called, SCB is in self.state + param_from_state = getattr(self.state, scb_name) + + key_name = prefix + f"{scb_name}" + + # We now only save in row-major. This format information is stored for backwards compatibility. + format_name = prefix + "weight_format" + + if not self.state.has_fp16_weights: + if param_from_weight is not None: + destination[key_name] = param_from_weight if keep_vars else param_from_weight.detach() + destination[format_name] = torch.tensor(0, dtype=torch.uint8) + elif param_from_state is not None: + destination[key_name] = param_from_state if keep_vars else param_from_state.detach() + destination[format_name] = torch.tensor(0, dtype=torch.uint8) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + unexpected_copy = list(unexpected_keys) + + for key in unexpected_copy: + input_name = key[len(prefix) :] + if input_name == "SCB": + if self.weight.SCB is None: + # buffers not yet initialized, can't access them directly without quantizing first + raise RuntimeError( + "Loading a quantized checkpoint into non-quantized Linear8bitLt is " + "not supported. Please call module.cuda() before module.load_state_dict()", + ) + + input_param = state_dict[key] + self.weight.SCB.copy_(input_param) + + if self.state.SCB is not None: + self.state.SCB = self.weight.SCB + + unexpected_keys.remove(key) + + def init_8bit_state(self): + self.state.CB = self.weight.CB + self.state.SCB = self.weight.SCB + self.weight.CB = None + self.weight.SCB = None + + def to(self, *args, **kwargs): + # Call the parent to() method to handle standard parameter/buffer movement + result = super().to(*args, **kwargs) + + device, _, _, _ = torch._C._nn._parse_to(*args, **kwargs) + + # Handle state tensors if needed. + if device is not None: + if result.state.CB is not None: + result.state.CB = result.state.CB.to(device) + if result.state.SCB is not None: + result.state.SCB = result.state.SCB.to(device) + + return result + + def forward(self, x: torch.Tensor): + self.state.is_training = self.training + if self.weight.CB is not None: + self.init_8bit_state() + + # weights are cast automatically as Int8Params, but the bias has to be cast manually + if self.bias is not None and self.bias.dtype != x.dtype: + self.bias.data = self.bias.data.to(x.dtype) + + out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state) + + if not self.state.has_fp16_weights and self.state.CB is not None: + self.weight.data = self.state.CB + + return out + + +class OutlierAwareLinear(nn.Linear): + def __init__(self, input_features, output_features, bias=True, device=None): + super().__init__(input_features, output_features, bias, device) + self.outlier_dim = None + self.is_quantized = False + + def forward_with_outliers(self, x, outlier_idx): + raise NotImplementedError("Please override the `forward_with_outliers(self, x, outlier_idx)` function") + + def quantize_weight(self, w, outlier_idx): + raise NotImplementedError("Please override the `quantize_weights(self, w, outlier_idx)` function") + + def forward(self, x): + if self.outlier_dim is None: + tracer = OutlierTracer.get_instance() + if not tracer.is_initialized(): + print("Please use OutlierTracer.initialize(model) before using the OutlierAwareLinear layer") + outlier_idx = tracer.get_outliers(self.weight) + # print(outlier_idx, tracer.get_hvalue(self.weight)) + self.outlier_dim = outlier_idx + + if not self.is_quantized: + w = self.quantize_weight(self.weight, self.outlier_dim) + self.weight.data.copy_(w) + self.is_quantized = True + + +class SwitchBackLinearBnb(nn.Linear): + def __init__( + self, + input_features, + output_features, + bias=True, + has_fp16_weights=True, + memory_efficient_backward=False, + threshold=0.0, + index=None, + device=None, + ): + super().__init__(input_features, output_features, bias, device) + self.state = bnb.MatmulLtState() + self.index = index + + self.state.threshold = threshold + self.state.has_fp16_weights = has_fp16_weights + self.state.memory_efficient_backward = memory_efficient_backward + if threshold > 0.0 and not has_fp16_weights: + self.state.use_pool = True + + self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights) + + def init_8bit_state(self): + self.state.CB = self.weight.CB + self.state.SCB = self.weight.SCB + self.weight.CB = None + self.weight.SCB = None + + def forward(self, x): + self.state.is_training = self.training + + if self.weight.CB is not None: + self.init_8bit_state() + + return bnb.matmul_mixed(x.half(), self.weight.half(), bias=None, state=self.state) + self.bias diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/nn/parametrize.py b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/parametrize.py new file mode 100644 index 0000000000000000000000000000000000000000..4a956c7fa8fa8343cf651080b72de79dd40bd532 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/parametrize.py @@ -0,0 +1,192 @@ +from functools import partial +from typing import Any, Literal, Optional + +import torch +import torch.nn as nn +import torch.nn.utils.parametrize as P + +from .. import functional as F + + +class Bnb4bitParametrization(nn.Module): + """ + A parametrization module that handles dequantization of a 4-bit quantized parameter. + + The parameter data is expected to be already quantized when this parametrization is applied. + This module will dequantize the parameter data to its original floating-point representation + when the forward method is called (i.e. when the parameter is accessed). + + Args: + quant_state (`F.QuantState`): + The quantization state containing the necessary information for dequantization. + """ + + def __init__(self, quant_state: F.QuantState): + super().__init__() + self.quant_state = quant_state + + @torch.no_grad() + def forward(self, quantized_param: torch.Tensor) -> torch.Tensor: + """ + Forward pass to dequantize the parameter. + + Args: + quantized_param (`torch.Tensor`): The quantized parameter tensor (from .original) + + Returns: + `torch.Tensor`: The dequantized parameter tensor in the original shape and dtype. + """ + return F.dequantize_4bit(quantized_param, self.quant_state) + + +def replace_parameter_4bit_prequantized( + module: nn.Module, param_name: str, qs_dict: dict[str, Any], device: torch.device +): + if not hasattr(module, param_name): + raise AttributeError(f"Module does not have parameter '{param_name}'") + + original_param = getattr(module, param_name) + + if not isinstance(original_param, nn.Parameter): + raise TypeError(f"Parameter '{param_name}' is not an instance of nn.Parameter") + + quant_state = F.QuantState.from_dict(qs_dict, device=device) + + # Apply a parametrization to the module to handle dequantization. + P.register_parametrization(module, param_name, Bnb4bitParametrization(quant_state), unsafe=True) + + # Next, register hooks. + _register_parametrization_hooks(module, param_name) + + +def replace_parameter_4bit( + module: nn.Module, + param_name: str, + compress_statistics: bool = False, + quant_type: Literal["nf4", "fp4"] = "nf4", + blocksize: Optional[int] = None, +): + """ + Replace a module parameter with a 4-bit quantized version using parametrization. + + This function quantizes an existing parameter in a PyTorch module to 4-bit precision + and sets up parametrization to handle automatic dequantization during forward passes. + The original parameter is replaced with quantized data, and a parametrization layer + is registered to manage the quantization state and dequantization process. + + Additional, it registers a state dict post-hook to ensure that the quantization state + is saved correctly when the model's state dict is saved. + + It is useful for MoE models or other scenarios where you want to quantize parameters + outside of nn.Linear layers without changing the model's architecture. + + This feature is experimental and may change in future releases. + + Args: + module (`nn.Module`): + The PyTorch module containing the parameter to be quantized. + param_name (`str`): + The name of the parameter within the module to quantize. + compress_statistics (`bool`, *optional*, defaults to `False`): + Whether to compress quantization statistics to reduce memory usage. + quant_type (`Literal["nf4", "fp4"]`, *optional*, defaults to `"nf4"`): + The quantization format to use. + blocksize (`int`, *optional*, defaults to `None`): + The block size for quantization. If None, uses the default block size. + + Raises: + AttributeError: If the module does not have the specified parameter. + TypeError: If the specified attribute is not an instance of nn.Parameter. + """ + + if not hasattr(module, param_name): + raise AttributeError(f"Module does not have parameter '{param_name}'") + + original_param = getattr(module, param_name) + + if not isinstance(original_param, nn.Parameter): + raise TypeError(f"Parameter '{param_name}' is not an instance of nn.Parameter") + + # Quantize the original parameter. + quantized_data, quant_state = F.quantize_4bit( + original_param.data, + blocksize=blocksize, + compress_statistics=compress_statistics, + quant_type=quant_type, + ) + + # Replace the parameter with the quantized data. + setattr(module, param_name, nn.Parameter(quantized_data, requires_grad=False)) + del original_param + + # Apply a parametrization to the module to handle dequantization. + P.register_parametrization(module, param_name, Bnb4bitParametrization(quant_state), unsafe=True) + + # Next, register hooks. + _register_parametrization_hooks(module, param_name) + + +def _disable_parametrization_cache(module: nn.Module, inputs: tuple[Any, ...], output: Any): + P._cache_enabled -= 1 + if not P._cache_enabled: + P._cache = {} + + +def _enable_parametrization_cache(module: nn.Module, inputs: tuple[Any, ...]): + P._cache_enabled += 1 + + +def _register_parametrization_hooks(module: nn.Module, param_name: str): + # Register a state dict hook for saving. Note that this requires torch >= 2.5.0. + if torch.__version__ >= (2, 5): + module.register_state_dict_post_hook( + partial( + _parametrized_state_dict_post_hook, + param_name=param_name, + ) + ) + + # Register hooks to enable caching for the dequantization parametrization. + # This helps preserve time and memory when the same quantized parameter + # is accessed multiple times in the forward computation. + module.register_forward_pre_hook(_enable_parametrization_cache) + module.register_forward_hook(_disable_parametrization_cache) + + +def _parametrized_state_dict_post_hook( + module: nn.Module, + state_dict: dict[str, Any], + prefix: str, + local_metadata: Any, + *, + param_name: str = "weight", + **kwargs: dict[str, Any], +) -> None: + """ + Hook to modify the state dict to include the quantization state. + """ + + original_key = f"{prefix}parametrizations.{param_name}.original" + + if original_key in state_dict: + # Create a clean entry. + # The `parametrizations.{param_name}.original` key will have the quantized data, + # but we would like it to keep it in the state_dict as `{param_name}`. + clean_key = f"{prefix}{param_name}" + state_dict[clean_key] = state_dict.pop(original_key) + + assert P.is_parametrized(module, param_name) + + # Find the parametrization, which should have the quantization state. + parametrization: Bnb4bitParametrization = next( + filter(lambda x: isinstance(x, Bnb4bitParametrization), module.parametrizations[param_name]), None + ) + + assert parametrization is not None, "Parametrization not found for the parameter." + + quant_state = parametrization.quant_state + + # Next, we need to store the quantization state. + if quant_state is not None: + for k, v in quant_state.as_dict(packed=True).items(): + state_dict[f"{prefix}{param_name}.{k}"] = v diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/nn/triton_based_modules.py b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/triton_based_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..aa849494266596317232256f90f118a4614d02ca --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/nn/triton_based_modules.py @@ -0,0 +1,264 @@ +from functools import partial + +import torch +import torch.nn as nn + +from bitsandbytes.triton.dequantize_rowwise import dequantize_rowwise +from bitsandbytes.triton.int8_matmul_mixed_dequantize import ( + int8_matmul_mixed_dequantize, +) +from bitsandbytes.triton.int8_matmul_rowwise_dequantize import ( + int8_matmul_rowwise_dequantize, +) +from bitsandbytes.triton.quantize_columnwise_and_transpose import ( + quantize_columnwise_and_transpose, +) +from bitsandbytes.triton.quantize_global import ( + quantize_global, + quantize_global_transpose, +) +from bitsandbytes.triton.quantize_rowwise import quantize_rowwise +from bitsandbytes.triton.triton_utils import is_triton_available + + +class _switchback_global(torch.autograd.Function): + @staticmethod + def forward(ctx, X_3D, W, bias): + # reshape input to [N * L, D] + X = X_3D.view(-1, X_3D.size(-1)) + + # rowwise quantize for X, global quantize for W + X_int8, state_X = quantize_rowwise(X) + W_int8, state_W = quantize_global(W) + + # save for backward. + ctx.save_for_backward = X, W + + # matmult, fused dequant and add bias + # call "mixed" because we are mixing rowwise quantized and global quantized + return int8_matmul_mixed_dequantize(X_int8, W_int8.t(), state_X, state_W, bias).view(*X_3D.size()[:-1], -1) + + @staticmethod + def backward(ctx, G_3D): + # reshape input to [N_out * L, D] + G = G_3D.reshape(-1, G_3D.size(-1)) + + grad_X = grad_W = grad_bias = None + + X, W = ctx.save_for_backward + if ctx.needs_input_grad[0]: + # rowwise quantize for G, global quantize for W + # for W, we also fuse the transpose operation because only A @ B^T is supported + # so we transpose once then call .t() in the matmul + G_int8, state_G = quantize_rowwise(G) + W_int8, state_W = quantize_global_transpose(W) + grad_X = int8_matmul_mixed_dequantize(G_int8, W_int8.t(), state_G, state_W, None).view( + *G_3D.size()[:-1], + -1, + ) + if ctx.needs_input_grad[1]: + # backward pass uses standard weight grad + grad_W = torch.matmul(G.t(), X.to(G.dtype)) + if ctx.needs_input_grad[2]: + grad_bias = G.sum(dim=0) + + return grad_X, grad_W, grad_bias + + +class _switchback_vectorrize(torch.autograd.Function): + @staticmethod + def forward(ctx, X_3D, W, bias): + # reshape input to [N * L, D] + X = X_3D.view(-1, X_3D.size(-1)) + + ctx.save_for_backward = X, W + # rowwise quantize for X + # columnwise quantize for W (first rowwise, transpose later) + X_int8, state_X = quantize_rowwise(X) + W_int8, state_W = quantize_rowwise(W) + + # matmult, fused dequant and add bias + # call kernel which expects rowwise quantized X and W + return int8_matmul_rowwise_dequantize(X_int8, W_int8.t(), state_X, state_W, bias).view(*X_3D.size()[:-1], -1) + + @staticmethod + def backward(ctx, G_3D): + X, W = ctx.save_for_backward + + G = G_3D.reshape(-1, G_3D.size(-1)) + + grad_X = grad_W = grad_bias = None + + if ctx.needs_input_grad[0]: + # rowwise quantize for G, columnwise quantize for W and fused transpose + # we call .t() for weight later because only A @ B^T is supported + G_int8, state_G = quantize_rowwise(G) + W_int8, state_W = quantize_columnwise_and_transpose(W) + grad_X = int8_matmul_rowwise_dequantize(G_int8, W_int8.t(), state_G, state_W, None).view( + *G_3D.size()[:-1], + -1, + ) + if ctx.needs_input_grad[1]: + # backward pass uses standard weight grad + grad_W = torch.matmul(G.t(), X.to(G.dtype)) + if ctx.needs_input_grad[2]: + grad_bias = G.sum(dim=0) + + return grad_X, grad_W, grad_bias + + +class _switchback_global_mem_efficient(torch.autograd.Function): + @staticmethod + def forward(ctx, X_3D, W, bias): + # reshape input to [N * L, D] + X = X_3D.view(-1, X_3D.size(-1)) + X_3D_sz = X_3D.size() + + # rowwise quantize for X, global quantize for W + X_int8, state_X = quantize_rowwise(X) + del X + W_int8, state_W = quantize_global(W) + + # save for backward. + ctx.save_for_backward = X_int8, state_X, W_int8, state_W + + # matmult, fused dequant and add bias + # call "mixed" because we are mixing rowwise quantized and global quantized + return int8_matmul_mixed_dequantize(X_int8, W_int8.t(), state_X, state_W, bias).view(*X_3D_sz[:-1], -1) + + @staticmethod + def backward(ctx, G_3D): + # reshape input to [N_out * L, D] + G = G_3D.reshape(-1, G_3D.size(-1)) + G_3D_sz = G_3D.size() + + grad_X = grad_W = grad_bias = None + + X_int8, state_X, W_int8, state_W = ctx.save_for_backward + if ctx.needs_input_grad[1]: + real_X = dequantize_rowwise(X_int8, state_X) + del X_int8 + grad_W = torch.matmul(G.t(), real_X.to(G.dtype)) + del real_X + if ctx.needs_input_grad[2]: + grad_bias = G.sum(dim=0) + if ctx.needs_input_grad[0]: + G_int8, state_G = quantize_rowwise(G) + del G + W_int8 = W_int8.t().contiguous() + grad_X = int8_matmul_mixed_dequantize(G_int8, W_int8.t(), state_G, state_W, None).view(*G_3D_sz[:-1], -1) + + return grad_X, grad_W, grad_bias + + +class SwitchBackLinear(nn.Linear): + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + device=None, + dtype=None, + vector_wise_quantization: bool = False, + mem_efficient: bool = False, + ): + super().__init__(in_features, out_features, bias, device, dtype) + + if not is_triton_available(): + raise ImportError("""Could not import triton. Please install triton to use SwitchBackLinear. + Alternatively, you can use bnb.nn.SwitchBackLinearBnb, but it will be slower""") + + # By default, we use the global quantization. + self.vector_wise_quantization = vector_wise_quantization + if self.vector_wise_quantization: + self._fn = _switchback_vectorrize + if mem_efficient: + print("mem efficient is not supported for vector-wise quantization.") + exit(1) + else: + if mem_efficient: + self._fn = _switchback_global_mem_efficient + else: + self._fn = _switchback_global + + def prepare_for_eval(self): + # If we just want to do eval, we can pre-quantize the weights instead of doing it on the forward pass. + # Note this is experimental and not tested thoroughly. + # Note this needs to be explicitly called with something like + # def cond_prepare(m): + # if hasattr(m, "prepare_for_eval"): + # m.prepare_for_eval() + # model.apply(cond_prepare) + print("=> preparing for eval.") + if self.vector_wise_quantization: + W_int8, state_W = quantize_rowwise(self.weight) + else: + W_int8, state_W = quantize_global(self.weight) + + self.register_buffer("W_int8", W_int8) + self.register_buffer("state_W", state_W) + + del self.weight + + def forward(self, x): + if self.training: + return self._fn.apply(x, self.weight, self.bias) + else: + # If it hasn't been "prepared for eval", run the standard forward pass. + if not hasattr(self, "W_int8"): + return self._fn.apply(x, self.weight, self.bias) + + # Otherwise, use pre-computed weights. + X = x.view(-1, x.size(-1)) + X_int8, state_X = quantize_rowwise(X) + + if self.vector_wise_quantization: + return int8_matmul_rowwise_dequantize(X_int8, self.W_int8.t(), state_X, self.state_W, self.bias).view( + *x.size()[:-1], + -1, + ) + else: + return int8_matmul_mixed_dequantize(X_int8, self.W_int8.t(), state_X, self.state_W, self.bias).view( + *x.size()[:-1], + -1, + ) + + +SwitchBackLinearGlobal = partial(SwitchBackLinear, vector_wise_quantization=False) +SwitchBackLinearGlobalMemEfficient = partial(SwitchBackLinear, vector_wise_quantization=False, mem_efficient=True) +SwitchBackLinearVectorwise = partial(SwitchBackLinear, vector_wise_quantization=True) + + +# This is just the standard linear function. +class StandardLinearFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, input, weight, bias=None): + X = input.view(-1, input.size(-1)) + + ctx.save_for_backward(X, weight, bias) + output = input.matmul(weight.t()) + if bias is not None: + output += bias.unsqueeze(0).expand_as(output) + return output.view(*input.size()[:-1], -1) + + @staticmethod + def backward(ctx, grad_output_3D): + input, weight, bias = ctx.saved_tensors + + grad_output = grad_output_3D.reshape(-1, grad_output_3D.size(-1)) + + grad_input = grad_weight = grad_bias = None + + if ctx.needs_input_grad[0]: + grad_input = grad_output.matmul(weight.to(grad_output.dtype)).view(*grad_output_3D.size()[:-1], -1) + if ctx.needs_input_grad[1]: + grad_weight = grad_output.t().matmul(input.to(grad_output.dtype)) + if bias is not None and ctx.needs_input_grad[2]: + grad_bias = grad_output.sum(0) + + return grad_input, grad_weight, grad_bias + + +class StandardLinear(nn.Linear): + def forward(self, x): + return StandardLinearFunction.apply(x, self.weight, self.bias) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..07174c38dfec8cbb6a3e93f8177af965026e168e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit +from .adam import Adam, Adam8bit, Adam32bit, PagedAdam, PagedAdam8bit, PagedAdam32bit +from .adamw import ( + AdamW, + AdamW8bit, + AdamW32bit, + PagedAdamW, + PagedAdamW8bit, + PagedAdamW32bit, +) +from .ademamix import AdEMAMix, AdEMAMix8bit, AdEMAMix32bit, PagedAdEMAMix, PagedAdEMAMix8bit, PagedAdEMAMix32bit +from .lamb import LAMB, LAMB8bit, LAMB32bit +from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS +from .lion import Lion, Lion8bit, Lion32bit, PagedLion, PagedLion8bit, PagedLion32bit +from .optimizer import GlobalOptimManager +from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit +from .sgd import SGD, SGD8bit, SGD32bit diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d5ae249e1093a5b24fc83ff6b003b76de1024b75 Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__pycache__/__init__.cpython-312.pyc differ diff 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a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__pycache__/sgd.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__pycache__/sgd.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..07e4fe128d60bbb8446e5ceceb6f2d1f0f952efd Binary files /dev/null and b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/__pycache__/sgd.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adagrad.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adagrad.py new file mode 100644 index 0000000000000000000000000000000000000000..7459dece1040ceea6e9541e32818676fa94591d1 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adagrad.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from bitsandbytes.optim.optimizer import Optimizer1State + + +class Adagrad(Optimizer1State): + def __init__( + self, + params, + lr=1e-2, + lr_decay=0, + weight_decay=0, + initial_accumulator_value=0, + eps=1e-10, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Base Adagrad optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-2): + The learning rate. + lr_decay (`int`, defaults to 0): + The learning rate decay. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + initial_accumulator_value (`int`, defaults to 0): + The initial momemtum values. + eps (`float`, defaults to 1e-10): + The epsilon value prevents division by zero in the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if not 0.0 <= lr: + raise ValueError(f"Invalid learning rate: {lr}") + if not 0.0 <= weight_decay: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + if not 0.0 <= eps: + raise ValueError(f"Invalid epsilon value: {eps}") + if initial_accumulator_value != 0.0: + raise ValueError("Initial accumulator value != 0.0 not supported!") + if lr_decay != 0.0: + raise ValueError("Lr Decay != 0.0 not supported!") + super().__init__( + "adagrad", + params, + lr, + (0.0, 0.0), + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) + + +class Adagrad8bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-2, + lr_decay=0, + weight_decay=0, + initial_accumulator_value=0, + eps=1e-10, + optim_bits=8, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + 8-bit Adagrad optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-2): + The learning rate. + lr_decay (`int`, defaults to 0): + The learning rate decay. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + initial_accumulator_value (`int`, defaults to 0): + The initial momemtum values. + eps (`float`, defaults to 1e-10): + The epsilon value prevents division by zero in the optimizer. + optim_bits (`int`, defaults to 8): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if not 0.0 <= lr: + raise ValueError(f"Invalid learning rate: {lr}") + if not 0.0 <= weight_decay: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + if not 0.0 <= eps: + raise ValueError(f"Invalid epsilon value: {eps}") + if initial_accumulator_value != 0.0: + raise ValueError("Initial accumulator value != 0.0 not supported!") + if lr_decay != 0.0: + raise ValueError("Lr Decay != 0.0 not supported!") + assert block_wise + super().__init__( + "adagrad", + params, + lr, + (0.0, 0.0), + eps, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) + + +class Adagrad32bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-2, + lr_decay=0, + weight_decay=0, + initial_accumulator_value=0, + eps=1e-10, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + 32-bit Adagrad optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-2): + The learning rate. + lr_decay (`int`, defaults to 0): + The learning rate decay. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + initial_accumulator_value (`int`, defaults to 0): + The initial momemtum values. + eps (`float`, defaults to 1e-10): + The epsilon value prevents division by zero in the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if not 0.0 <= lr: + raise ValueError(f"Invalid learning rate: {lr}") + if not 0.0 <= weight_decay: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + if not 0.0 <= eps: + raise ValueError(f"Invalid epsilon value: {eps}") + if initial_accumulator_value != 0.0: + raise ValueError("Initial accumulator value != 0.0 not supported!") + if lr_decay != 0.0: + raise ValueError("Lr Decay != 0.0 not supported!") + super().__init__( + "adagrad", + params, + lr, + (0.0, 0.0), + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adam.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adam.py new file mode 100644 index 0000000000000000000000000000000000000000..22a217c3b6af07cd54e77c742de94e639f135e6a --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adam.py @@ -0,0 +1,394 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from bitsandbytes.optim.optimizer import Optimizer2State + + +class Adam(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + Base Adam optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class Adam8bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 8-bit Adam optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + Note: This parameter is not supported in Adam8bit and must be False. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + Note: This parameter is not used in Adam8bit as it always uses 8-bit optimization. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + # Validate unsupported parameters + if amsgrad: + raise ValueError("Adam8bit does not support amsgrad=True") + + if optim_bits != 32: + # We allow the default value of 32 to maintain compatibility with the function signature, + # but any other value is invalid since Adam8bit always uses 8-bit optimization + raise ValueError("Adam8bit only supports optim_bits=32 (default value for compatibility)") + + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 8, # Hardcoded to 8 bits + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class Adam32bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 32-bit Adam optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class PagedAdam(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + Paged Adam optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) + + +class PagedAdam8bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 8-bit paged Adam optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + Note: This parameter is not supported in PagedAdam8bit and must be False. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + Note: This parameter is not used in PagedAdam8bit as it always uses 8-bit optimization. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + # Validate unsupported parameters + if amsgrad: + raise ValueError("PagedAdam8bit does not support amsgrad=True") + + if optim_bits != 32: + # We allow the default value of 32 to maintain compatibility with the function signature, + # but any other value is invalid since PagedAdam8bit always uses 8-bit optimization + raise ValueError("PagedAdam8bit only supports optim_bits=32 (default value for compatibility)") + + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 8, # Hardcoded to 8 bits + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) + + +class PagedAdam32bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + Paged 32-bit Adam optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adamw.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adamw.py new file mode 100644 index 0000000000000000000000000000000000000000..5f225c9ad9d51ced5f769cf0e495dbf04074bc16 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/adamw.py @@ -0,0 +1,385 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from bitsandbytes.optim.optimizer import Optimizer2State + + +class AdamW(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=1e-2, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + Base AdamW optimizer. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class AdamW8bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=1e-2, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 8-bit AdamW optimizer. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + Note: This parameter is not supported in AdamW8bit and must be False. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + Note: This parameter is not used in AdamW8bit as it always uses 8-bit optimization. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + # Validate unsupported parameters + if amsgrad: + raise ValueError("AdamW8bit does not support amsgrad=True") + + if optim_bits != 32: + # We allow the default value of 32 to maintain compatibility with the function signature, + # but any other value is invalid since AdamW8bit always uses 8-bit optimization + raise ValueError("AdamW8bit only supports optim_bits=32 (default value for compatibility)") + + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 8, # Hardcoded to 8 bits + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class AdamW32bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=1e-2, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 32-bit AdamW optimizer. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class PagedAdamW(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=1e-2, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Paged AdamW optimizer. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) + + +class PagedAdamW8bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=1e-2, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Paged 8-bit AdamW optimizer. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + Note: This parameter is not supported in PagedAdamW8bit and must be False. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + Note: This parameter is not used in PagedAdamW8bit as it always uses 8-bit optimization. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + # Validate unsupported parameters + if amsgrad: + raise ValueError("PagedAdamW8bit does not support amsgrad=True") + + if optim_bits != 32: + # We allow the default value of 32 to maintain compatibility with the function signature, + # but any other value is invalid since PagedAdamW8bit always uses 8-bit optimization + raise ValueError("PagedAdamW8bit only supports optim_bits=32 (default value for compatibility)") + + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 8, # Hardcoded to 8 bits + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) + + +class PagedAdamW32bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=1e-2, + amsgrad=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Paged 32-bit AdamW optimizer. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + super().__init__( + "adam", + params, + lr, + betas, + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/ademamix.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/ademamix.py new file mode 100644 index 0000000000000000000000000000000000000000..4cd27274744bd53dda63fa5a145ae3acb5a66dda --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/ademamix.py @@ -0,0 +1,416 @@ +from collections.abc import Iterable +import math +from typing import Literal, Optional + +import torch + +import bitsandbytes.functional as F +from bitsandbytes.optim.optimizer import Optimizer2State + + +class _ReferenceAdEMAMix(torch.optim.Optimizer): + """ + Reference: https://hf.co/papers/2409.03137 + """ + + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + eps: float = 1e-8, + weight_decay: float = 1e-2, # default 0.0 or 1e-2? + t_beta3: Optional[int] = None, + t_alpha: Optional[int] = None, + ): + defaults = dict( + lr=lr, betas=betas, alpha=alpha, eps=eps, weight_decay=weight_decay, t_beta3=t_beta3, t_alpha=t_alpha + ) + + super().__init__(params, defaults) + + @torch.no_grad() + def step(self, closure=None): + loss = None + + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + if "step" in group: + group["step"] += 1 + else: + group["step"] = 1 + + lr = group["lr"] + eps = group["eps"] + beta1, beta2, beta3 = group["betas"] + alpha = group["alpha"] + t_alpha = group["t_alpha"] + t_beta3 = group["t_beta3"] + weight_decay = group["weight_decay"] + + for p in group["params"]: + if p.grad is None: + continue + + grad = p.grad + state = self.state[p] + + # State initialization + if len(state) == 0: + # For parity with bnb implementation we combine both fast + # and slow EMA stats into one stacked tensor. + state["m1_m2"] = p.new_zeros((2, *p.size())) + state["nu"] = torch.zeros_like(p) # second moment estimate + + m1, m2, nu = state["m1_m2"][0], state["m1_m2"][1], state["nu"] + + bias_correction1 = 1 - beta1 ** group["step"] + + bias_correction2 = 1 - beta2 ** group["step"] + + # Apply scheduler for alpha + if t_alpha is not None: + alpha = min(group["step"] * alpha / t_alpha, alpha) + + # Apply scheduler for beta3 + if t_beta3 is not None: + ln_beta1 = math.log(beta1) + ln_beta3 = math.log(beta3) + step_scale = group["step"] / t_beta3 + beta3 = min( + math.exp((ln_beta1 * ln_beta3) / (((1 - step_scale) * ln_beta3) + (step_scale * ln_beta1))), + beta3, + ) + + # Update the EMAs + m1.mul_(beta1).add_(grad, alpha=1 - beta1) + m2.mul_(beta3).add_(grad, alpha=1 - beta3) + nu.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + + # Compute step + denom = (nu.sqrt() / (bias_correction2**0.5)).add(eps) + update = (m1.div(bias_correction1) + alpha * m2) / denom + + # Add weight decay + update.add_(p, alpha=weight_decay) + + # Apply update scaled by learning rate + p.add_(-lr * update) + + return loss + + +class AdEMAMix(Optimizer2State): + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + eps: float = 1e-8, + weight_decay: float = 1e-2, + optim_bits: Literal[8, 32] = 32, + min_8bit_size: int = 4096, + is_paged: bool = False, + ): + super().__init__( + "ademamix", + params=params, + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + optim_bits=optim_bits, + args=None, + min_8bit_size=min_8bit_size, + percentile_clipping=100, + block_wise=True, + is_paged=is_paged, + alpha=alpha, + t_alpha=t_alpha, + t_beta3=t_beta3, + ) + + @torch.no_grad() + def init_state(self, group, p, gindex, pindex): + # In our AdEMAMix implementation, we use `state` to hold + # both the fast and slow EMAs. Here we override the base + # `Optimizer2State` to allocate a buffer twice as large. + # Additional consideration: we do not support block_wise=False, + # percentile clipping, or max_unorm. + + config = self.get_config(gindex, pindex, group) + + if config["optim_bits"] == 32: + dtype = torch.float32 + elif config["optim_bits"] == 8: + dtype = torch.uint8 + else: + raise NotImplementedError(f"Amount of optimizer bits not supported: {config['optim_bits']}") + + if p.numel() < config["min_8bit_size"]: + dtype = torch.float32 + + state = self.state[p] + state["step"] = 0 + + if dtype == torch.uint8: + if "dynamic" not in self.name2qmap: + self.fill_qmap() + self.name2qmap["dynamic"] = state["qmap1"] = self.name2qmap["dynamic"].to(p.device) + self.name2qmap["udynamic"] = state["qmap2"] = self.name2qmap["udynamic"].to(p.device) + + blocksize = 256 + n = p.numel() + blocks = (n // blocksize) + bool(n % blocksize) + + state["absmax1"] = torch.zeros((2, blocks), dtype=torch.float32, device=p.device) + state["absmax2"] = torch.zeros((blocks,), dtype=torch.float32, device=p.device) + + state["state1"] = self._get_state_double_buffer(p, dtype=dtype) + state["state2"] = self.get_state_buffer(p, dtype=dtype) + + @torch.no_grad() + def update_step(self, group, p, gindex, pindex): + config = self.get_config(gindex, pindex, group) + + if not config["t_alpha"] and not config["t_beta3"]: + # Not using alpha/beta3 scheduler; we can fall through. + super().update_step(group, p, gindex, pindex) + return + + # Ensure contiguous memory layout + p.data = p.data.contiguous() + p.grad = p.grad.contiguous() + + state = self.state[p] + grad = p.grad + + state["step"] += 1 + step = state["step"] + + beta1, beta2, beta3 = config["betas"] + alpha = config["alpha"] + t_alpha = config["t_alpha"] + t_beta3 = config["t_beta3"] + + # Apply scheduler for alpha + if t_alpha: + alpha_t = min(step * alpha / t_alpha, alpha) + else: + alpha_t = alpha + + # Apply scheduler for beta3 + if t_beta3: + ln_beta1 = math.log(beta1) + ln_beta3 = math.log(beta3) + step_scale = step / t_beta3 + beta3_t = min( + math.exp((ln_beta1 * ln_beta3) / (((1 - step_scale) * ln_beta3) + (step_scale * ln_beta1))), beta3 + ) + else: + beta3_t = beta3 + + # Apply updates + if state["state1"].dtype == torch.float32: + F.optimizer_update_32bit( + self.optimizer_name, + grad, + p, + state["state1"], + beta1, + config["eps"], + step, + config["lr"], + state["state2"], + beta2, + beta3_t, + alpha_t, + config["weight_decay"], + gnorm_scale=1.0, + unorm_vec=state["unorm_vec"] if config["max_unorm"] > 0.0 else None, + max_unorm=config["max_unorm"], + skip_zeros=config["skip_zeros"], + ) + elif state["state1"].dtype == torch.uint8: + F.optimizer_update_8bit_blockwise( + self.optimizer_name, + grad, + p, + state["state1"], + state["state2"], + config["betas"][0], + config["betas"][1], + beta3_t, + alpha_t, + config["eps"], + step, + config["lr"], + state["qmap1"], + state["qmap2"], + state["absmax1"], + state["absmax2"], + config["weight_decay"], + gnorm_scale=1.0, + skip_zeros=config["skip_zeros"], + ) + + def _get_state_double_buffer(self, p, dtype=torch.float32): + if not self.is_paged or p.numel() < 1e5: + return torch.zeros((2, *p.size()), dtype=dtype, device=p.device) + else: + buff = F.get_paged(*(2, *p.size()), dtype=dtype, device=p.device) + F.fill(buff, 0) + self.page_mng.paged_tensors.append(buff) + return buff + + +class AdEMAMix8bit(AdEMAMix): + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + eps: float = 1e-8, + weight_decay: float = 1e-2, + min_8bit_size: int = 4096, + is_paged: bool = False, + ): + super().__init__( + params, + lr=lr, + betas=betas, + alpha=alpha, + t_alpha=t_alpha, + t_beta3=t_beta3, + eps=eps, + weight_decay=weight_decay, + optim_bits=8, + min_8bit_size=min_8bit_size, + is_paged=is_paged, + ) + + +class PagedAdEMAMix8bit(AdEMAMix8bit): + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + eps: float = 1e-8, + weight_decay: float = 1e-2, + min_8bit_size: int = 4096, + ): + super().__init__( + params, + lr=lr, + betas=betas, + alpha=alpha, + t_alpha=t_alpha, + t_beta3=t_beta3, + eps=eps, + weight_decay=weight_decay, + min_8bit_size=min_8bit_size, + is_paged=True, + ) + + +class PagedAdEMAMix(AdEMAMix): + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + eps: float = 1e-8, + weight_decay: float = 1e-2, + optim_bits: Literal[8, 32] = 32, + min_8bit_size: int = 4096, + ): + super().__init__( + params, + lr=lr, + betas=betas, + alpha=alpha, + t_alpha=t_alpha, + t_beta3=t_beta3, + eps=eps, + weight_decay=weight_decay, + optim_bits=optim_bits, + min_8bit_size=min_8bit_size, + is_paged=True, + ) + + +class AdEMAMix32bit(Optimizer2State): + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + eps: float = 1e-8, + weight_decay: float = 1e-2, + min_8bit_size: int = 4096, + is_paged: bool = False, + ): + super().__init__( + "ademamix", + params=params, + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + optim_bits=32, + args=None, + min_8bit_size=min_8bit_size, + percentile_clipping=100, + block_wise=True, + is_paged=is_paged, + alpha=alpha, + t_alpha=t_alpha, + t_beta3=t_beta3, + ) + + +class PagedAdEMAMix32bit(AdEMAMix32bit): + def __init__( + self, + params: Iterable[torch.nn.Parameter], + lr: float = 1e-3, + betas: tuple[float, float, float] = (0.9, 0.999, 0.9999), + alpha: float = 5.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + eps: float = 1e-8, + weight_decay: float = 1e-2, + min_8bit_size: int = 4096, + ): + super().__init__( + params, + lr=lr, + betas=betas, + alpha=alpha, + t_alpha=t_alpha, + t_beta3=t_beta3, + eps=eps, + weight_decay=weight_decay, + min_8bit_size=min_8bit_size, + is_paged=True, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lamb.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lamb.py new file mode 100644 index 0000000000000000000000000000000000000000..8d29cbbfe5021ab3967c4bb4c98ed0be3675ab29 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lamb.py @@ -0,0 +1,200 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from bitsandbytes.optim.optimizer import Optimizer2State + + +class LAMB(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + adam_w_mode=True, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=False, + max_unorm=1.0, + ): + """ + Base LAMB optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + bias_correction (`bool`, defaults to `True`): + Whether to apply bias correction to the first and second-order moments. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + adam_w_mode (`bool`, defaults to `True`): + Whether to use the AdamW variant. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + max_unorm (`float`, defaults to 1.0): + The maximum gradient norm. + """ + super().__init__( + "lamb", + params, + lr, + betas, + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + max_unorm=1.0, + ) + + +class LAMB8bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + adam_w_mode=True, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=False, + max_unorm=1.0, + ): + """ + 8-bit LAMB optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + bias_correction (`bool`, defaults to `True`): + Whether to apply bias correction to the first and second-order moments. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + adam_w_mode (`bool`, defaults to `True`): + Whether to use the AdamW variant. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + max_unorm (`float`, defaults to 1.0): + The maximum gradient norm. + """ + super().__init__( + "lamb", + params, + lr, + betas, + eps, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + block_wise, + max_unorm=1.0, + ) + + +class LAMB32bit(Optimizer2State): + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + adam_w_mode=True, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=False, + max_unorm=1.0, + ): + """ + 32-bit LAMB optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + bias_correction (`bool`, defaults to `True`): + Whether to apply bias correction to the first and second-order moments. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + amsgrad (`bool`, defaults to `False`): + Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. + adam_w_mode (`bool`, defaults to `True`): + Whether to use the AdamW variant. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + max_unorm (`float`, defaults to 1.0): + The maximum gradient norm. + """ + super().__init__( + "lamb", + params, + lr, + betas, + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + max_unorm=1.0, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lars.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lars.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2af57bcc9221b76ffcd30c23f39c63ef58d21e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lars.py @@ -0,0 +1,274 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import torch +from torch.optim import Optimizer + +from bitsandbytes.optim.optimizer import Optimizer1State + + +class LARS(Optimizer1State): + def __init__( + self, + params, + lr, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + max_unorm=0.02, + ): + """ + Base LARS optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`): + The learning rate. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + dampening (`float`, defaults to 0): + The dampening value reduces the momentum of the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + nesterov (`bool`, defaults to `False`): + Whether to use Nesterov momentum. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + max_unorm (`float`, defaults to 0.02): + The maximum gradient norm. + """ + if momentum == 0: + raise NotImplementedError("LARS without momentum is not supported!") + super().__init__( + "lars", + params, + lr, + (momentum, dampening), + 0.0, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + max_unorm=max_unorm, + block_wise=False, + ) + + +class LARS8bit(Optimizer1State): + def __init__( + self, + params, + lr, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + max_unorm=0.02, + ): + """ + 8-bit LARS optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`): + The learning rate. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + dampening (`float`, defaults to 0): + The dampening value reduces the momentum of the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + nesterov (`bool`, defaults to `False`): + Whether to use Nesterov momentum. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + max_unorm (`float`, defaults to 0.02): + The maximum gradient norm. + """ + if momentum == 0: + raise NotImplementedError("LARS without momentum is not supported!") + super().__init__( + "lars", + params, + lr, + (momentum, dampening), + 0.0, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + max_unorm=max_unorm, + block_wise=False, + ) + + +class LARS32bit(Optimizer1State): + def __init__( + self, + params, + lr, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + max_unorm=0.02, + ): + """ + 32-bit LARS optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`): + The learning rate. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + dampening (`float`, defaults to 0): + The dampening value reduces the momentum of the optimizer. + weight_decay (`float`, defaults to 1e-2): + The weight decay value for the optimizer. + nesterov (`bool`, defaults to `False`): + Whether to use Nesterov momentum. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + max_unorm (`float`, defaults to 0.02): + The maximum gradient norm. + """ + if momentum == 0: + raise NotImplementedError("LARS without momentum is not supported!") + super().__init__( + "lars", + params, + lr, + (momentum, dampening), + 0.0, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + max_unorm=max_unorm, + block_wise=False, + ) + + +class PytorchLARS(Optimizer): + def __init__( + self, + params, + lr=0.01, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + max_unorm=0.02, + ): + if lr < 0.0: + raise ValueError(f"Invalid learning rate: {lr}") + if momentum < 0.0: + raise ValueError(f"Invalid momentum value: {momentum}") + if weight_decay < 0.0: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + + defaults = dict( + lr=lr, + momentum=momentum, + dampening=dampening, + weight_decay=weight_decay, + nesterov=nesterov, + max_unorm=max_unorm, + ) + if nesterov and (momentum <= 0 or dampening != 0): + raise ValueError("Nesterov momentum requires a momentum and zero dampening") + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault("nesterov", False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + weight_decay = group["weight_decay"] + momentum = group["momentum"] + dampening = group["dampening"] + nesterov = group["nesterov"] + max_unorm = group["max_unorm"] + lr = group["lr"] + + for p in group["params"]: + if p.grad is None: + continue + + state = self.state[p] + d_p = p.grad + if weight_decay != 0: + d_p = d_p.add(p, alpha=weight_decay) + + if momentum != 0: + buf = state.get("momentum_buffer", None) + + if buf is None: + buf = torch.clone(d_p).detach() + state["momentum_buffer"] = buf + else: + buf.mul_(momentum).add_(d_p, alpha=1 - dampening) + + if nesterov: + update = d_p + buf * momentum + else: + update = buf + + update_scale = 1.0 + if max_unorm > 0.0: + assert p.dtype == torch.float32 + pnorm = torch.norm(p.detach()) + unorm = torch.norm(update) + if unorm > max_unorm * pnorm: + update_scale = max_unorm * pnorm / unorm + + p.add_(update, alpha=-lr * update_scale) + + return loss diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lion.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lion.py new file mode 100644 index 0000000000000000000000000000000000000000..2e4163694ae06476931588caba20e38c163c0735 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/lion.py @@ -0,0 +1,318 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from bitsandbytes.optim.optimizer import Optimizer1State + + +class Lion(Optimizer1State): + def __init__( + self, + params, + lr=1e-4, + betas=(0.9, 0.99), + weight_decay=0, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + Base Lion optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-4): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + weight_decay (`float`, defaults to 0): + The weight decay value for the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "lion", + params, + lr, + betas, + 0.0, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class Lion8bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-4, + betas=(0.9, 0.99), + weight_decay=0, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 8-bit Lion optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-4): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + weight_decay (`float`, defaults to 0): + The weight decay value for the optimizer. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "lion", + params, + lr, + betas, + 0.0, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class Lion32bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-4, + betas=(0.9, 0.99), + weight_decay=0, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + is_paged=False, + ): + """ + 32-bit Lion optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-4): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + weight_decay (`float`, defaults to 0): + The weight decay value for the optimizer. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__( + "lion", + params, + lr, + betas, + 0.0, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=is_paged, + ) + + +class PagedLion(Optimizer1State): + def __init__( + self, + params, + lr=1e-4, + betas=(0.9, 0.99), + weight_decay=0, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Paged Lion optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-4): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + weight_decay (`float`, defaults to 0): + The weight decay value for the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + super().__init__( + "lion", + params, + lr, + betas, + 0.0, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) + + +class PagedLion8bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-4, + betas=(0.9, 0.99), + weight_decay=0, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Paged 8-bit Lion optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-4): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + weight_decay (`float`, defaults to 0): + The weight decay value for the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + super().__init__( + "lion", + params, + lr, + betas, + 0.0, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) + + +class PagedLion32bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-4, + betas=(0.9, 0.99), + weight_decay=0, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Paged 32-bit Lion optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-4): + The learning rate. + betas (`tuple(float, float)`, defaults to (0.9, 0.999)): + The beta values are the decay rates of the first and second-order moment of the optimizer. + weight_decay (`float`, defaults to 0): + The weight decay value for the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + super().__init__( + "lion", + params, + lr, + betas, + 0.0, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + is_paged=True, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..ef904757b692c5aab08f5eb823acd15057003928 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py @@ -0,0 +1,841 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from collections import abc as container_abcs, defaultdict +from copy import deepcopy +from itertools import chain +from typing import Optional + +import torch + +import bitsandbytes.functional as F +from bitsandbytes.utils import sync_gpu + + +class MockArgs: + def __init__(self, initial_data): + for key in initial_data: + setattr(self, key, initial_data[key]) + + +class GlobalOptimManager: + """ + A global optimizer manager for enabling custom optimizer configs. + """ + + _instance = None + + def __init__(self): + raise RuntimeError("Call get_instance() instead") + + def initialize(self): + self.pid2config = {} + self.index2config = {} + self.optimizer = None + self.uses_config_override = False + self.module_weight_config_triple = [] + + @classmethod + def get_instance(cls): + if cls._instance is None: + cls._instance = cls.__new__(cls) + cls._instance.initialize() + return cls._instance + + def register_parameters(self, params): + param_groups = list(params) + if not isinstance(param_groups[0], dict): + param_groups = [{"params": param_groups}] + + for group_index, group in enumerate(param_groups): + for p_index, p in enumerate(group["params"]): + if id(p) in self.pid2config: + self.index2config[(group_index, p_index)] = self.pid2config[id(p)] + + def override_config(self, parameters, key=None, value=None, key_value_dict=None): + """ + Override initial optimizer config with specific hyperparameters. + + The key-values of the optimizer config for the input parameters are overridden + This can be both, optimizer parameters like `betas` or `lr`, or it can be + 8-bit specific parameters like `optim_bits` or `percentile_clipping`. + + Arguments: + parameters (`torch.Tensor` or `list(torch.Tensors)`): + The input parameters. + key (`str`): + The hyperparameter to override. + value: + The hyperparameter value. + key_value_dict (`dict`): + A dictionary with multiple key-values to override. + + Example: + + ```py + import torch + import bitsandbytes as bnb + + mng = bnb.optim.GlobalOptimManager.get_instance() + + model = MyModel() + mng.register_parameters(model.parameters()) # 1. register parameters while still on CPU + + model = model.cuda() + # use 8-bit optimizer states for all parameters + adam = bnb.optim.Adam(model.parameters(), lr=0.001, optim_bits=8) + + # 2. override: the parameter model.fc1.weight now uses 32-bit Adam + mng.override_config(model.fc1.weight, 'optim_bits', 32) + ``` + """ + self.uses_config_override = True + if isinstance(parameters, torch.nn.Parameter): + parameters = [parameters] + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + if key is not None and value is not None: + assert key_value_dict is None + key_value_dict = {key: value} + + if key_value_dict is not None: + for p in parameters: + if id(p) in self.pid2config: + self.pid2config[id(p)].update(key_value_dict) + else: + self.pid2config[id(p)] = key_value_dict + + def register_module_override(self, module, param_name, config): + self.module_weight_config_triple.append((module, param_name, config)) + + +class Optimizer8bit(torch.optim.Optimizer): + _FSDP_WRAPPED_QUANT_STATE_KEY = "__bnb_optimizer_quant_state__" + + def __init__(self, params, defaults, optim_bits=32, is_paged=False): + """ + Base 8-bit optimizer class. + + Arguments: + params (`torch.Tensor`): + The input parameters to optimize. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + super().__init__(params, defaults) + self.initialized = False + self.name2qmap = {} + self.is_paged = is_paged + self.page_mng = F.GlobalPageManager.get_instance() + + self.mng = GlobalOptimManager.get_instance() + self.non_castable_tensor_keys = { + "qmap1", + "qmap2", + "max1", + "max2", + "new_max1", + "new_max2", + "state1", + "state2", + "gnorm_vec", + "absmax1", + "absmax2", + "unorm_vec", + } + + if optim_bits == 8: + self.fill_qmap() + + def fill_qmap(self): + self.name2qmap["dynamic"] = F.create_dynamic_map(signed=True) + self.name2qmap["udynamic"] = F.create_dynamic_map(signed=False) + + def state_dict(self): + """Return optimizer state, wrapping quantization tensors for FSDP compatibility. + + FSDP's full_optim_state_dict gathers all tensor states across ranks. + Quantization states (state1, state2, absmax, etc.) have different shapes + than model parameters, causing gather operations to fail. By wrapping + these tensors in a nested dict, FSDP skips them during gathering. + """ + state_dict = super().state_dict() + + # Deep copy the state to avoid modifying the original optimizer state + # PyTorch's state_dict() only does a shallow copy + state_dict["state"] = { + k: {kk: vv for kk, vv in v.items()} if isinstance(v, dict) else v for k, v in state_dict["state"].items() + } + + # Wrap quantization-specific tensors in a nested dict to hide from FSDP + for param_state in state_dict["state"].values(): + if isinstance(param_state, dict): + quant_state = {} + keys_to_wrap = [k for k in param_state if k in self.non_castable_tensor_keys] + for key in keys_to_wrap: + quant_state[key] = param_state.pop(key) + if quant_state: + param_state[self._FSDP_WRAPPED_QUANT_STATE_KEY] = quant_state + + return state_dict + + def __setstate__(self, state): + super().__setstate__(state) + + def load_state_dict(self, state_dict, move_to_device=True): + """Load an optimizer state. + + Arguments: + state_dict (`dict`): + An optimizer state (should be returned from a call to `state_dict`) to load. + move_to_device (`bool`, defaults to `True`): + Whether to move the optimizer's state to the device. + """ + # deepcopy, to be consistent with module API + state_dict = deepcopy(state_dict) + + # Unwrap quantization states that were wrapped for FSDP compatibility + for param_state in state_dict["state"].values(): + if isinstance(param_state, dict) and self._FSDP_WRAPPED_QUANT_STATE_KEY in param_state: + quant_state = param_state.pop(self._FSDP_WRAPPED_QUANT_STATE_KEY) + param_state.update(quant_state) + + # Validate the state_dict + groups = self.param_groups + saved_groups = state_dict["param_groups"] + + if len(groups) != len(saved_groups): + raise ValueError("loaded state dict has a different number of parameter groups") + param_lens = (len(g["params"]) for g in groups) + saved_lens = (len(g["params"]) for g in saved_groups) + if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)): + raise ValueError( + "loaded state dict contains a parameter group that doesn't match the size of optimizer's group", + ) + + # Update the state + id_map = { + old_id: p + for old_id, p in zip( + chain.from_iterable(g["params"] for g in saved_groups), + chain.from_iterable(g["params"] for g in groups), + ) + } + + def cast(param, value): + r"""Make a deep copy of value, casting all tensors to device of param.""" + if isinstance(value, torch.Tensor): + # Floating-point types are a bit special here. They are the only ones + # that are assumed to always match the type of params. + if param.is_floating_point() and value.dtype != torch.uint8: + value = value.to(param.dtype) + return value + elif isinstance(value, dict): + for k, v in value.items(): + if k in self.non_castable_tensor_keys: + if move_to_device: + value[k] = v.to(param.device) + else: + value[k] = cast(param, v) + + return value + elif isinstance(value, container_abcs.Iterable): + return type(value)(cast(param, v) for v in value) + else: + return value + + # Copy state assigned to params (and cast tensors to appropriate types). + # State that is not assigned to params is copied as is (needed for + # backward compatibility). + state = defaultdict(dict) + for k, v in state_dict["state"].items(): + if k in id_map: + param = id_map[k] + state[param] = cast(param, v) + else: + state[k] = v + + # Update parameter groups, setting their 'params' value + def update_group(group, new_group): + new_group["params"] = group["params"] + return new_group + + param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)] + self.__setstate__({"state": state, "param_groups": param_groups}) + + def to_gpu(self): + for gindex, group in enumerate(self.param_groups): + for pindex, p in enumerate(group["params"]): + if p in self.state: + values = self.state[p] + for k, v in values.items(): + if isinstance(v, torch.Tensor): + is_paged = getattr(v, "is_paged", False) + if not is_paged: + self.state[p][k] = v.to(p.device) + + def check_overrides(self): + for module, attr, config in self.mng.module_weight_config_triple: + pmodule = getattr(module, attr) + assert pmodule is not None + assert isinstance(pmodule, torch.Tensor) or isinstance(pmodule, torch.Parameter) + found = False + for gindex, group in enumerate(self.param_groups): + if found: + break + for pindex, p in enumerate(group["params"]): + if found: + break + if id(p) == id(pmodule): + # found the matching parameter + # init override + self.mng.pid2config[id(p)] = config + self.mng.index2config[(gindex, pindex)] = self.mng.pid2config[id(p)] + found = True + + @torch.no_grad() + def step(self, closure=None): + """Perform a single optimization step. + + Arguments: + closure (`Callable`, *optional*, defaults to `None`): + A closure that reevaluates the model and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if not self.initialized: + self.check_overrides() + self.to_gpu() # needed for fairseq pure fp16 training + self.initialized = True + + # if self.is_paged: self.page_mng.prefetch_all() + p = None + for gindex, group in enumerate(self.param_groups): + for pindex, p in enumerate(group["params"]): + if p.grad is None: + continue + state = self.state[p] + if len(state) == 0: + self.init_state(group, p, gindex, pindex) + + self.prefetch_state(p) + self.update_step(group, p, gindex, pindex) + sync_gpu(p) + if self.is_paged and p is not None: + # all paged operations are asynchronous, we need + # to sync to make sure all tensors are in the right state + sync_gpu(p) + + return loss + + def get_config(self, gindex, pindex, group): + config = {} + config["betas"] = group["betas"] + config["eps"] = group["eps"] + config["weight_decay"] = group["weight_decay"] + config["lr"] = group["lr"] + config["alpha"] = group.get("alpha", 0.0) + config["t_alpha"] = group.get("t_alpha", None) + config["t_beta3"] = group.get("t_beta3", None) + config["optim_bits"] = self.args.optim_bits + config["min_8bit_size"] = self.args.min_8bit_size + config["percentile_clipping"] = self.args.percentile_clipping + config["block_wise"] = self.args.block_wise + config["max_unorm"] = self.args.max_unorm + config["skip_zeros"] = self.args.skip_zeros + + if (gindex, pindex) in self.mng.index2config: + config.update(self.mng.index2config[(gindex, pindex)]) + return config + + def init_state(self, group, p, gindex, pindex): + raise NotImplementedError("init_state method needs to be overridden") + + def update_step(self, group, p, gindex, pindex): + raise NotImplementedError("The update_step method needs to be overridden") + + def get_state_buffer(self, p, dtype=torch.float32): + if not self.is_paged or p.numel() < 1e5: + return torch.zeros_like(p, dtype=dtype, device=p.device) + else: + # > 1 MB + buff = F.get_paged(*p.shape, dtype=dtype, device=p.device) + F.fill(buff, 0) + self.page_mng.paged_tensors.append(buff) + return buff + + def prefetch_state(self, p): + if self.is_paged: + state = self.state[p] + s1 = state["state1"] + is_paged = getattr(s1, "is_paged", False) + if is_paged: + F.prefetch_tensor(state["state1"]) + if "state2" in state: + F.prefetch_tensor(state["state2"]) + + +class Optimizer2State(Optimizer8bit): + def __init__( + self, + optimizer_name, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0.0, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + max_unorm=0.0, + skip_zeros=False, + is_paged=False, + alpha=0.0, + t_alpha: Optional[int] = None, + t_beta3: Optional[int] = None, + ): + """ + Base 2-state update optimizer class. + + Arguments: + optimizer_name (`str`): + The name of the optimizer. + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple`, defaults to (0.9, 0.999)): + The beta values for the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value for the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + max_unorm (`float`, defaults to 0.0): + The maximum value to normalize each block with. + skip_zeros (`bool`, defaults to `False`): + Whether to skip zero values for sparse gradients and models to ensure correct updates. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + alpha (`float`, defaults to 0.0): + The alpha value for the AdEMAMix optimizer. + t_alpha (`Optional[int]`, defaults to `None`): + Number of iterations for alpha scheduling with AdEMAMix. + t_beta3 (`Optional[int]`, defaults to `None`): + Number of iterations for beta scheduling with AdEMAMix. + + """ + if not 0.0 <= lr: + raise ValueError(f"Invalid learning rate: {lr}") + if not 0.0 <= eps: + raise ValueError(f"Invalid epsilon value: {eps}") + if isinstance(betas, str): + # format: '(beta1, beta2)' + betas = betas.replace("(", "").replace(")", "").strip().split(",") + betas = [float(b) for b in betas] + for i in range(len(betas)): + if not 0.0 <= betas[i] < 1.0: + raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}") + if not 0.0 <= weight_decay: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + + defaults = dict( + lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, alpha=alpha, t_alpha=t_alpha, t_beta3=t_beta3 + ) + + super().__init__(params, defaults, optim_bits, is_paged) + + if args is None: + args = {} + args["optim_bits"] = optim_bits + args["min_8bit_size"] = min_8bit_size + args["percentile_clipping"] = percentile_clipping + args["block_wise"] = block_wise + args["max_unorm"] = max_unorm + args["skip_zeros"] = skip_zeros + + self.args = MockArgs(args) + else: + self.args = args + + self.optimizer_name = optimizer_name + + @torch.no_grad() + def init_state(self, group, p, gindex, pindex): + config = self.get_config(gindex, pindex, group) + + if config["optim_bits"] == 32: + dtype = torch.float32 + elif config["optim_bits"] == 8: + dtype = torch.uint8 + else: + raise NotImplementedError(f"Amount of optimizer bits not supported: {config['optim_bits']}") + + if p.numel() < config["min_8bit_size"]: + dtype = torch.float32 + + state = self.state[p] + state["step"] = 0 + + if dtype == torch.float32: + state["state1"] = self.get_state_buffer(p, dtype=torch.float32) + state["state2"] = self.get_state_buffer(p, dtype=torch.float32) + elif dtype == torch.uint8: + if state["step"] == 0: + if "dynamic" not in self.name2qmap: + self.fill_qmap() + self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(p.device) + self.name2qmap["udynamic"] = self.name2qmap["udynamic"].to(p.device) + + state["state1"] = self.get_state_buffer(p, dtype=torch.uint8) + state["qmap1"] = self.name2qmap["dynamic"] + + state["state2"] = self.get_state_buffer(p, dtype=torch.uint8) + state["qmap2"] = self.name2qmap["udynamic"] + + if config["block_wise"]: + blocksize = 256 + n = p.numel() + blocks = (n // blocksize) + bool(n % blocksize) + + state["absmax1"] = torch.zeros((blocks,), dtype=torch.float32, device=p.device) + state["absmax2"] = torch.zeros((blocks,), dtype=torch.float32, device=p.device) + else: + state["max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["new_max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["max2"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["new_max2"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + + if config["percentile_clipping"] < 100: + state["gnorm_vec"] = torch.zeros((100,), device=p.device) + + if config["max_unorm"] > 0.0: + state["unorm_vec"] = torch.zeros((1,), device=p.device) + + @torch.no_grad() + def update_step(self, group, p, gindex, pindex): + # avoid update error from non-contiguous memory layout + p.data = p.data.contiguous() + p.grad = p.grad.contiguous() + + state = self.state[p] + grad = p.grad + + config = self.get_config(gindex, pindex, group) + + state["step"] += 1 + step = state["step"] + + if config["percentile_clipping"] < 100: + _current_gnorm, _clip_value, gnorm_scale = F.percentile_clipping( + grad, + state["gnorm_vec"], + step, + config["percentile_clipping"], + ) + else: + gnorm_scale = 1.0 + + if state["state1"].dtype == torch.float: + F.optimizer_update_32bit( + self.optimizer_name, + grad, + p, + state["state1"], + config["betas"][0], + config["eps"], + step, + config["lr"], + state["state2"], + config["betas"][1], + config["betas"][2] if len(config["betas"]) >= 3 else 0.0, + config.get("alpha", 0.0), + config["weight_decay"], + gnorm_scale, + state["unorm_vec"] if config["max_unorm"] > 0.0 else None, + max_unorm=config["max_unorm"], + skip_zeros=config["skip_zeros"], + ) + + elif state["state1"].dtype == torch.uint8 and not config["block_wise"]: + F.optimizer_update_8bit( + self.optimizer_name, + grad, + p, + state["state1"], + state["state2"], + config["betas"][0], + config["betas"][1], + config["eps"], + step, + config["lr"], + state["qmap1"], + state["qmap2"], + state["max1"], + state["max2"], + state["new_max1"], + state["new_max2"], + config["weight_decay"], + gnorm_scale=gnorm_scale, + unorm_vec=state["unorm_vec"] if config["max_unorm"] > 0.0 else None, + max_unorm=config["max_unorm"], + ) + + # swap maxes + state["max1"], state["new_max1"] = state["new_max1"], state["max1"] + state["max2"], state["new_max2"] = state["new_max2"], state["max2"] + elif state["state1"].dtype == torch.uint8 and config["block_wise"]: + F.optimizer_update_8bit_blockwise( + self.optimizer_name, + grad, + p, + state["state1"], + state["state2"], + config["betas"][0], + config["betas"][1], + config["betas"][2] if len(config["betas"]) >= 3 else 0.0, + config.get("alpha", 0.0), + config["eps"], + step, + config["lr"], + state["qmap1"], + state["qmap2"], + state["absmax1"], + state["absmax2"], + config["weight_decay"], + gnorm_scale=gnorm_scale, + skip_zeros=config["skip_zeros"], + ) + + +class Optimizer1State(Optimizer8bit): + def __init__( + self, + optimizer_name, + params, + lr=1e-3, + betas=(0.9, 0.0), + eps=1e-8, + weight_decay=0.0, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + max_unorm=0.0, + skip_zeros=False, + is_paged=False, + ): + """ + Base 1-state update optimizer class. + + Arguments: + optimizer_name (`str`): + The name of the optimizer. + params (`torch.Tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-3): + The learning rate. + betas (`tuple`, defaults to (0.9, 0.0)): + The beta values for the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value for the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + max_unorm (`float`, defaults to 0.0): + The maximum value to normalize each block with. + skip_zeros (`bool`, defaults to `False`): + Whether to skip zero values for sparse gradients and models to ensure correct updates. + is_paged (`bool`, defaults to `False`): + Whether the optimizer is a paged optimizer or not. + """ + if not 0.0 <= lr: + raise ValueError(f"Invalid learning rate: {lr}") + if not 0.0 <= eps: + raise ValueError(f"Invalid epsilon value: {eps}") + for i in range(len(betas)): + if not 0.0 <= betas[i] < 1.0: + raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}") + if not 0.0 <= weight_decay: + raise ValueError(f"Invalid weight_decay value: {weight_decay}") + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + super().__init__(params, defaults, optim_bits, is_paged) + + if args is None: + args = {} + args["optim_bits"] = optim_bits + args["min_8bit_size"] = min_8bit_size + args["percentile_clipping"] = percentile_clipping + args["block_wise"] = block_wise + args["max_unorm"] = max_unorm + args["skip_zeros"] = skip_zeros + + self.args = MockArgs(args) + else: + self.args = args + + self.optimizer_name = optimizer_name + + @torch.no_grad() + def init_state(self, group, p, gindex, pindex): + config = self.get_config(gindex, pindex, group) + + if config["optim_bits"] == 32: + dtype = torch.float32 + elif config["optim_bits"] == 8: + dtype = torch.uint8 + else: + raise NotImplementedError(f"Amount of optimizer bits not supported: {config['optim_bits']}") + + if p.numel() < config["min_8bit_size"]: + dtype = torch.float32 + + state = self.state[p] + state["step"] = 0 + + if dtype == torch.float32: + state["state1"] = self.get_state_buffer(p, dtype=torch.float32) + elif dtype == torch.uint8: + if state["step"] == 0: + if "dynamic" not in self.name2qmap: + self.fill_qmap() + self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(p.device) + + state["state1"] = self.get_state_buffer(p, dtype=torch.uint8) + state["qmap1"] = self.name2qmap["dynamic"] + + if config["block_wise"]: + blocksize = 256 + n = p.numel() + blocks = (n // blocksize) + bool(n % blocksize) + + state["absmax1"] = torch.zeros((blocks,), dtype=torch.float32, device=p.device) + else: + state["max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["new_max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + + if config["percentile_clipping"] < 100: + state["gnorm_vec"] = torch.zeros((100,), device=p.device) + + if config["max_unorm"] > 0.0: + state["unorm_vec"] = torch.zeros((1,), device=p.device) + + @torch.no_grad() + def update_step(self, group, p, gindex, pindex): + # avoid update error from non-contiguous memory layout + p.data = p.data.contiguous() + p.grad = p.grad.contiguous() + + state = self.state[p] + grad = p.grad + + config = self.get_config(gindex, pindex, group) + + state["step"] += 1 + step = state["step"] + + if config["percentile_clipping"] < 100: + _current_gnorm, _clip_value, gnorm_scale = F.percentile_clipping( + grad, + state["gnorm_vec"], + step, + config["percentile_clipping"], + ) + else: + gnorm_scale = 1.0 + + if state["state1"].dtype == torch.float: + F.optimizer_update_32bit( + self.optimizer_name, + grad, + p, + state["state1"], + config["betas"][0], + config["eps"], + step, + config["lr"], + None, + config["betas"][1], + 0.0, + 0.0, + config["weight_decay"], + gnorm_scale, + state["unorm_vec"] if config["max_unorm"] > 0.0 else None, + max_unorm=config["max_unorm"], + skip_zeros=config["skip_zeros"], + ) + + elif state["state1"].dtype == torch.uint8 and not config["block_wise"]: + F.optimizer_update_8bit( + self.optimizer_name, + grad, + p, + state["state1"], + None, + config["betas"][0], + config["betas"][1], + config["eps"], + step, + config["lr"], + state["qmap1"], + None, + state["max1"], + None, + state["new_max1"], + None, + config["weight_decay"], + gnorm_scale, + state["unorm_vec"] if config["max_unorm"] > 0.0 else None, + max_unorm=config["max_unorm"], + ) + + state["max1"], state["new_max1"] = state["new_max1"], state["max1"] + elif state["state1"].dtype == torch.uint8 and config["block_wise"]: + F.optimizer_update_8bit_blockwise( + self.optimizer_name, + grad, + p, + state["state1"], + None, + config["betas"][0], + config["betas"][1], + 0.0, + 0.0, + config["eps"], + step, + config["lr"], + state["qmap1"], + None, + state["absmax1"], + None, + config["weight_decay"], + gnorm_scale=gnorm_scale, + skip_zeros=config["skip_zeros"], + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/rmsprop.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/rmsprop.py new file mode 100644 index 0000000000000000000000000000000000000000..25611309b5ceac9cd51ac169dbc43786f57d9220 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/rmsprop.py @@ -0,0 +1,196 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from bitsandbytes.optim.optimizer import Optimizer1State + + +class RMSprop(Optimizer1State): + def __init__( + self, + params, + lr=1e-2, + alpha=0.99, + eps=1e-8, + weight_decay=0, + momentum=0, + centered=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Base RMSprop optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-2): + The learning rate. + alpha (`float`, defaults to 0.99): + The alpha value is the decay rate of the squared gradients of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + centered (`bool`, defaults to `False`): + Whether the gradients are normalized by the variance. If `True`, it can help training at the expense of additional compute. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if alpha == 0: + raise NotImplementedError("RMSprop with alpha==0.0 is not supported!") + if centered: + raise NotImplementedError("Centered RMSprop is not supported!") + super().__init__( + "rmsprop", + params, + lr, + (alpha, momentum), + eps, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) + + +class RMSprop8bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-2, + alpha=0.99, + eps=1e-8, + weight_decay=0, + momentum=0, + centered=False, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + 8-bit RMSprop optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-2): + The learning rate. + alpha (`float`, defaults to 0.99): + The alpha value is the decay rate of the squared gradients of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + centered (`bool`, defaults to `False`): + Whether the gradients are normalized by the variance. If `True`, it can help training at the expense of additional compute. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if alpha == 0: + raise NotImplementedError("RMSprop with alpha==0.0 is not supported!") + if centered: + raise NotImplementedError("Centered RMSprop is not supported!") + super().__init__( + "rmsprop", + params, + lr, + (alpha, momentum), + eps, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) + + +class RMSprop32bit(Optimizer1State): + def __init__( + self, + params, + lr=1e-2, + alpha=0.99, + eps=1e-8, + weight_decay=0, + momentum=0, + centered=False, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + 32-bit RMSprop optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`, defaults to 1e-2): + The learning rate. + alpha (`float`, defaults to 0.99): + The alpha value is the decay rate of the squared gradients of the optimizer. + eps (`float`, defaults to 1e-8): + The epsilon value prevents division by zero in the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + centered (`bool`, defaults to `False`): + Whether the gradients are normalized by the variance. If `True`, it can help training at the expense of additional compute. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + + if alpha == 0: + raise NotImplementedError("RMSprop with alpha==0.0 is not supported!") + if centered: + raise NotImplementedError("Centered RMSprop is not supported!") + super().__init__( + "rmsprop", + params, + lr, + (alpha, momentum), + eps, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/optim/sgd.py b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/sgd.py new file mode 100644 index 0000000000000000000000000000000000000000..ec18f036c6b7176e742c585de1537cc0da61f506 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/optim/sgd.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +from bitsandbytes.optim.optimizer import Optimizer1State + + +class SGD(Optimizer1State): + def __init__( + self, + params, + lr, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + optim_bits=32, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + Base SGD optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`): + The learning rate. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + dampening (`float`, defaults to 0): + The dampening value reduces the momentum of the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + nesterov (`bool`, defaults to `False`): + Whether to use Nesterov momentum. + optim_bits (`int`, defaults to 32): + The number of bits of the optimizer state. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if momentum == 0: + raise NotImplementedError("SGD without momentum is not supported!") + super().__init__( + "momentum", + params, + lr, + (momentum, dampening), + 0.0, + weight_decay, + optim_bits, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) + + +class SGD8bit(Optimizer1State): + def __init__( + self, + params, + lr, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + 8-bit SGD optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`): + The learning rate. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + dampening (`float`, defaults to 0): + The dampening value reduces the momentum of the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + nesterov (`bool`, defaults to `False`): + Whether to use Nesterov momentum. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if momentum == 0: + raise NotImplementedError("SGD without momentum is not supported!") + super().__init__( + "momentum", + params, + lr, + (momentum, dampening), + 0.0, + weight_decay, + 8, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) + + +class SGD32bit(Optimizer1State): + def __init__( + self, + params, + lr, + momentum=0, + dampening=0, + weight_decay=0, + nesterov=False, + args=None, + min_8bit_size=4096, + percentile_clipping=100, + block_wise=True, + ): + """ + 32-bit SGD optimizer. + + Arguments: + params (`torch.tensor`): + The input parameters to optimize. + lr (`float`): + The learning rate. + momentum (`float`, defaults to 0): + The momentum value speeds up the optimizer by taking bigger steps. + dampening (`float`, defaults to 0): + The dampening value reduces the momentum of the optimizer. + weight_decay (`float`, defaults to 0.0): + The weight decay value for the optimizer. + nesterov (`bool`, defaults to `False`): + Whether to use Nesterov momentum. + args (`object`, defaults to `None`): + An object with additional arguments. + min_8bit_size (`int`, defaults to 4096): + The minimum number of elements of the parameter tensors for 8-bit optimization. + percentile_clipping (`int`, defaults to 100): + Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. + block_wise (`bool`, defaults to `True`): + Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. + """ + if momentum == 0: + raise NotImplementedError("SGD without momentum is not supported!") + super().__init__( + "momentum", + params, + lr, + (momentum, dampening), + 0.0, + weight_decay, + 32, + args, + min_8bit_size, + percentile_clipping, + block_wise, + ) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/research/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/research/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..31db4f282d6eff1c60d1b5696a9dc035d232b82b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/research/__init__.py @@ -0,0 +1,6 @@ +from . import nn +from .autograd._functions import ( + matmul_fp8_global, + matmul_fp8_mixed, + switchback_bnb, +) diff --git 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b/.venv/lib/python3.12/site-packages/bitsandbytes/research/autograd/_functions.py @@ -0,0 +1,396 @@ +from functools import reduce # Required in Python 3 +import operator +from typing import Optional +import warnings + +import torch + +from bitsandbytes.autograd._functions import GlobalOutlierPooler, MatmulLtState +import bitsandbytes.functional as F + + +# math.prod not compatible with python < 3.8 +def prod(iterable): + return reduce(operator.mul, iterable, 1) + + +class MatMulFP8Mixed(torch.autograd.Function): + # forward is the same, but we added the fallback for pre-turing GPUs + # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None") + + @staticmethod + def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024): + # default of pytorch behavior if inputs are empty + ctx.is_empty = False + if prod(A.shape) == 0: + ctx.is_empty = True + ctx.A = A + ctx.B = B + + B_shape = B.shape + if A.shape[-1] == B_shape[0]: + return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device) + else: + return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device) + + # 1. Dequantize + # 2. MatmulnN + cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=bsz) + fp8A = F.dequantize_blockwise(cA, state, blocksize=bsz).to(A.dtype) + + cB, state = F.quantize(B.float(), code=fw_code) + fp8B = F.dequantize(cB, state).to(B.dtype) + + output = torch.matmul(fp8A, fp8B) + + # output is half + + # 3. Save state + ctx.fw_code = fw_code + ctx.bw_code = bw_code + ctx.bsz = bsz + ctx.bsz2 = bsz2 + ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype + + if any(ctx.needs_input_grad[:2]): + # NOTE: we send back A, and re-quant. + ctx.tensors = (A, fp8B) + else: + ctx.tensors = (None, None) + + return output + + @staticmethod + def backward(ctx, grad_output): + if ctx.is_empty: + return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None + + req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad + A, B = ctx.tensors + + grad_A, grad_B = None, None + + # TODO: Fix blocksize to be output_dim + cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=ctx.bsz2) + fp8out = F.dequantize_blockwise(cgrad_out, state, blocksize=ctx.bsz2).to(grad_output.dtype) + + # cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code) + # fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype) + + # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous() + # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector') + # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose + # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2]) + + # not supported by PyTorch. TODO: create work-around + if req_gradA: + grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype) + + if req_gradB: + if len(A.shape) == 3: + At = A.transpose(2, 1).contiguous() + else: + At = A.transpose(1, 0).contiguous() + # cA, state = F.quantize(At.float(), code=ctx.fw_code) + # fp8At = F.dequantize(cA, state).to(A.dtype) + grad_B = torch.matmul(At.to(grad_output.dtype), grad_output).to(B.dtype) + + return grad_A, grad_B, None, None, None, None, None + + +class MatMulFP8Global(torch.autograd.Function): + # forward is the same, but we added the fallback for pre-turing GPUs + # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None") + + @staticmethod + def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024): + # default of pytorch behavior if inputs are empty + ctx.is_empty = False + if prod(A.shape) == 0: + ctx.is_empty = True + ctx.A = A + ctx.B = B + + B_shape = B.shape + if A.shape[-1] == B_shape[0]: + return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device) + else: + return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device) + + # 1. Dequantize + # 2. MatmulnN + cA, state = F.quantize(A.float(), code=fw_code) + fp8A = F.dequantize(cA, state).to(A.dtype) + + cB, state = F.quantize(B.float(), code=fw_code) + fp8B = F.dequantize(cB, state).to(B.dtype) + + output = torch.matmul(fp8A, fp8B) + + # output is half + + # 3. Save state + ctx.fw_code = fw_code + ctx.bw_code = bw_code + ctx.bsz = bsz + ctx.bsz2 = bsz2 + ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype + + if any(ctx.needs_input_grad[:2]): + # NOTE: we send back A, and re-quant. + ctx.tensors = (A, fp8B) + else: + ctx.tensors = (None, None) + + return output + + @staticmethod + def backward(ctx, grad_output): + if ctx.is_empty: + return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None + + req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad + A, B = ctx.tensors + + grad_A, grad_B = None, None + + # TODO: Fix blocksize to be output_dim + cgrad_out, state = F.quantize(grad_output.float(), code=ctx.bw_code) + fp8out = F.dequantize(cgrad_out, state).to(grad_output.dtype) + + # cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code) + # fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype) + + # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous() + # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector') + # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose + # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2]) + + # not supported by PyTorch. TODO: create work-around + if req_gradA: + grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype) + + if req_gradB: + if len(A.shape) == 3: + At = A.transpose(2, 1).contiguous() + else: + At = A.transpose(1, 0).contiguous() + cA, state = F.quantize(At.float(), code=ctx.fw_code) + fp8At = F.dequantize(cA, state).to(A.dtype) + grad_B = torch.matmul(fp8At.to(fp8out.dtype), fp8out).to(B.dtype) + + return grad_A, grad_B, None, None, None, None, None + + +class SwitchBackBnb(torch.autograd.Function): + @staticmethod + def forward(ctx, A, B, out=None, bias=None, state: Optional[MatmulLtState] = None): + state = state or MatmulLtState() + + # default to pytorch behavior if inputs are empty + ctx.is_empty = False + if prod(A.shape) == 0: + ctx.is_empty = True + ctx.A = A + ctx.B = B + ctx.bias = bias + if A.shape[-1] == B.shape[0]: + return torch.empty(A.shape[:-1] + B.shape[1:], dtype=A.dtype, device=A.device) + else: + return torch.empty(A.shape[:-1] + B.shape[:1], dtype=A.dtype, device=A.device) + + # 1. Quantize A + # 2. Quantize B + # 3. Matmul + # 4. Mixed-precision decomposition matmul + # 5. Save state + input_shape = A.shape + if state.outlier_pool is None: + state.outlier_pool = GlobalOutlierPooler.get_instance() + + # Cast A to fp16 + if A.dtype != torch.float16: + warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization") + + # 1. Quantize A + if len(A.shape) == 3: + A = A.view(-1, A.shape[-1]).contiguous() + CA, CAt, SCA, SCAt, outlier_cols = F.int8_double_quant(A.to(torch.float16), threshold=state.threshold) + + if state.threshold > 0.0 and outlier_cols is not None: + if state.has_fp16_weights: + idx = outlier_cols + CA[:, idx] = 0 + subA = A[:, idx] + state.subB = B[:, idx].t().contiguous() + state.idx = idx + else: + if state.SB is None: + state.SB = (state.CB.shape, "row") + else: + if not state.has_fp16_weights and state.SB is None: + state.SB = (state.CB.shape, "row") + subA = None + + # 2. Quantize B + if state.has_fp16_weights: + # print('B shape', B.shape) + has_grad = getattr(B, "grad", None) is not None + is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1) + if is_transposed: + B = B.contiguous() + + if (state.is_training and not has_grad) or state.SB is None: + state.reset_grads() + ( + state.CB, + state.CBt, + state.SCB, + state.SCBt, + _, + ) = F.int8_double_quant(B.to(torch.float16)) + state.SB = (state.CB.shape, "row") + else: + has_grad = False + + if outlier_cols is not None and not state.has_fp16_weights: + # extract outliers + state.idx = outlier_cols + outliers = state.CB[:, state.idx.long()].clone() + state.subB = (outliers * state.SCB.view(-1, 1) / 127.0).t().contiguous().to(A.dtype) + CA[:, state.idx.long()] = 0 + + subA = A[:, state.idx.long()] + + shapeB = state.SB[0] + + if len(input_shape) == 3: + output_shape = (input_shape[0], input_shape[1], shapeB[0]) + else: + output_shape = (input_shape[0], shapeB[0]) + + # 3. Matmul + out32 = F.int8_linear_matmul(CA, state.CB) + # we apply the fused bias here + + if bias is None or bias.dtype == torch.float16: + output = F.int8_mm_dequant(out32, SCA, state.SCB, bias=bias).to(A.dtype) + else: # apply bias separately + output = F.int8_mm_dequant(out32, SCA, state.SCB, bias=None).to(A.dtype) + output.add_(bias) + + # 4. Mixed-precision decomposition matmul + if outlier_cols is not None and subA is not None: + output += torch.matmul(subA, state.subB) + + # 5. Save state + ctx.state = state + + ctx.grad_shape = input_shape + ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype + + if any(ctx.needs_input_grad[:2]): + ctx.tensors = (CAt, subA, A) + ctx.tensor_states = (SCAt, state.idx) + else: + ctx.tensors = [None, None, None] + ctx.tensor_states = (None, None) + ctx.save_for_backward(None, None) + + clone_func = torch.clone if len(output_shape) == 3 else lambda x: x + return clone_func(output.view(output_shape)) + + @staticmethod + def backward(ctx, grad_output): + if ctx.is_empty: + bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias) + return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None + + req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad + _CAt, _subA, A = ctx.tensors + _SCAt, _idx = ctx.tensor_states + state = ctx.state + grad_A = grad_B = grad_bias = None + + if req_gradBias: + # compute grad_bias first before changing grad_output dtype + grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias) + + # Cast grad_output to fp16 + if len(grad_output.shape) == 3: + grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous() + + _Cgrad, _Cgradt, _SCgrad, _SCgradt, _outlier_cols = F.int8_double_quant(grad_output.to(torch.float16)) + + if req_gradB: + # print('back A shape', A.shape) + # print('grad output t shape', grad_output.t().shape) + grad_B = torch.matmul(grad_output.t(), A) + + if req_gradA: + if state.CB is not None: + CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0)) + grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A) + else: + raise Exception("State must contain either CBt or CB matrix for backward") + + return grad_A, grad_B, None, grad_bias, None + + +def get_block_sizes(input_matrix, weight_matrix): + input_features = input_matrix.shape[-1] + output_features = weight_matrix.shape[0] if weight_matrix.shape[1] == input_features else weight_matrix.shape[1] + array = [4096, 2048, 1024, 512, 256, 128, 64, 0] + bsz, bsz2 = 1024, 1024 + for i, k in enumerate(array): + if input_features > array[i + 1]: + bsz = k + break + for i, k in enumerate(array): + if output_features > array[i + 1]: + bsz2 = k + break + + return bsz, bsz2 + + +def matmul_fp8_global( + A: torch.Tensor, + B: torch.Tensor, + fw_code: torch.Tensor, + bw_code: torch.Tensor, + out: Optional[torch.Tensor] = None, + bsz: int = -1, + bsz2: int = -1, +): + if bsz == -1 or bsz2 == -1: + bsz, bsz2 = get_block_sizes(A, B) + return MatMulFP8Global.apply(A, B, out, fw_code, bw_code, bsz, bsz2) + + +def matmul_fp8_mixed( + A: torch.Tensor, + B: torch.Tensor, + fw_code: torch.Tensor, + bw_code: torch.Tensor, + out: Optional[torch.Tensor] = None, + bsz: int = -1, + bsz2: int = -1, +): + if bsz == -1 or bsz2 == -1: + bsz, bsz2 = get_block_sizes(A, B) + return MatMulFP8Mixed.apply(A, B, out, fw_code, bw_code, bsz, bsz2) + + +def switchback_bnb( + A: torch.Tensor, + B: torch.Tensor, + out: Optional[torch.Tensor] = None, + state: Optional[MatmulLtState] = None, + threshold=0.0, + bias=None, +): + state = state or MatmulLtState() + if threshold > 0.0: + state.threshold = threshold + return SwitchBackBnb.apply(A, B, out, bias, state) diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/research/nn/__init__.py b/.venv/lib/python3.12/site-packages/bitsandbytes/research/nn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4170112184deb9958d9846230a0e9a664c3f652b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/research/nn/__init__.py @@ -0,0 +1 @@ +from .modules import LinearFP8Global, LinearFP8Mixed diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/research/nn/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/bitsandbytes/research/nn/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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b/.venv/lib/python3.12/site-packages/bitsandbytes/research/nn/modules.py @@ -0,0 +1,76 @@ +from typing import TypeVar + +import torch +from torch import nn + +import bitsandbytes as bnb + +T = TypeVar("T", bound="torch.nn.Module") + + +class LinearFP8Mixed(nn.Linear): + def __init__(self, input_features, output_features, bias=True): + super().__init__(input_features, output_features, bias) + self.bw_code = None + self.fw_code = None + array = [4096, 2048, 1024, 512, 256, 128, 64, 0] + for i, k in enumerate(array): + if input_features > array[i + 1]: + self.bsz = k + break + for i, k in enumerate(array): + if output_features > array[i + 1]: + self.bsz2 = k + break + + def forward(self, x: torch.Tensor): + if self.fw_code is None: + self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device) + self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device) + + out = bnb.research.matmul_fp8_mixed( + x, + self.weight.t(), + fw_code=self.fw_code, + bw_code=self.bw_code, + bsz=self.bsz, + bsz2=self.bsz2, + ) + if self.bias is not None: + out += self.bias + + return out + + +class LinearFP8Global(nn.Linear): + def __init__(self, input_features, output_features, bias=True): + super().__init__(input_features, output_features, bias) + self.bw_code = None + self.fw_code = None + array = [4096, 2048, 1024, 512, 256, 128, 64, 0] + for i, k in enumerate(array): + if input_features > array[i + 1]: + self.bsz = k + break + for i, k in enumerate(array): + if output_features > array[i + 1]: + self.bsz2 = k + break + + def forward(self, x: torch.Tensor): + if self.fw_code is None: + self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device) + self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device) + + out = bnb.matmul_fp8_global( + x, + self.weight.t(), + fw_code=self.fw_code, + bw_code=self.bw_code, + bsz=self.bsz, + bsz2=self.bsz2, + ) + if self.bias is not None: + out += self.bias + + return out diff --git 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b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/dequantize_rowwise.py @@ -0,0 +1,64 @@ +import math + +import torch + +from bitsandbytes.triton.triton_utils import is_triton_available + +if not is_triton_available(): + + def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): + return None +else: + import triton + import triton.language as tl + + # rowwise quantize + + # TODO: autotune this better. + @triton.autotune( + configs=[ + triton.Config({}, num_stages=1, num_warps=8), + triton.Config({}, num_stages=2, num_warps=8), + triton.Config({}, num_stages=4, num_warps=8), + triton.Config({}, num_stages=8, num_warps=8), + triton.Config({}, num_stages=1), + triton.Config({}, num_stages=2), + triton.Config({}, num_stages=4), + triton.Config({}, num_stages=8), + triton.Config({}, num_warps=1), + triton.Config({}, num_warps=2), + triton.Config({}, num_warps=4), + triton.Config({}, num_warps=8), + ], + key=["n_elements"], + ) + @triton.jit + def _dequantize_rowwise( + x_ptr, + state_x, + output_ptr, + inv_127, + n_elements, + BLOCK_SIZE: tl.constexpr, + P2: tl.constexpr, + ): + pid = tl.program_id(axis=0) + block_start = pid * BLOCK_SIZE + arange = tl.arange(0, P2) + offsets = block_start + arange + row_mask = arange < BLOCK_SIZE + x = tl.load(x_ptr + offsets, mask=row_mask) + max_val = tl.load(state_x + pid) + output = max_val * x * inv_127 + tl.store(output_ptr + offsets, output, mask=row_mask) + + def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): + output = torch.empty(*x.shape, device=x.device, dtype=torch.float16) + + P2 = int(2 ** (math.ceil(math.log2(x.shape[1])))) + + assert x.is_cuda and output.is_cuda + n_elements = output.numel() + grid = lambda meta: (x.shape[0],) + _dequantize_rowwise[grid](x, state_x, output, 1.0 / 127, n_elements, BLOCK_SIZE=x.shape[1], P2=P2) + return output diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_mixed_dequantize.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_mixed_dequantize.py new file mode 100644 index 0000000000000000000000000000000000000000..5fcb927d42db2b1be38712926cfe244c7f51df52 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_mixed_dequantize.py @@ -0,0 +1,206 @@ +import torch + +from bitsandbytes.triton.triton_utils import is_triton_available + +if not is_triton_available(): + + def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias): + return None +else: + import triton + import triton.language as tl + + from .matmul_perf_model import early_config_prune, estimate_matmul_time + + # This is a matmul kernel based on triton.ops.matmul + # It is modified to support rowwise quantized input and global quantized weight + # It's purpose is fused matmul then dequantize + # It does support bias. + + def init_to_zero(name): + return lambda nargs: nargs[name].zero_() + + def get_configs_io_bound(): + configs = [] + for num_stages in [2, 3, 4, 5, 6]: + for block_m in [16, 32]: + for block_k in [32, 64]: + for block_n in [32, 64, 128, 256]: + num_warps = 2 if block_n <= 64 else 4 + configs.append( + triton.Config( + {"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": 1}, + num_stages=num_stages, + num_warps=num_warps, + ), + ) + # split_k + for split_k in [2, 4, 8, 16]: + configs.append( + triton.Config( + {"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": split_k}, + num_stages=num_stages, + num_warps=num_warps, + pre_hook=init_to_zero("C"), + ), + ) + return configs + + @triton.autotune( + configs=[ + # basic configs for compute-bound matmuls + triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=5, num_warps=2), + # good for int8 + triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=5, num_warps=2), + *get_configs_io_bound(), + ], + key=["M", "N", "K"], + prune_configs_by={"early_config_prune": early_config_prune, "perf_model": estimate_matmul_time, "top_k": 10}, + ) + @triton.heuristics( + { + "EVEN_K": lambda args: args["K"] % (args["BLOCK_K"] * args["SPLIT_K"]) == 0, + }, + ) + @triton.jit + def _int8_matmul_mixed_dequantize( + A, + B, + C, + bias, + state_x_ptr, + state_w_ptr, + M, + N, + K, + divfactor: tl.constexpr, + has_bias: tl.constexpr, + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + BLOCK_K: tl.constexpr, + GROUP_M: tl.constexpr, + SPLIT_K: tl.constexpr, + EVEN_K: tl.constexpr, + ACC_TYPE: tl.constexpr, + ): + # matrix multiplication + pid = tl.program_id(0) + pid_z = tl.program_id(1) + grid_m = tl.cdiv(M, BLOCK_M) + grid_n = tl.cdiv(N, BLOCK_N) + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + # do matrix multiplication + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K) + # pointers + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + + w_factor = tl.load(state_w_ptr) + x_factor = tl.load(state_x_ptr + ram)[:, None] + + # acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32) + for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + k_remaining = K - k * (BLOCK_K * SPLIT_K) + a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.0) + b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.0) + acc += tl.dot(a, b) + A += BLOCK_K * SPLIT_K * stride_ak + B += BLOCK_K * SPLIT_K * stride_bk + + acc = w_factor * (x_factor * (acc * divfactor)) + acc = acc.to(C.dtype.element_ty) + + # conditionally add bias + if has_bias: + bias = tl.load(bias + rn).to(C.dtype.element_ty) + acc = acc + bias[None, :] + + C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn) + mask = (rm < M)[:, None] & (rn < N)[None, :] + # handles write-back with reduction-splitting + if SPLIT_K == 1: + tl.store(C, acc, mask=mask) + else: + tl.atomic_add(C, acc, mask=mask) + + def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias): + device = a.device + divfactor = 1.0 / (127.0 * 127.0) + has_bias = 0 if bias is None else 1 + # handle non-contiguous inputs if necessary + if a.stride(0) > 1 and a.stride(1) > 1: + a = a.contiguous() + if b.stride(0) > 1 and b.stride(1) > 1: + b = b.contiguous() + # checks constraints + assert a.shape[1] == b.shape[0], "incompatible dimensions" + M, K = a.shape + _, N = b.shape + # allocates output + c = torch.empty((M, N), device=device, dtype=torch.float16) + # accumulator types + ACC_TYPE = tl.float32 # if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32 + # launch int8_matmul_mixed_dequantize kernel + grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]), META["SPLIT_K"]) + _int8_matmul_mixed_dequantize[grid]( + a, + b, + c, + bias, + state_x, + state_w, + M, + N, + K, + divfactor, + has_bias, + a.stride(0), + a.stride(1), + b.stride(0), + b.stride(1), + c.stride(0), + c.stride(1), + GROUP_M=8, + ACC_TYPE=ACC_TYPE, + ) + return c diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_rowwise_dequantize.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_rowwise_dequantize.py new file mode 100644 index 0000000000000000000000000000000000000000..05e30a4c92de6d4a2edbd6d1f3e811a3ebc85495 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_rowwise_dequantize.py @@ -0,0 +1,207 @@ +import torch + +from bitsandbytes.triton.triton_utils import is_triton_available + +if not is_triton_available(): + + def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): + return None +else: + import triton + import triton.language as tl + + from .matmul_perf_model import early_config_prune, estimate_matmul_time + + # This is a matmul kernel based on triton.ops.matmul + # It is modified to support rowwise quantized input and columnwise quantized weight + # It's purpose is fused matmul then dequantize + # It does support bias. + + def init_to_zero(name): + return lambda nargs: nargs[name].zero_() + + def get_configs_io_bound(): + configs = [] + for num_stages in [2, 3, 4, 5, 6]: + for block_m in [16, 32]: + for block_k in [32, 64]: + for block_n in [32, 64, 128, 256]: + num_warps = 2 if block_n <= 64 else 4 + configs.append( + triton.Config( + {"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": 1}, + num_stages=num_stages, + num_warps=num_warps, + ), + ) + # split_k + for split_k in [2, 4, 8, 16]: + configs.append( + triton.Config( + {"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": split_k}, + num_stages=num_stages, + num_warps=num_warps, + pre_hook=init_to_zero("C"), + ), + ) + return configs + + @triton.autotune( + configs=[ + # basic configs for compute-bound matmuls + triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=5, num_warps=2), + # good for int8 + triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8), + triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4), + triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=5, num_warps=2), + *get_configs_io_bound(), + ], + key=["M", "N", "K"], + prune_configs_by={"early_config_prune": early_config_prune, "perf_model": estimate_matmul_time, "top_k": 10}, + ) + @triton.heuristics( + { + "EVEN_K": lambda args: args["K"] % (args["BLOCK_K"] * args["SPLIT_K"]) == 0, + }, + ) + @triton.jit + def _int8_matmul_rowwise_dequantize( + A, + B, + C, + bias, + state_x_ptr, + state_w_ptr, + M, + N, + K, + divfactor, + has_bias: tl.constexpr, + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + BLOCK_K: tl.constexpr, + GROUP_M: tl.constexpr, + SPLIT_K: tl.constexpr, + EVEN_K: tl.constexpr, + ACC_TYPE: tl.constexpr, + ): + # matrix multiplication + pid = tl.program_id(0) + pid_z = tl.program_id(1) + grid_m = tl.cdiv(M, BLOCK_M) + grid_n = tl.cdiv(N, BLOCK_N) + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + # do matrix multiplication + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K) + # pointers + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + + w_factor = tl.load(state_w_ptr + rbn)[None, :] + x_factor = tl.load(state_x_ptr + ram)[:, None] + + # acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE) + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32) + for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + k_remaining = K - k * (BLOCK_K * SPLIT_K) + a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.0) + b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.0) + acc += tl.dot(a, b) + A += BLOCK_K * SPLIT_K * stride_ak + B += BLOCK_K * SPLIT_K * stride_bk + + acc = w_factor * (x_factor * (acc * divfactor)) + acc = acc.to(C.dtype.element_ty) + + if has_bias: + bias = tl.load(bias + rn).to(C.dtype.element_ty) + acc = acc + bias[None, :] + + C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn) + mask = (rm < M)[:, None] & (rn < N)[None, :] + # handles write-back with reduction-splitting + if SPLIT_K == 1: + tl.store(C, acc, mask=mask) + else: + tl.atomic_add(C, acc, mask=mask) + + def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): + divfactor = 1.0 / (127.0 * 127.0) + + has_bias = 0 if bias is None else 1 + + device = a.device + # handle non-contiguous inputs if necessary + if a.stride(0) > 1 and a.stride(1) > 1: + a = a.contiguous() + if b.stride(0) > 1 and b.stride(1) > 1: + b = b.contiguous() + # checks constraints + assert a.shape[1] == b.shape[0], "incompatible dimensions" + M, K = a.shape + _, N = b.shape + # allocates output + c = torch.empty((M, N), device=device, dtype=torch.float16) + # accumulator types + ACC_TYPE = tl.float32 # if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32 + # launch int8_matmul_rowwise_dequantize kernel + grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]), META["SPLIT_K"]) + _int8_matmul_rowwise_dequantize[grid]( + a, + b, + c, + bias, + state_x, + state_w, + M, + N, + K, + divfactor, + has_bias, + a.stride(0), + a.stride(1), + b.stride(0), + b.stride(1), + c.stride(0), + c.stride(1), + GROUP_M=8, + ACC_TYPE=ACC_TYPE, + ) + return c diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/matmul_perf_model.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/matmul_perf_model.py new file mode 100644 index 0000000000000000000000000000000000000000..e843a3a39f4e8dfcba55bf5649650f01fcf896b2 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/matmul_perf_model.py @@ -0,0 +1,211 @@ +# Adapted from https://github.com/triton-lang/kernels/blob/eeeebdd8be7d13629de22d600621e6234057eed3/kernels/matmul_perf_model.py +# https://github.com/triton-lang/kernels is licensed under the MIT License. + +import functools +import heapq + +import torch + +from triton import cdiv +from triton.runtime import driver +from triton.testing import ( + get_dram_gbps, + get_max_simd_tflops, + get_max_tensorcore_tflops, + nvsmi, +) + + +@functools.lru_cache +def get_clock_rate_in_khz(): + try: + return nvsmi(["clocks.max.sm"])[0] * 1e3 + except FileNotFoundError: + import pynvml + + pynvml.nvmlInit() + handle = pynvml.nvmlDeviceGetHandleByIndex(0) + return pynvml.nvmlDeviceGetMaxClockInfo(handle, pynvml.NVML_CLOCK_SM) * 1e3 + + +def get_tensorcore_tflops(device, num_ctas, num_warps, dtype): + """return compute throughput in TOPS""" + total_warps = num_ctas * min(num_warps, 4) + num_subcores = driver.active.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs + tflops = ( + min(num_subcores, total_warps) + / num_subcores + * get_max_tensorcore_tflops(dtype, get_clock_rate_in_khz(), device) + ) + return tflops + + +def get_simd_tflops(device, num_ctas, num_warps, dtype): + """return compute throughput in TOPS""" + total_warps = num_ctas * min(num_warps, 4) + num_subcores = driver.active.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs + tflops = ( + min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, get_clock_rate_in_khz(), device) + ) + return tflops + + +def get_tflops(device, num_ctas, num_warps, dtype): + capability = torch.cuda.get_device_capability(device) + if capability[0] < 8 and dtype == torch.float32: + return get_simd_tflops(device, num_ctas, num_warps, dtype) + return get_tensorcore_tflops(device, num_ctas, num_warps, dtype) + + +def estimate_matmul_time( + # backend, device, + num_warps, + num_stages, # + A, + B, + C, # + M, + N, + K, # + BLOCK_M, + BLOCK_N, + BLOCK_K, + SPLIT_K, # + debug=False, + **kwargs, # +): + """return estimated running time in ms + = max(compute, loading) + store""" + device = torch.cuda.current_device() + dtype = A.dtype + dtsize = A.element_size() + + num_cta_m = cdiv(M, BLOCK_M) + num_cta_n = cdiv(N, BLOCK_N) + num_cta_k = SPLIT_K + num_ctas = num_cta_m * num_cta_n * num_cta_k + + # If the input is smaller than the block size + M, N = max(M, BLOCK_M), max(N, BLOCK_N) + + # time to compute + total_ops = 2 * M * N * K / (1024 * 1024 * 1024) # GOPS + tput = get_tflops(device, num_ctas, num_warps, dtype) + compute_ms = total_ops / tput + + # time to load data + num_sm = driver.active.utils.get_device_properties(device)["multiprocessor_count"] + active_cta_ratio = min(1, num_ctas / num_sm) + active_cta_ratio_bw1 = min(1, num_ctas / 32) # 32 active ctas are enough to saturate + active_cta_ratio_bw2 = max(min(1, (num_ctas - 32) / (108 - 32)), 0) # 32-108, remaining 5% + dram_bw = get_dram_gbps(device) * (active_cta_ratio_bw1 * 0.95 + active_cta_ratio_bw2 * 0.05) # in GB/s + l2_bw = dram_bw * 4 # rough estimation (should be 4.7 for A100?) + # assume 80% of (following) loads are in L2 cache + load_a_dram = M * K * dtsize * (1 + 0.2 * (num_cta_n - 1)) + load_a_l2 = M * K * dtsize * 0.8 * (num_cta_n - 1) + load_b_dram = N * K * dtsize * (1 + 0.2 * (num_cta_m - 1)) + load_b_l2 = N * K * dtsize * 0.8 * (num_cta_m - 1) + # total + total_dram = (load_a_dram + load_b_dram) / (1024 * 1024) # MB + total_l2 = (load_a_l2 + load_b_l2) / (1024 * 1024) + # loading time in ms + load_ms = total_dram / dram_bw + total_l2 / l2_bw + + # estimate storing time + store_bw = dram_bw * 0.6 # :o + store_c_dram = M * N * dtsize * SPLIT_K / (1024 * 1024) # MB + if SPLIT_K == 1: + store_ms = store_c_dram / store_bw + else: + reduce_bw = store_bw + store_ms = store_c_dram / reduce_bw + # c.zero_() + zero_ms = M * N * 2 / (1024 * 1024) / store_bw + store_ms += zero_ms + + total_time_ms = max(compute_ms, load_ms) + store_ms + if debug: + print( + f"Total time: {total_time_ms}ms, compute time: {compute_ms}ms, " + f"loading time: {load_ms}ms, store time: {store_ms}ms, " + f"Activate CTAs: {active_cta_ratio * 100}%" + ) + return total_time_ms + + +def early_config_prune(configs, named_args, **kwargs): + device = torch.cuda.current_device() + capability = torch.cuda.get_device_capability() + # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages + dtsize = named_args["A"].element_size() + dtype = named_args["A"].dtype + + # 1. make sure we have enough smem + pruned_configs = [] + for config in configs: + kw = config.kwargs + BLOCK_M, BLOCK_N, BLOCK_K, num_stages = ( + kw["BLOCK_M"], + kw["BLOCK_N"], + kw["BLOCK_K"], + config.num_stages, + ) + + max_shared_memory = driver.active.utils.get_device_properties(device)["max_shared_mem"] + required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize + if required_shared_memory <= max_shared_memory: + pruned_configs.append(config) + configs = pruned_configs + + # Some dtypes do not allow atomic_add + if dtype not in [torch.float16, torch.float32]: + configs = [config for config in configs if config.kwargs["SPLIT_K"] == 1] + + # group configs by (BLOCK_M,_N,_K, SPLIT_K, num_warps) + configs_map = {} + for config in configs: + kw = config.kwargs + BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages = ( + kw["BLOCK_M"], + kw["BLOCK_N"], + kw["BLOCK_K"], + kw["SPLIT_K"], + config.num_warps, + config.num_stages, + ) + + key = (BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps) + if key in configs_map: + configs_map[key].append((config, num_stages)) + else: + configs_map[key] = [(config, num_stages)] + + pruned_configs = [] + for k, v in configs_map.items(): + BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k + if capability[0] >= 8: + # compute cycles (only works for ampere GPUs) + mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16) + mma_cycles = mmas / min(4, num_warps) * 8 + + ldgsts_latency = 300 # Does this matter? + optimal_num_stages = ldgsts_latency / mma_cycles + + # nearest stages, prefer large #stages + nearest = heapq.nsmallest( + 2, + v, + key=lambda x: ( + 10 + abs(x[1] - optimal_num_stages) + if (x[1] - optimal_num_stages) < 0 + else x[1] - optimal_num_stages + ), + ) + + for n in nearest: + pruned_configs.append(n[0]) + else: # Volta & Turing only supports num_stages <= 2 + random_config = v[0][0] + random_config.num_stages = 2 + pruned_configs.append(random_config) + return pruned_configs diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_columnwise_and_transpose.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_columnwise_and_transpose.py new file mode 100644 index 0000000000000000000000000000000000000000..b8eeffd0c6a67502b30e2a213845a9762f2106ee --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_columnwise_and_transpose.py @@ -0,0 +1,75 @@ +import math + +import torch + +from bitsandbytes.triton.triton_utils import is_triton_available + +if not is_triton_available(): + + def quantize_columnwise_and_transpose(x: torch.Tensor): + return None +else: + import triton + import triton.language as tl + + # This kernel does fused columnwise quantization and transpose. + + # TODO: autotune this better. + @triton.autotune( + configs=[ + triton.Config({}, num_stages=1), + triton.Config({}, num_stages=2), + triton.Config({}, num_stages=4), + triton.Config({}, num_stages=8), + triton.Config({}, num_stages=16), + triton.Config({}, num_stages=1, num_warps=8), + triton.Config({}, num_stages=2, num_warps=8), + triton.Config({}, num_stages=4, num_warps=8), + triton.Config({}, num_stages=8, num_warps=8), + triton.Config({}, num_stages=16, num_warps=8), + triton.Config({}, num_warps=1), + triton.Config({}, num_warps=2), + triton.Config({}, num_warps=4), + triton.Config({}, num_warps=8), + ], + key=["n_elements"], + ) + @triton.jit + def _quantize_columnwise_and_transpose( + x_ptr, + output_ptr, + output_maxs, + n_elements, + M: tl.constexpr, + N: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + P2: tl.constexpr, + ): + pid = tl.program_id(axis=0) + block_start = pid + p2_arange = tl.arange(0, P2) + p2_arange_mask = p2_arange < M + arange = p2_arange * N + offsets = block_start + arange + x = tl.load(x_ptr + offsets, mask=p2_arange_mask) + abs_x = tl.abs(x) + max_val = tl.max(tl.where(p2_arange_mask, abs_x, 0), axis=0) + output = tl.libdevice.llrint(127.0 * (x / max_val)) + + new_start = pid * M + new_offsets = new_start + p2_arange + tl.store(output_ptr + new_offsets, output, mask=p2_arange_mask) + tl.store(output_maxs + pid, max_val) + + def quantize_columnwise_and_transpose(x: torch.Tensor): + M, N = x.shape + output = torch.empty(N, M, device=x.device, dtype=torch.int8) + output_maxs = torch.empty(x.shape[1], device=x.device, dtype=torch.float16) + + P2 = int(2 ** (math.ceil(math.log2(M)))) + + assert x.is_cuda and output.is_cuda + n_elements = output.numel() + grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) + _quantize_columnwise_and_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2) + return output, output_maxs diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_global.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_global.py new file mode 100644 index 0000000000000000000000000000000000000000..f35bdd3040c575ea5cf171b2c6342c61c9740f12 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_global.py @@ -0,0 +1,124 @@ +import torch + +from bitsandbytes.triton.triton_utils import is_triton_available + +if not is_triton_available(): + + def quantize_global_transpose(input): + return None + + def quantize_global(x: torch.Tensor): + return None +else: + import triton + import triton.language as tl + + # global quantize + @triton.autotune( + configs=[ + triton.Config({"BLOCK_SIZE": 1024}, num_warps=4), + triton.Config({"BLOCK_SIZE": 2048}, num_stages=1), + ], + key=["n_elements"], + ) + @triton.jit + def _quantize_global( + x_ptr, + absmax_inv_ptr, + output_ptr, + n_elements, + BLOCK_SIZE: tl.constexpr, + ): + pid = tl.program_id(axis=0) + block_start = pid * BLOCK_SIZE + offsets = block_start + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + x = tl.load(x_ptr + offsets, mask=mask) + absmax_inv = tl.load(absmax_inv_ptr) + output = tl.libdevice.llrint(127.0 * (x * absmax_inv)) + tl.store(output_ptr + offsets, output, mask=mask) + + def quantize_global(x: torch.Tensor): + absmax = x.abs().max().unsqueeze(0) + absmax_inv = 1.0 / absmax + output = torch.empty(*x.shape, device="cuda", dtype=torch.int8) + assert x.is_cuda and output.is_cuda + n_elements = output.numel() + grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) + _quantize_global[grid](x, absmax_inv, output, n_elements) + return output, absmax + + # global quantize and transpose + @triton.autotune( + configs=[ + triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "GROUP_M": 8}, num_warps=4), + triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "GROUP_M": 8}, num_warps=4), + # ... + ], + key=["M", "N"], + ) + @triton.jit + def _quantize_global_transpose( + A, + absmax_inv_ptr, + B, + stride_am, + stride_an, + stride_bn, + stride_bm, + M, + N, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + GROUP_M: tl.constexpr, + ): + pid = tl.program_id(0) + grid_m = (M + BLOCK_M - 1) // BLOCK_M + grid_n = (N + BLOCK_N - 1) // BLOCK_N + + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // group_size + + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + A = A + (rm[:, None] * stride_am + rn[None, :] * stride_an) + mask = (rm < M)[:, None] & (rn < N)[None, :] + a = tl.load(A, mask=mask) + absmax_inv = tl.load(absmax_inv_ptr) + + # rematerialize to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + B = B + (rm[:, None] * stride_bm + rn[None, :] * stride_bn) + mask = (rm < M)[:, None] & (rn < N)[None, :] + + output = tl.libdevice.llrint(127.0 * (a * absmax_inv)) + + tl.store(B, output, mask=mask) + + def quantize_global_transpose(input): + absmax = input.abs().max().unsqueeze(0) + absmax_inv = 1.0 / absmax + M, N = input.shape + out = torch.empty(N, M, device="cuda", dtype=torch.int8) + + assert out.size(0) == N and out.size(1) == M + assert input.stride(0) == 1 or input.stride(1) == 1 + assert out.stride(0) == 1 or out.stride(1) == 1 + + grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),) + _quantize_global_transpose[grid]( + input, + absmax_inv, + out, + input.stride(0), + input.stride(1), + out.stride(0), + out.stride(1), + M, + N, + ) + return out, absmax diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_rowwise.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_rowwise.py new file mode 100644 index 0000000000000000000000000000000000000000..f92ace02c2d4c7c452eb1e935f13e8e0098c88e3 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/quantize_rowwise.py @@ -0,0 +1,67 @@ +import math + +import torch + +from bitsandbytes.triton.triton_utils import is_triton_available + +if not is_triton_available(): + + def quantize_rowwise(x: torch.Tensor): + return None +else: + import triton + import triton.language as tl + + # rowwise quantize + + # TODO: autotune this better. + @triton.autotune( + configs=[ + triton.Config({}, num_stages=1, num_warps=8), + triton.Config({}, num_stages=2, num_warps=8), + triton.Config({}, num_stages=4, num_warps=8), + triton.Config({}, num_stages=8, num_warps=8), + triton.Config({}, num_stages=1), + triton.Config({}, num_stages=2), + triton.Config({}, num_stages=4), + triton.Config({}, num_stages=8), + triton.Config({}, num_warps=1), + triton.Config({}, num_warps=2), + triton.Config({}, num_warps=4), + triton.Config({}, num_warps=8), + ], + key=["n_elements"], + ) + @triton.jit + def _quantize_rowwise( + x_ptr, + output_ptr, + output_maxs, + n_elements, + BLOCK_SIZE: tl.constexpr, + P2: tl.constexpr, + ): + pid = tl.program_id(axis=0) + block_start = pid * BLOCK_SIZE + arange = tl.arange(0, P2) + offsets = block_start + arange + row_mask = arange < BLOCK_SIZE + x = tl.load(x_ptr + offsets, mask=row_mask) + + abs_x = tl.abs(x) + max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0) + output = tl.libdevice.llrint(127.0 * (x / max_val)) + tl.store(output_ptr + offsets, output, mask=row_mask) + tl.store(output_maxs + pid, max_val) + + def quantize_rowwise(x: torch.Tensor): + output = torch.empty(*x.shape, device=x.device, dtype=torch.int8) + output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16) + + P2 = int(2 ** (math.ceil(math.log2(x.shape[1])))) + + assert x.is_cuda and output.is_cuda + n_elements = output.numel() + grid = lambda meta: (x.shape[0],) + _quantize_rowwise[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2) + return output, output_maxs diff --git a/.venv/lib/python3.12/site-packages/bitsandbytes/triton/triton_utils.py b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/triton_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f6bedd8cd22a6905318388e27698a63c5c654e72 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/bitsandbytes/triton/triton_utils.py @@ -0,0 +1,11 @@ +import functools + + +@functools.lru_cache(None) +def is_triton_available(): + try: + from torch.utils._triton import has_triton, has_triton_package + + return has_triton_package() and has_triton() + except Exception: + return False diff --git a/.venv/lib/python3.12/site-packages/dateutil/__init__.py b/.venv/lib/python3.12/site-packages/dateutil/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a2c19c06fe14476a9bfa4f1f60de7a997a41191c --- /dev/null +++ b/.venv/lib/python3.12/site-packages/dateutil/__init__.py @@ -0,0 +1,24 @@ +# -*- coding: utf-8 -*- +import sys + +try: + from ._version import version as __version__ +except ImportError: + __version__ = 'unknown' + +__all__ = ['easter', 'parser', 'relativedelta', 'rrule', 'tz', + 'utils', 'zoneinfo'] + +def __getattr__(name): + import importlib + + if name in __all__: + return importlib.import_module("." + name, __name__) + raise AttributeError( + "module {!r} has not attribute {!r}".format(__name__, name) + ) + + +def __dir__(): + # __dir__ should include all the lazy-importable modules as well. + return [x for x in globals() if x not in sys.modules] + __all__ diff --git a/.venv/lib/python3.12/site-packages/hf_xet/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/hf_xet/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ce3fe13004b4718a9c07e7a90c04e6be2df754ec Binary files /dev/null and b/.venv/lib/python3.12/site-packages/hf_xet/__pycache__/__init__.cpython-312.pyc differ diff --git a/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..4c3ec2d3446730e125759e8b6f57735a24b79d94 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/LICENSE @@ -0,0 +1,789 @@ +License agreement for matplotlib versions 1.3.0 and later +========================================================= + +1. 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Font Software. + "wncyss10" is a Reserved Font Name for this Font Software. + + This Font Software is licensed under the SIL Open Font License, Version 1.1. + This license is copied below, and is also available with a FAQ at: + http://scripts.sil.org/OFL + + ----------------------------------------------------------- + SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 + ----------------------------------------------------------- + + PREAMBLE + The goals of the Open Font License (OFL) are to stimulate worldwide + development of collaborative font projects, to support the font creation + efforts of academic and linguistic communities, and to provide a free and + open framework in which fonts may be shared and improved in partnership + with others. + + The OFL allows the licensed fonts to be used, studied, modified and + redistributed freely as long as they are not sold by themselves. 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The requirement for fonts to + remain under this license does not apply to any document created + using the Font Software. + + TERMINATION + This license becomes null and void if any of the above conditions are + not met. + + DISCLAIMER + THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT + OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. 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Malyshev. All Rights Reserved. + + Permission to copy and distribute these fonts for any purpose is + hereby granted without fee, provided that the above copyright notice, + author statement and this permission notice appear in all copies of + these fonts and related documentation. + + Permission to modify and distribute modified fonts for any purpose is + hereby granted without fee, provided that the copyright notice, + author statement, this permission notice and location of original + fonts (http://www.ctan.org/tex-archive/fonts/cm/ps-type1/bakoma) + appear in all copies of modified fonts and related documentation. + + Permission to use these fonts (embedding into PostScript, PDF, SVG + and printing by using any software) is hereby granted without fee. + It is not required to provide any notices about using these fonts. + + Basil K. Malyshev + INSTITUTE FOR HIGH ENERGY PHYSICS + IHEP, OMVT + Moscow Region + 142281 PROTVINO + RUSSIA + + E-Mail: bakoma@mail.ru + or malyshev@mail.ihep.ru + + + + +Name: ColorBrewer Color Schemes +Files: lib/matplotlib/_cm.py +Description: Color schemes from ColorBrewer +License: Apache-2.0 + Apache-Style Software License for ColorBrewer software and ColorBrewer Color Schemes + + Copyright (c) 2002 Cynthia Brewer, Mark Harrower, and The Pennsylvania State University. + + Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software distributed + under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. See the License for the + specific language governing permissions and limitations under the License. + + +Name: Courier 10 +Files: matplotlib/tests/Courier10PitchBT-Bold.pfb +Description: Courier 10 font, used in tests. +License: Bitstream-Charter + The Courier10PitchBT-Bold.pfb file is a Type-1 version of + Courier 10 Pitch BT Bold by Bitstream, obtained from + . It is included + here as test data only, but the following license applies. + + + (c) Copyright 1989-1992, Bitstream Inc., Cambridge, MA. + + You are hereby granted permission under all Bitstream propriety rights + to use, copy, modify, sublicense, sell, and redistribute the 4 Bitstream + Charter (r) Type 1 outline fonts and the 4 Courier Type 1 outline fonts + for any purpose and without restriction; provided, that this notice is + left intact on all copies of such fonts and that Bitstream's trademark + is acknowledged as shown below on all unmodified copies of the 4 Charter + Type 1 fonts. + + BITSTREAM CHARTER is a registered trademark of Bitstream Inc. + + + +Name: JSXTools resize observer +Files: +Description: Minimal polyfill for the ResizeObserver API +License: CC0-1.0 + # CC0 1.0 Universal + + ## Statement of Purpose + + The laws of most jurisdictions throughout the world automatically confer + exclusive Copyright and Related Rights (defined below) upon the creator and + subsequent owner(s) (each and all, an “owner”) of an original work of + authorship and/or a database (each, a “Work”). + + Certain owners wish to permanently relinquish those rights to a Work for the + purpose of contributing to a commons of creative, cultural and scientific works + (“Commons”) that the public can reliably and without fear of later claims of + infringement build upon, modify, incorporate in other works, reuse and + redistribute as freely as possible in any form whatsoever and for any purposes, + including without limitation commercial purposes. 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Bug reports or fixes may be sent to + qhull_bug@qhull.org; the authors may or may not act on them as + they desire. + + +Name: Qt4 Editor +Files: matplotlib/backends/qt_editor +Description: Module creating PyQt4 form dialogs/layouts to edit various type of parameters +License: MIT + Module creating PyQt4 form dialogs/layouts to edit various type of parameters + + + formlayout License Agreement (MIT License) + ------------------------------------------ + + Copyright (c) 2009 Pierre Raybaut + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without + restriction, including without limitation the rights to use, + copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the + Software is furnished to do so, subject to the following + conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES + OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. 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IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + THE SOFTWARE. + + +Name: Stix fonts +Files: matplotlib/mpl-data/fonts/ttf/STIX*.ttf +Description: STIX fonts +License: + TERMS AND CONDITIONS + + 1. Permission is hereby granted, free of charge, to any person + obtaining a copy of the STIX Fonts-TM set accompanying this license + (collectively, the "Fonts") and the associated documentation files + (collectively with the Fonts, the "Font Software"), to reproduce and + distribute the Font Software, including the rights to use, copy, merge + and publish copies of the Font Software, and to permit persons to whom + the Font Software is furnished to do so same, subject to the following + terms and conditions (the "License"). + + 2. The following copyright and trademark notice and these Terms and + Conditions shall be included in all copies of one or more of the Font + typefaces and any derivative work created as permitted under this + License: + + Copyright (c) 2001-2005 by the STI Pub Companies, consisting of + the American Institute of Physics, the American Chemical Society, the + American Mathematical Society, the American Physical Society, Elsevier, + Inc., and The Institute of Electrical and Electronic Engineers, Inc. + Portions copyright (c) 1998-2003 by MicroPress, Inc. Portions copyright + (c) 1990 by Elsevier, Inc. All rights reserved. STIX Fonts-TM is a + trademark of The Institute of Electrical and Electronics Engineers, Inc. + + 3. You may (a) convert the Fonts from one format to another (e.g., + from TrueType to PostScript), in which case the normal and reasonable + distortion that occurs during such conversion shall be permitted and (b) + embed or include a subset of the Fonts in a document for the purposes of + allowing users to read text in the document that utilizes the Fonts. In + each case, you may use the STIX Fonts-TM mark to designate the resulting + Fonts or subset of the Fonts. + + 4. You may also (a) add glyphs or characters to the Fonts, or modify + the shape of existing glyphs, so long as the base set of glyphs is not + removed and (b) delete glyphs or characters from the Fonts, provided + that the resulting font set is distributed with the following + disclaimer: "This [name] font does not include all the Unicode points + covered in the STIX Fonts-TM set but may include others." In each case, + the name used to denote the resulting font set shall not include the + term "STIX" or any similar term. + + 5. You may charge a fee in connection with the distribution of the + Font Software, provided that no copy of one or more of the individual + Font typefaces that form the STIX Fonts-TM set may be sold by itself. + + 6. THE FONT SOFTWARE IS PROVIDED "AS IS," WITHOUT WARRANTY OF ANY + KIND, EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES + OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT + OF COPYRIGHT, PATENT, TRADEMARK OR OTHER RIGHT. IN NO EVENT SHALL + MICROPRESS OR ANY OF THE STI PUB COMPANIES BE LIABLE FOR ANY CLAIM, + DAMAGES OR OTHER LIABILITY, INCLUDING, BUT NOT LIMITED TO, ANY GENERAL, + SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES, WHETHER IN AN + ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM OR OUT OF THE USE OR + INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT + SOFTWARE. + + 7. Except as contained in the notice set forth in Section 2, the + names MicroPress Inc. and STI Pub Companies, as well as the names of the + companies/organizations that compose the STI Pub Companies, shall not be + used in advertising or otherwise to promote the sale, use or other + dealings in the Font Software without the prior written consent of the + respective company or organization. + + 8. This License shall become null and void in the event of any + material breach of the Terms and Conditions herein by licensee. + + 9. A substantial portion of the STIX Fonts set was developed by + MicroPress Inc. for the STI Pub Companies. To obtain additional + mathematical fonts, please contact MicroPress, Inc., 68-30 Harrow + Street, Forest Hills, NY 11375, USA - Phone: (718) 575-1816. + + +Name: Yorick Colormaps +Files: lib/matplotlib/_cm.py +Description: Gist/Yorick colormaps +License: + BSD-style license for gist/yorick colormaps. + + Copyright: + + Copyright (c) 1996. The Regents of the University of California. + All rights reserved. + + Permission to use, copy, modify, and distribute this software for any + purpose without fee is hereby granted, provided that this entire + notice is included in all copies of any software which is or includes + a copy or modification of this software and in all copies of the + supporting documentation for such software. + + This work was produced at the University of California, Lawrence + Livermore National Laboratory under contract no. W-7405-ENG-48 between + the U.S. Department of Energy and The Regents of the University of + California for the operation of UC LLNL. + + + DISCLAIMER + + This software was prepared as an account of work sponsored by an + agency of the United States Government. Neither the United States + Government nor the University of California nor any of their + employees, makes any warranty, express or implied, or assumes any + liability or responsibility for the accuracy, completeness, or + usefulness of any information, apparatus, product, or process + disclosed, or represents that its use would not infringe + privately-owned rights. Reference herein to any specific commercial + products, process, or service by trade name, trademark, manufacturer, + or otherwise, does not necessarily constitute or imply its + endorsement, recommendation, or favoring by the United States + Government or the University of California. The views and opinions of + authors expressed herein do not necessarily state or reflect those of + the United States Government or the University of California, and + shall not be used for advertising or product endorsement purposes. + + + AUTHOR + + David H. Munro wrote Yorick and Gist. Berkeley Yacc (byacc) generated + the Yorick parser. The routines in Math are from LAPACK and FFTPACK; + MathC contains C translations by David H. Munro. The algorithms for + Yorick's random number generator and several special functions in + Yorick/include were taken from Numerical Recipes by Press, et. al., + although the Yorick implementations are unrelated to those in + Numerical Recipes. A small amount of code in Gist was adapted from + the X11R4 release, copyright M.I.T. -- the complete copyright notice + may be found in the (unused) file Gist/host.c. diff --git a/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/METADATA b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..502e1e68ef0283477468639308d430809b05f082 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/METADATA @@ -0,0 +1,907 @@ +Metadata-Version: 2.1 +Name: matplotlib +Version: 3.10.8 +Summary: Python plotting package +Author: John D. Hunter, Michael Droettboom +Author-Email: Unknown +License: License agreement for matplotlib versions 1.3.0 and later + ========================================================= + + 1. 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By copying, installing or otherwise using matplotlib, + Licensee agrees to be bound by the terms and conditions of this License + Agreement. + ---- + + This binary distrubution of Matplotlib can also bundle the following software + (depending on the build): + + Name: AMS Fonts + Files: matplotlib/tests/cmr10.pfb + Description: Type-1 version of one of Knuth's Computer Modern fonts + License: OFL-1.1 + The cmr10.pfb file is a Type-1 version of one of Knuth's Computer Modern fonts. + It is included here as test data only, but the following license applies. + + Copyright (c) 1997, 2009, American Mathematical Society (http://www.ams.org). + All Rights Reserved. + + "cmb10" is a Reserved Font Name for this Font Software. + "cmbsy10" is a Reserved Font Name for this Font Software. + "cmbsy5" is a Reserved Font Name for this Font Software. + "cmbsy6" is a Reserved Font Name for this Font Software. + "cmbsy7" is a Reserved Font Name for this Font Software. + "cmbsy8" is a Reserved Font Name 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this Font Software. + "wncyss10" is a Reserved Font Name for this Font Software. + + This Font Software is licensed under the SIL Open Font License, Version 1.1. + This license is copied below, and is also available with a FAQ at: + http://scripts.sil.org/OFL + + ----------------------------------------------------------- + SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 + ----------------------------------------------------------- + + PREAMBLE + The goals of the Open Font License (OFL) are to stimulate worldwide + development of collaborative font projects, to support the font creation + efforts of academic and linguistic communities, and to provide a free and + open framework in which fonts may be shared and improved in partnership + with others. + + The OFL allows the licensed fonts to be used, studied, modified and + redistributed freely as long as they are not sold by themselves. The + fonts, including any derivative works, can be bundled, embedded, + redistributed and/or sold with any software provided that any reserved + names are not used by derivative works. The fonts and derivatives, + however, cannot be released under any other type of license. The + requirement for fonts to remain under this license does not apply + to any document created using the fonts or their derivatives. + + DEFINITIONS + "Font Software" refers to the set of files released by the Copyright + Holder(s) under this license and clearly marked as such. This may + include source files, build scripts and documentation. + + "Reserved Font Name" refers to any names specified as such after the + copyright statement(s). + + "Original Version" refers to the collection of Font Software components as + distributed by the Copyright Holder(s). + + "Modified Version" refers to any derivative made by adding to, deleting, + or substituting -- in part or in whole -- any of the components of the + Original Version, by changing formats or by porting the Font Software to a + new environment. + + "Author" refers to any designer, engineer, programmer, technical + writer or other person who contributed to the Font Software. + + PERMISSION & CONDITIONS + Permission is hereby granted, free of charge, to any person obtaining + a copy of the Font Software, to use, study, copy, merge, embed, modify, + redistribute, and sell modified and unmodified copies of the Font + Software, subject to the following conditions: + + 1) Neither the Font Software nor any of its individual components, + in Original or Modified Versions, may be sold by itself. + + 2) Original or Modified Versions of the Font Software may be bundled, + redistributed and/or sold with any software, provided that each copy + contains the above copyright notice and this license. These can be + included either as stand-alone text files, human-readable headers or + in the appropriate machine-readable metadata fields within text or + binary files as long as those fields can be easily viewed by the user. + + 3) No Modified Version of the Font Software may use the Reserved Font + Name(s) unless explicit written permission is granted by the corresponding + Copyright Holder. This restriction only applies to the primary font name as + presented to the users. + + 4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font + Software shall not be used to promote, endorse or advertise any + Modified Version, except to acknowledge the contribution(s) of the + Copyright Holder(s) and the Author(s) or with their explicit written + permission. + + 5) The Font Software, modified or unmodified, in part or in whole, + must be distributed entirely under this license, and must not be + distributed under any other license. The requirement for fonts to + remain under this license does not apply to any document created + using the Font Software. + + TERMINATION + This license becomes null and void if any of the above conditions are + not met. + + DISCLAIMER + THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT + OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE + COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, + INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL + DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM + OTHER DEALINGS IN THE FONT SOFTWARE. + + + + Name: BaKoMa Fonts + Files: matplotlib/mpl-data/fonts/ttf/cm*.ttf matplotlib/mpl-data/fonts/afm/cm*.afm + Description: Computer Modern Fonts in PostScript Type 1 and TrueType font formats. + License: BaKoMa Fonts Licence + BaKoMa Fonts Licence + -------------------- + + This licence covers two font packs (known as BaKoMa Fonts Collection, + which is available at `CTAN:fonts/cm/ps-type1/bakoma/'): + + 1) BaKoMa-CM (1.1/12-Nov-94) + Computer Modern Fonts in PostScript Type 1 and TrueType font formats. + + 2) BaKoMa-AMS (1.2/19-Jan-95) + AMS TeX fonts in PostScript Type 1 and TrueType font formats. + + Copyright (C) 1994, 1995, Basil K. Malyshev. All Rights Reserved. + + Permission to copy and distribute these fonts for any purpose is + hereby granted without fee, provided that the above copyright notice, + author statement and this permission notice appear in all copies of + these fonts and related documentation. + + Permission to modify and distribute modified fonts for any purpose is + hereby granted without fee, provided that the copyright notice, + author statement, this permission notice and location of original + fonts (http://www.ctan.org/tex-archive/fonts/cm/ps-type1/bakoma) + appear in all copies of modified fonts and related documentation. + + Permission to use these fonts (embedding into PostScript, PDF, SVG + and printing by using any software) is hereby granted without fee. + It is not required to provide any notices about using these fonts. + + Basil K. Malyshev + INSTITUTE FOR HIGH ENERGY PHYSICS + IHEP, OMVT + Moscow Region + 142281 PROTVINO + RUSSIA + + E-Mail: bakoma@mail.ru + or malyshev@mail.ihep.ru + + + + + Name: ColorBrewer Color Schemes + Files: lib/matplotlib/_cm.py + Description: Color schemes from ColorBrewer + License: Apache-2.0 + Apache-Style Software License for ColorBrewer software and ColorBrewer Color Schemes + + Copyright (c) 2002 Cynthia Brewer, Mark Harrower, and The Pennsylvania State University. + + Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software distributed + under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. See the License for the + specific language governing permissions and limitations under the License. + + + Name: Courier 10 + Files: matplotlib/tests/Courier10PitchBT-Bold.pfb + Description: Courier 10 font, used in tests. + License: Bitstream-Charter + The Courier10PitchBT-Bold.pfb file is a Type-1 version of + Courier 10 Pitch BT Bold by Bitstream, obtained from + . 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Bug reports or fixes may be sent to + qhull_bug@qhull.org; the authors may or may not act on them as + they desire. + + + Name: Qt4 Editor + Files: matplotlib/backends/qt_editor + Description: Module creating PyQt4 form dialogs/layouts to edit various type of parameters + License: MIT + Module creating PyQt4 form dialogs/layouts to edit various type of parameters + + + formlayout License Agreement (MIT License) + ------------------------------------------ + + Copyright (c) 2009 Pierre Raybaut + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without + restriction, including without limitation the rights to use, + copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the + Software is furnished to do so, subject to the following + conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES + OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, + WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + """ + + + Name: Solarized + Files: matplotlib/mpl-data/stylelib/Solarize_Light2.mplstyle + Description: Solarized color scheme/style + License: MIT + https://github.com/altercation/solarized/blob/master/LICENSE + Copyright (c) 2011 Ethan Schoonover + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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The following copyright and trademark notice and these Terms and + Conditions shall be included in all copies of one or more of the Font + typefaces and any derivative work created as permitted under this + License: + + Copyright (c) 2001-2005 by the STI Pub Companies, consisting of + the American Institute of Physics, the American Chemical Society, the + American Mathematical Society, the American Physical Society, Elsevier, + Inc., and The Institute of Electrical and Electronic Engineers, Inc. + Portions copyright (c) 1998-2003 by MicroPress, Inc. Portions copyright + (c) 1990 by Elsevier, Inc. All rights reserved. STIX Fonts-TM is a + trademark of The Institute of Electrical and Electronics Engineers, Inc. + + 3. You may (a) convert the Fonts from one format to another (e.g., + from TrueType to PostScript), in which case the normal and reasonable + distortion that occurs during such conversion shall be permitted and (b) + embed or include a subset of the Fonts in a document for the purposes of + allowing users to read text in the document that utilizes the Fonts. In + each case, you may use the STIX Fonts-TM mark to designate the resulting + Fonts or subset of the Fonts. + + 4. You may also (a) add glyphs or characters to the Fonts, or modify + the shape of existing glyphs, so long as the base set of glyphs is not + removed and (b) delete glyphs or characters from the Fonts, provided + that the resulting font set is distributed with the following + disclaimer: "This [name] font does not include all the Unicode points + covered in the STIX Fonts-TM set but may include others." In each case, + the name used to denote the resulting font set shall not include the + term "STIX" or any similar term. + + 5. You may charge a fee in connection with the distribution of the + Font Software, provided that no copy of one or more of the individual + Font typefaces that form the STIX Fonts-TM set may be sold by itself. + + 6. THE FONT SOFTWARE IS PROVIDED "AS IS," WITHOUT WARRANTY OF ANY + KIND, EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES + OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT + OF COPYRIGHT, PATENT, TRADEMARK OR OTHER RIGHT. IN NO EVENT SHALL + MICROPRESS OR ANY OF THE STI PUB COMPANIES BE LIABLE FOR ANY CLAIM, + DAMAGES OR OTHER LIABILITY, INCLUDING, BUT NOT LIMITED TO, ANY GENERAL, + SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES, WHETHER IN AN + ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM OR OUT OF THE USE OR + INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT + SOFTWARE. + + 7. Except as contained in the notice set forth in Section 2, the + names MicroPress Inc. and STI Pub Companies, as well as the names of the + companies/organizations that compose the STI Pub Companies, shall not be + used in advertising or otherwise to promote the sale, use or other + dealings in the Font Software without the prior written consent of the + respective company or organization. + + 8. This License shall become null and void in the event of any + material breach of the Terms and Conditions herein by licensee. + + 9. A substantial portion of the STIX Fonts set was developed by + MicroPress Inc. for the STI Pub Companies. To obtain additional + mathematical fonts, please contact MicroPress, Inc., 68-30 Harrow + Street, Forest Hills, NY 11375, USA - Phone: (718) 575-1816. + + + Name: Yorick Colormaps + Files: lib/matplotlib/_cm.py + Description: Gist/Yorick colormaps + License: + BSD-style license for gist/yorick colormaps. + + Copyright: + + Copyright (c) 1996. The Regents of the University of California. + All rights reserved. + + Permission to use, copy, modify, and distribute this software for any + purpose without fee is hereby granted, provided that this entire + notice is included in all copies of any software which is or includes + a copy or modification of this software and in all copies of the + supporting documentation for such software. + + This work was produced at the University of California, Lawrence + Livermore National Laboratory under contract no. W-7405-ENG-48 between + the U.S. Department of Energy and The Regents of the University of + California for the operation of UC LLNL. + + + DISCLAIMER + + This software was prepared as an account of work sponsored by an + agency of the United States Government. Neither the United States + Government nor the University of California nor any of their + employees, makes any warranty, express or implied, or assumes any + liability or responsibility for the accuracy, completeness, or + usefulness of any information, apparatus, product, or process + disclosed, or represents that its use would not infringe + privately-owned rights. Reference herein to any specific commercial + products, process, or service by trade name, trademark, manufacturer, + or otherwise, does not necessarily constitute or imply its + endorsement, recommendation, or favoring by the United States + Government or the University of California. The views and opinions of + authors expressed herein do not necessarily state or reflect those of + the United States Government or the University of California, and + shall not be used for advertising or product endorsement purposes. + + + AUTHOR + + David H. Munro wrote Yorick and Gist. Berkeley Yacc (byacc) generated + the Yorick parser. The routines in Math are from LAPACK and FFTPACK; + MathC contains C translations by David H. Munro. The algorithms for + Yorick's random number generator and several special functions in + Yorick/include were taken from Numerical Recipes by Press, et. al., + although the Yorick implementations are unrelated to those in + Numerical Recipes. A small amount of code in Gist was adapted from + the X11R4 release, copyright M.I.T. -- the complete copyright notice + may be found in the (unused) file Gist/host.c. + +Classifier: Development Status :: 5 - Production/Stable +Classifier: Framework :: Matplotlib +Classifier: Intended Audience :: Science/Research +Classifier: Intended Audience :: Education +Classifier: License :: OSI Approved :: Python Software Foundation License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Topic :: Scientific/Engineering :: Visualization +Project-URL: Homepage, https://matplotlib.org +Project-URL: Download, https://matplotlib.org/stable/install/index.html +Project-URL: Documentation, https://matplotlib.org +Project-URL: Source Code, https://github.com/matplotlib/matplotlib +Project-URL: Bug Tracker, https://github.com/matplotlib/matplotlib/issues +Project-URL: Forum, https://discourse.matplotlib.org/ +Project-URL: Donate, https://numfocus.org/donate-to-matplotlib +Requires-Python: >=3.10 +Requires-Dist: contourpy>=1.0.1 +Requires-Dist: cycler>=0.10 +Requires-Dist: fonttools>=4.22.0 +Requires-Dist: kiwisolver>=1.3.1 +Requires-Dist: numpy>=1.23 +Requires-Dist: packaging>=20.0 +Requires-Dist: pillow>=8 +Requires-Dist: pyparsing>=3 +Requires-Dist: python-dateutil>=2.7 +Provides-Extra: dev +Requires-Dist: meson-python<0.17.0,>=0.13.1; extra == "dev" +Requires-Dist: pybind11!=2.13.3,>=2.13.2; extra == "dev" +Requires-Dist: setuptools_scm>=7; extra == "dev" +Requires-Dist: setuptools>=64; extra == "dev" +Description-Content-Type: text/markdown + +[![PyPi](https://img.shields.io/pypi/v/matplotlib)](https://pypi.org/project/matplotlib/) +[![Conda](https://img.shields.io/conda/vn/conda-forge/matplotlib)](https://anaconda.org/conda-forge/matplotlib) +[![Downloads](https://img.shields.io/pypi/dm/matplotlib)](https://pypi.org/project/matplotlib) +[![NUMFocus](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) + +[![Discourse help forum](https://img.shields.io/badge/help_forum-discourse-blue.svg)](https://discourse.matplotlib.org) +[![Gitter](https://badges.gitter.im/matplotlib/matplotlib.svg)](https://gitter.im/matplotlib/matplotlib) +[![GitHub issues](https://img.shields.io/badge/issue_tracking-github-blue.svg)](https://github.com/matplotlib/matplotlib/issues) +[![Contributing](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?)](https://matplotlib.org/stable/devel/index.html) + +[![GitHub actions status](https://github.com/matplotlib/matplotlib/workflows/Tests/badge.svg)](https://github.com/matplotlib/matplotlib/actions?query=workflow%3ATests) +[![Azure pipelines status](https://dev.azure.com/matplotlib/matplotlib/_apis/build/status/matplotlib.matplotlib?branchName=main)](https://dev.azure.com/matplotlib/matplotlib/_build/latest?definitionId=1&branchName=main) +[![AppVeyor status](https://ci.appveyor.com/api/projects/status/github/matplotlib/matplotlib?branch=main&svg=true)](https://ci.appveyor.com/project/matplotlib/matplotlib) +[![Codecov status](https://codecov.io/github/matplotlib/matplotlib/badge.svg?branch=main&service=github)](https://app.codecov.io/gh/matplotlib/matplotlib) +[![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver) + +![Matplotlib logotype](https://matplotlib.org/_static/logo2.svg) + +Matplotlib is a comprehensive library for creating static, animated, and +interactive visualizations in Python. + +Check out our [home page](https://matplotlib.org/) for more information. + +![image](https://matplotlib.org/_static/readme_preview.png) + +Matplotlib produces publication-quality figures in a variety of hardcopy +formats and interactive environments across platforms. Matplotlib can be +used in Python scripts, Python/IPython shells, web application servers, +and various graphical user interface toolkits. + +## Install + +See the [install +documentation](https://matplotlib.org/stable/users/installing/index.html), +which is generated from `/doc/install/index.rst` + +## Contribute + +You've discovered a bug or something else you want to change — excellent! + +You've worked out a way to fix it — even better! + +You want to tell us about it — best of all! + +Start at the [contributing +guide](https://matplotlib.org/devdocs/devel/contribute.html)! + +## Contact + +[Discourse](https://discourse.matplotlib.org/) is the discussion forum +for general questions and discussions and our recommended starting +point. + +Our active mailing lists (which are mirrored on Discourse) are: + +- [Users](https://mail.python.org/mailman/listinfo/matplotlib-users) + mailing list: +- [Announcement](https://mail.python.org/mailman/listinfo/matplotlib-announce) + mailing list: +- [Development](https://mail.python.org/mailman/listinfo/matplotlib-devel) + mailing list: + +[Gitter](https://gitter.im/matplotlib/matplotlib) is for coordinating +development and asking questions directly related to contributing to +matplotlib. + +## Citing Matplotlib + +If Matplotlib contributes to a project that leads to publication, please +acknowledge this by citing Matplotlib. + +[A ready-made citation +entry](https://matplotlib.org/stable/users/project/citing.html) is +available. diff --git a/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/RECORD b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..a24d08e940b45c538182c66b795610b2a784c9bc --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib-3.10.8.dist-info/RECORD @@ -0,0 +1,884 @@ +__pycache__/pylab.cpython-312.pyc,, +matplotlib-3.10.8.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 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is provided by the companion pyplot module, +which may be imported directly, e.g.:: + + import matplotlib.pyplot as plt + +or using ipython:: + + ipython + +at your terminal, followed by:: + + In [1]: %matplotlib + In [2]: import matplotlib.pyplot as plt + +at the ipython shell prompt. + +For the most part, direct use of the explicit object-oriented library is +encouraged when programming; the implicit pyplot interface is primarily for +working interactively. The exceptions to this suggestion are the pyplot +functions `.pyplot.figure`, `.pyplot.subplot`, `.pyplot.subplots`, and +`.pyplot.savefig`, which can greatly simplify scripting. See +:ref:`api_interfaces` for an explanation of the tradeoffs between the implicit +and explicit interfaces. + +Modules include: + +:mod:`matplotlib.axes` + The `~.axes.Axes` class. Most pyplot functions are wrappers for + `~.axes.Axes` methods. The axes module is the highest level of OO + access to the library. + +:mod:`matplotlib.figure` + The `.Figure` class. + +:mod:`matplotlib.artist` + The `.Artist` base class for all classes that draw things. + +:mod:`matplotlib.lines` + The `.Line2D` class for drawing lines and markers. + +:mod:`matplotlib.patches` + Classes for drawing polygons. + +:mod:`matplotlib.text` + The `.Text` and `.Annotation` classes. + +:mod:`matplotlib.image` + The `.AxesImage` and `.FigureImage` classes. + +:mod:`matplotlib.collections` + Classes for efficient drawing of groups of lines or polygons. + +:mod:`matplotlib.colors` + Color specifications and making colormaps. + +:mod:`matplotlib.cm` + Colormaps, and the `.ScalarMappable` mixin class for providing color + mapping functionality to other classes. + +:mod:`matplotlib.ticker` + Calculation of tick mark locations and formatting of tick labels. + +:mod:`matplotlib.backends` + A subpackage with modules for various GUI libraries and output formats. + +The base matplotlib namespace includes: + +`~matplotlib.rcParams` + Default configuration settings; their defaults may be overridden using + a :file:`matplotlibrc` file. + +`~matplotlib.use` + Setting the Matplotlib backend. This should be called before any + figure is created, because it is not possible to switch between + different GUI backends after that. + +The following environment variables can be used to customize the behavior: + +:envvar:`MPLBACKEND` + This optional variable can be set to choose the Matplotlib backend. See + :ref:`what-is-a-backend`. + +:envvar:`MPLCONFIGDIR` + This is the directory used to store user customizations to + Matplotlib, as well as some caches to improve performance. If + :envvar:`MPLCONFIGDIR` is not defined, :file:`{HOME}/.config/matplotlib` + and :file:`{HOME}/.cache/matplotlib` are used on Linux, and + :file:`{HOME}/.matplotlib` on other platforms, if they are + writable. Otherwise, the Python standard library's `tempfile.gettempdir` + is used to find a base directory in which the :file:`matplotlib` + subdirectory is created. + +Matplotlib was initially written by John D. Hunter (1968-2012) and is now +developed and maintained by a host of others. + +Occasionally the internal documentation (python docstrings) will refer +to MATLAB®, a registered trademark of The MathWorks, Inc. + +""" + +__all__ = [ + "__bibtex__", + "__version__", + "__version_info__", + "set_loglevel", + "ExecutableNotFoundError", + "get_configdir", + "get_cachedir", + "get_data_path", + "matplotlib_fname", + "MatplotlibDeprecationWarning", + "RcParams", + "rc_params", + "rc_params_from_file", + "rcParamsDefault", + "rcParams", + "rcParamsOrig", + "defaultParams", + "rc", + "rcdefaults", + "rc_file_defaults", + "rc_file", + "rc_context", + "use", + "get_backend", + "interactive", + "is_interactive", + "colormaps", + "multivar_colormaps", + "bivar_colormaps", + "color_sequences", +] + + +import atexit +from collections import namedtuple +from collections.abc import MutableMapping +import contextlib +import functools +import importlib +import inspect +from inspect import Parameter +import locale +import logging +import os +from pathlib import Path +import pprint +import re +import shutil +import subprocess +import sys +import tempfile + +from packaging.version import parse as parse_version + +# cbook must import matplotlib only within function +# definitions, so it is safe to import from it here. +from . import _api, _version, cbook, _docstring, rcsetup +from matplotlib._api import MatplotlibDeprecationWarning +from matplotlib.rcsetup import cycler # noqa: F401 + + +_log = logging.getLogger(__name__) + +__bibtex__ = r"""@Article{Hunter:2007, + Author = {Hunter, J. D.}, + Title = {Matplotlib: A 2D graphics environment}, + Journal = {Computing in Science \& Engineering}, + Volume = {9}, + Number = {3}, + Pages = {90--95}, + abstract = {Matplotlib is a 2D graphics package used for Python + for application development, interactive scripting, and + publication-quality image generation across user + interfaces and operating systems.}, + publisher = {IEEE COMPUTER SOC}, + year = 2007 +}""" + +# modelled after sys.version_info +_VersionInfo = namedtuple('_VersionInfo', + 'major, minor, micro, releaselevel, serial') + + +def _parse_to_version_info(version_str): + """ + Parse a version string to a namedtuple analogous to sys.version_info. + + See: + https://packaging.pypa.io/en/latest/version.html#packaging.version.parse + https://docs.python.org/3/library/sys.html#sys.version_info + """ + v = parse_version(version_str) + if v.pre is None and v.post is None and v.dev is None: + return _VersionInfo(v.major, v.minor, v.micro, 'final', 0) + elif v.dev is not None: + return _VersionInfo(v.major, v.minor, v.micro, 'alpha', v.dev) + elif v.pre is not None: + releaselevel = { + 'a': 'alpha', + 'b': 'beta', + 'rc': 'candidate'}.get(v.pre[0], 'alpha') + return _VersionInfo(v.major, v.minor, v.micro, releaselevel, v.pre[1]) + else: + # fallback for v.post: guess-next-dev scheme from setuptools_scm + return _VersionInfo(v.major, v.minor, v.micro + 1, 'alpha', v.post) + + +def _get_version(): + """Return the version string used for __version__.""" + # Only shell out to a git subprocess if really needed, i.e. when we are in + # a matplotlib git repo but not in a shallow clone, such as those used by + # CI, as the latter would trigger a warning from setuptools_scm. + root = Path(__file__).resolve().parents[2] + if ((root / ".matplotlib-repo").exists() + and (root / ".git").exists() + and not (root / ".git/shallow").exists()): + try: + import setuptools_scm + except ImportError: + pass + else: + return setuptools_scm.get_version( + root=root, + dist_name="matplotlib", + version_scheme="release-branch-semver", + local_scheme="node-and-date", + fallback_version=_version.version, + ) + # Get the version from the _version.py file if not in repo or setuptools_scm is + # unavailable. + return _version.version + + +@_api.caching_module_getattr +class __getattr__: + __version__ = property(lambda self: _get_version()) + __version_info__ = property( + lambda self: _parse_to_version_info(self.__version__)) + + +def _check_versions(): + + # Quickfix to ensure Microsoft Visual C++ redistributable + # DLLs are loaded before importing kiwisolver + from . import ft2font # noqa: F401 + + for modname, minver in [ + ("cycler", "0.10"), + ("dateutil", "2.7"), + ("kiwisolver", "1.3.1"), + ("numpy", "1.23"), + ("pyparsing", "2.3.1"), + ]: + module = importlib.import_module(modname) + if parse_version(module.__version__) < parse_version(minver): + raise ImportError(f"Matplotlib requires {modname}>={minver}; " + f"you have {module.__version__}") + + +_check_versions() + + +# The decorator ensures this always returns the same handler (and it is only +# attached once). +@functools.cache +def _ensure_handler(): + """ + The first time this function is called, attach a `StreamHandler` using the + same format as `logging.basicConfig` to the Matplotlib root logger. + + Return this handler every time this function is called. + """ + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT)) + _log.addHandler(handler) + return handler + + +def set_loglevel(level): + """ + Configure Matplotlib's logging levels. + + Matplotlib uses the standard library `logging` framework under the root + logger 'matplotlib'. This is a helper function to: + + - set Matplotlib's root logger level + - set the root logger handler's level, creating the handler + if it does not exist yet + + Typically, one should call ``set_loglevel("info")`` or + ``set_loglevel("debug")`` to get additional debugging information. + + Users or applications that are installing their own logging handlers + may want to directly manipulate ``logging.getLogger('matplotlib')`` rather + than use this function. + + Parameters + ---------- + level : {"notset", "debug", "info", "warning", "error", "critical"} + The log level of the handler. + + Notes + ----- + The first time this function is called, an additional handler is attached + to Matplotlib's root handler; this handler is reused every time and this + function simply manipulates the logger and handler's level. + + """ + _log.setLevel(level.upper()) + _ensure_handler().setLevel(level.upper()) + + +def _logged_cached(fmt, func=None): + """ + Decorator that logs a function's return value, and memoizes that value. + + After :: + + @_logged_cached(fmt) + def func(): ... + + the first call to *func* will log its return value at the DEBUG level using + %-format string *fmt*, and memoize it; later calls to *func* will directly + return that value. + """ + if func is None: # Return the actual decorator. + return functools.partial(_logged_cached, fmt) + + called = False + ret = None + + @functools.wraps(func) + def wrapper(**kwargs): + nonlocal called, ret + if not called: + ret = func(**kwargs) + called = True + _log.debug(fmt, ret) + return ret + + return wrapper + + +_ExecInfo = namedtuple("_ExecInfo", "executable raw_version version") + + +class ExecutableNotFoundError(FileNotFoundError): + """ + Error raised when an executable that Matplotlib optionally + depends on can't be found. + """ + pass + + +@functools.cache +def _get_executable_info(name): + """ + Get the version of some executable that Matplotlib optionally depends on. + + .. warning:: + The list of executables that this function supports is set according to + Matplotlib's internal needs, and may change without notice. + + Parameters + ---------- + name : str + The executable to query. The following values are currently supported: + "dvipng", "gs", "inkscape", "magick", "pdftocairo", "pdftops". This + list is subject to change without notice. + + Returns + ------- + tuple + A namedtuple with fields ``executable`` (`str`) and ``version`` + (`packaging.Version`, or ``None`` if the version cannot be determined). + + Raises + ------ + ExecutableNotFoundError + If the executable is not found or older than the oldest version + supported by Matplotlib. For debugging purposes, it is also + possible to "hide" an executable from Matplotlib by adding it to the + :envvar:`_MPLHIDEEXECUTABLES` environment variable (a comma-separated + list), which must be set prior to any calls to this function. + ValueError + If the executable is not one that we know how to query. + """ + + def impl(args, regex, min_ver=None, ignore_exit_code=False): + # Execute the subprocess specified by args; capture stdout and stderr. + # Search for a regex match in the output; if the match succeeds, the + # first group of the match is the version. + # Return an _ExecInfo if the executable exists, and has a version of + # at least min_ver (if set); else, raise ExecutableNotFoundError. + try: + output = subprocess.check_output( + args, stderr=subprocess.STDOUT, + text=True, errors="replace", timeout=30) + except subprocess.CalledProcessError as _cpe: + if ignore_exit_code: + output = _cpe.output + else: + raise ExecutableNotFoundError(str(_cpe)) from _cpe + except subprocess.TimeoutExpired as _te: + msg = f"Timed out running {cbook._pformat_subprocess(args)}" + raise ExecutableNotFoundError(msg) from _te + except OSError as _ose: + raise ExecutableNotFoundError(str(_ose)) from _ose + match = re.search(regex, output) + if match: + raw_version = match.group(1) + version = parse_version(raw_version) + if min_ver is not None and version < parse_version(min_ver): + raise ExecutableNotFoundError( + f"You have {args[0]} version {version} but the minimum " + f"version supported by Matplotlib is {min_ver}") + return _ExecInfo(args[0], raw_version, version) + else: + raise ExecutableNotFoundError( + f"Failed to determine the version of {args[0]} from " + f"{' '.join(args)}, which output {output}") + + if name in os.environ.get("_MPLHIDEEXECUTABLES", "").split(","): + raise ExecutableNotFoundError(f"{name} was hidden") + + if name == "dvipng": + return impl(["dvipng", "-version"], "(?m)^dvipng(?: .*)? (.+)", "1.6") + elif name == "gs": + execs = (["gswin32c", "gswin64c", "mgs", "gs"] # "mgs" for miktex. + if sys.platform == "win32" else + ["gs"]) + for e in execs: + try: + return impl([e, "--version"], "(.*)", "9") + except ExecutableNotFoundError: + pass + message = "Failed to find a Ghostscript installation" + raise ExecutableNotFoundError(message) + elif name == "inkscape": + try: + # Try headless option first (needed for Inkscape version < 1.0): + return impl(["inkscape", "--without-gui", "-V"], + "Inkscape ([^ ]*)") + except ExecutableNotFoundError: + pass # Suppress exception chaining. + # If --without-gui is not accepted, we may be using Inkscape >= 1.0 so + # try without it: + return impl(["inkscape", "-V"], "Inkscape ([^ ]*)") + elif name == "magick": + if sys.platform == "win32": + # Check the registry to avoid confusing ImageMagick's convert with + # Windows's builtin convert.exe. + import winreg + binpath = "" + for flag in [0, winreg.KEY_WOW64_32KEY, winreg.KEY_WOW64_64KEY]: + try: + with winreg.OpenKeyEx( + winreg.HKEY_LOCAL_MACHINE, + r"Software\Imagemagick\Current", + 0, winreg.KEY_QUERY_VALUE | flag) as hkey: + binpath = winreg.QueryValueEx(hkey, "BinPath")[0] + except OSError: + pass + path = None + if binpath: + for name in ["convert.exe", "magick.exe"]: + candidate = Path(binpath, name) + if candidate.exists(): + path = str(candidate) + break + if path is None: + raise ExecutableNotFoundError( + "Failed to find an ImageMagick installation") + else: + path = "convert" + info = impl([path, "--version"], r"^Version: ImageMagick (\S*)") + if info.raw_version == "7.0.10-34": + # https://github.com/ImageMagick/ImageMagick/issues/2720 + raise ExecutableNotFoundError( + f"You have ImageMagick {info.version}, which is unsupported") + return info + elif name == "pdftocairo": + return impl(["pdftocairo", "-v"], "pdftocairo version (.*)") + elif name == "pdftops": + info = impl(["pdftops", "-v"], "^pdftops version (.*)", + ignore_exit_code=True) + if info and not ( + 3 <= info.version.major or + # poppler version numbers. + parse_version("0.9") <= info.version < parse_version("1.0")): + raise ExecutableNotFoundError( + f"You have pdftops version {info.version} but the minimum " + f"version supported by Matplotlib is 3.0") + return info + else: + raise ValueError(f"Unknown executable: {name!r}") + + +def _get_xdg_config_dir(): + """ + Return the XDG configuration directory, according to the XDG base + directory spec: + + https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html + """ + return os.environ.get('XDG_CONFIG_HOME') or str(Path.home() / ".config") + + +def _get_xdg_cache_dir(): + """ + Return the XDG cache directory, according to the XDG base directory spec: + + https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html + """ + return os.environ.get('XDG_CACHE_HOME') or str(Path.home() / ".cache") + + +def _get_config_or_cache_dir(xdg_base_getter): + configdir = os.environ.get('MPLCONFIGDIR') + if configdir: + configdir = Path(configdir) + elif sys.platform.startswith(('linux', 'freebsd')): + # Only call _xdg_base_getter here so that MPLCONFIGDIR is tried first, + # as _xdg_base_getter can throw. + configdir = Path(xdg_base_getter(), "matplotlib") + else: + configdir = Path.home() / ".matplotlib" + # Resolve the path to handle potential issues with inaccessible symlinks. + configdir = configdir.resolve() + try: + configdir.mkdir(parents=True, exist_ok=True) + except OSError as exc: + _log.warning("mkdir -p failed for path %s: %s", configdir, exc) + else: + if os.access(str(configdir), os.W_OK) and configdir.is_dir(): + return str(configdir) + _log.warning("%s is not a writable directory", configdir) + # If the config or cache directory cannot be created or is not a writable + # directory, create a temporary one. + try: + tmpdir = tempfile.mkdtemp(prefix="matplotlib-") + except OSError as exc: + raise OSError( + f"Matplotlib requires access to a writable cache directory, but there " + f"was an issue with the default path ({configdir}), and a temporary " + f"directory could not be created; set the MPLCONFIGDIR environment " + f"variable to a writable directory") from exc + os.environ["MPLCONFIGDIR"] = tmpdir + atexit.register(shutil.rmtree, tmpdir) + _log.warning( + "Matplotlib created a temporary cache directory at %s because there was " + "an issue with the default path (%s); it is highly recommended to set the " + "MPLCONFIGDIR environment variable to a writable directory, in particular to " + "speed up the import of Matplotlib and to better support multiprocessing.", + tmpdir, configdir) + return tmpdir + + +@_logged_cached('CONFIGDIR=%s') +def get_configdir(): + """ + Return the string path of the configuration directory. + + The directory is chosen as follows: + + 1. If the MPLCONFIGDIR environment variable is supplied, choose that. + 2. On Linux, follow the XDG specification and look first in + ``$XDG_CONFIG_HOME``, if defined, or ``$HOME/.config``. On other + platforms, choose ``$HOME/.matplotlib``. + 3. If the chosen directory exists and is writable, use that as the + configuration directory. + 4. Else, create a temporary directory, and use it as the configuration + directory. + """ + return _get_config_or_cache_dir(_get_xdg_config_dir) + + +@_logged_cached('CACHEDIR=%s') +def get_cachedir(): + """ + Return the string path of the cache directory. + + The procedure used to find the directory is the same as for + `get_configdir`, except using ``$XDG_CACHE_HOME``/``$HOME/.cache`` instead. + """ + return _get_config_or_cache_dir(_get_xdg_cache_dir) + + +@_logged_cached('matplotlib data path: %s') +def get_data_path(): + """Return the path to Matplotlib data.""" + return str(Path(__file__).with_name("mpl-data")) + + +def matplotlib_fname(): + """ + Get the location of the config file. + + The file location is determined in the following order + + - ``$PWD/matplotlibrc`` + - ``$MATPLOTLIBRC`` if it is not a directory + - ``$MATPLOTLIBRC/matplotlibrc`` + - ``$MPLCONFIGDIR/matplotlibrc`` + - On Linux, + - ``$XDG_CONFIG_HOME/matplotlib/matplotlibrc`` (if ``$XDG_CONFIG_HOME`` + is defined) + - or ``$HOME/.config/matplotlib/matplotlibrc`` (if ``$XDG_CONFIG_HOME`` + is not defined) + - On other platforms, + - ``$HOME/.matplotlib/matplotlibrc`` if ``$HOME`` is defined + - Lastly, it looks in ``$MATPLOTLIBDATA/matplotlibrc``, which should always + exist. + """ + + def gen_candidates(): + # rely on down-stream code to make absolute. This protects us + # from having to directly get the current working directory + # which can fail if the user has ended up with a cwd that is + # non-existent. + yield 'matplotlibrc' + try: + matplotlibrc = os.environ['MATPLOTLIBRC'] + except KeyError: + pass + else: + yield matplotlibrc + yield os.path.join(matplotlibrc, 'matplotlibrc') + yield os.path.join(get_configdir(), 'matplotlibrc') + yield os.path.join(get_data_path(), 'matplotlibrc') + + for fname in gen_candidates(): + if os.path.exists(fname) and not os.path.isdir(fname): + return fname + + raise RuntimeError("Could not find matplotlibrc file; your Matplotlib " + "install is broken") + + +# rcParams deprecated and automatically mapped to another key. +# Values are tuples of (version, new_name, f_old2new, f_new2old). +_deprecated_map = {} +# rcParams deprecated; some can manually be mapped to another key. +# Values are tuples of (version, new_name_or_None). +_deprecated_ignore_map = {} +# rcParams deprecated; can use None to suppress warnings; remain actually +# listed in the rcParams. +# Values are tuples of (version,) +_deprecated_remain_as_none = {} + + +@_docstring.Substitution( + "\n".join(map("- {}".format, sorted(rcsetup._validators, key=str.lower))) +) +class RcParams(MutableMapping, dict): + """ + A dict-like key-value store for config parameters, including validation. + + Validating functions are defined and associated with rc parameters in + :mod:`matplotlib.rcsetup`. + + The list of rcParams is: + + %s + + See Also + -------- + :ref:`customizing-with-matplotlibrc-files` + """ + + validate = rcsetup._validators + + # validate values on the way in + def __init__(self, *args, **kwargs): + self.update(*args, **kwargs) + + def _set(self, key, val): + """ + Directly write data bypassing deprecation and validation logic. + + Notes + ----- + As end user or downstream library you almost always should use + ``rcParams[key] = val`` and not ``_set()``. + + There are only very few special cases that need direct data access. + These cases previously used ``dict.__setitem__(rcParams, key, val)``, + which is now deprecated and replaced by ``rcParams._set(key, val)``. + + Even though private, we guarantee API stability for ``rcParams._set``, + i.e. it is subject to Matplotlib's API and deprecation policy. + + :meta public: + """ + dict.__setitem__(self, key, val) + + def _get(self, key): + """ + Directly read data bypassing deprecation, backend and validation + logic. + + Notes + ----- + As end user or downstream library you almost always should use + ``val = rcParams[key]`` and not ``_get()``. + + There are only very few special cases that need direct data access. + These cases previously used ``dict.__getitem__(rcParams, key, val)``, + which is now deprecated and replaced by ``rcParams._get(key)``. + + Even though private, we guarantee API stability for ``rcParams._get``, + i.e. it is subject to Matplotlib's API and deprecation policy. + + :meta public: + """ + return dict.__getitem__(self, key) + + def _update_raw(self, other_params): + """ + Directly update the data from *other_params*, bypassing deprecation, + backend and validation logic on both sides. + + This ``rcParams._update_raw(params)`` replaces the previous pattern + ``dict.update(rcParams, params)``. + + Parameters + ---------- + other_params : dict or `.RcParams` + The input mapping from which to update. + """ + if isinstance(other_params, RcParams): + other_params = dict.items(other_params) + dict.update(self, other_params) + + def _ensure_has_backend(self): + """ + Ensure that a "backend" entry exists. + + Normally, the default matplotlibrc file contains *no* entry for "backend" (the + corresponding line starts with ##, not #; we fill in _auto_backend_sentinel + in that case. However, packagers can set a different default backend + (resulting in a normal `#backend: foo` line) in which case we should *not* + fill in _auto_backend_sentinel. + """ + dict.setdefault(self, "backend", rcsetup._auto_backend_sentinel) + + def __setitem__(self, key, val): + try: + if key in _deprecated_map: + version, alt_key, alt_val, inverse_alt = _deprecated_map[key] + _api.warn_deprecated( + version, name=key, obj_type="rcparam", alternative=alt_key) + key = alt_key + val = alt_val(val) + elif key in _deprecated_remain_as_none and val is not None: + version, = _deprecated_remain_as_none[key] + _api.warn_deprecated(version, name=key, obj_type="rcparam") + elif key in _deprecated_ignore_map: + version, alt_key = _deprecated_ignore_map[key] + _api.warn_deprecated( + version, name=key, obj_type="rcparam", alternative=alt_key) + return + elif key == 'backend': + if val is rcsetup._auto_backend_sentinel: + if 'backend' in self: + return + try: + cval = self.validate[key](val) + except ValueError as ve: + raise ValueError(f"Key {key}: {ve}") from None + self._set(key, cval) + except KeyError as err: + raise KeyError( + f"{key} is not a valid rc parameter (see rcParams.keys() for " + f"a list of valid parameters)") from err + + def __getitem__(self, key): + if key in _deprecated_map: + version, alt_key, alt_val, inverse_alt = _deprecated_map[key] + _api.warn_deprecated( + version, name=key, obj_type="rcparam", alternative=alt_key) + return inverse_alt(self._get(alt_key)) + + elif key in _deprecated_ignore_map: + version, alt_key = _deprecated_ignore_map[key] + _api.warn_deprecated( + version, name=key, obj_type="rcparam", alternative=alt_key) + return self._get(alt_key) if alt_key else None + + # In theory, this should only ever be used after the global rcParams + # has been set up, but better be safe e.g. in presence of breakpoints. + elif key == "backend" and self is globals().get("rcParams"): + val = self._get(key) + if val is rcsetup._auto_backend_sentinel: + from matplotlib import pyplot as plt + plt.switch_backend(rcsetup._auto_backend_sentinel) + + return self._get(key) + + def _get_backend_or_none(self): + """Get the requested backend, if any, without triggering resolution.""" + backend = self._get("backend") + return None if backend is rcsetup._auto_backend_sentinel else backend + + def __repr__(self): + class_name = self.__class__.__name__ + indent = len(class_name) + 1 + with _api.suppress_matplotlib_deprecation_warning(): + repr_split = pprint.pformat(dict(self), indent=1, + width=80 - indent).split('\n') + repr_indented = ('\n' + ' ' * indent).join(repr_split) + return f'{class_name}({repr_indented})' + + def __str__(self): + return '\n'.join(map('{0[0]}: {0[1]}'.format, sorted(self.items()))) + + def __iter__(self): + """Yield sorted list of keys.""" + with _api.suppress_matplotlib_deprecation_warning(): + yield from sorted(dict.__iter__(self)) + + def __len__(self): + return dict.__len__(self) + + def find_all(self, pattern): + """ + Return the subset of this RcParams dictionary whose keys match, + using :func:`re.search`, the given ``pattern``. + + .. note:: + + Changes to the returned dictionary are *not* propagated to + the parent RcParams dictionary. + + """ + pattern_re = re.compile(pattern) + return RcParams((key, value) + for key, value in self.items() + if pattern_re.search(key)) + + def copy(self): + """Copy this RcParams instance.""" + rccopy = RcParams() + for k in self: # Skip deprecations and revalidation. + rccopy._set(k, self._get(k)) + return rccopy + + +def rc_params(fail_on_error=False): + """Construct a `RcParams` instance from the default Matplotlib rc file.""" + return rc_params_from_file(matplotlib_fname(), fail_on_error) + + +@functools.cache +def _get_ssl_context(): + try: + import certifi + except ImportError: + _log.debug("Could not import certifi.") + return None + import ssl + return ssl.create_default_context(cafile=certifi.where()) + + +@contextlib.contextmanager +def _open_file_or_url(fname): + if (isinstance(fname, str) + and fname.startswith(('http://', 'https://', 'ftp://', 'file:'))): + import urllib.request + ssl_ctx = _get_ssl_context() + if ssl_ctx is None: + _log.debug( + "Could not get certifi ssl context, https may not work." + ) + with urllib.request.urlopen(fname, context=ssl_ctx) as f: + yield (line.decode('utf-8') for line in f) + else: + fname = os.path.expanduser(fname) + with open(fname, encoding='utf-8') as f: + yield f + + +def _rc_params_in_file(fname, transform=lambda x: x, fail_on_error=False): + """ + Construct a `RcParams` instance from file *fname*. + + Unlike `rc_params_from_file`, the configuration class only contains the + parameters specified in the file (i.e. default values are not filled in). + + Parameters + ---------- + fname : path-like + The loaded file. + transform : callable, default: the identity function + A function called on each individual line of the file to transform it, + before further parsing. + fail_on_error : bool, default: False + Whether invalid entries should result in an exception or a warning. + """ + import matplotlib as mpl + rc_temp = {} + with _open_file_or_url(fname) as fd: + try: + for line_no, line in enumerate(fd, 1): + line = transform(line) + strippedline = cbook._strip_comment(line) + if not strippedline: + continue + tup = strippedline.split(':', 1) + if len(tup) != 2: + _log.warning('Missing colon in file %r, line %d (%r)', + fname, line_no, line.rstrip('\n')) + continue + key, val = tup + key = key.strip() + val = val.strip() + if val.startswith('"') and val.endswith('"'): + val = val[1:-1] # strip double quotes + if key in rc_temp: + _log.warning('Duplicate key in file %r, line %d (%r)', + fname, line_no, line.rstrip('\n')) + rc_temp[key] = (val, line, line_no) + except UnicodeDecodeError: + _log.warning('Cannot decode configuration file %r as utf-8.', + fname) + raise + + config = RcParams() + + for key, (val, line, line_no) in rc_temp.items(): + if key in rcsetup._validators: + if fail_on_error: + config[key] = val # try to convert to proper type or raise + else: + try: + config[key] = val # try to convert to proper type or skip + except Exception as msg: + _log.warning('Bad value in file %r, line %d (%r): %s', + fname, line_no, line.rstrip('\n'), msg) + elif key in _deprecated_ignore_map: + version, alt_key = _deprecated_ignore_map[key] + _api.warn_deprecated( + version, name=key, alternative=alt_key, obj_type='rcparam', + addendum="Please update your matplotlibrc.") + else: + # __version__ must be looked up as an attribute to trigger the + # module-level __getattr__. + version = ('main' if '.post' in mpl.__version__ + else f'v{mpl.__version__}') + _log.warning(""" +Bad key %(key)s in file %(fname)s, line %(line_no)s (%(line)r) +You probably need to get an updated matplotlibrc file from +https://github.com/matplotlib/matplotlib/blob/%(version)s/lib/matplotlib/mpl-data/matplotlibrc +or from the matplotlib source distribution""", + dict(key=key, fname=fname, line_no=line_no, + line=line.rstrip('\n'), version=version)) + return config + + +def rc_params_from_file(fname, fail_on_error=False, use_default_template=True): + """ + Construct a `RcParams` from file *fname*. + + Parameters + ---------- + fname : str or path-like + A file with Matplotlib rc settings. + fail_on_error : bool + If True, raise an error when the parser fails to convert a parameter. + use_default_template : bool + If True, initialize with default parameters before updating with those + in the given file. If False, the configuration class only contains the + parameters specified in the file. (Useful for updating dicts.) + """ + config_from_file = _rc_params_in_file(fname, fail_on_error=fail_on_error) + + if not use_default_template: + return config_from_file + + with _api.suppress_matplotlib_deprecation_warning(): + config = RcParams({**rcParamsDefault, **config_from_file}) + + if "".join(config['text.latex.preamble']): + _log.info(""" +***************************************************************** +You have the following UNSUPPORTED LaTeX preamble customizations: +%s +Please do not ask for support with these customizations active. +***************************************************************** +""", '\n'.join(config['text.latex.preamble'])) + _log.debug('loaded rc file %s', fname) + + return config + + +rcParamsDefault = _rc_params_in_file( + cbook._get_data_path("matplotlibrc"), + # Strip leading comment. + transform=lambda line: line[1:] if line.startswith("#") else line, + fail_on_error=True) +rcParamsDefault._update_raw(rcsetup._hardcoded_defaults) +rcParamsDefault._ensure_has_backend() + +rcParams = RcParams() # The global instance. +rcParams._update_raw(rcParamsDefault) +rcParams._update_raw(_rc_params_in_file(matplotlib_fname())) +rcParamsOrig = rcParams.copy() +with _api.suppress_matplotlib_deprecation_warning(): + # This also checks that all rcParams are indeed listed in the template. + # Assigning to rcsetup.defaultParams is left only for backcompat. + defaultParams = rcsetup.defaultParams = { + # We want to resolve deprecated rcParams, but not backend... + key: [(rcsetup._auto_backend_sentinel if key == "backend" else + rcParamsDefault[key]), + validator] + for key, validator in rcsetup._validators.items()} +if rcParams['axes.formatter.use_locale']: + locale.setlocale(locale.LC_ALL, '') + + +def rc(group, **kwargs): + """ + Set the current `.rcParams`. *group* is the grouping for the rc, e.g., + for ``lines.linewidth`` the group is ``lines``, for + ``axes.facecolor``, the group is ``axes``, and so on. Group may + also be a list or tuple of group names, e.g., (*xtick*, *ytick*). + *kwargs* is a dictionary attribute name/value pairs, e.g.,:: + + rc('lines', linewidth=2, color='r') + + sets the current `.rcParams` and is equivalent to:: + + rcParams['lines.linewidth'] = 2 + rcParams['lines.color'] = 'r' + + The following aliases are available to save typing for interactive users: + + ===== ================= + Alias Property + ===== ================= + 'lw' 'linewidth' + 'ls' 'linestyle' + 'c' 'color' + 'fc' 'facecolor' + 'ec' 'edgecolor' + 'mew' 'markeredgewidth' + 'aa' 'antialiased' + ===== ================= + + Thus you could abbreviate the above call as:: + + rc('lines', lw=2, c='r') + + Note you can use python's kwargs dictionary facility to store + dictionaries of default parameters. e.g., you can customize the + font rc as follows:: + + font = {'family' : 'monospace', + 'weight' : 'bold', + 'size' : 'larger'} + rc('font', **font) # pass in the font dict as kwargs + + This enables you to easily switch between several configurations. Use + ``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to + restore the default `.rcParams` after changes. + + Notes + ----- + Similar functionality is available by using the normal dict interface, i.e. + ``rcParams.update({"lines.linewidth": 2, ...})`` (but ``rcParams.update`` + does not support abbreviations or grouping). + """ + + aliases = { + 'lw': 'linewidth', + 'ls': 'linestyle', + 'c': 'color', + 'fc': 'facecolor', + 'ec': 'edgecolor', + 'mew': 'markeredgewidth', + 'aa': 'antialiased', + } + + if isinstance(group, str): + group = (group,) + for g in group: + for k, v in kwargs.items(): + name = aliases.get(k) or k + key = f'{g}.{name}' + try: + rcParams[key] = v + except KeyError as err: + raise KeyError(('Unrecognized key "%s" for group "%s" and ' + 'name "%s"') % (key, g, name)) from err + + +def rcdefaults(): + """ + Restore the `.rcParams` from Matplotlib's internal default style. + + Style-blacklisted `.rcParams` (defined in + ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated. + + See Also + -------- + matplotlib.rc_file_defaults + Restore the `.rcParams` from the rc file originally loaded by + Matplotlib. + matplotlib.style.use + Use a specific style file. Call ``style.use('default')`` to restore + the default style. + """ + # Deprecation warnings were already handled when creating rcParamsDefault, + # no need to reemit them here. + with _api.suppress_matplotlib_deprecation_warning(): + from .style.core import STYLE_BLACKLIST + rcParams.clear() + rcParams.update({k: v for k, v in rcParamsDefault.items() + if k not in STYLE_BLACKLIST}) + + +def rc_file_defaults(): + """ + Restore the `.rcParams` from the original rc file loaded by Matplotlib. + + Style-blacklisted `.rcParams` (defined in + ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated. + """ + # Deprecation warnings were already handled when creating rcParamsOrig, no + # need to reemit them here. + with _api.suppress_matplotlib_deprecation_warning(): + from .style.core import STYLE_BLACKLIST + rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig + if k not in STYLE_BLACKLIST}) + + +def rc_file(fname, *, use_default_template=True): + """ + Update `.rcParams` from file. + + Style-blacklisted `.rcParams` (defined in + ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated. + + Parameters + ---------- + fname : str or path-like + A file with Matplotlib rc settings. + + use_default_template : bool + If True, initialize with default parameters before updating with those + in the given file. If False, the current configuration persists + and only the parameters specified in the file are updated. + """ + # Deprecation warnings were already handled in rc_params_from_file, no need + # to reemit them here. + with _api.suppress_matplotlib_deprecation_warning(): + from .style.core import STYLE_BLACKLIST + rc_from_file = rc_params_from_file( + fname, use_default_template=use_default_template) + rcParams.update({k: rc_from_file[k] for k in rc_from_file + if k not in STYLE_BLACKLIST}) + + +@contextlib.contextmanager +def rc_context(rc=None, fname=None): + """ + Return a context manager for temporarily changing rcParams. + + The :rc:`backend` will not be reset by the context manager. + + rcParams changed both through the context manager invocation and + in the body of the context will be reset on context exit. + + Parameters + ---------- + rc : dict + The rcParams to temporarily set. + fname : str or path-like + A file with Matplotlib rc settings. If both *fname* and *rc* are given, + settings from *rc* take precedence. + + See Also + -------- + :ref:`customizing-with-matplotlibrc-files` + + Examples + -------- + Passing explicit values via a dict:: + + with mpl.rc_context({'interactive': False}): + fig, ax = plt.subplots() + ax.plot(range(3), range(3)) + fig.savefig('example.png') + plt.close(fig) + + Loading settings from a file:: + + with mpl.rc_context(fname='print.rc'): + plt.plot(x, y) # uses 'print.rc' + + Setting in the context body:: + + with mpl.rc_context(): + # will be reset + mpl.rcParams['lines.linewidth'] = 5 + plt.plot(x, y) + + """ + orig = dict(rcParams.copy()) + del orig['backend'] + try: + if fname: + rc_file(fname) + if rc: + rcParams.update(rc) + yield + finally: + rcParams._update_raw(orig) # Revert to the original rcs. + + +def use(backend, *, force=True): + """ + Select the backend used for rendering and GUI integration. + + If pyplot is already imported, `~matplotlib.pyplot.switch_backend` is used + and if the new backend is different than the current backend, all Figures + will be closed. + + Parameters + ---------- + backend : str + The backend to switch to. This can either be one of the standard + backend names, which are case-insensitive: + + - interactive backends: + GTK3Agg, GTK3Cairo, GTK4Agg, GTK4Cairo, MacOSX, nbAgg, notebook, QtAgg, + QtCairo, TkAgg, TkCairo, WebAgg, WX, WXAgg, WXCairo, Qt5Agg, Qt5Cairo + + - non-interactive backends: + agg, cairo, pdf, pgf, ps, svg, template + + or a string of the form: ``module://my.module.name``. + + notebook is a synonym for nbAgg. + + Switching to an interactive backend is not possible if an unrelated + event loop has already been started (e.g., switching to GTK3Agg if a + TkAgg window has already been opened). Switching to a non-interactive + backend is always possible. + + force : bool, default: True + If True (the default), raise an `ImportError` if the backend cannot be + set up (either because it fails to import, or because an incompatible + GUI interactive framework is already running); if False, silently + ignore the failure. + + See Also + -------- + :ref:`backends` + matplotlib.get_backend + matplotlib.pyplot.switch_backend + + """ + name = rcsetup.validate_backend(backend) + # don't (prematurely) resolve the "auto" backend setting + if rcParams._get_backend_or_none() == name: + # Nothing to do if the requested backend is already set + pass + else: + # if pyplot is not already imported, do not import it. Doing + # so may trigger a `plt.switch_backend` to the _default_ backend + # before we get a chance to change to the one the user just requested + plt = sys.modules.get('matplotlib.pyplot') + # if pyplot is imported, then try to change backends + if plt is not None: + try: + # we need this import check here to re-raise if the + # user does not have the libraries to support their + # chosen backend installed. + plt.switch_backend(name) + except ImportError: + if force: + raise + # if we have not imported pyplot, then we can set the rcParam + # value which will be respected when the user finally imports + # pyplot + else: + rcParams['backend'] = backend + # if the user has asked for a given backend, do not helpfully + # fallback + rcParams['backend_fallback'] = False + + +if os.environ.get('MPLBACKEND'): + rcParams['backend'] = os.environ.get('MPLBACKEND') + + +def get_backend(*, auto_select=True): + """ + Return the name of the current backend. + + Parameters + ---------- + auto_select : bool, default: True + Whether to trigger backend resolution if no backend has been + selected so far. If True, this ensures that a valid backend + is returned. If False, this returns None if no backend has been + selected so far. + + .. versionadded:: 3.10 + + .. admonition:: Provisional + + The *auto_select* flag is provisional. It may be changed or removed + without prior warning. + + See Also + -------- + matplotlib.use + """ + if auto_select: + return rcParams['backend'] + else: + backend = rcParams._get('backend') + if backend is rcsetup._auto_backend_sentinel: + return None + else: + return backend + + +def interactive(b): + """ + Set whether to redraw after every plotting command (e.g. `.pyplot.xlabel`). + """ + rcParams['interactive'] = b + + +def is_interactive(): + """ + Return whether to redraw after every plotting command. + + .. note:: + + This function is only intended for use in backends. End users should + use `.pyplot.isinteractive` instead. + """ + return rcParams['interactive'] + + +def _val_or_rc(val, rc_name): + """ + If *val* is None, return ``mpl.rcParams[rc_name]``, otherwise return val. + """ + return val if val is not None else rcParams[rc_name] + + +def _init_tests(): + # The version of FreeType to install locally for running the tests. This must match + # the value in `meson.build`. + LOCAL_FREETYPE_VERSION = '2.6.1' + + from matplotlib import ft2font + if (ft2font.__freetype_version__ != LOCAL_FREETYPE_VERSION or + ft2font.__freetype_build_type__ != 'local'): + _log.warning( + "Matplotlib is not built with the correct FreeType version to run tests. " + "Rebuild without setting system-freetype=true in Meson setup options. " + "Expect many image comparison failures below. " + "Expected freetype version %s. " + "Found freetype version %s. " + "Freetype build type is %slocal.", + LOCAL_FREETYPE_VERSION, + ft2font.__freetype_version__, + "" if ft2font.__freetype_build_type__ == 'local' else "not ") + + +def _replacer(data, value): + """ + Either returns ``data[value]`` or passes ``data`` back, converts either to + a sequence. + """ + try: + # if key isn't a string don't bother + if isinstance(value, str): + # try to use __getitem__ + value = data[value] + except Exception: + # key does not exist, silently fall back to key + pass + return cbook.sanitize_sequence(value) + + +def _label_from_arg(y, default_name): + try: + return y.name + except AttributeError: + if isinstance(default_name, str): + return default_name + return None + + +def _add_data_doc(docstring, replace_names): + """ + Add documentation for a *data* field to the given docstring. + + Parameters + ---------- + docstring : str + The input docstring. + replace_names : list of str or None + The list of parameter names which arguments should be replaced by + ``data[name]`` (if ``data[name]`` does not throw an exception). If + None, replacement is attempted for all arguments. + + Returns + ------- + str + The augmented docstring. + """ + if (docstring is None + or replace_names is not None and len(replace_names) == 0): + return docstring + docstring = inspect.cleandoc(docstring) + + data_doc = ("""\ + If given, all parameters also accept a string ``s``, which is + interpreted as ``data[s]`` if ``s`` is a key in ``data``.""" + if replace_names is None else f"""\ + If given, the following parameters also accept a string ``s``, which is + interpreted as ``data[s]`` if ``s`` is a key in ``data``: + + {', '.join(map('*{}*'.format, replace_names))}""") + # using string replacement instead of formatting has the advantages + # 1) simpler indent handling + # 2) prevent problems with formatting characters '{', '%' in the docstring + if _log.level <= logging.DEBUG: + # test_data_parameter_replacement() tests against these log messages + # make sure to keep message and test in sync + if "data : indexable object, optional" not in docstring: + _log.debug("data parameter docstring error: no data parameter") + if 'DATA_PARAMETER_PLACEHOLDER' not in docstring: + _log.debug("data parameter docstring error: missing placeholder") + return docstring.replace(' DATA_PARAMETER_PLACEHOLDER', data_doc) + + +def _preprocess_data(func=None, *, replace_names=None, label_namer=None): + """ + A decorator to add a 'data' kwarg to a function. + + When applied:: + + @_preprocess_data() + def func(ax, *args, **kwargs): ... + + the signature is modified to ``decorated(ax, *args, data=None, **kwargs)`` + with the following behavior: + + - if called with ``data=None``, forward the other arguments to ``func``; + - otherwise, *data* must be a mapping; for any argument passed in as a + string ``name``, replace the argument by ``data[name]`` (if this does not + throw an exception), then forward the arguments to ``func``. + + In either case, any argument that is a `MappingView` is also converted to a + list. + + Parameters + ---------- + replace_names : list of str or None, default: None + The list of parameter names for which lookup into *data* should be + attempted. If None, replacement is attempted for all arguments. + label_namer : str, default: None + If set e.g. to "namer" (which must be a kwarg in the function's + signature -- not as ``**kwargs``), if the *namer* argument passed in is + a (string) key of *data* and no *label* kwarg is passed, then use the + (string) value of the *namer* as *label*. :: + + @_preprocess_data(label_namer="foo") + def func(foo, label=None): ... + + func("key", data={"key": value}) + # is equivalent to + func.__wrapped__(value, label="key") + """ + + if func is None: # Return the actual decorator. + return functools.partial( + _preprocess_data, + replace_names=replace_names, label_namer=label_namer) + + sig = inspect.signature(func) + varargs_name = None + varkwargs_name = None + arg_names = [] + params = list(sig.parameters.values()) + for p in params: + if p.kind is Parameter.VAR_POSITIONAL: + varargs_name = p.name + elif p.kind is Parameter.VAR_KEYWORD: + varkwargs_name = p.name + else: + arg_names.append(p.name) + data_param = Parameter("data", Parameter.KEYWORD_ONLY, default=None) + if varkwargs_name: + params.insert(-1, data_param) + else: + params.append(data_param) + new_sig = sig.replace(parameters=params) + arg_names = arg_names[1:] # remove the first "ax" / self arg + + assert {*arg_names}.issuperset(replace_names or []) or varkwargs_name, ( + "Matplotlib internal error: invalid replace_names " + f"({replace_names!r}) for {func.__name__!r}") + assert label_namer is None or label_namer in arg_names, ( + "Matplotlib internal error: invalid label_namer " + f"({label_namer!r}) for {func.__name__!r}") + + @functools.wraps(func) + def inner(ax, *args, data=None, **kwargs): + if data is None: + return func( + ax, + *map(cbook.sanitize_sequence, args), + **{k: cbook.sanitize_sequence(v) for k, v in kwargs.items()}) + + bound = new_sig.bind(ax, *args, **kwargs) + auto_label = (bound.arguments.get(label_namer) + or bound.kwargs.get(label_namer)) + + for k, v in bound.arguments.items(): + if k == varkwargs_name: + for k1, v1 in v.items(): + if replace_names is None or k1 in replace_names: + v[k1] = _replacer(data, v1) + elif k == varargs_name: + if replace_names is None: + bound.arguments[k] = tuple(_replacer(data, v1) for v1 in v) + else: + if replace_names is None or k in replace_names: + bound.arguments[k] = _replacer(data, v) + + new_args = bound.args + new_kwargs = bound.kwargs + + args_and_kwargs = {**bound.arguments, **bound.kwargs} + if label_namer and "label" not in args_and_kwargs: + new_kwargs["label"] = _label_from_arg( + args_and_kwargs.get(label_namer), auto_label) + + return func(*new_args, **new_kwargs) + + inner.__doc__ = _add_data_doc(inner.__doc__, replace_names) + inner.__signature__ = new_sig + return inner + + +_log.debug('interactive is %s', is_interactive()) +_log.debug('platform is %s', sys.platform) + + +@_api.deprecated("3.10", alternative="matplotlib.cbook.sanitize_sequence") +def sanitize_sequence(data): + return cbook.sanitize_sequence(data) + + +@_api.deprecated("3.10", alternative="matplotlib.rcsetup.validate_backend") +def validate_backend(s): + return rcsetup.validate_backend(s) + + +# workaround: we must defer colormaps import to after loading rcParams, because +# colormap creation depends on rcParams +from matplotlib.cm import _colormaps as colormaps # noqa: E402 +from matplotlib.cm import _multivar_colormaps as multivar_colormaps # noqa: E402 +from matplotlib.cm import _bivar_colormaps as bivar_colormaps # noqa: E402 +from matplotlib.colors import _color_sequences as color_sequences # noqa: E402 diff --git a/.venv/lib/python3.12/site-packages/matplotlib/__init__.pyi b/.venv/lib/python3.12/site-packages/matplotlib/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..88058ffd7def28740b4942d3851a5f43e49b086a --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/__init__.pyi @@ -0,0 +1,124 @@ +__all__ = [ + "__bibtex__", + "__version__", + "__version_info__", + "set_loglevel", + "ExecutableNotFoundError", + "get_configdir", + "get_cachedir", + "get_data_path", + "matplotlib_fname", + "MatplotlibDeprecationWarning", + "RcParams", + "rc_params", + "rc_params_from_file", + "rcParamsDefault", + "rcParams", + "rcParamsOrig", + "defaultParams", + "rc", + "rcdefaults", + "rc_file_defaults", + "rc_file", + "rc_context", + "use", + "get_backend", + "interactive", + "is_interactive", + "colormaps", + "color_sequences", +] + +import os +from pathlib import Path + +from collections.abc import Callable, Generator +import contextlib +from packaging.version import Version + +from matplotlib._api import MatplotlibDeprecationWarning +from typing import Any, Literal, NamedTuple, overload + +class _VersionInfo(NamedTuple): + major: int + minor: int + micro: int + releaselevel: str + serial: int + +__bibtex__: str +__version__: str +__version_info__: _VersionInfo + +def set_loglevel(level: str) -> None: ... + +class _ExecInfo(NamedTuple): + executable: str + raw_version: str + version: Version + +class ExecutableNotFoundError(FileNotFoundError): ... + +def _get_executable_info(name: str) -> _ExecInfo: ... +def get_configdir() -> str: ... +def get_cachedir() -> str: ... +def get_data_path() -> str: ... +def matplotlib_fname() -> str: ... + +class RcParams(dict[str, Any]): + validate: dict[str, Callable] + def __init__(self, *args, **kwargs) -> None: ... + def _set(self, key: str, val: Any) -> None: ... + def _get(self, key: str) -> Any: ... + + def _update_raw(self, other_params: dict | RcParams) -> None: ... + + def _ensure_has_backend(self) -> None: ... + def __setitem__(self, key: str, val: Any) -> None: ... + def __getitem__(self, key: str) -> Any: ... + def __iter__(self) -> Generator[str, None, None]: ... + def __len__(self) -> int: ... + def find_all(self, pattern: str) -> RcParams: ... + def copy(self) -> RcParams: ... + +def rc_params(fail_on_error: bool = ...) -> RcParams: ... +def rc_params_from_file( + fname: str | Path | os.PathLike, + fail_on_error: bool = ..., + use_default_template: bool = ..., +) -> RcParams: ... + +rcParamsDefault: RcParams +rcParams: RcParams +rcParamsOrig: RcParams +defaultParams: dict[str, Any] + +def rc(group: str, **kwargs) -> None: ... +def rcdefaults() -> None: ... +def rc_file_defaults() -> None: ... +def rc_file( + fname: str | Path | os.PathLike, *, use_default_template: bool = ... +) -> None: ... +@contextlib.contextmanager +def rc_context( + rc: dict[str, Any] | None = ..., fname: str | Path | os.PathLike | None = ... +) -> Generator[None, None, None]: ... +def use(backend: str, *, force: bool = ...) -> None: ... +@overload +def get_backend(*, auto_select: Literal[True] = True) -> str: ... +@overload +def get_backend(*, auto_select: Literal[False]) -> str | None: ... +def interactive(b: bool) -> None: ... +def is_interactive() -> bool: ... + +def _preprocess_data( + func: Callable | None = ..., + *, + replace_names: list[str] | None = ..., + label_namer: str | None = ... +) -> Callable: ... + +from matplotlib.cm import _colormaps as colormaps # noqa: E402 +from matplotlib.cm import _multivar_colormaps as multivar_colormaps # noqa: E402 +from matplotlib.cm import _bivar_colormaps as bivar_colormaps # noqa: E402 +from matplotlib.colors import _color_sequences as color_sequences # noqa: E402 diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_afm.py b/.venv/lib/python3.12/site-packages/matplotlib/_afm.py new file mode 100644 index 0000000000000000000000000000000000000000..558efe16392f7d386f642c86a8b028d10a778f25 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_afm.py @@ -0,0 +1,532 @@ +""" +A python interface to Adobe Font Metrics Files. + +Although a number of other Python implementations exist, and may be more +complete than this, it was decided not to go with them because they were +either: + +1) copyrighted or used a non-BSD compatible license +2) had too many dependencies and a free standing lib was needed +3) did more than needed and it was easier to write afresh rather than + figure out how to get just what was needed. + +It is pretty easy to use, and has no external dependencies: + +>>> import matplotlib as mpl +>>> from pathlib import Path +>>> afm_path = Path(mpl.get_data_path(), 'fonts', 'afm', 'ptmr8a.afm') +>>> +>>> from matplotlib.afm import AFM +>>> with afm_path.open('rb') as fh: +... afm = AFM(fh) +>>> afm.string_width_height('What the heck?') +(6220.0, 694) +>>> afm.get_fontname() +'Times-Roman' +>>> afm.get_kern_dist('A', 'f') +0 +>>> afm.get_kern_dist('A', 'y') +-92.0 +>>> afm.get_bbox_char('!') +[130, -9, 238, 676] + +As in the Adobe Font Metrics File Format Specification, all dimensions +are given in units of 1/1000 of the scale factor (point size) of the font +being used. +""" + +from collections import namedtuple +import logging +import re + +from ._mathtext_data import uni2type1 + + +_log = logging.getLogger(__name__) + + +def _to_int(x): + # Some AFM files have floats where we are expecting ints -- there is + # probably a better way to handle this (support floats, round rather than + # truncate). But I don't know what the best approach is now and this + # change to _to_int should at least prevent Matplotlib from crashing on + # these. JDH (2009-11-06) + return int(float(x)) + + +def _to_float(x): + # Some AFM files use "," instead of "." as decimal separator -- this + # shouldn't be ambiguous (unless someone is wicked enough to use "," as + # thousands separator...). + if isinstance(x, bytes): + # Encoding doesn't really matter -- if we have codepoints >127 the call + # to float() will error anyways. + x = x.decode('latin-1') + return float(x.replace(',', '.')) + + +def _to_str(x): + return x.decode('utf8') + + +def _to_list_of_ints(s): + s = s.replace(b',', b' ') + return [_to_int(val) for val in s.split()] + + +def _to_list_of_floats(s): + return [_to_float(val) for val in s.split()] + + +def _to_bool(s): + if s.lower().strip() in (b'false', b'0', b'no'): + return False + else: + return True + + +def _parse_header(fh): + """ + Read the font metrics header (up to the char metrics) and returns + a dictionary mapping *key* to *val*. *val* will be converted to the + appropriate python type as necessary; e.g.: + + * 'False'->False + * '0'->0 + * '-168 -218 1000 898'-> [-168, -218, 1000, 898] + + Dictionary keys are + + StartFontMetrics, FontName, FullName, FamilyName, Weight, + ItalicAngle, IsFixedPitch, FontBBox, UnderlinePosition, + UnderlineThickness, Version, Notice, EncodingScheme, CapHeight, + XHeight, Ascender, Descender, StartCharMetrics + """ + header_converters = { + b'StartFontMetrics': _to_float, + b'FontName': _to_str, + b'FullName': _to_str, + b'FamilyName': _to_str, + b'Weight': _to_str, + b'ItalicAngle': _to_float, + b'IsFixedPitch': _to_bool, + b'FontBBox': _to_list_of_ints, + b'UnderlinePosition': _to_float, + b'UnderlineThickness': _to_float, + b'Version': _to_str, + # Some AFM files have non-ASCII characters (which are not allowed by + # the spec). Given that there is actually no public API to even access + # this field, just return it as straight bytes. + b'Notice': lambda x: x, + b'EncodingScheme': _to_str, + b'CapHeight': _to_float, # Is the second version a mistake, or + b'Capheight': _to_float, # do some AFM files contain 'Capheight'? -JKS + b'XHeight': _to_float, + b'Ascender': _to_float, + b'Descender': _to_float, + b'StdHW': _to_float, + b'StdVW': _to_float, + b'StartCharMetrics': _to_int, + b'CharacterSet': _to_str, + b'Characters': _to_int, + } + d = {} + first_line = True + for line in fh: + line = line.rstrip() + if line.startswith(b'Comment'): + continue + lst = line.split(b' ', 1) + key = lst[0] + if first_line: + # AFM spec, Section 4: The StartFontMetrics keyword + # [followed by a version number] must be the first line in + # the file, and the EndFontMetrics keyword must be the + # last non-empty line in the file. We just check the + # first header entry. + if key != b'StartFontMetrics': + raise RuntimeError('Not an AFM file') + first_line = False + if len(lst) == 2: + val = lst[1] + else: + val = b'' + try: + converter = header_converters[key] + except KeyError: + _log.error("Found an unknown keyword in AFM header (was %r)", key) + continue + try: + d[key] = converter(val) + except ValueError: + _log.error('Value error parsing header in AFM: %s, %s', key, val) + continue + if key == b'StartCharMetrics': + break + else: + raise RuntimeError('Bad parse') + return d + + +CharMetrics = namedtuple('CharMetrics', 'width, name, bbox') +CharMetrics.__doc__ = """ + Represents the character metrics of a single character. + + Notes + ----- + The fields do currently only describe a subset of character metrics + information defined in the AFM standard. + """ +CharMetrics.width.__doc__ = """The character width (WX).""" +CharMetrics.name.__doc__ = """The character name (N).""" +CharMetrics.bbox.__doc__ = """ + The bbox of the character (B) as a tuple (*llx*, *lly*, *urx*, *ury*).""" + + +def _parse_char_metrics(fh): + """ + Parse the given filehandle for character metrics information and return + the information as dicts. + + It is assumed that the file cursor is on the line behind + 'StartCharMetrics'. + + Returns + ------- + ascii_d : dict + A mapping "ASCII num of the character" to `.CharMetrics`. + name_d : dict + A mapping "character name" to `.CharMetrics`. + + Notes + ----- + This function is incomplete per the standard, but thus far parses + all the sample afm files tried. + """ + required_keys = {'C', 'WX', 'N', 'B'} + + ascii_d = {} + name_d = {} + for line in fh: + # We are defensively letting values be utf8. The spec requires + # ascii, but there are non-compliant fonts in circulation + line = _to_str(line.rstrip()) # Convert from byte-literal + if line.startswith('EndCharMetrics'): + return ascii_d, name_d + # Split the metric line into a dictionary, keyed by metric identifiers + vals = dict(s.strip().split(' ', 1) for s in line.split(';') if s) + # There may be other metrics present, but only these are needed + if not required_keys.issubset(vals): + raise RuntimeError('Bad char metrics line: %s' % line) + num = _to_int(vals['C']) + wx = _to_float(vals['WX']) + name = vals['N'] + bbox = _to_list_of_floats(vals['B']) + bbox = list(map(int, bbox)) + metrics = CharMetrics(wx, name, bbox) + # Workaround: If the character name is 'Euro', give it the + # corresponding character code, according to WinAnsiEncoding (see PDF + # Reference). + if name == 'Euro': + num = 128 + elif name == 'minus': + num = ord("\N{MINUS SIGN}") # 0x2212 + if num != -1: + ascii_d[num] = metrics + name_d[name] = metrics + raise RuntimeError('Bad parse') + + +def _parse_kern_pairs(fh): + """ + Return a kern pairs dictionary; keys are (*char1*, *char2*) tuples and + values are the kern pair value. For example, a kern pairs line like + ``KPX A y -50`` + + will be represented as:: + + d[ ('A', 'y') ] = -50 + + """ + + line = next(fh) + if not line.startswith(b'StartKernPairs'): + raise RuntimeError('Bad start of kern pairs data: %s' % line) + + d = {} + for line in fh: + line = line.rstrip() + if not line: + continue + if line.startswith(b'EndKernPairs'): + next(fh) # EndKernData + return d + vals = line.split() + if len(vals) != 4 or vals[0] != b'KPX': + raise RuntimeError('Bad kern pairs line: %s' % line) + c1, c2, val = _to_str(vals[1]), _to_str(vals[2]), _to_float(vals[3]) + d[(c1, c2)] = val + raise RuntimeError('Bad kern pairs parse') + + +CompositePart = namedtuple('CompositePart', 'name, dx, dy') +CompositePart.__doc__ = """ + Represents the information on a composite element of a composite char.""" +CompositePart.name.__doc__ = """Name of the part, e.g. 'acute'.""" +CompositePart.dx.__doc__ = """x-displacement of the part from the origin.""" +CompositePart.dy.__doc__ = """y-displacement of the part from the origin.""" + + +def _parse_composites(fh): + """ + Parse the given filehandle for composites information return them as a + dict. + + It is assumed that the file cursor is on the line behind 'StartComposites'. + + Returns + ------- + dict + A dict mapping composite character names to a parts list. The parts + list is a list of `.CompositePart` entries describing the parts of + the composite. + + Examples + -------- + A composite definition line:: + + CC Aacute 2 ; PCC A 0 0 ; PCC acute 160 170 ; + + will be represented as:: + + composites['Aacute'] = [CompositePart(name='A', dx=0, dy=0), + CompositePart(name='acute', dx=160, dy=170)] + + """ + composites = {} + for line in fh: + line = line.rstrip() + if not line: + continue + if line.startswith(b'EndComposites'): + return composites + vals = line.split(b';') + cc = vals[0].split() + name, _num_parts = cc[1], _to_int(cc[2]) + pccParts = [] + for s in vals[1:-1]: + pcc = s.split() + part = CompositePart(pcc[1], _to_float(pcc[2]), _to_float(pcc[3])) + pccParts.append(part) + composites[name] = pccParts + + raise RuntimeError('Bad composites parse') + + +def _parse_optional(fh): + """ + Parse the optional fields for kern pair data and composites. + + Returns + ------- + kern_data : dict + A dict containing kerning information. May be empty. + See `._parse_kern_pairs`. + composites : dict + A dict containing composite information. May be empty. + See `._parse_composites`. + """ + optional = { + b'StartKernData': _parse_kern_pairs, + b'StartComposites': _parse_composites, + } + + d = {b'StartKernData': {}, + b'StartComposites': {}} + for line in fh: + line = line.rstrip() + if not line: + continue + key = line.split()[0] + + if key in optional: + d[key] = optional[key](fh) + + return d[b'StartKernData'], d[b'StartComposites'] + + +class AFM: + + def __init__(self, fh): + """Parse the AFM file in file object *fh*.""" + self._header = _parse_header(fh) + self._metrics, self._metrics_by_name = _parse_char_metrics(fh) + self._kern, self._composite = _parse_optional(fh) + + def get_bbox_char(self, c, isord=False): + if not isord: + c = ord(c) + return self._metrics[c].bbox + + def string_width_height(self, s): + """ + Return the string width (including kerning) and string height + as a (*w*, *h*) tuple. + """ + if not len(s): + return 0, 0 + total_width = 0 + namelast = None + miny = 1e9 + maxy = 0 + for c in s: + if c == '\n': + continue + wx, name, bbox = self._metrics[ord(c)] + + total_width += wx + self._kern.get((namelast, name), 0) + l, b, w, h = bbox + miny = min(miny, b) + maxy = max(maxy, b + h) + + namelast = name + + return total_width, maxy - miny + + def get_str_bbox_and_descent(self, s): + """Return the string bounding box and the maximal descent.""" + if not len(s): + return 0, 0, 0, 0, 0 + total_width = 0 + namelast = None + miny = 1e9 + maxy = 0 + left = 0 + if not isinstance(s, str): + s = _to_str(s) + for c in s: + if c == '\n': + continue + name = uni2type1.get(ord(c), f"uni{ord(c):04X}") + try: + wx, _, bbox = self._metrics_by_name[name] + except KeyError: + name = 'question' + wx, _, bbox = self._metrics_by_name[name] + total_width += wx + self._kern.get((namelast, name), 0) + l, b, w, h = bbox + left = min(left, l) + miny = min(miny, b) + maxy = max(maxy, b + h) + + namelast = name + + return left, miny, total_width, maxy - miny, -miny + + def get_str_bbox(self, s): + """Return the string bounding box.""" + return self.get_str_bbox_and_descent(s)[:4] + + def get_name_char(self, c, isord=False): + """Get the name of the character, i.e., ';' is 'semicolon'.""" + if not isord: + c = ord(c) + return self._metrics[c].name + + def get_width_char(self, c, isord=False): + """ + Get the width of the character from the character metric WX field. + """ + if not isord: + c = ord(c) + return self._metrics[c].width + + def get_width_from_char_name(self, name): + """Get the width of the character from a type1 character name.""" + return self._metrics_by_name[name].width + + def get_height_char(self, c, isord=False): + """Get the bounding box (ink) height of character *c* (space is 0).""" + if not isord: + c = ord(c) + return self._metrics[c].bbox[-1] + + def get_kern_dist(self, c1, c2): + """ + Return the kerning pair distance (possibly 0) for chars *c1* and *c2*. + """ + name1, name2 = self.get_name_char(c1), self.get_name_char(c2) + return self.get_kern_dist_from_name(name1, name2) + + def get_kern_dist_from_name(self, name1, name2): + """ + Return the kerning pair distance (possibly 0) for chars + *name1* and *name2*. + """ + return self._kern.get((name1, name2), 0) + + def get_fontname(self): + """Return the font name, e.g., 'Times-Roman'.""" + return self._header[b'FontName'] + + @property + def postscript_name(self): # For consistency with FT2Font. + return self.get_fontname() + + def get_fullname(self): + """Return the font full name, e.g., 'Times-Roman'.""" + name = self._header.get(b'FullName') + if name is None: # use FontName as a substitute + name = self._header[b'FontName'] + return name + + def get_familyname(self): + """Return the font family name, e.g., 'Times'.""" + name = self._header.get(b'FamilyName') + if name is not None: + return name + + # FamilyName not specified so we'll make a guess + name = self.get_fullname() + extras = (r'(?i)([ -](regular|plain|italic|oblique|bold|semibold|' + r'light|ultralight|extra|condensed))+$') + return re.sub(extras, '', name) + + @property + def family_name(self): + """The font family name, e.g., 'Times'.""" + return self.get_familyname() + + def get_weight(self): + """Return the font weight, e.g., 'Bold' or 'Roman'.""" + return self._header[b'Weight'] + + def get_angle(self): + """Return the fontangle as float.""" + return self._header[b'ItalicAngle'] + + def get_capheight(self): + """Return the cap height as float.""" + return self._header[b'CapHeight'] + + def get_xheight(self): + """Return the xheight as float.""" + return self._header[b'XHeight'] + + def get_underline_thickness(self): + """Return the underline thickness as float.""" + return self._header[b'UnderlineThickness'] + + def get_horizontal_stem_width(self): + """ + Return the standard horizontal stem width as float, or *None* if + not specified in AFM file. + """ + return self._header.get(b'StdHW', None) + + def get_vertical_stem_width(self): + """ + Return the standard vertical stem width as float, or *None* if + not specified in AFM file. + """ + return self._header.get(b'StdVW', None) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_animation_data.py b/.venv/lib/python3.12/site-packages/matplotlib/_animation_data.py new file mode 100644 index 0000000000000000000000000000000000000000..8cbd312d8f142d8b4ec3a3ad3e005a14f380ec13 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_animation_data.py @@ -0,0 +1,262 @@ +# JavaScript template for HTMLWriter +JS_INCLUDE = """ + + +""" + + +# Style definitions for the HTML template +STYLE_INCLUDE = """ + +""" + + +# HTML template for HTMLWriter +DISPLAY_TEMPLATE = """ +
+ +
+ +
+ + + + + + + + + +
+
+ + + + + + +
+
+
+ + + +""" # noqa: E501 + + +INCLUDED_FRAMES = """ + for (var i=0; i<{Nframes}; i++){{ + frames[i] = "{frame_dir}/frame" + ("0000000" + i).slice(-7) + + ".{frame_format}"; + }} +""" diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_blocking_input.py b/.venv/lib/python3.12/site-packages/matplotlib/_blocking_input.py new file mode 100644 index 0000000000000000000000000000000000000000..45f0775714431e73c283fede1f0cf12d7eaabb8d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_blocking_input.py @@ -0,0 +1,30 @@ +def blocking_input_loop(figure, event_names, timeout, handler): + """ + Run *figure*'s event loop while listening to interactive events. + + The events listed in *event_names* are passed to *handler*. + + This function is used to implement `.Figure.waitforbuttonpress`, + `.Figure.ginput`, and `.Axes.clabel`. + + Parameters + ---------- + figure : `~matplotlib.figure.Figure` + event_names : list of str + The names of the events passed to *handler*. + timeout : float + If positive, the event loop is stopped after *timeout* seconds. + handler : Callable[[Event], Any] + Function called for each event; it can force an early exit of the event + loop by calling ``canvas.stop_event_loop()``. + """ + if figure.canvas.manager: + figure.show() # Ensure that the figure is shown if we are managing it. + # Connect the events to the on_event function call. + cids = [figure.canvas.mpl_connect(name, handler) for name in event_names] + try: + figure.canvas.start_event_loop(timeout) # Start event loop. + finally: # Run even on exception like ctrl-c. + # Disconnect the callbacks. + for cid in cids: + figure.canvas.mpl_disconnect(cid) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_c_internal_utils.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_c_internal_utils.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ccc172cde27a35ad930cc618b86fdf5c8d44728e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_c_internal_utils.pyi @@ -0,0 +1,8 @@ +def display_is_valid() -> bool: ... +def xdisplay_is_valid() -> bool: ... + +def Win32_GetForegroundWindow() -> int | None: ... +def Win32_SetForegroundWindow(hwnd: int) -> None: ... +def Win32_SetProcessDpiAwareness_max() -> None: ... +def Win32_SetCurrentProcessExplicitAppUserModelID(appid: str) -> None: ... +def Win32_GetCurrentProcessExplicitAppUserModelID() -> str | None: ... diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_cm.py b/.venv/lib/python3.12/site-packages/matplotlib/_cm.py new file mode 100644 index 0000000000000000000000000000000000000000..b942d1697934789f26c522b3e7592671540919a6 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_cm.py @@ -0,0 +1,1460 @@ +""" +Nothing here but dictionaries for generating LinearSegmentedColormaps, +and a dictionary of these dictionaries. + +Documentation for each is in pyplot.colormaps(). Please update this +with the purpose and type of your colormap if you add data for one here. +""" + +from functools import partial + +import numpy as np + +_binary_data = { + 'red': ((0., 1., 1.), (1., 0., 0.)), + 'green': ((0., 1., 1.), (1., 0., 0.)), + 'blue': ((0., 1., 1.), (1., 0., 0.)) + } + +_autumn_data = {'red': ((0., 1.0, 1.0), (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), (1.0, 1.0, 1.0)), + 'blue': ((0., 0., 0.), (1.0, 0., 0.))} + +_bone_data = {'red': ((0., 0., 0.), + (0.746032, 0.652778, 0.652778), + (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), + (0.365079, 0.319444, 0.319444), + (0.746032, 0.777778, 0.777778), + (1.0, 1.0, 1.0)), + 'blue': ((0., 0., 0.), + (0.365079, 0.444444, 0.444444), + (1.0, 1.0, 1.0))} + +_cool_data = {'red': ((0., 0., 0.), (1.0, 1.0, 1.0)), + 'green': ((0., 1., 1.), (1.0, 0., 0.)), + 'blue': ((0., 1., 1.), (1.0, 1., 1.))} + +_copper_data = {'red': ((0., 0., 0.), + (0.809524, 1.000000, 1.000000), + (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), + (1.0, 0.7812, 0.7812)), + 'blue': ((0., 0., 0.), + (1.0, 0.4975, 0.4975))} + +def _flag_red(x): return 0.75 * np.sin((x * 31.5 + 0.25) * np.pi) + 0.5 +def _flag_green(x): return np.sin(x * 31.5 * np.pi) +def _flag_blue(x): return 0.75 * np.sin((x * 31.5 - 0.25) * np.pi) + 0.5 +_flag_data = {'red': _flag_red, 'green': _flag_green, 'blue': _flag_blue} + +def _prism_red(x): return 0.75 * np.sin((x * 20.9 + 0.25) * np.pi) + 0.67 +def _prism_green(x): return 0.75 * np.sin((x * 20.9 - 0.25) * np.pi) + 0.33 +def _prism_blue(x): return -1.1 * np.sin((x * 20.9) * np.pi) +_prism_data = {'red': _prism_red, 'green': _prism_green, 'blue': _prism_blue} + +def _ch_helper(gamma, s, r, h, p0, p1, x): + """Helper function for generating picklable cubehelix colormaps.""" + # Apply gamma factor to emphasise low or high intensity values + xg = x ** gamma + # Calculate amplitude and angle of deviation from the black to white + # diagonal in the plane of constant perceived intensity. + a = h * xg * (1 - xg) / 2 + phi = 2 * np.pi * (s / 3 + r * x) + return xg + a * (p0 * np.cos(phi) + p1 * np.sin(phi)) + +def cubehelix(gamma=1.0, s=0.5, r=-1.5, h=1.0): + """ + Return custom data dictionary of (r, g, b) conversion functions, which can + be used with `.ColormapRegistry.register`, for the cubehelix color scheme. + + Unlike most other color schemes cubehelix was designed by D.A. Green to + be monotonically increasing in terms of perceived brightness. + Also, when printed on a black and white postscript printer, the scheme + results in a greyscale with monotonically increasing brightness. + This color scheme is named cubehelix because the (r, g, b) values produced + can be visualised as a squashed helix around the diagonal in the + (r, g, b) color cube. + + For a unit color cube (i.e. 3D coordinates for (r, g, b) each in the + range 0 to 1) the color scheme starts at (r, g, b) = (0, 0, 0), i.e. black, + and finishes at (r, g, b) = (1, 1, 1), i.e. white. For some fraction *x*, + between 0 and 1, the color is the corresponding grey value at that + fraction along the black to white diagonal (x, x, x) plus a color + element. This color element is calculated in a plane of constant + perceived intensity and controlled by the following parameters. + + Parameters + ---------- + gamma : float, default: 1 + Gamma factor emphasizing either low intensity values (gamma < 1), or + high intensity values (gamma > 1). + s : float, default: 0.5 (purple) + The starting color. + r : float, default: -1.5 + The number of r, g, b rotations in color that are made from the start + to the end of the color scheme. The default of -1.5 corresponds to -> + B -> G -> R -> B. + h : float, default: 1 + The hue, i.e. how saturated the colors are. If this parameter is zero + then the color scheme is purely a greyscale. + """ + return {'red': partial(_ch_helper, gamma, s, r, h, -0.14861, 1.78277), + 'green': partial(_ch_helper, gamma, s, r, h, -0.29227, -0.90649), + 'blue': partial(_ch_helper, gamma, s, r, h, 1.97294, 0.0)} + +_cubehelix_data = cubehelix() + +_bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0)) +_brg_data = ((0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (0.0, 1.0, 0.0)) + +# Gnuplot palette functions +def _g0(x): return 0 +def _g1(x): return 0.5 +def _g2(x): return 1 +def _g3(x): return x +def _g4(x): return x ** 2 +def _g5(x): return x ** 3 +def _g6(x): return x ** 4 +def _g7(x): return np.sqrt(x) +def _g8(x): return np.sqrt(np.sqrt(x)) +def _g9(x): return np.sin(x * np.pi / 2) +def _g10(x): return np.cos(x * np.pi / 2) +def _g11(x): return np.abs(x - 0.5) +def _g12(x): return (2 * x - 1) ** 2 +def _g13(x): return np.sin(x * np.pi) +def _g14(x): return np.abs(np.cos(x * np.pi)) +def _g15(x): return np.sin(x * 2 * np.pi) +def _g16(x): return np.cos(x * 2 * np.pi) +def _g17(x): return np.abs(np.sin(x * 2 * np.pi)) +def _g18(x): return np.abs(np.cos(x * 2 * np.pi)) +def _g19(x): return np.abs(np.sin(x * 4 * np.pi)) +def _g20(x): return np.abs(np.cos(x * 4 * np.pi)) +def _g21(x): return 3 * x +def _g22(x): return 3 * x - 1 +def _g23(x): return 3 * x - 2 +def _g24(x): return np.abs(3 * x - 1) +def _g25(x): return np.abs(3 * x - 2) +def _g26(x): return (3 * x - 1) / 2 +def _g27(x): return (3 * x - 2) / 2 +def _g28(x): return np.abs((3 * x - 1) / 2) +def _g29(x): return np.abs((3 * x - 2) / 2) +def _g30(x): return x / 0.32 - 0.78125 +def _g31(x): return 2 * x - 0.84 +def _g32(x): + ret = np.zeros(len(x)) + m = (x < 0.25) + ret[m] = 4 * x[m] + m = (x >= 0.25) & (x < 0.92) + ret[m] = -2 * x[m] + 1.84 + m = (x >= 0.92) + ret[m] = x[m] / 0.08 - 11.5 + return ret +def _g33(x): return np.abs(2 * x - 0.5) +def _g34(x): return 2 * x +def _g35(x): return 2 * x - 0.5 +def _g36(x): return 2 * x - 1 + +gfunc = {i: globals()[f"_g{i}"] for i in range(37)} + +_gnuplot_data = { + 'red': gfunc[7], + 'green': gfunc[5], + 'blue': gfunc[15], +} + +_gnuplot2_data = { + 'red': gfunc[30], + 'green': gfunc[31], + 'blue': gfunc[32], +} + +_ocean_data = { + 'red': gfunc[23], + 'green': gfunc[28], + 'blue': gfunc[3], +} + +_afmhot_data = { + 'red': gfunc[34], + 'green': gfunc[35], + 'blue': gfunc[36], +} + +_rainbow_data = { + 'red': gfunc[33], + 'green': gfunc[13], + 'blue': gfunc[10], +} + +_seismic_data = ( + (0.0, 0.0, 0.3), (0.0, 0.0, 1.0), + (1.0, 1.0, 1.0), (1.0, 0.0, 0.0), + (0.5, 0.0, 0.0)) + +_terrain_data = ( + (0.00, (0.2, 0.2, 0.6)), + (0.15, (0.0, 0.6, 1.0)), + (0.25, (0.0, 0.8, 0.4)), + (0.50, (1.0, 1.0, 0.6)), + (0.75, (0.5, 0.36, 0.33)), + (1.00, (1.0, 1.0, 1.0))) + +_gray_data = {'red': ((0., 0, 0), (1., 1, 1)), + 'green': ((0., 0, 0), (1., 1, 1)), + 'blue': ((0., 0, 0), (1., 1, 1))} + +_hot_data = {'red': ((0., 0.0416, 0.0416), + (0.365079, 1.000000, 1.000000), + (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), + (0.365079, 0.000000, 0.000000), + (0.746032, 1.000000, 1.000000), + (1.0, 1.0, 1.0)), + 'blue': ((0., 0., 0.), + (0.746032, 0.000000, 0.000000), + (1.0, 1.0, 1.0))} + +_hsv_data = {'red': ((0., 1., 1.), + (0.158730, 1.000000, 1.000000), + (0.174603, 0.968750, 0.968750), + (0.333333, 0.031250, 0.031250), + (0.349206, 0.000000, 0.000000), + (0.666667, 0.000000, 0.000000), + (0.682540, 0.031250, 0.031250), + (0.841270, 0.968750, 0.968750), + (0.857143, 1.000000, 1.000000), + (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), + (0.158730, 0.937500, 0.937500), + (0.174603, 1.000000, 1.000000), + (0.507937, 1.000000, 1.000000), + (0.666667, 0.062500, 0.062500), + (0.682540, 0.000000, 0.000000), + (1.0, 0., 0.)), + 'blue': ((0., 0., 0.), + (0.333333, 0.000000, 0.000000), + (0.349206, 0.062500, 0.062500), + (0.507937, 1.000000, 1.000000), + (0.841270, 1.000000, 1.000000), + (0.857143, 0.937500, 0.937500), + (1.0, 0.09375, 0.09375))} + +_jet_data = {'red': ((0.00, 0, 0), + (0.35, 0, 0), + (0.66, 1, 1), + (0.89, 1, 1), + (1.00, 0.5, 0.5)), + 'green': ((0.000, 0, 0), + (0.125, 0, 0), + (0.375, 1, 1), + (0.640, 1, 1), + (0.910, 0, 0), + (1.000, 0, 0)), + 'blue': ((0.00, 0.5, 0.5), + (0.11, 1, 1), + (0.34, 1, 1), + (0.65, 0, 0), + (1.00, 0, 0))} + +_pink_data = {'red': ((0., 0.1178, 0.1178), (0.015873, 0.195857, 0.195857), + (0.031746, 0.250661, 0.250661), + (0.047619, 0.295468, 0.295468), + (0.063492, 0.334324, 0.334324), + (0.079365, 0.369112, 0.369112), + (0.095238, 0.400892, 0.400892), + (0.111111, 0.430331, 0.430331), + (0.126984, 0.457882, 0.457882), + (0.142857, 0.483867, 0.483867), + (0.158730, 0.508525, 0.508525), + (0.174603, 0.532042, 0.532042), + (0.190476, 0.554563, 0.554563), + (0.206349, 0.576204, 0.576204), + (0.222222, 0.597061, 0.597061), + (0.238095, 0.617213, 0.617213), + (0.253968, 0.636729, 0.636729), + (0.269841, 0.655663, 0.655663), + (0.285714, 0.674066, 0.674066), + (0.301587, 0.691980, 0.691980), + (0.317460, 0.709441, 0.709441), + (0.333333, 0.726483, 0.726483), + (0.349206, 0.743134, 0.743134), + (0.365079, 0.759421, 0.759421), + (0.380952, 0.766356, 0.766356), + (0.396825, 0.773229, 0.773229), + (0.412698, 0.780042, 0.780042), + (0.428571, 0.786796, 0.786796), + (0.444444, 0.793492, 0.793492), + (0.460317, 0.800132, 0.800132), + (0.476190, 0.806718, 0.806718), + (0.492063, 0.813250, 0.813250), + (0.507937, 0.819730, 0.819730), + (0.523810, 0.826160, 0.826160), + (0.539683, 0.832539, 0.832539), + (0.555556, 0.838870, 0.838870), + (0.571429, 0.845154, 0.845154), + (0.587302, 0.851392, 0.851392), + (0.603175, 0.857584, 0.857584), + (0.619048, 0.863731, 0.863731), + (0.634921, 0.869835, 0.869835), + (0.650794, 0.875897, 0.875897), + (0.666667, 0.881917, 0.881917), + (0.682540, 0.887896, 0.887896), + (0.698413, 0.893835, 0.893835), + (0.714286, 0.899735, 0.899735), + (0.730159, 0.905597, 0.905597), + (0.746032, 0.911421, 0.911421), + (0.761905, 0.917208, 0.917208), + (0.777778, 0.922958, 0.922958), + (0.793651, 0.928673, 0.928673), + (0.809524, 0.934353, 0.934353), + (0.825397, 0.939999, 0.939999), + (0.841270, 0.945611, 0.945611), + (0.857143, 0.951190, 0.951190), + (0.873016, 0.956736, 0.956736), + (0.888889, 0.962250, 0.962250), + (0.904762, 0.967733, 0.967733), + (0.920635, 0.973185, 0.973185), + (0.936508, 0.978607, 0.978607), + (0.952381, 0.983999, 0.983999), + (0.968254, 0.989361, 0.989361), + (0.984127, 0.994695, 0.994695), (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), (0.015873, 0.102869, 0.102869), + (0.031746, 0.145479, 0.145479), + (0.047619, 0.178174, 0.178174), + (0.063492, 0.205738, 0.205738), + (0.079365, 0.230022, 0.230022), + (0.095238, 0.251976, 0.251976), + (0.111111, 0.272166, 0.272166), + (0.126984, 0.290957, 0.290957), + (0.142857, 0.308607, 0.308607), + (0.158730, 0.325300, 0.325300), + (0.174603, 0.341178, 0.341178), + (0.190476, 0.356348, 0.356348), + (0.206349, 0.370899, 0.370899), + (0.222222, 0.384900, 0.384900), + (0.238095, 0.398410, 0.398410), + (0.253968, 0.411476, 0.411476), + (0.269841, 0.424139, 0.424139), + (0.285714, 0.436436, 0.436436), + (0.301587, 0.448395, 0.448395), + (0.317460, 0.460044, 0.460044), + (0.333333, 0.471405, 0.471405), + (0.349206, 0.482498, 0.482498), + (0.365079, 0.493342, 0.493342), + (0.380952, 0.517549, 0.517549), + (0.396825, 0.540674, 0.540674), + (0.412698, 0.562849, 0.562849), + (0.428571, 0.584183, 0.584183), + (0.444444, 0.604765, 0.604765), + (0.460317, 0.624669, 0.624669), + (0.476190, 0.643958, 0.643958), + (0.492063, 0.662687, 0.662687), + (0.507937, 0.680900, 0.680900), + (0.523810, 0.698638, 0.698638), + (0.539683, 0.715937, 0.715937), + (0.555556, 0.732828, 0.732828), + (0.571429, 0.749338, 0.749338), + (0.587302, 0.765493, 0.765493), + (0.603175, 0.781313, 0.781313), + (0.619048, 0.796819, 0.796819), + (0.634921, 0.812029, 0.812029), + (0.650794, 0.826960, 0.826960), + (0.666667, 0.841625, 0.841625), + (0.682540, 0.856040, 0.856040), + (0.698413, 0.870216, 0.870216), + (0.714286, 0.884164, 0.884164), + (0.730159, 0.897896, 0.897896), + (0.746032, 0.911421, 0.911421), + (0.761905, 0.917208, 0.917208), + (0.777778, 0.922958, 0.922958), + (0.793651, 0.928673, 0.928673), + (0.809524, 0.934353, 0.934353), + (0.825397, 0.939999, 0.939999), + (0.841270, 0.945611, 0.945611), + (0.857143, 0.951190, 0.951190), + (0.873016, 0.956736, 0.956736), + (0.888889, 0.962250, 0.962250), + (0.904762, 0.967733, 0.967733), + (0.920635, 0.973185, 0.973185), + (0.936508, 0.978607, 0.978607), + (0.952381, 0.983999, 0.983999), + (0.968254, 0.989361, 0.989361), + (0.984127, 0.994695, 0.994695), (1.0, 1.0, 1.0)), + 'blue': ((0., 0., 0.), (0.015873, 0.102869, 0.102869), + (0.031746, 0.145479, 0.145479), + (0.047619, 0.178174, 0.178174), + (0.063492, 0.205738, 0.205738), + (0.079365, 0.230022, 0.230022), + (0.095238, 0.251976, 0.251976), + (0.111111, 0.272166, 0.272166), + (0.126984, 0.290957, 0.290957), + (0.142857, 0.308607, 0.308607), + (0.158730, 0.325300, 0.325300), + (0.174603, 0.341178, 0.341178), + (0.190476, 0.356348, 0.356348), + (0.206349, 0.370899, 0.370899), + (0.222222, 0.384900, 0.384900), + (0.238095, 0.398410, 0.398410), + (0.253968, 0.411476, 0.411476), + (0.269841, 0.424139, 0.424139), + (0.285714, 0.436436, 0.436436), + (0.301587, 0.448395, 0.448395), + (0.317460, 0.460044, 0.460044), + (0.333333, 0.471405, 0.471405), + (0.349206, 0.482498, 0.482498), + (0.365079, 0.493342, 0.493342), + (0.380952, 0.503953, 0.503953), + (0.396825, 0.514344, 0.514344), + (0.412698, 0.524531, 0.524531), + (0.428571, 0.534522, 0.534522), + (0.444444, 0.544331, 0.544331), + (0.460317, 0.553966, 0.553966), + (0.476190, 0.563436, 0.563436), + (0.492063, 0.572750, 0.572750), + (0.507937, 0.581914, 0.581914), + (0.523810, 0.590937, 0.590937), + (0.539683, 0.599824, 0.599824), + (0.555556, 0.608581, 0.608581), + (0.571429, 0.617213, 0.617213), + (0.587302, 0.625727, 0.625727), + (0.603175, 0.634126, 0.634126), + (0.619048, 0.642416, 0.642416), + (0.634921, 0.650600, 0.650600), + (0.650794, 0.658682, 0.658682), + (0.666667, 0.666667, 0.666667), + (0.682540, 0.674556, 0.674556), + (0.698413, 0.682355, 0.682355), + (0.714286, 0.690066, 0.690066), + (0.730159, 0.697691, 0.697691), + (0.746032, 0.705234, 0.705234), + (0.761905, 0.727166, 0.727166), + (0.777778, 0.748455, 0.748455), + (0.793651, 0.769156, 0.769156), + (0.809524, 0.789314, 0.789314), + (0.825397, 0.808969, 0.808969), + (0.841270, 0.828159, 0.828159), + (0.857143, 0.846913, 0.846913), + (0.873016, 0.865261, 0.865261), + (0.888889, 0.883229, 0.883229), + (0.904762, 0.900837, 0.900837), + (0.920635, 0.918109, 0.918109), + (0.936508, 0.935061, 0.935061), + (0.952381, 0.951711, 0.951711), + (0.968254, 0.968075, 0.968075), + (0.984127, 0.984167, 0.984167), (1.0, 1.0, 1.0))} + +_spring_data = {'red': ((0., 1., 1.), (1.0, 1.0, 1.0)), + 'green': ((0., 0., 0.), (1.0, 1.0, 1.0)), + 'blue': ((0., 1., 1.), (1.0, 0.0, 0.0))} + + +_summer_data = {'red': ((0., 0., 0.), (1.0, 1.0, 1.0)), + 'green': ((0., 0.5, 0.5), (1.0, 1.0, 1.0)), + 'blue': ((0., 0.4, 0.4), (1.0, 0.4, 0.4))} + + +_winter_data = {'red': ((0., 0., 0.), (1.0, 0.0, 0.0)), + 'green': ((0., 0., 0.), (1.0, 1.0, 1.0)), + 'blue': ((0., 1., 1.), (1.0, 0.5, 0.5))} + +_nipy_spectral_data = { + 'red': [ + (0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667), + (0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0), + (0.20, 0.0, 0.0), (0.25, 0.0, 0.0), + (0.30, 0.0, 0.0), (0.35, 0.0, 0.0), + (0.40, 0.0, 0.0), (0.45, 0.0, 0.0), + (0.50, 0.0, 0.0), (0.55, 0.0, 0.0), + (0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333), + (0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0), + (0.80, 1.0, 1.0), (0.85, 1.0, 1.0), + (0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80), + (1.0, 0.80, 0.80), + ], + 'green': [ + (0.0, 0.0, 0.0), (0.05, 0.0, 0.0), + (0.10, 0.0, 0.0), (0.15, 0.0, 0.0), + (0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667), + (0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667), + (0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000), + (0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667), + (0.60, 1.0, 1.0), (0.65, 1.0, 1.0), + (0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000), + (0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0), + (0.90, 0.0, 0.0), (0.95, 0.0, 0.0), + (1.0, 0.80, 0.80), + ], + 'blue': [ + (0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333), + (0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667), + (0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667), + (0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667), + (0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0), + (0.5, 0.0, 0.0), (0.55, 0.0, 0.0), + (0.60, 0.0, 0.0), (0.65, 0.0, 0.0), + (0.70, 0.0, 0.0), (0.75, 0.0, 0.0), + (0.80, 0.0, 0.0), (0.85, 0.0, 0.0), + (0.90, 0.0, 0.0), (0.95, 0.0, 0.0), + (1.0, 0.80, 0.80), + ], +} + + +# 34 colormaps based on color specifications and designs +# developed by Cynthia Brewer (https://colorbrewer2.org/). +# The ColorBrewer palettes have been included under the terms +# of an Apache-stype license (for details, see the file +# LICENSE_COLORBREWER in the license directory of the matplotlib +# source distribution). + +# RGB values taken from Brewer's Excel sheet, divided by 255 + +_Blues_data = ( + (0.96862745098039216, 0.98431372549019602, 1.0 ), + (0.87058823529411766, 0.92156862745098034, 0.96862745098039216), + (0.77647058823529413, 0.85882352941176465, 0.93725490196078431), + (0.61960784313725492, 0.792156862745098 , 0.88235294117647056), + (0.41960784313725491, 0.68235294117647061, 0.83921568627450982), + (0.25882352941176473, 0.5725490196078431 , 0.77647058823529413), + (0.12941176470588237, 0.44313725490196076, 0.70980392156862748), + (0.03137254901960784, 0.31764705882352939, 0.61176470588235299), + (0.03137254901960784, 0.18823529411764706, 0.41960784313725491) + ) + +_BrBG_data = ( + (0.32941176470588235, 0.18823529411764706, 0.0196078431372549 ), + (0.5490196078431373 , 0.31764705882352939, 0.0392156862745098 ), + (0.74901960784313726, 0.50588235294117645, 0.17647058823529413), + (0.87450980392156863, 0.76078431372549016, 0.49019607843137253), + (0.96470588235294119, 0.90980392156862744, 0.76470588235294112), + (0.96078431372549022, 0.96078431372549022, 0.96078431372549022), + (0.7803921568627451 , 0.91764705882352937, 0.89803921568627454), + (0.50196078431372548, 0.80392156862745101, 0.75686274509803919), + (0.20784313725490197, 0.59215686274509804, 0.5607843137254902 ), + (0.00392156862745098, 0.4 , 0.36862745098039218), + (0.0 , 0.23529411764705882, 0.18823529411764706) + ) + +_BuGn_data = ( + (0.96862745098039216, 0.9882352941176471 , 0.99215686274509807), + (0.89803921568627454, 0.96078431372549022, 0.97647058823529409), + (0.8 , 0.92549019607843142, 0.90196078431372551), + (0.6 , 0.84705882352941175, 0.78823529411764703), + (0.4 , 0.76078431372549016, 0.64313725490196083), + (0.25490196078431371, 0.68235294117647061, 0.46274509803921571), + (0.13725490196078433, 0.54509803921568623, 0.27058823529411763), + (0.0 , 0.42745098039215684, 0.17254901960784313), + (0.0 , 0.26666666666666666, 0.10588235294117647) + ) + +_BuPu_data = ( + (0.96862745098039216, 0.9882352941176471 , 0.99215686274509807), + (0.8784313725490196 , 0.92549019607843142, 0.95686274509803926), + (0.74901960784313726, 0.82745098039215681, 0.90196078431372551), + (0.61960784313725492, 0.73725490196078436, 0.85490196078431369), + (0.5490196078431373 , 0.58823529411764708, 0.77647058823529413), + (0.5490196078431373 , 0.41960784313725491, 0.69411764705882351), + (0.53333333333333333, 0.25490196078431371, 0.61568627450980395), + (0.50588235294117645, 0.05882352941176471, 0.48627450980392156), + (0.30196078431372547, 0.0 , 0.29411764705882354) + ) + +_GnBu_data = ( + (0.96862745098039216, 0.9882352941176471 , 0.94117647058823528), + (0.8784313725490196 , 0.95294117647058818, 0.85882352941176465), + (0.8 , 0.92156862745098034, 0.77254901960784317), + (0.6588235294117647 , 0.8666666666666667 , 0.70980392156862748), + (0.4823529411764706 , 0.8 , 0.7686274509803922 ), + (0.30588235294117649, 0.70196078431372544, 0.82745098039215681), + (0.16862745098039217, 0.5490196078431373 , 0.74509803921568629), + (0.03137254901960784, 0.40784313725490196, 0.67450980392156867), + (0.03137254901960784, 0.25098039215686274, 0.50588235294117645) + ) + +_Greens_data = ( + (0.96862745098039216, 0.9882352941176471 , 0.96078431372549022), + (0.89803921568627454, 0.96078431372549022, 0.8784313725490196 ), + (0.7803921568627451 , 0.9137254901960784 , 0.75294117647058822), + (0.63137254901960782, 0.85098039215686272, 0.60784313725490191), + (0.45490196078431372, 0.7686274509803922 , 0.46274509803921571), + (0.25490196078431371, 0.6705882352941176 , 0.36470588235294116), + (0.13725490196078433, 0.54509803921568623, 0.27058823529411763), + (0.0 , 0.42745098039215684, 0.17254901960784313), + (0.0 , 0.26666666666666666, 0.10588235294117647) + ) + +_Greys_data = ( + (1.0 , 1.0 , 1.0 ), + (0.94117647058823528, 0.94117647058823528, 0.94117647058823528), + (0.85098039215686272, 0.85098039215686272, 0.85098039215686272), + (0.74117647058823533, 0.74117647058823533, 0.74117647058823533), + (0.58823529411764708, 0.58823529411764708, 0.58823529411764708), + (0.45098039215686275, 0.45098039215686275, 0.45098039215686275), + (0.32156862745098042, 0.32156862745098042, 0.32156862745098042), + (0.14509803921568629, 0.14509803921568629, 0.14509803921568629), + (0.0 , 0.0 , 0.0 ) + ) + +_Oranges_data = ( + (1.0 , 0.96078431372549022, 0.92156862745098034), + (0.99607843137254903, 0.90196078431372551, 0.80784313725490198), + (0.99215686274509807, 0.81568627450980391, 0.63529411764705879), + (0.99215686274509807, 0.68235294117647061, 0.41960784313725491), + (0.99215686274509807, 0.55294117647058827, 0.23529411764705882), + (0.94509803921568625, 0.41176470588235292, 0.07450980392156863), + (0.85098039215686272, 0.28235294117647058, 0.00392156862745098), + (0.65098039215686276, 0.21176470588235294, 0.01176470588235294), + (0.49803921568627452, 0.15294117647058825, 0.01568627450980392) + ) + +_OrRd_data = ( + (1.0 , 0.96862745098039216, 0.92549019607843142), + (0.99607843137254903, 0.90980392156862744, 0.78431372549019607), + (0.99215686274509807, 0.83137254901960789, 0.61960784313725492), + (0.99215686274509807, 0.73333333333333328, 0.51764705882352946), + (0.9882352941176471 , 0.55294117647058827, 0.34901960784313724), + (0.93725490196078431, 0.396078431372549 , 0.28235294117647058), + (0.84313725490196079, 0.18823529411764706, 0.12156862745098039), + (0.70196078431372544, 0.0 , 0.0 ), + (0.49803921568627452, 0.0 , 0.0 ) + ) + +_PiYG_data = ( + (0.55686274509803924, 0.00392156862745098, 0.32156862745098042), + (0.77254901960784317, 0.10588235294117647, 0.49019607843137253), + (0.87058823529411766, 0.46666666666666667, 0.68235294117647061), + (0.94509803921568625, 0.71372549019607845, 0.85490196078431369), + (0.99215686274509807, 0.8784313725490196 , 0.93725490196078431), + (0.96862745098039216, 0.96862745098039216, 0.96862745098039216), + (0.90196078431372551, 0.96078431372549022, 0.81568627450980391), + (0.72156862745098038, 0.88235294117647056, 0.52549019607843139), + (0.49803921568627452, 0.73725490196078436, 0.25490196078431371), + (0.30196078431372547, 0.5725490196078431 , 0.12941176470588237), + (0.15294117647058825, 0.39215686274509803, 0.09803921568627451) + ) + +_PRGn_data = ( + (0.25098039215686274, 0.0 , 0.29411764705882354), + (0.46274509803921571, 0.16470588235294117, 0.51372549019607838), + (0.6 , 0.4392156862745098 , 0.6705882352941176 ), + (0.76078431372549016, 0.6470588235294118 , 0.81176470588235294), + (0.90588235294117647, 0.83137254901960789, 0.90980392156862744), + (0.96862745098039216, 0.96862745098039216, 0.96862745098039216), + (0.85098039215686272, 0.94117647058823528, 0.82745098039215681), + (0.65098039215686276, 0.85882352941176465, 0.62745098039215685), + (0.35294117647058826, 0.68235294117647061, 0.38039215686274508), + (0.10588235294117647, 0.47058823529411764, 0.21568627450980393), + (0.0 , 0.26666666666666666, 0.10588235294117647) + ) + +_PuBu_data = ( + (1.0 , 0.96862745098039216, 0.98431372549019602), + (0.92549019607843142, 0.90588235294117647, 0.94901960784313721), + (0.81568627450980391, 0.81960784313725488, 0.90196078431372551), + (0.65098039215686276, 0.74117647058823533, 0.85882352941176465), + (0.45490196078431372, 0.66274509803921566, 0.81176470588235294), + (0.21176470588235294, 0.56470588235294117, 0.75294117647058822), + (0.0196078431372549 , 0.4392156862745098 , 0.69019607843137254), + (0.01568627450980392, 0.35294117647058826, 0.55294117647058827), + (0.00784313725490196, 0.2196078431372549 , 0.34509803921568627) + ) + +_PuBuGn_data = ( + (1.0 , 0.96862745098039216, 0.98431372549019602), + (0.92549019607843142, 0.88627450980392153, 0.94117647058823528), + (0.81568627450980391, 0.81960784313725488, 0.90196078431372551), + (0.65098039215686276, 0.74117647058823533, 0.85882352941176465), + (0.40392156862745099, 0.66274509803921566, 0.81176470588235294), + (0.21176470588235294, 0.56470588235294117, 0.75294117647058822), + (0.00784313725490196, 0.50588235294117645, 0.54117647058823526), + (0.00392156862745098, 0.42352941176470588, 0.34901960784313724), + (0.00392156862745098, 0.27450980392156865, 0.21176470588235294) + ) + +_PuOr_data = ( + (0.49803921568627452, 0.23137254901960785, 0.03137254901960784), + (0.70196078431372544, 0.34509803921568627, 0.02352941176470588), + (0.8784313725490196 , 0.50980392156862742, 0.07843137254901961), + (0.99215686274509807, 0.72156862745098038, 0.38823529411764707), + (0.99607843137254903, 0.8784313725490196 , 0.71372549019607845), + (0.96862745098039216, 0.96862745098039216, 0.96862745098039216), + (0.84705882352941175, 0.85490196078431369, 0.92156862745098034), + (0.69803921568627447, 0.6705882352941176 , 0.82352941176470584), + (0.50196078431372548, 0.45098039215686275, 0.67450980392156867), + (0.32941176470588235, 0.15294117647058825, 0.53333333333333333), + (0.17647058823529413, 0.0 , 0.29411764705882354) + ) + +_PuRd_data = ( + (0.96862745098039216, 0.95686274509803926, 0.97647058823529409), + (0.90588235294117647, 0.88235294117647056, 0.93725490196078431), + (0.83137254901960789, 0.72549019607843135, 0.85490196078431369), + (0.78823529411764703, 0.58039215686274515, 0.7803921568627451 ), + (0.87450980392156863, 0.396078431372549 , 0.69019607843137254), + (0.90588235294117647, 0.16078431372549021, 0.54117647058823526), + (0.80784313725490198, 0.07058823529411765, 0.33725490196078434), + (0.59607843137254901, 0.0 , 0.2627450980392157 ), + (0.40392156862745099, 0.0 , 0.12156862745098039) + ) + +_Purples_data = ( + (0.9882352941176471 , 0.98431372549019602, 0.99215686274509807), + (0.93725490196078431, 0.92941176470588238, 0.96078431372549022), + (0.85490196078431369, 0.85490196078431369, 0.92156862745098034), + (0.73725490196078436, 0.74117647058823533, 0.86274509803921573), + (0.61960784313725492, 0.60392156862745094, 0.78431372549019607), + (0.50196078431372548, 0.49019607843137253, 0.72941176470588232), + (0.41568627450980394, 0.31764705882352939, 0.63921568627450975), + (0.32941176470588235, 0.15294117647058825, 0.5607843137254902 ), + (0.24705882352941178, 0.0 , 0.49019607843137253) + ) + +_RdBu_data = ( + (0.40392156862745099, 0.0 , 0.12156862745098039), + (0.69803921568627447, 0.09411764705882353, 0.16862745098039217), + (0.83921568627450982, 0.37647058823529411, 0.30196078431372547), + (0.95686274509803926, 0.6470588235294118 , 0.50980392156862742), + (0.99215686274509807, 0.85882352941176465, 0.7803921568627451 ), + (0.96862745098039216, 0.96862745098039216, 0.96862745098039216), + (0.81960784313725488, 0.89803921568627454, 0.94117647058823528), + (0.5725490196078431 , 0.77254901960784317, 0.87058823529411766), + (0.2627450980392157 , 0.57647058823529407, 0.76470588235294112), + (0.12941176470588237, 0.4 , 0.67450980392156867), + (0.0196078431372549 , 0.18823529411764706, 0.38039215686274508) + ) + +_RdGy_data = ( + (0.40392156862745099, 0.0 , 0.12156862745098039), + (0.69803921568627447, 0.09411764705882353, 0.16862745098039217), + (0.83921568627450982, 0.37647058823529411, 0.30196078431372547), + (0.95686274509803926, 0.6470588235294118 , 0.50980392156862742), + (0.99215686274509807, 0.85882352941176465, 0.7803921568627451 ), + (1.0 , 1.0 , 1.0 ), + (0.8784313725490196 , 0.8784313725490196 , 0.8784313725490196 ), + (0.72941176470588232, 0.72941176470588232, 0.72941176470588232), + (0.52941176470588236, 0.52941176470588236, 0.52941176470588236), + (0.30196078431372547, 0.30196078431372547, 0.30196078431372547), + (0.10196078431372549, 0.10196078431372549, 0.10196078431372549) + ) + +_RdPu_data = ( + (1.0 , 0.96862745098039216, 0.95294117647058818), + (0.99215686274509807, 0.8784313725490196 , 0.86666666666666667), + (0.9882352941176471 , 0.77254901960784317, 0.75294117647058822), + (0.98039215686274506, 0.62352941176470589, 0.70980392156862748), + (0.96862745098039216, 0.40784313725490196, 0.63137254901960782), + (0.86666666666666667, 0.20392156862745098, 0.59215686274509804), + (0.68235294117647061, 0.00392156862745098, 0.49411764705882355), + (0.47843137254901963, 0.00392156862745098, 0.46666666666666667), + (0.28627450980392155, 0.0 , 0.41568627450980394) + ) + +_RdYlBu_data = ( + (0.6470588235294118 , 0.0 , 0.14901960784313725), + (0.84313725490196079, 0.18823529411764706 , 0.15294117647058825), + (0.95686274509803926, 0.42745098039215684 , 0.2627450980392157 ), + (0.99215686274509807, 0.68235294117647061 , 0.38039215686274508), + (0.99607843137254903, 0.8784313725490196 , 0.56470588235294117), + (1.0 , 1.0 , 0.74901960784313726), + (0.8784313725490196 , 0.95294117647058818 , 0.97254901960784312), + (0.6705882352941176 , 0.85098039215686272 , 0.9137254901960784 ), + (0.45490196078431372, 0.67843137254901964 , 0.81960784313725488), + (0.27058823529411763, 0.45882352941176469 , 0.70588235294117652), + (0.19215686274509805, 0.21176470588235294 , 0.58431372549019611) + ) + +_RdYlGn_data = ( + (0.6470588235294118 , 0.0 , 0.14901960784313725), + (0.84313725490196079, 0.18823529411764706 , 0.15294117647058825), + (0.95686274509803926, 0.42745098039215684 , 0.2627450980392157 ), + (0.99215686274509807, 0.68235294117647061 , 0.38039215686274508), + (0.99607843137254903, 0.8784313725490196 , 0.54509803921568623), + (1.0 , 1.0 , 0.74901960784313726), + (0.85098039215686272, 0.93725490196078431 , 0.54509803921568623), + (0.65098039215686276, 0.85098039215686272 , 0.41568627450980394), + (0.4 , 0.74117647058823533 , 0.38823529411764707), + (0.10196078431372549, 0.59607843137254901 , 0.31372549019607843), + (0.0 , 0.40784313725490196 , 0.21568627450980393) + ) + +_Reds_data = ( + (1.0 , 0.96078431372549022 , 0.94117647058823528), + (0.99607843137254903, 0.8784313725490196 , 0.82352941176470584), + (0.9882352941176471 , 0.73333333333333328 , 0.63137254901960782), + (0.9882352941176471 , 0.5725490196078431 , 0.44705882352941179), + (0.98431372549019602, 0.41568627450980394 , 0.29019607843137257), + (0.93725490196078431, 0.23137254901960785 , 0.17254901960784313), + (0.79607843137254897, 0.094117647058823528, 0.11372549019607843), + (0.6470588235294118 , 0.058823529411764705, 0.08235294117647058), + (0.40392156862745099, 0.0 , 0.05098039215686274) + ) + +_Spectral_data = ( + (0.61960784313725492, 0.003921568627450980, 0.25882352941176473), + (0.83529411764705885, 0.24313725490196078 , 0.30980392156862746), + (0.95686274509803926, 0.42745098039215684 , 0.2627450980392157 ), + (0.99215686274509807, 0.68235294117647061 , 0.38039215686274508), + (0.99607843137254903, 0.8784313725490196 , 0.54509803921568623), + (1.0 , 1.0 , 0.74901960784313726), + (0.90196078431372551, 0.96078431372549022 , 0.59607843137254901), + (0.6705882352941176 , 0.8666666666666667 , 0.64313725490196083), + (0.4 , 0.76078431372549016 , 0.6470588235294118 ), + (0.19607843137254902, 0.53333333333333333 , 0.74117647058823533), + (0.36862745098039218, 0.30980392156862746 , 0.63529411764705879) + ) + +_YlGn_data = ( + (1.0 , 1.0 , 0.89803921568627454), + (0.96862745098039216, 0.9882352941176471 , 0.72549019607843135), + (0.85098039215686272, 0.94117647058823528 , 0.63921568627450975), + (0.67843137254901964, 0.8666666666666667 , 0.55686274509803924), + (0.47058823529411764, 0.77647058823529413 , 0.47450980392156861), + (0.25490196078431371, 0.6705882352941176 , 0.36470588235294116), + (0.13725490196078433, 0.51764705882352946 , 0.2627450980392157 ), + (0.0 , 0.40784313725490196 , 0.21568627450980393), + (0.0 , 0.27058823529411763 , 0.16078431372549021) + ) + +_YlGnBu_data = ( + (1.0 , 1.0 , 0.85098039215686272), + (0.92941176470588238, 0.97254901960784312 , 0.69411764705882351), + (0.7803921568627451 , 0.9137254901960784 , 0.70588235294117652), + (0.49803921568627452, 0.80392156862745101 , 0.73333333333333328), + (0.25490196078431371, 0.71372549019607845 , 0.7686274509803922 ), + (0.11372549019607843, 0.56862745098039214 , 0.75294117647058822), + (0.13333333333333333, 0.36862745098039218 , 0.6588235294117647 ), + (0.14509803921568629, 0.20392156862745098 , 0.58039215686274515), + (0.03137254901960784, 0.11372549019607843 , 0.34509803921568627) + ) + +_YlOrBr_data = ( + (1.0 , 1.0 , 0.89803921568627454), + (1.0 , 0.96862745098039216 , 0.73725490196078436), + (0.99607843137254903, 0.8901960784313725 , 0.56862745098039214), + (0.99607843137254903, 0.7686274509803922 , 0.30980392156862746), + (0.99607843137254903, 0.6 , 0.16078431372549021), + (0.92549019607843142, 0.4392156862745098 , 0.07843137254901961), + (0.8 , 0.29803921568627451 , 0.00784313725490196), + (0.6 , 0.20392156862745098 , 0.01568627450980392), + (0.4 , 0.14509803921568629 , 0.02352941176470588) + ) + +_YlOrRd_data = ( + (1.0 , 1.0 , 0.8 ), + (1.0 , 0.92941176470588238 , 0.62745098039215685), + (0.99607843137254903, 0.85098039215686272 , 0.46274509803921571), + (0.99607843137254903, 0.69803921568627447 , 0.29803921568627451), + (0.99215686274509807, 0.55294117647058827 , 0.23529411764705882), + (0.9882352941176471 , 0.30588235294117649 , 0.16470588235294117), + (0.8901960784313725 , 0.10196078431372549 , 0.10980392156862745), + (0.74117647058823533, 0.0 , 0.14901960784313725), + (0.50196078431372548, 0.0 , 0.14901960784313725) + ) + + +# ColorBrewer's qualitative maps, implemented using ListedColormap +# for use with mpl.colors.NoNorm + +_Accent_data = ( + (0.49803921568627452, 0.78823529411764703, 0.49803921568627452), + (0.74509803921568629, 0.68235294117647061, 0.83137254901960789), + (0.99215686274509807, 0.75294117647058822, 0.52549019607843139), + (1.0, 1.0, 0.6 ), + (0.2196078431372549, 0.42352941176470588, 0.69019607843137254), + (0.94117647058823528, 0.00784313725490196, 0.49803921568627452), + (0.74901960784313726, 0.35686274509803922, 0.09019607843137254), + (0.4, 0.4, 0.4 ), + ) + +_Dark2_data = ( + (0.10588235294117647, 0.61960784313725492, 0.46666666666666667), + (0.85098039215686272, 0.37254901960784315, 0.00784313725490196), + (0.45882352941176469, 0.4392156862745098, 0.70196078431372544), + (0.90588235294117647, 0.16078431372549021, 0.54117647058823526), + (0.4, 0.65098039215686276, 0.11764705882352941), + (0.90196078431372551, 0.6705882352941176, 0.00784313725490196), + (0.65098039215686276, 0.46274509803921571, 0.11372549019607843), + (0.4, 0.4, 0.4 ), + ) + +_Paired_data = ( + (0.65098039215686276, 0.80784313725490198, 0.8901960784313725 ), + (0.12156862745098039, 0.47058823529411764, 0.70588235294117652), + (0.69803921568627447, 0.87450980392156863, 0.54117647058823526), + (0.2, 0.62745098039215685, 0.17254901960784313), + (0.98431372549019602, 0.60392156862745094, 0.6 ), + (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), + (0.99215686274509807, 0.74901960784313726, 0.43529411764705883), + (1.0, 0.49803921568627452, 0.0 ), + (0.792156862745098, 0.69803921568627447, 0.83921568627450982), + (0.41568627450980394, 0.23921568627450981, 0.60392156862745094), + (1.0, 1.0, 0.6 ), + (0.69411764705882351, 0.34901960784313724, 0.15686274509803921), + ) + +_Pastel1_data = ( + (0.98431372549019602, 0.70588235294117652, 0.68235294117647061), + (0.70196078431372544, 0.80392156862745101, 0.8901960784313725 ), + (0.8, 0.92156862745098034, 0.77254901960784317), + (0.87058823529411766, 0.79607843137254897, 0.89411764705882357), + (0.99607843137254903, 0.85098039215686272, 0.65098039215686276), + (1.0, 1.0, 0.8 ), + (0.89803921568627454, 0.84705882352941175, 0.74117647058823533), + (0.99215686274509807, 0.85490196078431369, 0.92549019607843142), + (0.94901960784313721, 0.94901960784313721, 0.94901960784313721), + ) + +_Pastel2_data = ( + (0.70196078431372544, 0.88627450980392153, 0.80392156862745101), + (0.99215686274509807, 0.80392156862745101, 0.67450980392156867), + (0.79607843137254897, 0.83529411764705885, 0.90980392156862744), + (0.95686274509803926, 0.792156862745098, 0.89411764705882357), + (0.90196078431372551, 0.96078431372549022, 0.78823529411764703), + (1.0, 0.94901960784313721, 0.68235294117647061), + (0.94509803921568625, 0.88627450980392153, 0.8 ), + (0.8, 0.8, 0.8 ), + ) + +_Set1_data = ( + (0.89411764705882357, 0.10196078431372549, 0.10980392156862745), + (0.21568627450980393, 0.49411764705882355, 0.72156862745098038), + (0.30196078431372547, 0.68627450980392157, 0.29019607843137257), + (0.59607843137254901, 0.30588235294117649, 0.63921568627450975), + (1.0, 0.49803921568627452, 0.0 ), + (1.0, 1.0, 0.2 ), + (0.65098039215686276, 0.33725490196078434, 0.15686274509803921), + (0.96862745098039216, 0.50588235294117645, 0.74901960784313726), + (0.6, 0.6, 0.6), + ) + +_Set2_data = ( + (0.4, 0.76078431372549016, 0.6470588235294118 ), + (0.9882352941176471, 0.55294117647058827, 0.3843137254901961 ), + (0.55294117647058827, 0.62745098039215685, 0.79607843137254897), + (0.90588235294117647, 0.54117647058823526, 0.76470588235294112), + (0.65098039215686276, 0.84705882352941175, 0.32941176470588235), + (1.0, 0.85098039215686272, 0.18431372549019609), + (0.89803921568627454, 0.7686274509803922, 0.58039215686274515), + (0.70196078431372544, 0.70196078431372544, 0.70196078431372544), + ) + +_Set3_data = ( + (0.55294117647058827, 0.82745098039215681, 0.7803921568627451 ), + (1.0, 1.0, 0.70196078431372544), + (0.74509803921568629, 0.72941176470588232, 0.85490196078431369), + (0.98431372549019602, 0.50196078431372548, 0.44705882352941179), + (0.50196078431372548, 0.69411764705882351, 0.82745098039215681), + (0.99215686274509807, 0.70588235294117652, 0.3843137254901961 ), + (0.70196078431372544, 0.87058823529411766, 0.41176470588235292), + (0.9882352941176471, 0.80392156862745101, 0.89803921568627454), + (0.85098039215686272, 0.85098039215686272, 0.85098039215686272), + (0.73725490196078436, 0.50196078431372548, 0.74117647058823533), + (0.8, 0.92156862745098034, 0.77254901960784317), + (1.0, 0.92941176470588238, 0.43529411764705883), + ) + + +# The next 7 palettes are from the Yorick scientific visualization package, +# an evolution of the GIST package, both by David H. Munro. +# They are released under a BSD-like license (see LICENSE_YORICK in +# the license directory of the matplotlib source distribution). +# +# Most palette functions have been reduced to simple function descriptions +# by Reinier Heeres, since the rgb components were mostly straight lines. +# gist_earth_data and gist_ncar_data were simplified by a script and some +# manual effort. + +_gist_earth_data = { + 'red': ( + (0.0, 0.0, 0.0000), + (0.2824, 0.1882, 0.1882), + (0.4588, 0.2714, 0.2714), + (0.5490, 0.4719, 0.4719), + (0.6980, 0.7176, 0.7176), + (0.7882, 0.7553, 0.7553), + (1.0000, 0.9922, 0.9922), + ), + 'green': ( + (0.0, 0.0, 0.0000), + (0.0275, 0.0000, 0.0000), + (0.1098, 0.1893, 0.1893), + (0.1647, 0.3035, 0.3035), + (0.2078, 0.3841, 0.3841), + (0.2824, 0.5020, 0.5020), + (0.5216, 0.6397, 0.6397), + (0.6980, 0.7171, 0.7171), + (0.7882, 0.6392, 0.6392), + (0.7922, 0.6413, 0.6413), + (0.8000, 0.6447, 0.6447), + (0.8078, 0.6481, 0.6481), + (0.8157, 0.6549, 0.6549), + (0.8667, 0.6991, 0.6991), + (0.8745, 0.7103, 0.7103), + (0.8824, 0.7216, 0.7216), + (0.8902, 0.7323, 0.7323), + (0.8980, 0.7430, 0.7430), + (0.9412, 0.8275, 0.8275), + (0.9569, 0.8635, 0.8635), + (0.9647, 0.8816, 0.8816), + (0.9961, 0.9733, 0.9733), + (1.0000, 0.9843, 0.9843), + ), + 'blue': ( + (0.0, 0.0, 0.0000), + (0.0039, 0.1684, 0.1684), + (0.0078, 0.2212, 0.2212), + (0.0275, 0.4329, 0.4329), + (0.0314, 0.4549, 0.4549), + (0.2824, 0.5004, 0.5004), + (0.4667, 0.2748, 0.2748), + (0.5451, 0.3205, 0.3205), + (0.7843, 0.3961, 0.3961), + (0.8941, 0.6651, 0.6651), + (1.0000, 0.9843, 0.9843), + ) +} + +_gist_gray_data = { + 'red': gfunc[3], + 'green': gfunc[3], + 'blue': gfunc[3], +} + +def _gist_heat_red(x): return 1.5 * x +def _gist_heat_green(x): return 2 * x - 1 +def _gist_heat_blue(x): return 4 * x - 3 +_gist_heat_data = { + 'red': _gist_heat_red, 'green': _gist_heat_green, 'blue': _gist_heat_blue} + +_gist_ncar_data = { + 'red': ( + (0.0, 0.0, 0.0000), + (0.3098, 0.0000, 0.0000), + (0.3725, 0.3993, 0.3993), + (0.4235, 0.5003, 0.5003), + (0.5333, 1.0000, 1.0000), + (0.7922, 1.0000, 1.0000), + (0.8471, 0.6218, 0.6218), + (0.8980, 0.9235, 0.9235), + (1.0000, 0.9961, 0.9961), + ), + 'green': ( + (0.0, 0.0, 0.0000), + (0.0510, 0.3722, 0.3722), + (0.1059, 0.0000, 0.0000), + (0.1569, 0.7202, 0.7202), + (0.1608, 0.7537, 0.7537), + (0.1647, 0.7752, 0.7752), + (0.2157, 1.0000, 1.0000), + (0.2588, 0.9804, 0.9804), + (0.2706, 0.9804, 0.9804), + (0.3176, 1.0000, 1.0000), + (0.3686, 0.8081, 0.8081), + (0.4275, 1.0000, 1.0000), + (0.5216, 1.0000, 1.0000), + (0.6314, 0.7292, 0.7292), + (0.6863, 0.2796, 0.2796), + (0.7451, 0.0000, 0.0000), + (0.7922, 0.0000, 0.0000), + (0.8431, 0.1753, 0.1753), + (0.8980, 0.5000, 0.5000), + (1.0000, 0.9725, 0.9725), + ), + 'blue': ( + (0.0, 0.5020, 0.5020), + (0.0510, 0.0222, 0.0222), + (0.1098, 1.0000, 1.0000), + (0.2039, 1.0000, 1.0000), + (0.2627, 0.6145, 0.6145), + (0.3216, 0.0000, 0.0000), + (0.4157, 0.0000, 0.0000), + (0.4745, 0.2342, 0.2342), + (0.5333, 0.0000, 0.0000), + (0.5804, 0.0000, 0.0000), + (0.6314, 0.0549, 0.0549), + (0.6902, 0.0000, 0.0000), + (0.7373, 0.0000, 0.0000), + (0.7922, 0.9738, 0.9738), + (0.8000, 1.0000, 1.0000), + (0.8431, 1.0000, 1.0000), + (0.8980, 0.9341, 0.9341), + (1.0000, 0.9961, 0.9961), + ) +} + +_gist_rainbow_data = ( + (0.000, (1.00, 0.00, 0.16)), + (0.030, (1.00, 0.00, 0.00)), + (0.215, (1.00, 1.00, 0.00)), + (0.400, (0.00, 1.00, 0.00)), + (0.586, (0.00, 1.00, 1.00)), + (0.770, (0.00, 0.00, 1.00)), + (0.954, (1.00, 0.00, 1.00)), + (1.000, (1.00, 0.00, 0.75)) +) + +_gist_stern_data = { + 'red': ( + (0.000, 0.000, 0.000), (0.0547, 1.000, 1.000), + (0.250, 0.027, 0.250), # (0.2500, 0.250, 0.250), + (1.000, 1.000, 1.000)), + 'green': ((0, 0, 0), (1, 1, 1)), + 'blue': ( + (0.000, 0.000, 0.000), (0.500, 1.000, 1.000), + (0.735, 0.000, 0.000), (1.000, 1.000, 1.000)) +} + +def _gist_yarg(x): return 1 - x +_gist_yarg_data = {'red': _gist_yarg, 'green': _gist_yarg, 'blue': _gist_yarg} + +# This bipolar colormap was generated from CoolWarmFloat33.csv of +# "Diverging Color Maps for Scientific Visualization" by Kenneth Moreland. +# +_coolwarm_data = { + 'red': [ + (0.0, 0.2298057, 0.2298057), + (0.03125, 0.26623388, 0.26623388), + (0.0625, 0.30386891, 0.30386891), + (0.09375, 0.342804478, 0.342804478), + (0.125, 0.38301334, 0.38301334), + (0.15625, 0.424369608, 0.424369608), + (0.1875, 0.46666708, 0.46666708), + (0.21875, 0.509635204, 0.509635204), + (0.25, 0.552953156, 0.552953156), + (0.28125, 0.596262162, 0.596262162), + (0.3125, 0.639176211, 0.639176211), + (0.34375, 0.681291281, 0.681291281), + (0.375, 0.722193294, 0.722193294), + (0.40625, 0.761464949, 0.761464949), + (0.4375, 0.798691636, 0.798691636), + (0.46875, 0.833466556, 0.833466556), + (0.5, 0.865395197, 0.865395197), + (0.53125, 0.897787179, 0.897787179), + (0.5625, 0.924127593, 0.924127593), + (0.59375, 0.944468518, 0.944468518), + (0.625, 0.958852946, 0.958852946), + (0.65625, 0.96732803, 0.96732803), + (0.6875, 0.969954137, 0.969954137), + (0.71875, 0.966811177, 0.966811177), + (0.75, 0.958003065, 0.958003065), + (0.78125, 0.943660866, 0.943660866), + (0.8125, 0.923944917, 0.923944917), + (0.84375, 0.89904617, 0.89904617), + (0.875, 0.869186849, 0.869186849), + (0.90625, 0.834620542, 0.834620542), + (0.9375, 0.795631745, 0.795631745), + (0.96875, 0.752534934, 0.752534934), + (1.0, 0.705673158, 0.705673158)], + 'green': [ + (0.0, 0.298717966, 0.298717966), + (0.03125, 0.353094838, 0.353094838), + (0.0625, 0.406535296, 0.406535296), + (0.09375, 0.458757618, 0.458757618), + (0.125, 0.50941904, 0.50941904), + (0.15625, 0.558148092, 0.558148092), + (0.1875, 0.604562568, 0.604562568), + (0.21875, 0.648280772, 0.648280772), + (0.25, 0.688929332, 0.688929332), + (0.28125, 0.726149107, 0.726149107), + (0.3125, 0.759599947, 0.759599947), + (0.34375, 0.788964712, 0.788964712), + (0.375, 0.813952739, 0.813952739), + (0.40625, 0.834302879, 0.834302879), + (0.4375, 0.849786142, 0.849786142), + (0.46875, 0.860207984, 0.860207984), + (0.5, 0.86541021, 0.86541021), + (0.53125, 0.848937047, 0.848937047), + (0.5625, 0.827384882, 0.827384882), + (0.59375, 0.800927443, 0.800927443), + (0.625, 0.769767752, 0.769767752), + (0.65625, 0.734132809, 0.734132809), + (0.6875, 0.694266682, 0.694266682), + (0.71875, 0.650421156, 0.650421156), + (0.75, 0.602842431, 0.602842431), + (0.78125, 0.551750968, 0.551750968), + (0.8125, 0.49730856, 0.49730856), + (0.84375, 0.439559467, 0.439559467), + (0.875, 0.378313092, 0.378313092), + (0.90625, 0.312874446, 0.312874446), + (0.9375, 0.24128379, 0.24128379), + (0.96875, 0.157246067, 0.157246067), + (1.0, 0.01555616, 0.01555616)], + 'blue': [ + (0.0, 0.753683153, 0.753683153), + (0.03125, 0.801466763, 0.801466763), + (0.0625, 0.84495867, 0.84495867), + (0.09375, 0.883725899, 0.883725899), + (0.125, 0.917387822, 0.917387822), + (0.15625, 0.945619588, 0.945619588), + (0.1875, 0.968154911, 0.968154911), + (0.21875, 0.98478814, 0.98478814), + (0.25, 0.995375608, 0.995375608), + (0.28125, 0.999836203, 0.999836203), + (0.3125, 0.998151185, 0.998151185), + (0.34375, 0.990363227, 0.990363227), + (0.375, 0.976574709, 0.976574709), + (0.40625, 0.956945269, 0.956945269), + (0.4375, 0.931688648, 0.931688648), + (0.46875, 0.901068838, 0.901068838), + (0.5, 0.865395561, 0.865395561), + (0.53125, 0.820880546, 0.820880546), + (0.5625, 0.774508472, 0.774508472), + (0.59375, 0.726736146, 0.726736146), + (0.625, 0.678007945, 0.678007945), + (0.65625, 0.628751763, 0.628751763), + (0.6875, 0.579375448, 0.579375448), + (0.71875, 0.530263762, 0.530263762), + (0.75, 0.481775914, 0.481775914), + (0.78125, 0.434243684, 0.434243684), + (0.8125, 0.387970225, 0.387970225), + (0.84375, 0.343229596, 0.343229596), + (0.875, 0.300267182, 0.300267182), + (0.90625, 0.259301199, 0.259301199), + (0.9375, 0.220525627, 0.220525627), + (0.96875, 0.184115123, 0.184115123), + (1.0, 0.150232812, 0.150232812)] + } + +# Implementation of Carey Rappaport's CMRmap. +# See `A Color Map for Effective Black-and-White Rendering of Color-Scale +# Images' by Carey Rappaport +# https://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m +_CMRmap_data = {'red': ((0.000, 0.00, 0.00), + (0.125, 0.15, 0.15), + (0.250, 0.30, 0.30), + (0.375, 0.60, 0.60), + (0.500, 1.00, 1.00), + (0.625, 0.90, 0.90), + (0.750, 0.90, 0.90), + (0.875, 0.90, 0.90), + (1.000, 1.00, 1.00)), + 'green': ((0.000, 0.00, 0.00), + (0.125, 0.15, 0.15), + (0.250, 0.15, 0.15), + (0.375, 0.20, 0.20), + (0.500, 0.25, 0.25), + (0.625, 0.50, 0.50), + (0.750, 0.75, 0.75), + (0.875, 0.90, 0.90), + (1.000, 1.00, 1.00)), + 'blue': ((0.000, 0.00, 0.00), + (0.125, 0.50, 0.50), + (0.250, 0.75, 0.75), + (0.375, 0.50, 0.50), + (0.500, 0.15, 0.15), + (0.625, 0.00, 0.00), + (0.750, 0.10, 0.10), + (0.875, 0.50, 0.50), + (1.000, 1.00, 1.00))} + + +# An MIT licensed, colorblind-friendly heatmap from Wistia: +# https://github.com/wistia/heatmap-palette +# https://wistia.com/learn/culture/heatmaps-for-colorblindness +# +# >>> import matplotlib.colors as c +# >>> colors = ["#e4ff7a", "#ffe81a", "#ffbd00", "#ffa000", "#fc7f00"] +# >>> cm = c.LinearSegmentedColormap.from_list('wistia', colors) +# >>> _wistia_data = cm._segmentdata +# >>> del _wistia_data['alpha'] +# +_wistia_data = { + 'red': [(0.0, 0.8941176470588236, 0.8941176470588236), + (0.25, 1.0, 1.0), + (0.5, 1.0, 1.0), + (0.75, 1.0, 1.0), + (1.0, 0.9882352941176471, 0.9882352941176471)], + 'green': [(0.0, 1.0, 1.0), + (0.25, 0.9098039215686274, 0.9098039215686274), + (0.5, 0.7411764705882353, 0.7411764705882353), + (0.75, 0.6274509803921569, 0.6274509803921569), + (1.0, 0.4980392156862745, 0.4980392156862745)], + 'blue': [(0.0, 0.47843137254901963, 0.47843137254901963), + (0.25, 0.10196078431372549, 0.10196078431372549), + (0.5, 0.0, 0.0), + (0.75, 0.0, 0.0), + (1.0, 0.0, 0.0)], +} + + +# Categorical palettes from Vega: +# https://github.com/vega/vega/wiki/Scales +# (divided by 255) +# + +_tab10_data = ( + (0.12156862745098039, 0.4666666666666667, 0.7058823529411765 ), # 1f77b4 + (1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e + (0.17254901960784313, 0.6274509803921569, 0.17254901960784313 ), # 2ca02c + (0.8392156862745098, 0.15294117647058825, 0.1568627450980392 ), # d62728 + (0.5803921568627451, 0.403921568627451, 0.7411764705882353 ), # 9467bd + (0.5490196078431373, 0.33725490196078434, 0.29411764705882354 ), # 8c564b + (0.8901960784313725, 0.4666666666666667, 0.7607843137254902 ), # e377c2 + (0.4980392156862745, 0.4980392156862745, 0.4980392156862745 ), # 7f7f7f + (0.7372549019607844, 0.7411764705882353, 0.13333333333333333 ), # bcbd22 + (0.09019607843137255, 0.7450980392156863, 0.8117647058823529), # 17becf +) + +_tab20_data = ( + (0.12156862745098039, 0.4666666666666667, 0.7058823529411765 ), # 1f77b4 + (0.6823529411764706, 0.7803921568627451, 0.9098039215686274 ), # aec7e8 + (1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e + (1.0, 0.7333333333333333, 0.47058823529411764 ), # ffbb78 + (0.17254901960784313, 0.6274509803921569, 0.17254901960784313 ), # 2ca02c + (0.596078431372549, 0.8745098039215686, 0.5411764705882353 ), # 98df8a + (0.8392156862745098, 0.15294117647058825, 0.1568627450980392 ), # d62728 + (1.0, 0.596078431372549, 0.5882352941176471 ), # ff9896 + (0.5803921568627451, 0.403921568627451, 0.7411764705882353 ), # 9467bd + (0.7725490196078432, 0.6901960784313725, 0.8352941176470589 ), # c5b0d5 + (0.5490196078431373, 0.33725490196078434, 0.29411764705882354 ), # 8c564b + (0.7686274509803922, 0.611764705882353, 0.5803921568627451 ), # c49c94 + (0.8901960784313725, 0.4666666666666667, 0.7607843137254902 ), # e377c2 + (0.9686274509803922, 0.7137254901960784, 0.8235294117647058 ), # f7b6d2 + (0.4980392156862745, 0.4980392156862745, 0.4980392156862745 ), # 7f7f7f + (0.7803921568627451, 0.7803921568627451, 0.7803921568627451 ), # c7c7c7 + (0.7372549019607844, 0.7411764705882353, 0.13333333333333333 ), # bcbd22 + (0.8588235294117647, 0.8588235294117647, 0.5529411764705883 ), # dbdb8d + (0.09019607843137255, 0.7450980392156863, 0.8117647058823529 ), # 17becf + (0.6196078431372549, 0.8549019607843137, 0.8980392156862745), # 9edae5 +) + +_tab20b_data = ( + (0.2235294117647059, 0.23137254901960785, 0.4745098039215686 ), # 393b79 + (0.3215686274509804, 0.32941176470588235, 0.6392156862745098 ), # 5254a3 + (0.4196078431372549, 0.43137254901960786, 0.8117647058823529 ), # 6b6ecf + (0.611764705882353, 0.6196078431372549, 0.8705882352941177 ), # 9c9ede + (0.38823529411764707, 0.4745098039215686, 0.2235294117647059 ), # 637939 + (0.5490196078431373, 0.6352941176470588, 0.3215686274509804 ), # 8ca252 + (0.7098039215686275, 0.8117647058823529, 0.4196078431372549 ), # b5cf6b + (0.807843137254902, 0.8588235294117647, 0.611764705882353 ), # cedb9c + (0.5490196078431373, 0.42745098039215684, 0.19215686274509805), # 8c6d31 + (0.7411764705882353, 0.6196078431372549, 0.2235294117647059 ), # bd9e39 + (0.9058823529411765, 0.7294117647058823, 0.3215686274509804 ), # e7ba52 + (0.9058823529411765, 0.796078431372549, 0.5803921568627451 ), # e7cb94 + (0.5176470588235295, 0.23529411764705882, 0.2235294117647059 ), # 843c39 + (0.6784313725490196, 0.28627450980392155, 0.2901960784313726 ), # ad494a + (0.8392156862745098, 0.3803921568627451, 0.4196078431372549 ), # d6616b + (0.9058823529411765, 0.5882352941176471, 0.611764705882353 ), # e7969c + (0.4823529411764706, 0.2549019607843137, 0.45098039215686275), # 7b4173 + (0.6470588235294118, 0.3176470588235294, 0.5803921568627451 ), # a55194 + (0.807843137254902, 0.42745098039215684, 0.7411764705882353 ), # ce6dbd + (0.8705882352941177, 0.6196078431372549, 0.8392156862745098 ), # de9ed6 +) + +_tab20c_data = ( + (0.19215686274509805, 0.5098039215686274, 0.7411764705882353 ), # 3182bd + (0.4196078431372549, 0.6823529411764706, 0.8392156862745098 ), # 6baed6 + (0.6196078431372549, 0.792156862745098, 0.8823529411764706 ), # 9ecae1 + (0.7764705882352941, 0.8588235294117647, 0.9372549019607843 ), # c6dbef + (0.9019607843137255, 0.3333333333333333, 0.050980392156862744), # e6550d + (0.9921568627450981, 0.5529411764705883, 0.23529411764705882 ), # fd8d3c + (0.9921568627450981, 0.6823529411764706, 0.4196078431372549 ), # fdae6b + (0.9921568627450981, 0.8156862745098039, 0.6352941176470588 ), # fdd0a2 + (0.19215686274509805, 0.6392156862745098, 0.32941176470588235 ), # 31a354 + (0.4549019607843137, 0.7686274509803922, 0.4627450980392157 ), # 74c476 + (0.6313725490196078, 0.8509803921568627, 0.6078431372549019 ), # a1d99b + (0.7803921568627451, 0.9137254901960784, 0.7529411764705882 ), # c7e9c0 + (0.4588235294117647, 0.4196078431372549, 0.6941176470588235 ), # 756bb1 + (0.6196078431372549, 0.6039215686274509, 0.7843137254901961 ), # 9e9ac8 + (0.7372549019607844, 0.7411764705882353, 0.8627450980392157 ), # bcbddc + (0.8549019607843137, 0.8549019607843137, 0.9215686274509803 ), # dadaeb + (0.38823529411764707, 0.38823529411764707, 0.38823529411764707 ), # 636363 + (0.5882352941176471, 0.5882352941176471, 0.5882352941176471 ), # 969696 + (0.7411764705882353, 0.7411764705882353, 0.7411764705882353 ), # bdbdbd + (0.8509803921568627, 0.8509803921568627, 0.8509803921568627 ), # d9d9d9 +) + + +_petroff10_data = ( + (0.24705882352941178, 0.5647058823529412, 0.8549019607843137), # 3f90da + (1.0, 0.6627450980392157, 0.054901960784313725), # ffa90e + (0.7411764705882353, 0.12156862745098039, 0.00392156862745098), # bd1f01 + (0.5803921568627451, 0.6431372549019608, 0.6352941176470588), # 94a4a2 + (0.5137254901960784, 0.17647058823529413, 0.7137254901960784), # 832db6 + (0.6627450980392157, 0.4196078431372549, 0.34901960784313724), # a96b59 + (0.9058823529411765, 0.38823529411764707, 0.0), # e76300 + (0.7254901960784313, 0.6745098039215687, 0.4392156862745098), # b9ac70 + (0.44313725490196076, 0.4588235294117647, 0.5058823529411764), # 717581 + (0.5725490196078431, 0.8549019607843137, 0.8666666666666667), # 92dadd +) + + +datad = { + 'Blues': _Blues_data, + 'BrBG': _BrBG_data, + 'BuGn': _BuGn_data, + 'BuPu': _BuPu_data, + 'CMRmap': _CMRmap_data, + 'GnBu': _GnBu_data, + 'Greens': _Greens_data, + 'Greys': _Greys_data, + 'OrRd': _OrRd_data, + 'Oranges': _Oranges_data, + 'PRGn': _PRGn_data, + 'PiYG': _PiYG_data, + 'PuBu': _PuBu_data, + 'PuBuGn': _PuBuGn_data, + 'PuOr': _PuOr_data, + 'PuRd': _PuRd_data, + 'Purples': _Purples_data, + 'RdBu': _RdBu_data, + 'RdGy': _RdGy_data, + 'RdPu': _RdPu_data, + 'RdYlBu': _RdYlBu_data, + 'RdYlGn': _RdYlGn_data, + 'Reds': _Reds_data, + 'Spectral': _Spectral_data, + 'Wistia': _wistia_data, + 'YlGn': _YlGn_data, + 'YlGnBu': _YlGnBu_data, + 'YlOrBr': _YlOrBr_data, + 'YlOrRd': _YlOrRd_data, + 'afmhot': _afmhot_data, + 'autumn': _autumn_data, + 'binary': _binary_data, + 'bone': _bone_data, + 'brg': _brg_data, + 'bwr': _bwr_data, + 'cool': _cool_data, + 'coolwarm': _coolwarm_data, + 'copper': _copper_data, + 'cubehelix': _cubehelix_data, + 'flag': _flag_data, + 'gist_earth': _gist_earth_data, + 'gist_gray': _gist_gray_data, + 'gist_heat': _gist_heat_data, + 'gist_ncar': _gist_ncar_data, + 'gist_rainbow': _gist_rainbow_data, + 'gist_stern': _gist_stern_data, + 'gist_yarg': _gist_yarg_data, + 'gnuplot': _gnuplot_data, + 'gnuplot2': _gnuplot2_data, + 'gray': _gray_data, + 'hot': _hot_data, + 'hsv': _hsv_data, + 'jet': _jet_data, + 'nipy_spectral': _nipy_spectral_data, + 'ocean': _ocean_data, + 'pink': _pink_data, + 'prism': _prism_data, + 'rainbow': _rainbow_data, + 'seismic': _seismic_data, + 'spring': _spring_data, + 'summer': _summer_data, + 'terrain': _terrain_data, + 'winter': _winter_data, + # Qualitative + 'Accent': {'listed': _Accent_data}, + 'Dark2': {'listed': _Dark2_data}, + 'Paired': {'listed': _Paired_data}, + 'Pastel1': {'listed': _Pastel1_data}, + 'Pastel2': {'listed': _Pastel2_data}, + 'Set1': {'listed': _Set1_data}, + 'Set2': {'listed': _Set2_data}, + 'Set3': {'listed': _Set3_data}, + 'tab10': {'listed': _tab10_data}, + 'tab20': {'listed': _tab20_data}, + 'tab20b': {'listed': _tab20b_data}, + 'tab20c': {'listed': _tab20c_data}, +} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_cm_bivar.py b/.venv/lib/python3.12/site-packages/matplotlib/_cm_bivar.py new file mode 100644 index 0000000000000000000000000000000000000000..53c0d48d7d6c5d3818a33df783a284e11c16662d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_cm_bivar.py @@ -0,0 +1,1312 @@ +# auto-generated by https://github.com/trygvrad/multivariate_colormaps +# date: 2024-05-24 + +import numpy as np +from matplotlib.colors import SegmentedBivarColormap + +BiPeak = np.array( + [0.000, 0.674, 0.931, 0.000, 0.680, 0.922, 0.000, 0.685, 0.914, 0.000, + 0.691, 0.906, 0.000, 0.696, 0.898, 0.000, 0.701, 0.890, 0.000, 0.706, + 0.882, 0.000, 0.711, 0.875, 0.000, 0.715, 0.867, 0.000, 0.720, 0.860, + 0.000, 0.725, 0.853, 0.000, 0.729, 0.845, 0.000, 0.733, 0.838, 0.000, + 0.737, 0.831, 0.000, 0.741, 0.824, 0.000, 0.745, 0.816, 0.000, 0.749, + 0.809, 0.000, 0.752, 0.802, 0.000, 0.756, 0.794, 0.000, 0.759, 0.787, + 0.000, 0.762, 0.779, 0.000, 0.765, 0.771, 0.000, 0.767, 0.764, 0.000, + 0.770, 0.755, 0.000, 0.772, 0.747, 0.000, 0.774, 0.739, 0.000, 0.776, + 0.730, 0.000, 0.777, 0.721, 0.000, 0.779, 0.712, 0.021, 0.780, 0.702, + 0.055, 0.781, 0.693, 0.079, 0.782, 0.682, 0.097, 0.782, 0.672, 0.111, + 0.782, 0.661, 0.122, 0.782, 0.650, 0.132, 0.782, 0.639, 0.140, 0.781, + 0.627, 0.147, 0.781, 0.615, 0.154, 0.780, 0.602, 0.159, 0.778, 0.589, + 0.164, 0.777, 0.576, 0.169, 0.775, 0.563, 0.173, 0.773, 0.549, 0.177, + 0.771, 0.535, 0.180, 0.768, 0.520, 0.184, 0.766, 0.505, 0.187, 0.763, + 0.490, 0.190, 0.760, 0.474, 0.193, 0.756, 0.458, 0.196, 0.753, 0.442, + 0.200, 0.749, 0.425, 0.203, 0.745, 0.408, 0.206, 0.741, 0.391, 0.210, + 0.736, 0.373, 0.213, 0.732, 0.355, 0.216, 0.727, 0.337, 0.220, 0.722, + 0.318, 0.224, 0.717, 0.298, 0.227, 0.712, 0.278, 0.231, 0.707, 0.258, + 0.235, 0.701, 0.236, 0.239, 0.696, 0.214, 0.242, 0.690, 0.190, 0.246, + 0.684, 0.165, 0.250, 0.678, 0.136, 0.000, 0.675, 0.934, 0.000, 0.681, + 0.925, 0.000, 0.687, 0.917, 0.000, 0.692, 0.909, 0.000, 0.697, 0.901, + 0.000, 0.703, 0.894, 0.000, 0.708, 0.886, 0.000, 0.713, 0.879, 0.000, + 0.718, 0.872, 0.000, 0.722, 0.864, 0.000, 0.727, 0.857, 0.000, 0.731, + 0.850, 0.000, 0.736, 0.843, 0.000, 0.740, 0.836, 0.000, 0.744, 0.829, + 0.000, 0.748, 0.822, 0.000, 0.752, 0.815, 0.000, 0.755, 0.808, 0.000, + 0.759, 0.800, 0.000, 0.762, 0.793, 0.000, 0.765, 0.786, 0.000, 0.768, + 0.778, 0.000, 0.771, 0.770, 0.000, 0.773, 0.762, 0.051, 0.776, 0.754, + 0.087, 0.778, 0.746, 0.111, 0.780, 0.737, 0.131, 0.782, 0.728, 0.146, + 0.783, 0.719, 0.159, 0.784, 0.710, 0.171, 0.785, 0.700, 0.180, 0.786, + 0.690, 0.189, 0.786, 0.680, 0.196, 0.787, 0.669, 0.202, 0.787, 0.658, + 0.208, 0.786, 0.647, 0.213, 0.786, 0.635, 0.217, 0.785, 0.623, 0.221, + 0.784, 0.610, 0.224, 0.782, 0.597, 0.227, 0.781, 0.584, 0.230, 0.779, + 0.570, 0.232, 0.777, 0.556, 0.234, 0.775, 0.542, 0.236, 0.772, 0.527, + 0.238, 0.769, 0.512, 0.240, 0.766, 0.497, 0.242, 0.763, 0.481, 0.244, + 0.760, 0.465, 0.246, 0.756, 0.448, 0.248, 0.752, 0.432, 0.250, 0.748, + 0.415, 0.252, 0.744, 0.397, 0.254, 0.739, 0.379, 0.256, 0.735, 0.361, + 0.259, 0.730, 0.343, 0.261, 0.725, 0.324, 0.264, 0.720, 0.304, 0.266, + 0.715, 0.284, 0.269, 0.709, 0.263, 0.271, 0.704, 0.242, 0.274, 0.698, + 0.220, 0.277, 0.692, 0.196, 0.280, 0.686, 0.170, 0.283, 0.680, 0.143, + 0.000, 0.676, 0.937, 0.000, 0.682, 0.928, 0.000, 0.688, 0.920, 0.000, + 0.694, 0.913, 0.000, 0.699, 0.905, 0.000, 0.704, 0.897, 0.000, 0.710, + 0.890, 0.000, 0.715, 0.883, 0.000, 0.720, 0.876, 0.000, 0.724, 0.869, + 0.000, 0.729, 0.862, 0.000, 0.734, 0.855, 0.000, 0.738, 0.848, 0.000, + 0.743, 0.841, 0.000, 0.747, 0.834, 0.000, 0.751, 0.827, 0.000, 0.755, + 0.820, 0.000, 0.759, 0.813, 0.000, 0.762, 0.806, 0.003, 0.766, 0.799, + 0.066, 0.769, 0.792, 0.104, 0.772, 0.784, 0.131, 0.775, 0.777, 0.152, + 0.777, 0.769, 0.170, 0.780, 0.761, 0.185, 0.782, 0.753, 0.198, 0.784, + 0.744, 0.209, 0.786, 0.736, 0.219, 0.787, 0.727, 0.228, 0.788, 0.717, + 0.236, 0.789, 0.708, 0.243, 0.790, 0.698, 0.249, 0.791, 0.688, 0.254, + 0.791, 0.677, 0.259, 0.791, 0.666, 0.263, 0.791, 0.654, 0.266, 0.790, + 0.643, 0.269, 0.789, 0.631, 0.272, 0.788, 0.618, 0.274, 0.787, 0.605, + 0.276, 0.785, 0.592, 0.278, 0.783, 0.578, 0.279, 0.781, 0.564, 0.280, + 0.779, 0.549, 0.282, 0.776, 0.535, 0.283, 0.773, 0.519, 0.284, 0.770, + 0.504, 0.285, 0.767, 0.488, 0.286, 0.763, 0.472, 0.287, 0.759, 0.455, + 0.288, 0.756, 0.438, 0.289, 0.751, 0.421, 0.291, 0.747, 0.403, 0.292, + 0.742, 0.385, 0.293, 0.738, 0.367, 0.295, 0.733, 0.348, 0.296, 0.728, + 0.329, 0.298, 0.723, 0.310, 0.300, 0.717, 0.290, 0.302, 0.712, 0.269, + 0.304, 0.706, 0.247, 0.306, 0.700, 0.225, 0.308, 0.694, 0.201, 0.310, + 0.688, 0.176, 0.312, 0.682, 0.149, 0.000, 0.678, 0.939, 0.000, 0.683, + 0.931, 0.000, 0.689, 0.923, 0.000, 0.695, 0.916, 0.000, 0.701, 0.908, + 0.000, 0.706, 0.901, 0.000, 0.711, 0.894, 0.000, 0.717, 0.887, 0.000, + 0.722, 0.880, 0.000, 0.727, 0.873, 0.000, 0.732, 0.866, 0.000, 0.736, + 0.859, 0.000, 0.741, 0.853, 0.000, 0.745, 0.846, 0.000, 0.750, 0.839, + 0.000, 0.754, 0.833, 0.035, 0.758, 0.826, 0.091, 0.762, 0.819, 0.126, + 0.765, 0.812, 0.153, 0.769, 0.805, 0.174, 0.772, 0.798, 0.193, 0.775, + 0.791, 0.209, 0.778, 0.783, 0.223, 0.781, 0.776, 0.236, 0.784, 0.768, + 0.247, 0.786, 0.760, 0.257, 0.788, 0.752, 0.266, 0.790, 0.743, 0.273, + 0.791, 0.734, 0.280, 0.793, 0.725, 0.287, 0.794, 0.715, 0.292, 0.794, + 0.706, 0.297, 0.795, 0.695, 0.301, 0.795, 0.685, 0.305, 0.795, 0.674, + 0.308, 0.795, 0.662, 0.310, 0.794, 0.651, 0.312, 0.794, 0.638, 0.314, + 0.792, 0.626, 0.316, 0.791, 0.613, 0.317, 0.789, 0.599, 0.318, 0.787, + 0.586, 0.319, 0.785, 0.571, 0.320, 0.783, 0.557, 0.320, 0.780, 0.542, + 0.321, 0.777, 0.527, 0.321, 0.774, 0.511, 0.322, 0.770, 0.495, 0.322, + 0.767, 0.478, 0.323, 0.763, 0.462, 0.323, 0.759, 0.445, 0.324, 0.755, + 0.427, 0.325, 0.750, 0.410, 0.325, 0.745, 0.391, 0.326, 0.741, 0.373, + 0.327, 0.736, 0.354, 0.328, 0.730, 0.335, 0.329, 0.725, 0.315, 0.330, + 0.720, 0.295, 0.331, 0.714, 0.274, 0.333, 0.708, 0.253, 0.334, 0.702, + 0.230, 0.336, 0.696, 0.207, 0.337, 0.690, 0.182, 0.339, 0.684, 0.154, + 0.000, 0.679, 0.942, 0.000, 0.685, 0.934, 0.000, 0.691, 0.927, 0.000, + 0.696, 0.919, 0.000, 0.702, 0.912, 0.000, 0.708, 0.905, 0.000, 0.713, + 0.898, 0.000, 0.718, 0.891, 0.000, 0.724, 0.884, 0.000, 0.729, 0.877, + 0.000, 0.734, 0.871, 0.000, 0.739, 0.864, 0.000, 0.743, 0.857, 0.035, + 0.748, 0.851, 0.096, 0.752, 0.844, 0.133, 0.757, 0.838, 0.161, 0.761, + 0.831, 0.185, 0.765, 0.825, 0.205, 0.769, 0.818, 0.223, 0.772, 0.811, + 0.238, 0.776, 0.804, 0.252, 0.779, 0.797, 0.265, 0.782, 0.790, 0.276, + 0.785, 0.783, 0.286, 0.788, 0.775, 0.296, 0.790, 0.767, 0.304, 0.792, + 0.759, 0.311, 0.794, 0.751, 0.318, 0.796, 0.742, 0.324, 0.797, 0.733, + 0.329, 0.798, 0.723, 0.334, 0.799, 0.714, 0.338, 0.799, 0.703, 0.341, + 0.800, 0.693, 0.344, 0.800, 0.682, 0.347, 0.799, 0.670, 0.349, 0.799, + 0.659, 0.351, 0.798, 0.646, 0.352, 0.797, 0.634, 0.353, 0.795, 0.621, + 0.354, 0.794, 0.607, 0.354, 0.792, 0.593, 0.355, 0.789, 0.579, 0.355, + 0.787, 0.564, 0.355, 0.784, 0.549, 0.355, 0.781, 0.534, 0.355, 0.778, + 0.518, 0.355, 0.774, 0.502, 0.355, 0.770, 0.485, 0.355, 0.766, 0.468, + 0.355, 0.762, 0.451, 0.355, 0.758, 0.434, 0.355, 0.753, 0.416, 0.356, + 0.748, 0.397, 0.356, 0.743, 0.379, 0.356, 0.738, 0.360, 0.357, 0.733, + 0.340, 0.357, 0.728, 0.321, 0.358, 0.722, 0.300, 0.359, 0.716, 0.279, + 0.360, 0.710, 0.258, 0.361, 0.704, 0.235, 0.361, 0.698, 0.212, 0.362, + 0.692, 0.187, 0.363, 0.686, 0.160, 0.000, 0.680, 0.945, 0.000, 0.686, + 0.937, 0.000, 0.692, 0.930, 0.000, 0.698, 0.922, 0.000, 0.703, 0.915, + 0.000, 0.709, 0.908, 0.000, 0.715, 0.901, 0.000, 0.720, 0.894, 0.000, + 0.726, 0.888, 0.000, 0.731, 0.881, 0.007, 0.736, 0.875, 0.084, 0.741, + 0.869, 0.127, 0.746, 0.862, 0.159, 0.751, 0.856, 0.185, 0.755, 0.850, + 0.208, 0.760, 0.843, 0.227, 0.764, 0.837, 0.245, 0.768, 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0.507, 0.713, 0.951, 0.508, 0.701, + 0.954, 0.508, 0.690, 0.957, 0.508, 0.678, 0.960, 0.508, 0.666, 0.962, + 0.507, 0.653, 0.965, 0.507, 0.640, 0.967, 0.505, 0.627, 0.968, 0.504, + 0.613, 0.970, 0.502, 0.599, 0.971, 0.500, 0.585, 0.972, 0.498, 0.570, + 0.973, 0.495, 0.555, 0.973, 0.493, 0.540, 0.973, 0.490, 0.525, 0.973, + 0.486, 0.509, 0.973, 0.483, 0.493, 0.972, 0.479, 0.476, 0.971, 0.475, + 0.460, 0.970, 0.471, 0.443, 0.969, 0.467, 0.426, 0.967, 0.463, 0.409, + 0.965, 0.458, 0.391, 0.963, 0.453, 0.374, 0.961, 0.449, 0.356, 0.958, + 0.444, 0.338, 0.956, 0.438, 0.319, 0.953, 0.433, 0.301, 0.949, 0.428, + 0.282, 0.946, 0.422, 0.263, 0.943, 0.417, 0.243, 0.939, 0.411, 0.223, + 0.935, 0.405, 0.202, 0.931, 0.399, 0.181, 0.927, 0.393, 0.158, 0.923, + 0.387, 0.134, 0.918, 0.381, 0.107, + ]).reshape((65, 65, 3)) + +BiOrangeBlue = np.array( + [0.000, 0.000, 0.000, 0.000, 0.062, 0.125, 0.000, 0.125, 0.250, 0.000, + 0.188, 0.375, 0.000, 0.250, 0.500, 0.000, 0.312, 0.625, 0.000, 0.375, + 0.750, 0.000, 0.438, 0.875, 0.000, 0.500, 1.000, 0.125, 0.062, 0.000, + 0.125, 0.125, 0.125, 0.125, 0.188, 0.250, 0.125, 0.250, 0.375, 0.125, + 0.312, 0.500, 0.125, 0.375, 0.625, 0.125, 0.438, 0.750, 0.125, 0.500, + 0.875, 0.125, 0.562, 1.000, 0.250, 0.125, 0.000, 0.250, 0.188, 0.125, + 0.250, 0.250, 0.250, 0.250, 0.312, 0.375, 0.250, 0.375, 0.500, 0.250, + 0.438, 0.625, 0.250, 0.500, 0.750, 0.250, 0.562, 0.875, 0.250, 0.625, + 1.000, 0.375, 0.188, 0.000, 0.375, 0.250, 0.125, 0.375, 0.312, 0.250, + 0.375, 0.375, 0.375, 0.375, 0.438, 0.500, 0.375, 0.500, 0.625, 0.375, + 0.562, 0.750, 0.375, 0.625, 0.875, 0.375, 0.688, 1.000, 0.500, 0.250, + 0.000, 0.500, 0.312, 0.125, 0.500, 0.375, 0.250, 0.500, 0.438, 0.375, + 0.500, 0.500, 0.500, 0.500, 0.562, 0.625, 0.500, 0.625, 0.750, 0.500, + 0.688, 0.875, 0.500, 0.750, 1.000, 0.625, 0.312, 0.000, 0.625, 0.375, + 0.125, 0.625, 0.438, 0.250, 0.625, 0.500, 0.375, 0.625, 0.562, 0.500, + 0.625, 0.625, 0.625, 0.625, 0.688, 0.750, 0.625, 0.750, 0.875, 0.625, + 0.812, 1.000, 0.750, 0.375, 0.000, 0.750, 0.438, 0.125, 0.750, 0.500, + 0.250, 0.750, 0.562, 0.375, 0.750, 0.625, 0.500, 0.750, 0.688, 0.625, + 0.750, 0.750, 0.750, 0.750, 0.812, 0.875, 0.750, 0.875, 1.000, 0.875, + 0.438, 0.000, 0.875, 0.500, 0.125, 0.875, 0.562, 0.250, 0.875, 0.625, + 0.375, 0.875, 0.688, 0.500, 0.875, 0.750, 0.625, 0.875, 0.812, 0.750, + 0.875, 0.875, 0.875, 0.875, 0.938, 1.000, 1.000, 0.500, 0.000, 1.000, + 0.562, 0.125, 1.000, 0.625, 0.250, 1.000, 0.688, 0.375, 1.000, 0.750, + 0.500, 1.000, 0.812, 0.625, 1.000, 0.875, 0.750, 1.000, 0.938, 0.875, + 1.000, 1.000, 1.000, + ]).reshape((9, 9, 3)) + +cmaps = { + "BiPeak": SegmentedBivarColormap( + BiPeak, 256, "square", (.5, .5), name="BiPeak"), + "BiOrangeBlue": SegmentedBivarColormap( + BiOrangeBlue, 256, "square", (0, 0), name="BiOrangeBlue"), + "BiCone": SegmentedBivarColormap(BiPeak, 256, "circle", (.5, .5), name="BiCone"), +} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_cm_listed.py b/.venv/lib/python3.12/site-packages/matplotlib/_cm_listed.py new file mode 100644 index 0000000000000000000000000000000000000000..b90e0a23acb08a80a71e4911ee6e487f4a297bc4 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_cm_listed.py @@ -0,0 +1,2847 @@ +from .colors import ListedColormap + +_magma_data = [[0.001462, 0.000466, 0.013866], + [0.002258, 0.001295, 0.018331], + [0.003279, 0.002305, 0.023708], + [0.004512, 0.003490, 0.029965], + [0.005950, 0.004843, 0.037130], + [0.007588, 0.006356, 0.044973], + [0.009426, 0.008022, 0.052844], + [0.011465, 0.009828, 0.060750], + [0.013708, 0.011771, 0.068667], + [0.016156, 0.013840, 0.076603], + [0.018815, 0.016026, 0.084584], + [0.021692, 0.018320, 0.092610], + [0.024792, 0.020715, 0.100676], + [0.028123, 0.023201, 0.108787], + [0.031696, 0.025765, 0.116965], + [0.035520, 0.028397, 0.125209], + [0.039608, 0.031090, 0.133515], + [0.043830, 0.033830, 0.141886], + [0.048062, 0.036607, 0.150327], + [0.052320, 0.039407, 0.158841], + [0.056615, 0.042160, 0.167446], + [0.060949, 0.044794, 0.176129], + [0.065330, 0.047318, 0.184892], + [0.069764, 0.049726, 0.193735], + [0.074257, 0.052017, 0.202660], + [0.078815, 0.054184, 0.211667], + [0.083446, 0.056225, 0.220755], + [0.088155, 0.058133, 0.229922], + [0.092949, 0.059904, 0.239164], + [0.097833, 0.061531, 0.248477], + [0.102815, 0.063010, 0.257854], + [0.107899, 0.064335, 0.267289], + [0.113094, 0.065492, 0.276784], + [0.118405, 0.066479, 0.286321], + [0.123833, 0.067295, 0.295879], + [0.129380, 0.067935, 0.305443], + [0.135053, 0.068391, 0.315000], + [0.140858, 0.068654, 0.324538], + [0.146785, 0.068738, 0.334011], + [0.152839, 0.068637, 0.343404], + [0.159018, 0.068354, 0.352688], + [0.165308, 0.067911, 0.361816], + [0.171713, 0.067305, 0.370771], + [0.178212, 0.066576, 0.379497], + [0.184801, 0.065732, 0.387973], + [0.191460, 0.064818, 0.396152], + [0.198177, 0.063862, 0.404009], + [0.204935, 0.062907, 0.411514], + [0.211718, 0.061992, 0.418647], + [0.218512, 0.061158, 0.425392], + [0.225302, 0.060445, 0.431742], + [0.232077, 0.059889, 0.437695], + [0.238826, 0.059517, 0.443256], + [0.245543, 0.059352, 0.448436], + [0.252220, 0.059415, 0.453248], + [0.258857, 0.059706, 0.457710], + [0.265447, 0.060237, 0.461840], + [0.271994, 0.060994, 0.465660], + [0.278493, 0.061978, 0.469190], + [0.284951, 0.063168, 0.472451], + [0.291366, 0.064553, 0.475462], + [0.297740, 0.066117, 0.478243], + [0.304081, 0.067835, 0.480812], + [0.310382, 0.069702, 0.483186], + [0.316654, 0.071690, 0.485380], + [0.322899, 0.073782, 0.487408], + [0.329114, 0.075972, 0.489287], + [0.335308, 0.078236, 0.491024], + [0.341482, 0.080564, 0.492631], + [0.347636, 0.082946, 0.494121], + [0.353773, 0.085373, 0.495501], + [0.359898, 0.087831, 0.496778], + [0.366012, 0.090314, 0.497960], + [0.372116, 0.092816, 0.499053], + [0.378211, 0.095332, 0.500067], + [0.384299, 0.097855, 0.501002], + [0.390384, 0.100379, 0.501864], + [0.396467, 0.102902, 0.502658], + [0.402548, 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+ [0.12782, 0.077586, 0.10529], + [0.12422, 0.077332, 0.1019], + [0.12091, 0.077161, 0.098724], + [0.11793, 0.077088, 0.095739], + [0.11512, 0.077124, 0.092921], + [0.11267, 0.077278, 0.090344], + [0.11042, 0.077557, 0.087858], + [0.10835, 0.077968, 0.085431], + [0.10665, 0.078516, 0.083233], + [0.105, 0.079207, 0.081185], + [0.10368, 0.080048, 0.079202], + [0.10245, 0.081036, 0.077408], + [0.10143, 0.082173, 0.075793], + [0.1006, 0.083343, 0.074344], + [0.099957, 0.084733, 0.073021], + [0.099492, 0.086174, 0.071799], + [0.099204, 0.087868, 0.070716], + [0.099092, 0.089631, 0.069813], + [0.099154, 0.091582, 0.069047], + [0.099384, 0.093597, 0.068337], + [0.099759, 0.095871, 0.067776], + [0.10029, 0.098368, 0.067351], + [0.10099, 0.101, 0.067056], + [0.10185, 0.1039, 0.066891], + [0.1029, 0.10702, 0.066853], + [0.10407, 0.11031, 0.066942], + [0.10543, 0.1138, 0.067155], + [0.10701, 0.1175, 0.067485], + [0.10866, 0.12142, 0.067929], + [0.11059, 0.12561, 0.06849], + [0.11265, 0.12998, 0.069162], + [0.11483, 0.13453, 0.069842], + [0.11725, 0.13923, 0.07061], + [0.11985, 0.14422, 0.071528], + [0.12259, 0.14937, 0.072403], + [0.12558, 0.15467, 0.073463], + [0.12867, 0.16015, 0.074429], + [0.13196, 0.16584, 0.075451], + [0.1354, 0.17169, 0.076499], + [0.13898, 0.17771, 0.077615], + [0.14273, 0.18382, 0.078814], + [0.14658, 0.1901, 0.080098], + [0.15058, 0.19654, 0.081473], + [0.15468, 0.20304, 0.08282], + [0.15891, 0.20968, 0.084315], + [0.16324, 0.21644, 0.085726], + [0.16764, 0.22326, 0.087378], + [0.17214, 0.23015, 0.088955], + [0.17673, 0.23717, 0.090617], + [0.18139, 0.24418, 0.092314], + [0.18615, 0.25132, 0.094071], + [0.19092, 0.25846, 0.095839], + [0.19578, 0.26567, 0.097702], + [0.20067, 0.2729, 0.099539], + [0.20564, 0.28016, 0.10144], + [0.21062, 0.28744, 0.10342], + [0.21565, 0.29475, 0.10534], + [0.22072, 0.30207, 0.10737], + [0.22579, 0.30942, 0.10942], + [0.23087, 0.31675, 0.11146], + [0.236, 0.32407, 0.11354], + [0.24112, 0.3314, 0.11563], + [0.24625, 0.33874, 0.11774], + [0.25142, 0.34605, 0.11988], + [0.25656, 0.35337, 0.12202], + [0.26171, 0.36065, 0.12422], + [0.26686, 0.36793, 0.12645], + [0.272, 0.37519, 0.12865], + [0.27717, 0.38242, 0.13092], + [0.28231, 0.38964, 0.13316], + [0.28741, 0.39682, 0.13541], + [0.29253, 0.40398, 0.13773], + [0.29763, 0.41111, 0.13998], + [0.30271, 0.4182, 0.14232], + [0.30778, 0.42527, 0.14466], + [0.31283, 0.43231, 0.14699], + [0.31787, 0.43929, 0.14937], + [0.32289, 0.44625, 0.15173], + [0.32787, 0.45318, 0.15414], + [0.33286, 0.46006, 0.1566], + [0.33781, 0.46693, 0.15904], + [0.34276, 0.47374, 0.16155], + [0.34769, 0.48054, 0.16407], + [0.3526, 0.48733, 0.16661], + [0.35753, 0.4941, 0.16923], + [0.36245, 0.50086, 0.17185], + [0.36738, 0.50764, 0.17458], + [0.37234, 0.51443, 0.17738], + [0.37735, 0.52125, 0.18022], + [0.38238, 0.52812, 0.18318], + [0.38746, 0.53505, 0.18626], + [0.39261, 0.54204, 0.18942], + [0.39783, 0.54911, 0.19272], + [0.40311, 0.55624, 0.19616], + [0.40846, 0.56348, 0.1997], + [0.4139, 0.57078, 0.20345], + [0.41942, 0.57819, 0.20734], + [0.42503, 0.5857, 0.2114], + [0.43071, 0.59329, 0.21565], + [0.43649, 0.60098, 0.22009], + [0.44237, 0.60878, 0.2247], + [0.44833, 0.61667, 0.22956], + [0.45439, 0.62465, 0.23468], + [0.46053, 0.63274, 0.23997], + [0.46679, 0.64092, 0.24553], + [0.47313, 0.64921, 0.25138], + [0.47959, 0.6576, 0.25745], + [0.48612, 0.66608, 0.26382], + [0.49277, 0.67466, 0.27047], + [0.49951, 0.68335, 0.2774], + [0.50636, 0.69213, 0.28464], + [0.51331, 0.70101, 0.2922], + [0.52035, 0.70998, 0.30008], + [0.5275, 0.71905, 0.30828], + [0.53474, 0.72821, 0.31682], + [0.54207, 0.73747, 0.32567], + [0.5495, 0.74682, 0.33491], + [0.55702, 0.75625, 0.34443], + [0.56461, 0.76577, 0.35434], + [0.5723, 0.77537, 0.36457], + [0.58006, 0.78506, 0.37515], + [0.58789, 0.79482, 0.38607], + [0.59581, 0.80465, 0.39734], + [0.60379, 0.81455, 0.40894], + [0.61182, 0.82453, 0.42086], + [0.61991, 0.83457, 0.43311], + [0.62805, 0.84467, 0.44566], + [0.63623, 0.85482, 0.45852], + [0.64445, 0.86503, 0.47168], + [0.6527, 0.8753, 0.48511], + [0.66099, 0.88562, 0.49882], + [0.6693, 0.89599, 0.51278], + [0.67763, 0.90641, 0.52699], + [0.68597, 0.91687, 0.54141], + [0.69432, 0.92738, 0.55605], + [0.70269, 0.93794, 0.5709], + [0.71107, 0.94855, 0.58593], + [0.71945, 0.9592, 0.60112], + [0.72782, 0.96989, 0.61646], + [0.7362, 0.98063, 0.63191], + [0.74458, 0.99141, 0.64748]] + +cmaps = { + name: ListedColormap(data, name=name) for name, data in [ + ('magma', _magma_data), + ('inferno', _inferno_data), + ('plasma', _plasma_data), + ('viridis', _viridis_data), + ('cividis', _cividis_data), + ('twilight', _twilight_data), + ('twilight_shifted', _twilight_shifted_data), + ('turbo', _turbo_data), + ('berlin', _berlin_data), + ('managua', _managua_data), + ('vanimo', _vanimo_data), + ]} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_cm_multivar.py b/.venv/lib/python3.12/site-packages/matplotlib/_cm_multivar.py new file mode 100644 index 0000000000000000000000000000000000000000..610d7c40935b0999fe7de38515f316ce816023f7 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_cm_multivar.py @@ -0,0 +1,166 @@ +# auto-generated by https://github.com/trygvrad/multivariate_colormaps +# date: 2024-05-28 + +from .colors import LinearSegmentedColormap, MultivarColormap +import matplotlib as mpl +_LUTSIZE = mpl.rcParams['image.lut'] + +_2VarAddA0_data = [[0.000, 0.000, 0.000], + [0.020, 0.026, 0.031], + [0.049, 0.068, 0.085], + [0.075, 0.107, 0.135], + [0.097, 0.144, 0.183], + [0.116, 0.178, 0.231], + [0.133, 0.212, 0.279], + [0.148, 0.244, 0.326], + [0.161, 0.276, 0.374], + [0.173, 0.308, 0.422], + [0.182, 0.339, 0.471], + [0.190, 0.370, 0.521], + [0.197, 0.400, 0.572], + [0.201, 0.431, 0.623], + [0.204, 0.461, 0.675], + [0.204, 0.491, 0.728], + [0.202, 0.520, 0.783], + [0.197, 0.549, 0.838], + [0.187, 0.577, 0.895]] + +_2VarAddA1_data = [[0.000, 0.000, 0.000], + [0.030, 0.023, 0.018], + [0.079, 0.060, 0.043], + [0.125, 0.093, 0.065], + [0.170, 0.123, 0.083], + [0.213, 0.151, 0.098], + [0.255, 0.177, 0.110], + [0.298, 0.202, 0.120], + [0.341, 0.226, 0.128], + [0.384, 0.249, 0.134], + [0.427, 0.271, 0.138], + [0.472, 0.292, 0.141], + [0.517, 0.313, 0.142], + [0.563, 0.333, 0.141], + [0.610, 0.353, 0.139], + [0.658, 0.372, 0.134], + [0.708, 0.390, 0.127], + [0.759, 0.407, 0.118], + [0.813, 0.423, 0.105]] + +_2VarSubA0_data = [[1.000, 1.000, 1.000], + [0.959, 0.973, 0.986], + [0.916, 0.948, 0.974], + [0.874, 0.923, 0.965], + [0.832, 0.899, 0.956], + [0.790, 0.875, 0.948], + [0.748, 0.852, 0.940], + [0.707, 0.829, 0.934], + [0.665, 0.806, 0.927], + [0.624, 0.784, 0.921], + [0.583, 0.762, 0.916], + [0.541, 0.740, 0.910], + [0.500, 0.718, 0.905], + [0.457, 0.697, 0.901], + [0.414, 0.675, 0.896], + [0.369, 0.652, 0.892], + [0.320, 0.629, 0.888], + [0.266, 0.604, 0.884], + [0.199, 0.574, 0.881]] + +_2VarSubA1_data = [[1.000, 1.000, 1.000], + [0.982, 0.967, 0.955], + [0.966, 0.935, 0.908], + [0.951, 0.902, 0.860], + [0.937, 0.870, 0.813], + [0.923, 0.838, 0.765], + [0.910, 0.807, 0.718], + [0.898, 0.776, 0.671], + [0.886, 0.745, 0.624], + [0.874, 0.714, 0.577], + [0.862, 0.683, 0.530], + [0.851, 0.653, 0.483], + [0.841, 0.622, 0.435], + [0.831, 0.592, 0.388], + [0.822, 0.561, 0.340], + [0.813, 0.530, 0.290], + [0.806, 0.498, 0.239], + [0.802, 0.464, 0.184], + [0.801, 0.426, 0.119]] + +_3VarAddA0_data = [[0.000, 0.000, 0.000], + [0.018, 0.023, 0.028], + [0.040, 0.056, 0.071], + [0.059, 0.087, 0.110], + [0.074, 0.114, 0.147], + [0.086, 0.139, 0.183], + [0.095, 0.163, 0.219], + [0.101, 0.187, 0.255], + [0.105, 0.209, 0.290], + [0.107, 0.230, 0.326], + [0.105, 0.251, 0.362], + [0.101, 0.271, 0.398], + [0.091, 0.291, 0.434], + [0.075, 0.309, 0.471], + [0.046, 0.325, 0.507], + [0.021, 0.341, 0.546], + [0.021, 0.363, 0.584], + [0.022, 0.385, 0.622], + [0.023, 0.408, 0.661]] + +_3VarAddA1_data = [[0.000, 0.000, 0.000], + [0.020, 0.024, 0.016], + [0.047, 0.058, 0.034], + [0.072, 0.088, 0.048], + [0.093, 0.116, 0.059], + [0.113, 0.142, 0.067], + [0.131, 0.167, 0.071], + [0.149, 0.190, 0.074], + [0.166, 0.213, 0.074], + [0.182, 0.235, 0.072], + [0.198, 0.256, 0.068], + [0.215, 0.276, 0.061], + [0.232, 0.296, 0.051], + [0.249, 0.314, 0.037], + [0.270, 0.330, 0.018], + [0.288, 0.347, 0.000], + [0.302, 0.369, 0.000], + [0.315, 0.391, 0.000], + [0.328, 0.414, 0.000]] + +_3VarAddA2_data = [[0.000, 0.000, 0.000], + [0.029, 0.020, 0.023], + [0.072, 0.045, 0.055], + [0.111, 0.067, 0.084], + [0.148, 0.085, 0.109], + [0.184, 0.101, 0.133], + [0.219, 0.115, 0.155], + [0.254, 0.127, 0.176], + [0.289, 0.138, 0.195], + [0.323, 0.147, 0.214], + [0.358, 0.155, 0.232], + [0.393, 0.161, 0.250], + [0.429, 0.166, 0.267], + [0.467, 0.169, 0.283], + [0.507, 0.168, 0.298], + [0.546, 0.168, 0.313], + [0.580, 0.172, 0.328], + [0.615, 0.175, 0.341], + [0.649, 0.178, 0.355]] + +cmaps = { + name: LinearSegmentedColormap.from_list(name, data, _LUTSIZE) for name, data in [ + ('2VarAddA0', _2VarAddA0_data), + ('2VarAddA1', _2VarAddA1_data), + ('2VarSubA0', _2VarSubA0_data), + ('2VarSubA1', _2VarSubA1_data), + ('3VarAddA0', _3VarAddA0_data), + ('3VarAddA1', _3VarAddA1_data), + ('3VarAddA2', _3VarAddA2_data), + ]} + +cmap_families = { + '2VarAddA': MultivarColormap([cmaps[f'2VarAddA{i}'] for i in range(2)], + 'sRGB_add', name='2VarAddA'), + '2VarSubA': MultivarColormap([cmaps[f'2VarSubA{i}'] for i in range(2)], + 'sRGB_sub', name='2VarSubA'), + '3VarAddA': MultivarColormap([cmaps[f'3VarAddA{i}'] for i in range(3)], + 'sRGB_add', name='3VarAddA'), +} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_color_data.py b/.venv/lib/python3.12/site-packages/matplotlib/_color_data.py new file mode 100644 index 0000000000000000000000000000000000000000..44f97adbb76aeaec2578cedfe60219a3278fd2ca --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_color_data.py @@ -0,0 +1,1141 @@ +BASE_COLORS = { + 'b': (0, 0, 1), # blue + 'g': (0, 0.5, 0), # green + 'r': (1, 0, 0), # red + 'c': (0, 0.75, 0.75), # cyan + 'm': (0.75, 0, 0.75), # magenta + 'y': (0.75, 0.75, 0), # yellow + 'k': (0, 0, 0), # black + 'w': (1, 1, 1), # white +} + + +# These colors are from Tableau +TABLEAU_COLORS = { + 'tab:blue': '#1f77b4', + 'tab:orange': '#ff7f0e', + 'tab:green': '#2ca02c', + 'tab:red': '#d62728', + 'tab:purple': '#9467bd', + 'tab:brown': '#8c564b', + 'tab:pink': '#e377c2', + 'tab:gray': '#7f7f7f', + 'tab:olive': '#bcbd22', + 'tab:cyan': '#17becf', +} + + +# This mapping of color names -> hex values is taken from +# a survey run by Randall Munroe see: +# https://blog.xkcd.com/2010/05/03/color-survey-results/ +# for more details. The results are hosted at +# https://xkcd.com/color/rgb/ +# and also available as a text file at +# https://xkcd.com/color/rgb.txt +# +# License: https://creativecommons.org/publicdomain/zero/1.0/ +XKCD_COLORS = { + 'cloudy blue': '#acc2d9', + 'dark pastel green': '#56ae57', + 'dust': '#b2996e', + 'electric lime': '#a8ff04', + 'fresh green': '#69d84f', + 'light eggplant': '#894585', + 'nasty green': '#70b23f', + 'really light blue': '#d4ffff', + 'tea': '#65ab7c', + 'warm purple': '#952e8f', + 'yellowish tan': '#fcfc81', + 'cement': '#a5a391', + 'dark grass green': '#388004', + 'dusty teal': '#4c9085', + 'grey teal': '#5e9b8a', + 'macaroni and cheese': '#efb435', + 'pinkish tan': '#d99b82', + 'spruce': '#0a5f38', + 'strong blue': '#0c06f7', + 'toxic green': '#61de2a', + 'windows blue': '#3778bf', + 'blue blue': '#2242c7', + 'blue with a hint of purple': '#533cc6', + 'booger': '#9bb53c', + 'bright sea green': '#05ffa6', + 'dark green blue': '#1f6357', + 'deep turquoise': '#017374', + 'green teal': '#0cb577', + 'strong pink': '#ff0789', + 'bland': '#afa88b', + 'deep aqua': '#08787f', + 'lavender pink': '#dd85d7', + 'light moss green': '#a6c875', + 'light seafoam green': '#a7ffb5', + 'olive yellow': '#c2b709', + 'pig pink': '#e78ea5', + 'deep lilac': '#966ebd', + 'desert': '#ccad60', + 'dusty lavender': '#ac86a8', + 'purpley grey': '#947e94', + 'purply': '#983fb2', + 'candy pink': '#ff63e9', + 'light pastel green': '#b2fba5', + 'boring green': '#63b365', + 'kiwi green': '#8ee53f', + 'light grey green': '#b7e1a1', + 'orange pink': '#ff6f52', + 'tea green': '#bdf8a3', + 'very light brown': '#d3b683', + 'egg shell': '#fffcc4', + 'eggplant purple': '#430541', + 'powder pink': '#ffb2d0', + 'reddish grey': '#997570', + 'baby shit brown': '#ad900d', + 'liliac': '#c48efd', + 'stormy blue': '#507b9c', + 'ugly brown': '#7d7103', + 'custard': '#fffd78', + 'darkish pink': '#da467d', + 'deep brown': '#410200', + 'greenish beige': '#c9d179', + 'manilla': '#fffa86', + 'off blue': '#5684ae', + 'battleship grey': '#6b7c85', + 'browny green': '#6f6c0a', + 'bruise': '#7e4071', + 'kelley green': '#009337', + 'sickly yellow': '#d0e429', + 'sunny yellow': '#fff917', + 'azul': '#1d5dec', + 'darkgreen': '#054907', + 'green/yellow': '#b5ce08', + 'lichen': '#8fb67b', + 'light light green': '#c8ffb0', + 'pale gold': '#fdde6c', + 'sun yellow': '#ffdf22', + 'tan green': '#a9be70', + 'burple': '#6832e3', + 'butterscotch': '#fdb147', + 'toupe': '#c7ac7d', + 'dark cream': '#fff39a', + 'indian red': '#850e04', + 'light lavendar': '#efc0fe', + 'poison green': '#40fd14', + 'baby puke green': '#b6c406', + 'bright yellow green': '#9dff00', + 'charcoal grey': '#3c4142', + 'squash': '#f2ab15', + 'cinnamon': '#ac4f06', + 'light pea green': '#c4fe82', + 'radioactive green': '#2cfa1f', + 'raw sienna': '#9a6200', + 'baby purple': '#ca9bf7', + 'cocoa': '#875f42', + 'light royal blue': '#3a2efe', + 'orangeish': '#fd8d49', + 'rust brown': '#8b3103', + 'sand brown': '#cba560', + 'swamp': '#698339', + 'tealish green': '#0cdc73', + 'burnt siena': '#b75203', + 'camo': '#7f8f4e', + 'dusk blue': '#26538d', + 'fern': '#63a950', + 'old rose': '#c87f89', + 'pale light green': '#b1fc99', + 'peachy pink': '#ff9a8a', + 'rosy pink': '#f6688e', + 'light bluish green': '#76fda8', + 'light bright green': '#53fe5c', + 'light neon green': '#4efd54', + 'light seafoam': '#a0febf', + 'tiffany blue': '#7bf2da', + 'washed out green': '#bcf5a6', + 'browny orange': '#ca6b02', + 'nice blue': '#107ab0', + 'sapphire': '#2138ab', + 'greyish teal': '#719f91', + 'orangey yellow': '#fdb915', + 'parchment': '#fefcaf', + 'straw': '#fcf679', + 'very dark brown': '#1d0200', + 'terracota': '#cb6843', + 'ugly blue': '#31668a', + 'clear blue': '#247afd', + 'creme': '#ffffb6', + 'foam green': '#90fda9', + 'grey/green': '#86a17d', + 'light gold': '#fddc5c', + 'seafoam blue': '#78d1b6', + 'topaz': '#13bbaf', + 'violet pink': '#fb5ffc', + 'wintergreen': '#20f986', + 'yellow tan': '#ffe36e', + 'dark fuchsia': '#9d0759', + 'indigo blue': '#3a18b1', + 'light yellowish green': '#c2ff89', + 'pale magenta': '#d767ad', + 'rich purple': '#720058', + 'sunflower yellow': '#ffda03', + 'green/blue': '#01c08d', + 'leather': '#ac7434', + 'racing green': '#014600', + 'vivid purple': '#9900fa', + 'dark royal blue': '#02066f', + 'hazel': '#8e7618', + 'muted pink': '#d1768f', + 'booger green': '#96b403', + 'canary': '#fdff63', + 'cool grey': '#95a3a6', + 'dark taupe': '#7f684e', + 'darkish purple': '#751973', + 'true green': '#089404', + 'coral pink': '#ff6163', + 'dark sage': '#598556', + 'dark slate blue': '#214761', + 'flat blue': '#3c73a8', + 'mushroom': '#ba9e88', + 'rich blue': '#021bf9', + 'dirty purple': '#734a65', + 'greenblue': '#23c48b', + 'icky green': '#8fae22', + 'light khaki': '#e6f2a2', + 'warm blue': '#4b57db', + 'dark hot pink': '#d90166', + 'deep sea blue': '#015482', + 'carmine': '#9d0216', + 'dark yellow green': '#728f02', + 'pale peach': '#ffe5ad', + 'plum purple': '#4e0550', + 'golden rod': '#f9bc08', + 'neon red': '#ff073a', + 'old pink': '#c77986', + 'very pale blue': '#d6fffe', + 'blood orange': '#fe4b03', + 'grapefruit': '#fd5956', + 'sand yellow': '#fce166', + 'clay brown': '#b2713d', + 'dark blue grey': '#1f3b4d', + 'flat green': '#699d4c', + 'light green blue': '#56fca2', + 'warm pink': '#fb5581', + 'dodger blue': '#3e82fc', + 'gross green': '#a0bf16', + 'ice': '#d6fffa', + 'metallic blue': '#4f738e', + 'pale salmon': '#ffb19a', + 'sap green': '#5c8b15', + 'algae': '#54ac68', + 'bluey grey': '#89a0b0', + 'greeny grey': '#7ea07a', + 'highlighter green': '#1bfc06', + 'light light blue': '#cafffb', + 'light mint': '#b6ffbb', + 'raw umber': '#a75e09', + 'vivid blue': '#152eff', + 'deep lavender': '#8d5eb7', + 'dull teal': '#5f9e8f', + 'light greenish blue': '#63f7b4', + 'mud green': '#606602', + 'pinky': '#fc86aa', + 'red wine': '#8c0034', + 'shit green': '#758000', + 'tan brown': '#ab7e4c', + 'darkblue': '#030764', + 'rosa': '#fe86a4', + 'lipstick': '#d5174e', + 'pale mauve': '#fed0fc', + 'claret': '#680018', + 'dandelion': '#fedf08', + 'orangered': '#fe420f', + 'poop green': '#6f7c00', + 'ruby': '#ca0147', + 'dark': '#1b2431', + 'greenish turquoise': '#00fbb0', + 'pastel red': '#db5856', + 'piss yellow': '#ddd618', + 'bright cyan': '#41fdfe', + 'dark coral': '#cf524e', + 'algae green': '#21c36f', + 'darkish red': '#a90308', + 'reddy brown': '#6e1005', + 'blush pink': '#fe828c', + 'camouflage green': '#4b6113', + 'lawn green': '#4da409', + 'putty': '#beae8a', + 'vibrant blue': '#0339f8', + 'dark sand': '#a88f59', + 'purple/blue': '#5d21d0', + 'saffron': '#feb209', + 'twilight': '#4e518b', + 'warm brown': '#964e02', + 'bluegrey': '#85a3b2', + 'bubble gum pink': '#ff69af', + 'duck egg blue': '#c3fbf4', + 'greenish cyan': '#2afeb7', + 'petrol': '#005f6a', + 'royal': '#0c1793', + 'butter': '#ffff81', + 'dusty orange': '#f0833a', + 'off yellow': '#f1f33f', + 'pale olive green': '#b1d27b', + 'orangish': '#fc824a', + 'leaf': '#71aa34', + 'light blue grey': '#b7c9e2', + 'dried blood': '#4b0101', + 'lightish purple': '#a552e6', + 'rusty red': '#af2f0d', + 'lavender blue': '#8b88f8', + 'light grass green': '#9af764', + 'light mint green': '#a6fbb2', + 'sunflower': '#ffc512', + 'velvet': '#750851', + 'brick orange': '#c14a09', + 'lightish red': '#fe2f4a', + 'pure blue': '#0203e2', + 'twilight blue': '#0a437a', + 'violet red': '#a50055', + 'yellowy brown': '#ae8b0c', + 'carnation': '#fd798f', + 'muddy yellow': '#bfac05', + 'dark seafoam green': '#3eaf76', + 'deep rose': '#c74767', + 'dusty red': '#b9484e', + 'grey/blue': '#647d8e', + 'lemon lime': '#bffe28', + 'purple/pink': '#d725de', + 'brown yellow': '#b29705', + 'purple brown': '#673a3f', + 'wisteria': '#a87dc2', + 'banana yellow': '#fafe4b', + 'lipstick red': '#c0022f', + 'water blue': '#0e87cc', + 'brown grey': '#8d8468', + 'vibrant purple': '#ad03de', + 'baby green': '#8cff9e', + 'barf green': '#94ac02', + 'eggshell blue': '#c4fff7', + 'sandy yellow': '#fdee73', + 'cool green': '#33b864', + 'pale': '#fff9d0', + 'blue/grey': '#758da3', + 'hot magenta': '#f504c9', + 'greyblue': '#77a1b5', + 'purpley': '#8756e4', + 'baby shit green': '#889717', + 'brownish pink': '#c27e79', + 'dark aquamarine': '#017371', + 'diarrhea': '#9f8303', + 'light mustard': '#f7d560', + 'pale sky blue': '#bdf6fe', + 'turtle green': '#75b84f', + 'bright olive': '#9cbb04', + 'dark grey blue': '#29465b', + 'greeny brown': '#696006', + 'lemon green': '#adf802', + 'light periwinkle': '#c1c6fc', + 'seaweed green': '#35ad6b', + 'sunshine yellow': '#fffd37', + 'ugly purple': '#a442a0', + 'medium pink': '#f36196', + 'puke brown': '#947706', + 'very light pink': '#fff4f2', + 'viridian': '#1e9167', + 'bile': '#b5c306', + 'faded yellow': '#feff7f', + 'very pale green': '#cffdbc', + 'vibrant green': '#0add08', + 'bright lime': '#87fd05', + 'spearmint': '#1ef876', + 'light aquamarine': '#7bfdc7', + 'light sage': '#bcecac', + 'yellowgreen': '#bbf90f', + 'baby poo': '#ab9004', + 'dark seafoam': '#1fb57a', + 'deep teal': '#00555a', + 'heather': '#a484ac', + 'rust orange': '#c45508', + 'dirty blue': '#3f829d', + 'fern green': '#548d44', + 'bright lilac': '#c95efb', + 'weird green': '#3ae57f', + 'peacock blue': '#016795', + 'avocado green': '#87a922', + 'faded orange': '#f0944d', + 'grape purple': '#5d1451', + 'hot green': '#25ff29', + 'lime yellow': '#d0fe1d', + 'mango': '#ffa62b', + 'shamrock': '#01b44c', + 'bubblegum': '#ff6cb5', + 'purplish brown': '#6b4247', + 'vomit yellow': '#c7c10c', + 'pale cyan': '#b7fffa', + 'key lime': '#aeff6e', + 'tomato red': '#ec2d01', + 'lightgreen': '#76ff7b', + 'merlot': '#730039', + 'night blue': '#040348', + 'purpleish pink': '#df4ec8', + 'apple': '#6ecb3c', + 'baby poop green': '#8f9805', + 'green apple': '#5edc1f', + 'heliotrope': '#d94ff5', + 'yellow/green': '#c8fd3d', + 'almost black': '#070d0d', + 'cool blue': '#4984b8', + 'leafy green': '#51b73b', + 'mustard brown': '#ac7e04', + 'dusk': '#4e5481', + 'dull brown': '#876e4b', + 'frog green': '#58bc08', + 'vivid green': '#2fef10', + 'bright light green': '#2dfe54', + 'fluro green': '#0aff02', + 'kiwi': '#9cef43', + 'seaweed': '#18d17b', + 'navy green': '#35530a', + 'ultramarine blue': '#1805db', + 'iris': '#6258c4', + 'pastel orange': '#ff964f', + 'yellowish orange': '#ffab0f', + 'perrywinkle': '#8f8ce7', + 'tealish': '#24bca8', + 'dark plum': '#3f012c', + 'pear': '#cbf85f', + 'pinkish orange': '#ff724c', + 'midnight purple': '#280137', + 'light urple': '#b36ff6', + 'dark mint': '#48c072', + 'greenish tan': '#bccb7a', + 'light burgundy': '#a8415b', + 'turquoise blue': '#06b1c4', + 'ugly pink': '#cd7584', + 'sandy': '#f1da7a', + 'electric pink': '#ff0490', + 'muted purple': '#805b87', + 'mid green': '#50a747', + 'greyish': '#a8a495', + 'neon yellow': '#cfff04', + 'banana': '#ffff7e', + 'carnation pink': '#ff7fa7', + 'tomato': '#ef4026', + 'sea': '#3c9992', + 'muddy brown': '#886806', + 'turquoise green': '#04f489', + 'buff': '#fef69e', + 'fawn': '#cfaf7b', + 'muted blue': '#3b719f', + 'pale rose': '#fdc1c5', + 'dark mint green': '#20c073', + 'amethyst': '#9b5fc0', + 'blue/green': '#0f9b8e', + 'chestnut': '#742802', + 'sick green': '#9db92c', + 'pea': '#a4bf20', + 'rusty orange': '#cd5909', + 'stone': '#ada587', + 'rose red': '#be013c', + 'pale aqua': '#b8ffeb', + 'deep orange': '#dc4d01', + 'earth': '#a2653e', + 'mossy green': '#638b27', + 'grassy green': '#419c03', + 'pale lime green': '#b1ff65', + 'light grey blue': '#9dbcd4', + 'pale grey': '#fdfdfe', + 'asparagus': '#77ab56', + 'blueberry': '#464196', + 'purple red': '#990147', + 'pale lime': '#befd73', + 'greenish teal': '#32bf84', + 'caramel': '#af6f09', + 'deep magenta': '#a0025c', + 'light peach': '#ffd8b1', + 'milk chocolate': '#7f4e1e', + 'ocher': '#bf9b0c', + 'off green': '#6ba353', + 'purply pink': '#f075e6', + 'lightblue': '#7bc8f6', + 'dusky blue': '#475f94', + 'golden': '#f5bf03', + 'light beige': '#fffeb6', + 'butter yellow': '#fffd74', + 'dusky purple': '#895b7b', + 'french blue': '#436bad', + 'ugly yellow': '#d0c101', + 'greeny yellow': '#c6f808', + 'orangish red': '#f43605', + 'shamrock green': '#02c14d', + 'orangish brown': '#b25f03', + 'tree green': '#2a7e19', + 'deep violet': '#490648', + 'gunmetal': '#536267', + 'blue/purple': '#5a06ef', + 'cherry': '#cf0234', + 'sandy brown': '#c4a661', + 'warm grey': '#978a84', + 'dark indigo': '#1f0954', + 'midnight': '#03012d', + 'bluey green': '#2bb179', + 'grey pink': '#c3909b', + 'soft purple': '#a66fb5', + 'blood': '#770001', + 'brown red': '#922b05', + 'medium grey': '#7d7f7c', + 'berry': '#990f4b', + 'poo': '#8f7303', + 'purpley pink': '#c83cb9', + 'light salmon': '#fea993', + 'snot': '#acbb0d', + 'easter purple': '#c071fe', + 'light yellow green': '#ccfd7f', + 'dark navy blue': '#00022e', + 'drab': '#828344', + 'light rose': '#ffc5cb', + 'rouge': '#ab1239', + 'purplish red': '#b0054b', + 'slime green': '#99cc04', + 'baby poop': '#937c00', + 'irish green': '#019529', + 'pink/purple': '#ef1de7', + 'dark navy': '#000435', + 'greeny blue': '#42b395', + 'light plum': '#9d5783', + 'pinkish grey': '#c8aca9', + 'dirty orange': '#c87606', + 'rust red': '#aa2704', + 'pale lilac': '#e4cbff', + 'orangey red': '#fa4224', + 'primary blue': '#0804f9', + 'kermit green': '#5cb200', + 'brownish purple': '#76424e', + 'murky green': '#6c7a0e', + 'wheat': '#fbdd7e', + 'very dark purple': '#2a0134', + 'bottle green': '#044a05', + 'watermelon': '#fd4659', + 'deep sky blue': '#0d75f8', + 'fire engine red': '#fe0002', + 'yellow ochre': '#cb9d06', + 'pumpkin orange': '#fb7d07', + 'pale olive': '#b9cc81', + 'light lilac': '#edc8ff', + 'lightish green': '#61e160', + 'carolina blue': '#8ab8fe', + 'mulberry': '#920a4e', + 'shocking pink': '#fe02a2', + 'auburn': '#9a3001', + 'bright lime green': '#65fe08', + 'celadon': '#befdb7', + 'pinkish brown': '#b17261', + 'poo brown': '#885f01', + 'bright sky blue': '#02ccfe', + 'celery': '#c1fd95', + 'dirt brown': '#836539', + 'strawberry': '#fb2943', + 'dark lime': '#84b701', + 'copper': '#b66325', + 'medium brown': '#7f5112', + 'muted green': '#5fa052', + "robin's egg": '#6dedfd', + 'bright aqua': '#0bf9ea', + 'bright lavender': '#c760ff', + 'ivory': '#ffffcb', + 'very light purple': '#f6cefc', + 'light navy': '#155084', + 'pink red': '#f5054f', + 'olive brown': '#645403', + 'poop brown': '#7a5901', + 'mustard green': '#a8b504', + 'ocean green': '#3d9973', + 'very dark blue': '#000133', + 'dusty green': '#76a973', + 'light navy blue': '#2e5a88', + 'minty green': '#0bf77d', + 'adobe': '#bd6c48', + 'barney': '#ac1db8', + 'jade green': '#2baf6a', + 'bright light blue': '#26f7fd', + 'light lime': '#aefd6c', + 'dark khaki': '#9b8f55', + 'orange yellow': '#ffad01', + 'ocre': '#c69c04', + 'maize': '#f4d054', + 'faded pink': '#de9dac', + 'british racing green': '#05480d', + 'sandstone': '#c9ae74', + 'mud brown': '#60460f', + 'light sea green': '#98f6b0', + 'robin egg blue': '#8af1fe', + 'aqua marine': '#2ee8bb', + 'dark sea green': '#11875d', + 'soft pink': '#fdb0c0', + 'orangey brown': '#b16002', + 'cherry red': '#f7022a', + 'burnt yellow': '#d5ab09', + 'brownish grey': '#86775f', + 'camel': '#c69f59', + 'purplish grey': '#7a687f', + 'marine': '#042e60', + 'greyish pink': '#c88d94', + 'pale turquoise': '#a5fbd5', + 'pastel yellow': '#fffe71', + 'bluey purple': '#6241c7', + 'canary yellow': '#fffe40', + 'faded red': '#d3494e', + 'sepia': '#985e2b', + 'coffee': '#a6814c', + 'bright magenta': '#ff08e8', + 'mocha': '#9d7651', + 'ecru': '#feffca', + 'purpleish': '#98568d', + 'cranberry': '#9e003a', + 'darkish green': '#287c37', + 'brown orange': '#b96902', + 'dusky rose': '#ba6873', + 'melon': '#ff7855', + 'sickly green': '#94b21c', + 'silver': '#c5c9c7', + 'purply blue': '#661aee', + 'purpleish blue': '#6140ef', + 'hospital green': '#9be5aa', + 'shit brown': '#7b5804', + 'mid blue': '#276ab3', + 'amber': '#feb308', + 'easter green': '#8cfd7e', + 'soft blue': '#6488ea', + 'cerulean blue': '#056eee', + 'golden brown': '#b27a01', + 'bright turquoise': '#0ffef9', + 'red pink': '#fa2a55', + 'red purple': '#820747', + 'greyish brown': '#7a6a4f', + 'vermillion': '#f4320c', + 'russet': '#a13905', + 'steel grey': '#6f828a', + 'lighter purple': '#a55af4', + 'bright violet': '#ad0afd', + 'prussian blue': '#004577', + 'slate green': '#658d6d', + 'dirty pink': '#ca7b80', + 'dark blue green': '#005249', + 'pine': '#2b5d34', + 'yellowy green': '#bff128', + 'dark gold': '#b59410', + 'bluish': '#2976bb', + 'darkish blue': '#014182', + 'dull red': '#bb3f3f', + 'pinky red': '#fc2647', + 'bronze': '#a87900', + 'pale teal': '#82cbb2', + 'military green': '#667c3e', + 'barbie pink': '#fe46a5', + 'bubblegum pink': '#fe83cc', + 'pea soup green': '#94a617', + 'dark mustard': '#a88905', + 'shit': '#7f5f00', + 'medium purple': '#9e43a2', + 'very dark green': '#062e03', + 'dirt': '#8a6e45', + 'dusky pink': '#cc7a8b', + 'red violet': '#9e0168', + 'lemon yellow': '#fdff38', + 'pistachio': '#c0fa8b', + 'dull yellow': '#eedc5b', + 'dark lime green': '#7ebd01', + 'denim blue': '#3b5b92', + 'teal blue': '#01889f', + 'lightish blue': '#3d7afd', + 'purpley blue': '#5f34e7', + 'light indigo': '#6d5acf', + 'swamp green': '#748500', + 'brown green': '#706c11', + 'dark maroon': '#3c0008', + 'hot purple': '#cb00f5', + 'dark forest green': '#002d04', + 'faded blue': '#658cbb', + 'drab green': '#749551', + 'light lime green': '#b9ff66', + 'snot green': '#9dc100', + 'yellowish': '#faee66', + 'light blue green': '#7efbb3', + 'bordeaux': '#7b002c', + 'light mauve': '#c292a1', + 'ocean': '#017b92', + 'marigold': '#fcc006', + 'muddy green': '#657432', + 'dull orange': '#d8863b', + 'steel': '#738595', + 'electric purple': '#aa23ff', + 'fluorescent green': '#08ff08', + 'yellowish brown': '#9b7a01', + 'blush': '#f29e8e', + 'soft green': '#6fc276', + 'bright orange': '#ff5b00', + 'lemon': '#fdff52', + 'purple grey': '#866f85', + 'acid green': '#8ffe09', + 'pale lavender': '#eecffe', + 'violet blue': '#510ac9', + 'light forest green': '#4f9153', + 'burnt red': '#9f2305', + 'khaki green': '#728639', + 'cerise': '#de0c62', + 'faded purple': '#916e99', + 'apricot': '#ffb16d', + 'dark olive green': '#3c4d03', + 'grey brown': '#7f7053', + 'green grey': '#77926f', + 'true blue': '#010fcc', + 'pale violet': '#ceaefa', + 'periwinkle blue': '#8f99fb', + 'light sky blue': '#c6fcff', + 'blurple': '#5539cc', + 'green brown': '#544e03', + 'bluegreen': '#017a79', + 'bright teal': '#01f9c6', + 'brownish yellow': '#c9b003', + 'pea soup': '#929901', + 'forest': '#0b5509', + 'barney purple': '#a00498', + 'ultramarine': '#2000b1', + 'purplish': '#94568c', + 'puke yellow': '#c2be0e', + 'bluish grey': '#748b97', + 'dark periwinkle': '#665fd1', + 'dark lilac': '#9c6da5', + 'reddish': '#c44240', + 'light maroon': '#a24857', + 'dusty purple': '#825f87', + 'terra cotta': '#c9643b', + 'avocado': '#90b134', + 'marine blue': '#01386a', + 'teal green': '#25a36f', + 'slate grey': '#59656d', + 'lighter green': '#75fd63', + 'electric green': '#21fc0d', + 'dusty blue': '#5a86ad', + 'golden yellow': '#fec615', + 'bright yellow': '#fffd01', + 'light lavender': '#dfc5fe', + 'umber': '#b26400', + 'poop': '#7f5e00', + 'dark peach': '#de7e5d', + 'jungle green': '#048243', + 'eggshell': '#ffffd4', + 'denim': '#3b638c', + 'yellow brown': '#b79400', + 'dull purple': '#84597e', + 'chocolate brown': '#411900', + 'wine red': '#7b0323', + 'neon blue': '#04d9ff', + 'dirty green': '#667e2c', + 'light tan': '#fbeeac', + 'ice blue': '#d7fffe', + 'cadet blue': '#4e7496', + 'dark mauve': '#874c62', + 'very light blue': '#d5ffff', + 'grey purple': '#826d8c', + 'pastel pink': '#ffbacd', + 'very light green': '#d1ffbd', + 'dark sky blue': '#448ee4', + 'evergreen': '#05472a', + 'dull pink': '#d5869d', + 'aubergine': '#3d0734', + 'mahogany': '#4a0100', + 'reddish orange': '#f8481c', + 'deep green': '#02590f', + 'vomit green': '#89a203', + 'purple pink': '#e03fd8', + 'dusty pink': '#d58a94', + 'faded green': '#7bb274', + 'camo green': '#526525', + 'pinky purple': '#c94cbe', + 'pink purple': '#db4bda', + 'brownish red': '#9e3623', + 'dark rose': '#b5485d', + 'mud': '#735c12', + 'brownish': '#9c6d57', + 'emerald green': '#028f1e', + 'pale brown': '#b1916e', + 'dull blue': '#49759c', + 'burnt umber': '#a0450e', + 'medium green': '#39ad48', + 'clay': '#b66a50', + 'light aqua': '#8cffdb', + 'light olive green': '#a4be5c', + 'brownish orange': '#cb7723', + 'dark aqua': '#05696b', + 'purplish pink': '#ce5dae', + 'dark salmon': '#c85a53', + 'greenish grey': '#96ae8d', + 'jade': '#1fa774', + 'ugly green': '#7a9703', + 'dark beige': '#ac9362', + 'emerald': '#01a049', + 'pale red': '#d9544d', + 'light magenta': '#fa5ff7', + 'sky': '#82cafc', + 'light cyan': '#acfffc', + 'yellow orange': '#fcb001', + 'reddish purple': '#910951', + 'reddish pink': '#fe2c54', + 'orchid': '#c875c4', + 'dirty yellow': '#cdc50a', + 'orange red': '#fd411e', + 'deep red': '#9a0200', + 'orange brown': '#be6400', + 'cobalt blue': '#030aa7', + 'neon pink': '#fe019a', + 'rose pink': '#f7879a', + 'greyish purple': '#887191', + 'raspberry': '#b00149', + 'aqua green': '#12e193', + 'salmon pink': '#fe7b7c', + 'tangerine': '#ff9408', + 'brownish green': '#6a6e09', + 'red brown': '#8b2e16', + 'greenish brown': '#696112', + 'pumpkin': '#e17701', + 'pine green': '#0a481e', + 'charcoal': '#343837', + 'baby pink': '#ffb7ce', + 'cornflower': '#6a79f7', + 'blue violet': '#5d06e9', + 'chocolate': '#3d1c02', + 'greyish green': '#82a67d', + 'scarlet': '#be0119', + 'green yellow': '#c9ff27', + 'dark olive': '#373e02', + 'sienna': '#a9561e', + 'pastel purple': '#caa0ff', + 'terracotta': '#ca6641', + 'aqua blue': '#02d8e9', + 'sage green': '#88b378', + 'blood red': '#980002', + 'deep pink': '#cb0162', + 'grass': '#5cac2d', + 'moss': '#769958', + 'pastel blue': '#a2bffe', + 'bluish green': '#10a674', + 'green blue': '#06b48b', + 'dark tan': '#af884a', + 'greenish blue': '#0b8b87', + 'pale orange': '#ffa756', + 'vomit': '#a2a415', + 'forrest green': '#154406', + 'dark lavender': '#856798', + 'dark violet': '#34013f', + 'purple blue': '#632de9', + 'dark cyan': '#0a888a', + 'olive drab': '#6f7632', + 'pinkish': '#d46a7e', + 'cobalt': '#1e488f', + 'neon purple': '#bc13fe', + 'light turquoise': '#7ef4cc', + 'apple green': '#76cd26', + 'dull green': '#74a662', + 'wine': '#80013f', + 'powder blue': '#b1d1fc', + 'off white': '#ffffe4', + 'electric blue': '#0652ff', + 'dark turquoise': '#045c5a', + 'blue purple': '#5729ce', + 'azure': '#069af3', + 'bright red': '#ff000d', + 'pinkish red': '#f10c45', + 'cornflower blue': '#5170d7', + 'light olive': '#acbf69', + 'grape': '#6c3461', + 'greyish blue': '#5e819d', + 'purplish blue': '#601ef9', + 'yellowish green': '#b0dd16', + 'greenish yellow': '#cdfd02', + 'medium blue': '#2c6fbb', + 'dusty rose': '#c0737a', + 'light violet': '#d6b4fc', + 'midnight blue': '#020035', + 'bluish purple': '#703be7', + 'red orange': '#fd3c06', + 'dark magenta': '#960056', + 'greenish': '#40a368', + 'ocean blue': '#03719c', + 'coral': '#fc5a50', + 'cream': '#ffffc2', + 'reddish brown': '#7f2b0a', + 'burnt sienna': '#b04e0f', + 'brick': '#a03623', + 'sage': '#87ae73', + 'grey green': '#789b73', + 'white': '#ffffff', + "robin's egg blue": '#98eff9', + 'moss green': '#658b38', + 'steel blue': '#5a7d9a', + 'eggplant': '#380835', + 'light yellow': '#fffe7a', + 'leaf green': '#5ca904', + 'light grey': '#d8dcd6', + 'puke': '#a5a502', + 'pinkish purple': '#d648d7', + 'sea blue': '#047495', + 'pale purple': '#b790d4', + 'slate blue': '#5b7c99', + 'blue grey': '#607c8e', + 'hunter green': '#0b4008', + 'fuchsia': '#ed0dd9', + 'crimson': '#8c000f', + 'pale yellow': '#ffff84', + 'ochre': '#bf9005', + 'mustard yellow': '#d2bd0a', + 'light red': '#ff474c', + 'cerulean': '#0485d1', + 'pale pink': '#ffcfdc', + 'deep blue': '#040273', + 'rust': '#a83c09', + 'light teal': '#90e4c1', + 'slate': '#516572', + 'goldenrod': '#fac205', + 'dark yellow': '#d5b60a', + 'dark grey': '#363737', + 'army green': '#4b5d16', + 'grey blue': '#6b8ba4', + 'seafoam': '#80f9ad', + 'puce': '#a57e52', + 'spring green': '#a9f971', + 'dark orange': '#c65102', + 'sand': '#e2ca76', + 'pastel green': '#b0ff9d', + 'mint': '#9ffeb0', + 'light orange': '#fdaa48', + 'bright pink': '#fe01b1', + 'chartreuse': '#c1f80a', + 'deep purple': '#36013f', + 'dark brown': '#341c02', + 'taupe': '#b9a281', + 'pea green': '#8eab12', + 'puke green': '#9aae07', + 'kelly green': '#02ab2e', + 'seafoam green': '#7af9ab', + 'blue green': '#137e6d', + 'khaki': '#aaa662', + 'burgundy': '#610023', + 'dark teal': '#014d4e', + 'brick red': '#8f1402', + 'royal purple': '#4b006e', + 'plum': '#580f41', + 'mint green': '#8fff9f', + 'gold': '#dbb40c', + 'baby blue': '#a2cffe', + 'yellow green': '#c0fb2d', + 'bright purple': '#be03fd', + 'dark red': '#840000', + 'pale blue': '#d0fefe', + 'grass green': '#3f9b0b', + 'navy': '#01153e', + 'aquamarine': '#04d8b2', + 'burnt orange': '#c04e01', + 'neon green': '#0cff0c', + 'bright blue': '#0165fc', + 'rose': '#cf6275', + 'light pink': '#ffd1df', + 'mustard': '#ceb301', + 'indigo': '#380282', + 'lime': '#aaff32', + 'sea green': '#53fca1', + 'periwinkle': '#8e82fe', + 'dark pink': '#cb416b', + 'olive green': '#677a04', + 'peach': '#ffb07c', + 'pale green': '#c7fdb5', + 'light brown': '#ad8150', + 'hot pink': '#ff028d', + 'black': '#000000', + 'lilac': '#cea2fd', + 'navy blue': '#001146', + 'royal blue': '#0504aa', + 'beige': '#e6daa6', + 'salmon': '#ff796c', + 'olive': '#6e750e', + 'maroon': '#650021', + 'bright green': '#01ff07', + 'dark purple': '#35063e', + 'mauve': '#ae7181', + 'forest green': '#06470c', + 'aqua': '#13eac9', + 'cyan': '#00ffff', + 'tan': '#d1b26f', + 'dark blue': '#00035b', + 'lavender': '#c79fef', + 'turquoise': '#06c2ac', + 'dark green': '#033500', + 'violet': '#9a0eea', + 'light purple': '#bf77f6', + 'lime green': '#89fe05', + 'grey': '#929591', + 'sky blue': '#75bbfd', + 'yellow': '#ffff14', + 'magenta': '#c20078', + 'light green': '#96f97b', + 'orange': '#f97306', + 'teal': '#029386', + 'light blue': '#95d0fc', + 'red': '#e50000', + 'brown': '#653700', + 'pink': '#ff81c0', + 'blue': '#0343df', + 'green': '#15b01a', + 'purple': '#7e1e9c'} + +# Normalize name to "xkcd:" to avoid name collisions. +XKCD_COLORS = {'xkcd:' + name: value for name, value in XKCD_COLORS.items()} + + +# https://drafts.csswg.org/css-color-4/#named-colors +CSS4_COLORS = { + 'aliceblue': '#F0F8FF', + 'antiquewhite': '#FAEBD7', + 'aqua': '#00FFFF', + 'aquamarine': '#7FFFD4', + 'azure': '#F0FFFF', + 'beige': '#F5F5DC', + 'bisque': '#FFE4C4', + 'black': '#000000', + 'blanchedalmond': '#FFEBCD', + 'blue': '#0000FF', + 'blueviolet': '#8A2BE2', + 'brown': '#A52A2A', + 'burlywood': '#DEB887', + 'cadetblue': '#5F9EA0', + 'chartreuse': '#7FFF00', + 'chocolate': '#D2691E', + 'coral': '#FF7F50', + 'cornflowerblue': '#6495ED', + 'cornsilk': '#FFF8DC', + 'crimson': '#DC143C', + 'cyan': '#00FFFF', + 'darkblue': '#00008B', + 'darkcyan': '#008B8B', + 'darkgoldenrod': '#B8860B', + 'darkgray': '#A9A9A9', + 'darkgreen': '#006400', + 'darkgrey': '#A9A9A9', + 'darkkhaki': '#BDB76B', + 'darkmagenta': '#8B008B', + 'darkolivegreen': '#556B2F', + 'darkorange': '#FF8C00', + 'darkorchid': '#9932CC', + 'darkred': '#8B0000', + 'darksalmon': '#E9967A', + 'darkseagreen': '#8FBC8F', + 'darkslateblue': '#483D8B', + 'darkslategray': '#2F4F4F', + 'darkslategrey': '#2F4F4F', + 'darkturquoise': '#00CED1', + 'darkviolet': '#9400D3', + 'deeppink': '#FF1493', + 'deepskyblue': '#00BFFF', + 'dimgray': '#696969', + 'dimgrey': '#696969', + 'dodgerblue': '#1E90FF', + 'firebrick': '#B22222', + 'floralwhite': '#FFFAF0', + 'forestgreen': '#228B22', + 'fuchsia': '#FF00FF', + 'gainsboro': '#DCDCDC', + 'ghostwhite': '#F8F8FF', + 'gold': '#FFD700', + 'goldenrod': '#DAA520', + 'gray': '#808080', + 'green': '#008000', + 'greenyellow': '#ADFF2F', + 'grey': '#808080', + 'honeydew': '#F0FFF0', + 'hotpink': '#FF69B4', + 'indianred': '#CD5C5C', + 'indigo': '#4B0082', + 'ivory': '#FFFFF0', + 'khaki': '#F0E68C', + 'lavender': '#E6E6FA', + 'lavenderblush': '#FFF0F5', + 'lawngreen': '#7CFC00', + 'lemonchiffon': '#FFFACD', + 'lightblue': '#ADD8E6', + 'lightcoral': '#F08080', + 'lightcyan': '#E0FFFF', + 'lightgoldenrodyellow': '#FAFAD2', + 'lightgray': '#D3D3D3', + 'lightgreen': '#90EE90', + 'lightgrey': '#D3D3D3', + 'lightpink': '#FFB6C1', + 'lightsalmon': '#FFA07A', + 'lightseagreen': '#20B2AA', + 'lightskyblue': '#87CEFA', + 'lightslategray': '#778899', + 'lightslategrey': '#778899', + 'lightsteelblue': '#B0C4DE', + 'lightyellow': '#FFFFE0', + 'lime': '#00FF00', + 'limegreen': '#32CD32', + 'linen': '#FAF0E6', + 'magenta': '#FF00FF', + 'maroon': '#800000', + 'mediumaquamarine': '#66CDAA', + 'mediumblue': '#0000CD', + 'mediumorchid': '#BA55D3', + 'mediumpurple': '#9370DB', + 'mediumseagreen': '#3CB371', + 'mediumslateblue': '#7B68EE', + 'mediumspringgreen': '#00FA9A', + 'mediumturquoise': '#48D1CC', + 'mediumvioletred': '#C71585', + 'midnightblue': '#191970', + 'mintcream': '#F5FFFA', + 'mistyrose': '#FFE4E1', + 'moccasin': '#FFE4B5', + 'navajowhite': '#FFDEAD', + 'navy': '#000080', + 'oldlace': '#FDF5E6', + 'olive': '#808000', + 'olivedrab': '#6B8E23', + 'orange': '#FFA500', + 'orangered': '#FF4500', + 'orchid': '#DA70D6', + 'palegoldenrod': '#EEE8AA', + 'palegreen': '#98FB98', + 'paleturquoise': '#AFEEEE', + 'palevioletred': '#DB7093', + 'papayawhip': '#FFEFD5', + 'peachpuff': '#FFDAB9', + 'peru': '#CD853F', + 'pink': '#FFC0CB', + 'plum': '#DDA0DD', + 'powderblue': '#B0E0E6', + 'purple': '#800080', + 'rebeccapurple': '#663399', + 'red': '#FF0000', + 'rosybrown': '#BC8F8F', + 'royalblue': '#4169E1', + 'saddlebrown': '#8B4513', + 'salmon': '#FA8072', + 'sandybrown': '#F4A460', + 'seagreen': '#2E8B57', + 'seashell': '#FFF5EE', + 'sienna': '#A0522D', + 'silver': '#C0C0C0', + 'skyblue': '#87CEEB', + 'slateblue': '#6A5ACD', + 'slategray': '#708090', + 'slategrey': '#708090', + 'snow': '#FFFAFA', + 'springgreen': '#00FF7F', + 'steelblue': '#4682B4', + 'tan': '#D2B48C', + 'teal': '#008080', + 'thistle': '#D8BFD8', + 'tomato': '#FF6347', + 'turquoise': '#40E0D0', + 'violet': '#EE82EE', + 'wheat': '#F5DEB3', + 'white': '#FFFFFF', + 'whitesmoke': '#F5F5F5', + 'yellow': '#FFFF00', + 'yellowgreen': '#9ACD32'} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_color_data.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_color_data.pyi new file mode 100644 index 0000000000000000000000000000000000000000..feb3de9c3043d55910e2649804352ae96ccbd1ec --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_color_data.pyi @@ -0,0 +1,6 @@ +from .typing import ColorType + +BASE_COLORS: dict[str, ColorType] +TABLEAU_COLORS: dict[str, ColorType] +XKCD_COLORS: dict[str, ColorType] +CSS4_COLORS: dict[str, ColorType] diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_constrained_layout.py b/.venv/lib/python3.12/site-packages/matplotlib/_constrained_layout.py new file mode 100644 index 0000000000000000000000000000000000000000..f5f23581bd9dc519a33753dbe171e00ee29c28d4 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_constrained_layout.py @@ -0,0 +1,805 @@ +""" +Adjust subplot layouts so that there are no overlapping Axes or Axes +decorations. All Axes decorations are dealt with (labels, ticks, titles, +ticklabels) and some dependent artists are also dealt with (colorbar, +suptitle). + +Layout is done via `~matplotlib.gridspec`, with one constraint per gridspec, +so it is possible to have overlapping Axes if the gridspecs overlap (i.e. +using `~matplotlib.gridspec.GridSpecFromSubplotSpec`). Axes placed using +``figure.subplots()`` or ``figure.add_subplots()`` will participate in the +layout. Axes manually placed via ``figure.add_axes()`` will not. + +See Tutorial: :ref:`constrainedlayout_guide` + +General idea: +------------- + +First, a figure has a gridspec that divides the figure into nrows and ncols, +with heights and widths set by ``height_ratios`` and ``width_ratios``, +often just set to 1 for an equal grid. + +Subplotspecs that are derived from this gridspec can contain either a +``SubPanel``, a ``GridSpecFromSubplotSpec``, or an ``Axes``. The ``SubPanel`` +and ``GridSpecFromSubplotSpec`` are dealt with recursively and each contain an +analogous layout. + +Each ``GridSpec`` has a ``_layoutgrid`` attached to it. The ``_layoutgrid`` +has the same logical layout as the ``GridSpec``. Each row of the grid spec +has a top and bottom "margin" and each column has a left and right "margin". +The "inner" height of each row is constrained to be the same (or as modified +by ``height_ratio``), and the "inner" width of each column is +constrained to be the same (as modified by ``width_ratio``), where "inner" +is the width or height of each column/row minus the size of the margins. + +Then the size of the margins for each row and column are determined as the +max width of the decorators on each Axes that has decorators in that margin. +For instance, a normal Axes would have a left margin that includes the +left ticklabels, and the ylabel if it exists. The right margin may include a +colorbar, the bottom margin the xaxis decorations, and the top margin the +title. + +With these constraints, the solver then finds appropriate bounds for the +columns and rows. It's possible that the margins take up the whole figure, +in which case the algorithm is not applied and a warning is raised. + +See the tutorial :ref:`constrainedlayout_guide` +for more discussion of the algorithm with examples. +""" + +import logging + +import numpy as np + +from matplotlib import _api, artist as martist +import matplotlib.transforms as mtransforms +import matplotlib._layoutgrid as mlayoutgrid + + +_log = logging.getLogger(__name__) + + +###################################################### +def do_constrained_layout(fig, h_pad, w_pad, + hspace=None, wspace=None, rect=(0, 0, 1, 1), + compress=False): + """ + Do the constrained_layout. Called at draw time in + ``figure.constrained_layout()`` + + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + `.Figure` instance to do the layout in. + + h_pad, w_pad : float + Padding around the Axes elements in figure-normalized units. + + hspace, wspace : float + Fraction of the figure to dedicate to space between the + Axes. These are evenly spread between the gaps between the Axes. + A value of 0.2 for a three-column layout would have a space + of 0.1 of the figure width between each column. + If h/wspace < h/w_pad, then the pads are used instead. + + rect : tuple of 4 floats + Rectangle in figure coordinates to perform constrained layout in + [left, bottom, width, height], each from 0-1. + + compress : bool + Whether to shift Axes so that white space in between them is + removed. This is useful for simple grids of fixed-aspect Axes (e.g. + a grid of images). + + Returns + ------- + layoutgrid : private debugging structure + """ + + renderer = fig._get_renderer() + # make layoutgrid tree... + layoutgrids = make_layoutgrids(fig, None, rect=rect) + if not layoutgrids['hasgrids']: + _api.warn_external('There are no gridspecs with layoutgrids. ' + 'Possibly did not call parent GridSpec with the' + ' "figure" keyword') + return + + for _ in range(2): + # do the algorithm twice. This has to be done because decorations + # change size after the first re-position (i.e. x/yticklabels get + # larger/smaller). This second reposition tends to be much milder, + # so doing twice makes things work OK. + + # make margins for all the Axes and subfigures in the + # figure. Add margins for colorbars... + make_layout_margins(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad, hspace=hspace, wspace=wspace) + make_margin_suptitles(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad) + + # if a layout is such that a columns (or rows) margin has no + # constraints, we need to make all such instances in the grid + # match in margin size. + match_submerged_margins(layoutgrids, fig) + + # update all the variables in the layout. + layoutgrids[fig].update_variables() + + warn_collapsed = ('constrained_layout not applied because ' + 'axes sizes collapsed to zero. Try making ' + 'figure larger or Axes decorations smaller.') + if check_no_collapsed_axes(layoutgrids, fig): + reposition_axes(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad, hspace=hspace, wspace=wspace) + if compress: + layoutgrids = compress_fixed_aspect(layoutgrids, fig) + layoutgrids[fig].update_variables() + if check_no_collapsed_axes(layoutgrids, fig): + reposition_axes(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad, hspace=hspace, wspace=wspace) + else: + _api.warn_external(warn_collapsed) + + if ((suptitle := fig._suptitle) is not None and + suptitle.get_in_layout() and suptitle._autopos): + x, _ = suptitle.get_position() + suptitle.set_position( + (x, layoutgrids[fig].get_inner_bbox().y1 + h_pad)) + suptitle.set_verticalalignment('bottom') + else: + _api.warn_external(warn_collapsed) + reset_margins(layoutgrids, fig) + return layoutgrids + + +def make_layoutgrids(fig, layoutgrids, rect=(0, 0, 1, 1)): + """ + Make the layoutgrid tree. + + (Sub)Figures get a layoutgrid so we can have figure margins. + + Gridspecs that are attached to Axes get a layoutgrid so Axes + can have margins. + """ + + if layoutgrids is None: + layoutgrids = dict() + layoutgrids['hasgrids'] = False + if not hasattr(fig, '_parent'): + # top figure; pass rect as parent to allow user-specified + # margins + layoutgrids[fig] = mlayoutgrid.LayoutGrid(parent=rect, name='figlb') + else: + # subfigure + gs = fig._subplotspec.get_gridspec() + # it is possible the gridspec containing this subfigure hasn't + # been added to the tree yet: + layoutgrids = make_layoutgrids_gs(layoutgrids, gs) + # add the layoutgrid for the subfigure: + parentlb = layoutgrids[gs] + layoutgrids[fig] = mlayoutgrid.LayoutGrid( + parent=parentlb, + name='panellb', + parent_inner=True, + nrows=1, ncols=1, + parent_pos=(fig._subplotspec.rowspan, + fig._subplotspec.colspan)) + # recursively do all subfigures in this figure... + for sfig in fig.subfigs: + layoutgrids = make_layoutgrids(sfig, layoutgrids) + + # for each Axes at the local level add its gridspec: + for ax in fig._localaxes: + gs = ax.get_gridspec() + if gs is not None: + layoutgrids = make_layoutgrids_gs(layoutgrids, gs) + + return layoutgrids + + +def make_layoutgrids_gs(layoutgrids, gs): + """ + Make the layoutgrid for a gridspec (and anything nested in the gridspec) + """ + + if gs in layoutgrids or gs.figure is None: + return layoutgrids + # in order to do constrained_layout there has to be at least *one* + # gridspec in the tree: + layoutgrids['hasgrids'] = True + if not hasattr(gs, '_subplot_spec'): + # normal gridspec + parent = layoutgrids[gs.figure] + layoutgrids[gs] = mlayoutgrid.LayoutGrid( + parent=parent, + parent_inner=True, + name='gridspec', + ncols=gs._ncols, nrows=gs._nrows, + width_ratios=gs.get_width_ratios(), + height_ratios=gs.get_height_ratios()) + else: + # this is a gridspecfromsubplotspec: + subplot_spec = gs._subplot_spec + parentgs = subplot_spec.get_gridspec() + # if a nested gridspec it is possible the parent is not in there yet: + if parentgs not in layoutgrids: + layoutgrids = make_layoutgrids_gs(layoutgrids, parentgs) + subspeclb = layoutgrids[parentgs] + # gridspecfromsubplotspec need an outer container: + # get a unique representation: + rep = (gs, 'top') + if rep not in layoutgrids: + layoutgrids[rep] = mlayoutgrid.LayoutGrid( + parent=subspeclb, + name='top', + nrows=1, ncols=1, + parent_pos=(subplot_spec.rowspan, subplot_spec.colspan)) + layoutgrids[gs] = mlayoutgrid.LayoutGrid( + parent=layoutgrids[rep], + name='gridspec', + nrows=gs._nrows, ncols=gs._ncols, + width_ratios=gs.get_width_ratios(), + height_ratios=gs.get_height_ratios()) + return layoutgrids + + +def check_no_collapsed_axes(layoutgrids, fig): + """ + Check that no Axes have collapsed to zero size. + """ + for sfig in fig.subfigs: + ok = check_no_collapsed_axes(layoutgrids, sfig) + if not ok: + return False + for ax in fig.axes: + gs = ax.get_gridspec() + if gs in layoutgrids: # also implies gs is not None. + lg = layoutgrids[gs] + for i in range(gs.nrows): + for j in range(gs.ncols): + bb = lg.get_inner_bbox(i, j) + if bb.width <= 0 or bb.height <= 0: + return False + return True + + +def compress_fixed_aspect(layoutgrids, fig): + gs = None + for ax in fig.axes: + if ax.get_subplotspec() is None: + continue + ax.apply_aspect() + sub = ax.get_subplotspec() + _gs = sub.get_gridspec() + if gs is None: + gs = _gs + extraw = np.zeros(gs.ncols) + extrah = np.zeros(gs.nrows) + elif _gs != gs: + raise ValueError('Cannot do compressed layout if Axes are not' + 'all from the same gridspec') + orig = ax.get_position(original=True) + actual = ax.get_position(original=False) + dw = orig.width - actual.width + if dw > 0: + extraw[sub.colspan] = np.maximum(extraw[sub.colspan], dw) + dh = orig.height - actual.height + if dh > 0: + extrah[sub.rowspan] = np.maximum(extrah[sub.rowspan], dh) + + if gs is None: + raise ValueError('Cannot do compressed layout if no Axes ' + 'are part of a gridspec.') + w = np.sum(extraw) / 2 + layoutgrids[fig].edit_margin_min('left', w) + layoutgrids[fig].edit_margin_min('right', w) + + h = np.sum(extrah) / 2 + layoutgrids[fig].edit_margin_min('top', h) + layoutgrids[fig].edit_margin_min('bottom', h) + return layoutgrids + + +def get_margin_from_padding(obj, *, w_pad=0, h_pad=0, + hspace=0, wspace=0): + + ss = obj._subplotspec + gs = ss.get_gridspec() + + if hasattr(gs, 'hspace'): + _hspace = (gs.hspace if gs.hspace is not None else hspace) + _wspace = (gs.wspace if gs.wspace is not None else wspace) + else: + _hspace = (gs._hspace if gs._hspace is not None else hspace) + _wspace = (gs._wspace if gs._wspace is not None else wspace) + + _wspace = _wspace / 2 + _hspace = _hspace / 2 + + nrows, ncols = gs.get_geometry() + # there are two margins for each direction. The "cb" + # margins are for pads and colorbars, the non-"cb" are + # for the Axes decorations (labels etc). + margin = {'leftcb': w_pad, 'rightcb': w_pad, + 'bottomcb': h_pad, 'topcb': h_pad, + 'left': 0, 'right': 0, + 'top': 0, 'bottom': 0} + if _wspace / ncols > w_pad: + if ss.colspan.start > 0: + margin['leftcb'] = _wspace / ncols + if ss.colspan.stop < ncols: + margin['rightcb'] = _wspace / ncols + if _hspace / nrows > h_pad: + if ss.rowspan.stop < nrows: + margin['bottomcb'] = _hspace / nrows + if ss.rowspan.start > 0: + margin['topcb'] = _hspace / nrows + + return margin + + +def make_layout_margins(layoutgrids, fig, renderer, *, w_pad=0, h_pad=0, + hspace=0, wspace=0): + """ + For each Axes, make a margin between the *pos* layoutbox and the + *axes* layoutbox be a minimum size that can accommodate the + decorations on the axis. + + Then make room for colorbars. + + Parameters + ---------- + layoutgrids : dict + fig : `~matplotlib.figure.Figure` + `.Figure` instance to do the layout in. + renderer : `~matplotlib.backend_bases.RendererBase` subclass. + The renderer to use. + w_pad, h_pad : float, default: 0 + Width and height padding (in fraction of figure). + hspace, wspace : float, default: 0 + Width and height padding as fraction of figure size divided by + number of columns or rows. + """ + for sfig in fig.subfigs: # recursively make child panel margins + ss = sfig._subplotspec + gs = ss.get_gridspec() + + make_layout_margins(layoutgrids, sfig, renderer, + w_pad=w_pad, h_pad=h_pad, + hspace=hspace, wspace=wspace) + + margins = get_margin_from_padding(sfig, w_pad=0, h_pad=0, + hspace=hspace, wspace=wspace) + layoutgrids[gs].edit_outer_margin_mins(margins, ss) + + for ax in fig._localaxes: + if not ax.get_subplotspec() or not ax.get_in_layout(): + continue + + ss = ax.get_subplotspec() + gs = ss.get_gridspec() + + if gs not in layoutgrids: + return + + margin = get_margin_from_padding(ax, w_pad=w_pad, h_pad=h_pad, + hspace=hspace, wspace=wspace) + pos, bbox = get_pos_and_bbox(ax, renderer) + # the margin is the distance between the bounding box of the Axes + # and its position (plus the padding from above) + margin['left'] += pos.x0 - bbox.x0 + margin['right'] += bbox.x1 - pos.x1 + # remember that rows are ordered from top: + margin['bottom'] += pos.y0 - bbox.y0 + margin['top'] += bbox.y1 - pos.y1 + + # make margin for colorbars. These margins go in the + # padding margin, versus the margin for Axes decorators. + for cbax in ax._colorbars: + # note pad is a fraction of the parent width... + pad = colorbar_get_pad(layoutgrids, cbax) + # colorbars can be child of more than one subplot spec: + cbp_rspan, cbp_cspan = get_cb_parent_spans(cbax) + loc = cbax._colorbar_info['location'] + cbpos, cbbbox = get_pos_and_bbox(cbax, renderer) + if loc == 'right': + if cbp_cspan.stop == ss.colspan.stop: + # only increase if the colorbar is on the right edge + margin['rightcb'] += cbbbox.width + pad + elif loc == 'left': + if cbp_cspan.start == ss.colspan.start: + # only increase if the colorbar is on the left edge + margin['leftcb'] += cbbbox.width + pad + elif loc == 'top': + if cbp_rspan.start == ss.rowspan.start: + margin['topcb'] += cbbbox.height + pad + else: + if cbp_rspan.stop == ss.rowspan.stop: + margin['bottomcb'] += cbbbox.height + pad + # If the colorbars are wider than the parent box in the + # cross direction + if loc in ['top', 'bottom']: + if (cbp_cspan.start == ss.colspan.start and + cbbbox.x0 < bbox.x0): + margin['left'] += bbox.x0 - cbbbox.x0 + if (cbp_cspan.stop == ss.colspan.stop and + cbbbox.x1 > bbox.x1): + margin['right'] += cbbbox.x1 - bbox.x1 + # or taller: + if loc in ['left', 'right']: + if (cbp_rspan.stop == ss.rowspan.stop and + cbbbox.y0 < bbox.y0): + margin['bottom'] += bbox.y0 - cbbbox.y0 + if (cbp_rspan.start == ss.rowspan.start and + cbbbox.y1 > bbox.y1): + margin['top'] += cbbbox.y1 - bbox.y1 + # pass the new margins down to the layout grid for the solution... + layoutgrids[gs].edit_outer_margin_mins(margin, ss) + + # make margins for figure-level legends: + for leg in fig.legends: + inv_trans_fig = None + if leg._outside_loc and leg._bbox_to_anchor is None: + if inv_trans_fig is None: + inv_trans_fig = fig.transFigure.inverted().transform_bbox + bbox = inv_trans_fig(leg.get_tightbbox(renderer)) + w = bbox.width + 2 * w_pad + h = bbox.height + 2 * h_pad + legendloc = leg._outside_loc + if legendloc == 'lower': + layoutgrids[fig].edit_margin_min('bottom', h) + elif legendloc == 'upper': + layoutgrids[fig].edit_margin_min('top', h) + if legendloc == 'right': + layoutgrids[fig].edit_margin_min('right', w) + elif legendloc == 'left': + layoutgrids[fig].edit_margin_min('left', w) + + +def make_margin_suptitles(layoutgrids, fig, renderer, *, w_pad=0, h_pad=0): + # Figure out how large the suptitle is and make the + # top level figure margin larger. + + inv_trans_fig = fig.transFigure.inverted().transform_bbox + # get the h_pad and w_pad as distances in the local subfigure coordinates: + padbox = mtransforms.Bbox([[0, 0], [w_pad, h_pad]]) + padbox = (fig.transFigure - + fig.transSubfigure).transform_bbox(padbox) + h_pad_local = padbox.height + w_pad_local = padbox.width + + for sfig in fig.subfigs: + make_margin_suptitles(layoutgrids, sfig, renderer, + w_pad=w_pad, h_pad=h_pad) + + if fig._suptitle is not None and fig._suptitle.get_in_layout(): + p = fig._suptitle.get_position() + if getattr(fig._suptitle, '_autopos', False): + fig._suptitle.set_position((p[0], 1 - h_pad_local)) + bbox = inv_trans_fig(fig._suptitle.get_tightbbox(renderer)) + layoutgrids[fig].edit_margin_min('top', bbox.height + 2 * h_pad) + + if fig._supxlabel is not None and fig._supxlabel.get_in_layout(): + p = fig._supxlabel.get_position() + if getattr(fig._supxlabel, '_autopos', False): + fig._supxlabel.set_position((p[0], h_pad_local)) + bbox = inv_trans_fig(fig._supxlabel.get_tightbbox(renderer)) + layoutgrids[fig].edit_margin_min('bottom', + bbox.height + 2 * h_pad) + + if fig._supylabel is not None and fig._supylabel.get_in_layout(): + p = fig._supylabel.get_position() + if getattr(fig._supylabel, '_autopos', False): + fig._supylabel.set_position((w_pad_local, p[1])) + bbox = inv_trans_fig(fig._supylabel.get_tightbbox(renderer)) + layoutgrids[fig].edit_margin_min('left', bbox.width + 2 * w_pad) + + +def match_submerged_margins(layoutgrids, fig): + """ + Make the margins that are submerged inside an Axes the same size. + + This allows Axes that span two columns (or rows) that are offset + from one another to have the same size. + + This gives the proper layout for something like:: + fig = plt.figure(constrained_layout=True) + axs = fig.subplot_mosaic("AAAB\nCCDD") + + Without this routine, the Axes D will be wider than C, because the + margin width between the two columns in C has no width by default, + whereas the margins between the two columns of D are set by the + width of the margin between A and B. However, obviously the user would + like C and D to be the same size, so we need to add constraints to these + "submerged" margins. + + This routine makes all the interior margins the same, and the spacing + between the three columns in A and the two column in C are all set to the + margins between the two columns of D. + + See test_constrained_layout::test_constrained_layout12 for an example. + """ + + axsdone = [] + for sfig in fig.subfigs: + axsdone += match_submerged_margins(layoutgrids, sfig) + + axs = [a for a in fig.get_axes() + if (a.get_subplotspec() is not None and a.get_in_layout() and + a not in axsdone)] + + for ax1 in axs: + ss1 = ax1.get_subplotspec() + if ss1.get_gridspec() not in layoutgrids: + axs.remove(ax1) + continue + lg1 = layoutgrids[ss1.get_gridspec()] + + # interior columns: + if len(ss1.colspan) > 1: + maxsubl = np.max( + lg1.margin_vals['left'][ss1.colspan[1:]] + + lg1.margin_vals['leftcb'][ss1.colspan[1:]] + ) + maxsubr = np.max( + lg1.margin_vals['right'][ss1.colspan[:-1]] + + lg1.margin_vals['rightcb'][ss1.colspan[:-1]] + ) + for ax2 in axs: + ss2 = ax2.get_subplotspec() + lg2 = layoutgrids[ss2.get_gridspec()] + if lg2 is not None and len(ss2.colspan) > 1: + maxsubl2 = np.max( + lg2.margin_vals['left'][ss2.colspan[1:]] + + lg2.margin_vals['leftcb'][ss2.colspan[1:]]) + if maxsubl2 > maxsubl: + maxsubl = maxsubl2 + maxsubr2 = np.max( + lg2.margin_vals['right'][ss2.colspan[:-1]] + + lg2.margin_vals['rightcb'][ss2.colspan[:-1]]) + if maxsubr2 > maxsubr: + maxsubr = maxsubr2 + for i in ss1.colspan[1:]: + lg1.edit_margin_min('left', maxsubl, cell=i) + for i in ss1.colspan[:-1]: + lg1.edit_margin_min('right', maxsubr, cell=i) + + # interior rows: + if len(ss1.rowspan) > 1: + maxsubt = np.max( + lg1.margin_vals['top'][ss1.rowspan[1:]] + + lg1.margin_vals['topcb'][ss1.rowspan[1:]] + ) + maxsubb = np.max( + lg1.margin_vals['bottom'][ss1.rowspan[:-1]] + + lg1.margin_vals['bottomcb'][ss1.rowspan[:-1]] + ) + + for ax2 in axs: + ss2 = ax2.get_subplotspec() + lg2 = layoutgrids[ss2.get_gridspec()] + if lg2 is not None: + if len(ss2.rowspan) > 1: + maxsubt = np.max([np.max( + lg2.margin_vals['top'][ss2.rowspan[1:]] + + lg2.margin_vals['topcb'][ss2.rowspan[1:]] + ), maxsubt]) + maxsubb = np.max([np.max( + lg2.margin_vals['bottom'][ss2.rowspan[:-1]] + + lg2.margin_vals['bottomcb'][ss2.rowspan[:-1]] + ), maxsubb]) + for i in ss1.rowspan[1:]: + lg1.edit_margin_min('top', maxsubt, cell=i) + for i in ss1.rowspan[:-1]: + lg1.edit_margin_min('bottom', maxsubb, cell=i) + + return axs + + +def get_cb_parent_spans(cbax): + """ + Figure out which subplotspecs this colorbar belongs to. + + Parameters + ---------- + cbax : `~matplotlib.axes.Axes` + Axes for the colorbar. + """ + rowstart = np.inf + rowstop = -np.inf + colstart = np.inf + colstop = -np.inf + for parent in cbax._colorbar_info['parents']: + ss = parent.get_subplotspec() + rowstart = min(ss.rowspan.start, rowstart) + rowstop = max(ss.rowspan.stop, rowstop) + colstart = min(ss.colspan.start, colstart) + colstop = max(ss.colspan.stop, colstop) + + rowspan = range(rowstart, rowstop) + colspan = range(colstart, colstop) + return rowspan, colspan + + +def get_pos_and_bbox(ax, renderer): + """ + Get the position and the bbox for the Axes. + + Parameters + ---------- + ax : `~matplotlib.axes.Axes` + renderer : `~matplotlib.backend_bases.RendererBase` subclass. + + Returns + ------- + pos : `~matplotlib.transforms.Bbox` + Position in figure coordinates. + bbox : `~matplotlib.transforms.Bbox` + Tight bounding box in figure coordinates. + """ + fig = ax.get_figure(root=False) + pos = ax.get_position(original=True) + # pos is in panel co-ords, but we need in figure for the layout + pos = pos.transformed(fig.transSubfigure - fig.transFigure) + tightbbox = martist._get_tightbbox_for_layout_only(ax, renderer) + if tightbbox is None: + bbox = pos + else: + bbox = tightbbox.transformed(fig.transFigure.inverted()) + return pos, bbox + + +def reposition_axes(layoutgrids, fig, renderer, *, + w_pad=0, h_pad=0, hspace=0, wspace=0): + """ + Reposition all the Axes based on the new inner bounding box. + """ + trans_fig_to_subfig = fig.transFigure - fig.transSubfigure + for sfig in fig.subfigs: + bbox = layoutgrids[sfig].get_outer_bbox() + sfig._redo_transform_rel_fig( + bbox=bbox.transformed(trans_fig_to_subfig)) + reposition_axes(layoutgrids, sfig, renderer, + w_pad=w_pad, h_pad=h_pad, + wspace=wspace, hspace=hspace) + + for ax in fig._localaxes: + if ax.get_subplotspec() is None or not ax.get_in_layout(): + continue + + # grid bbox is in Figure coordinates, but we specify in panel + # coordinates... + ss = ax.get_subplotspec() + gs = ss.get_gridspec() + if gs not in layoutgrids: + return + + bbox = layoutgrids[gs].get_inner_bbox(rows=ss.rowspan, + cols=ss.colspan) + + # transform from figure to panel for set_position: + newbbox = trans_fig_to_subfig.transform_bbox(bbox) + ax._set_position(newbbox) + + # move the colorbars: + # we need to keep track of oldw and oldh if there is more than + # one colorbar: + offset = {'left': 0, 'right': 0, 'bottom': 0, 'top': 0} + for nn, cbax in enumerate(ax._colorbars[::-1]): + if ax == cbax._colorbar_info['parents'][0]: + reposition_colorbar(layoutgrids, cbax, renderer, + offset=offset) + + +def reposition_colorbar(layoutgrids, cbax, renderer, *, offset=None): + """ + Place the colorbar in its new place. + + Parameters + ---------- + layoutgrids : dict + cbax : `~matplotlib.axes.Axes` + Axes for the colorbar. + renderer : `~matplotlib.backend_bases.RendererBase` subclass. + The renderer to use. + offset : array-like + Offset the colorbar needs to be pushed to in order to + account for multiple colorbars. + """ + + parents = cbax._colorbar_info['parents'] + gs = parents[0].get_gridspec() + fig = cbax.get_figure(root=False) + trans_fig_to_subfig = fig.transFigure - fig.transSubfigure + + cb_rspans, cb_cspans = get_cb_parent_spans(cbax) + bboxparent = layoutgrids[gs].get_bbox_for_cb(rows=cb_rspans, + cols=cb_cspans) + pb = layoutgrids[gs].get_inner_bbox(rows=cb_rspans, cols=cb_cspans) + + location = cbax._colorbar_info['location'] + anchor = cbax._colorbar_info['anchor'] + fraction = cbax._colorbar_info['fraction'] + aspect = cbax._colorbar_info['aspect'] + shrink = cbax._colorbar_info['shrink'] + + cbpos, cbbbox = get_pos_and_bbox(cbax, renderer) + + # Colorbar gets put at extreme edge of outer bbox of the subplotspec + # It needs to be moved in by: 1) a pad 2) its "margin" 3) by + # any colorbars already added at this location: + cbpad = colorbar_get_pad(layoutgrids, cbax) + if location in ('left', 'right'): + # fraction and shrink are fractions of parent + pbcb = pb.shrunk(fraction, shrink).anchored(anchor, pb) + # The colorbar is at the left side of the parent. Need + # to translate to right (or left) + if location == 'right': + lmargin = cbpos.x0 - cbbbox.x0 + dx = bboxparent.x1 - pbcb.x0 + offset['right'] + dx += cbpad + lmargin + offset['right'] += cbbbox.width + cbpad + pbcb = pbcb.translated(dx, 0) + else: + lmargin = cbpos.x0 - cbbbox.x0 + dx = bboxparent.x0 - pbcb.x0 # edge of parent + dx += -cbbbox.width - cbpad + lmargin - offset['left'] + offset['left'] += cbbbox.width + cbpad + pbcb = pbcb.translated(dx, 0) + else: # horizontal axes: + pbcb = pb.shrunk(shrink, fraction).anchored(anchor, pb) + if location == 'top': + bmargin = cbpos.y0 - cbbbox.y0 + dy = bboxparent.y1 - pbcb.y0 + offset['top'] + dy += cbpad + bmargin + offset['top'] += cbbbox.height + cbpad + pbcb = pbcb.translated(0, dy) + else: + bmargin = cbpos.y0 - cbbbox.y0 + dy = bboxparent.y0 - pbcb.y0 + dy += -cbbbox.height - cbpad + bmargin - offset['bottom'] + offset['bottom'] += cbbbox.height + cbpad + pbcb = pbcb.translated(0, dy) + + pbcb = trans_fig_to_subfig.transform_bbox(pbcb) + cbax.set_transform(fig.transSubfigure) + cbax._set_position(pbcb) + cbax.set_anchor(anchor) + if location in ['bottom', 'top']: + aspect = 1 / aspect + cbax.set_box_aspect(aspect) + cbax.set_aspect('auto') + return offset + + +def reset_margins(layoutgrids, fig): + """ + Reset the margins in the layoutboxes of *fig*. + + Margins are usually set as a minimum, so if the figure gets smaller + the minimum needs to be zero in order for it to grow again. + """ + for sfig in fig.subfigs: + reset_margins(layoutgrids, sfig) + for ax in fig.axes: + if ax.get_in_layout(): + gs = ax.get_gridspec() + if gs in layoutgrids: # also implies gs is not None. + layoutgrids[gs].reset_margins() + layoutgrids[fig].reset_margins() + + +def colorbar_get_pad(layoutgrids, cax): + parents = cax._colorbar_info['parents'] + gs = parents[0].get_gridspec() + + cb_rspans, cb_cspans = get_cb_parent_spans(cax) + bboxouter = layoutgrids[gs].get_inner_bbox(rows=cb_rspans, cols=cb_cspans) + + if cax._colorbar_info['location'] in ['right', 'left']: + size = bboxouter.width + else: + size = bboxouter.height + + return cax._colorbar_info['pad'] * size diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_docstring.py b/.venv/lib/python3.12/site-packages/matplotlib/_docstring.py new file mode 100644 index 0000000000000000000000000000000000000000..8cc7d623efe5c3bb3421e78bccf63b75ee76614d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_docstring.py @@ -0,0 +1,141 @@ +import inspect + +from . import _api + + +def kwarg_doc(text): + """ + Decorator for defining the kwdoc documentation of artist properties. + + This decorator can be applied to artist property setter methods. + The given text is stored in a private attribute ``_kwarg_doc`` on + the method. It is used to overwrite auto-generated documentation + in the *kwdoc list* for artists. The kwdoc list is used to document + ``**kwargs`` when they are properties of an artist. See e.g. the + ``**kwargs`` section in `.Axes.text`. + + The text should contain the supported types, as well as the default + value if applicable, e.g.: + + @_docstring.kwarg_doc("bool, default: :rc:`text.usetex`") + def set_usetex(self, usetex): + + See Also + -------- + matplotlib.artist.kwdoc + + """ + def decorator(func): + func._kwarg_doc = text + return func + return decorator + + +class Substitution: + """ + A decorator that performs %-substitution on an object's docstring. + + This decorator should be robust even if ``obj.__doc__`` is None (for + example, if -OO was passed to the interpreter). + + Usage: construct a docstring.Substitution with a sequence or dictionary + suitable for performing substitution; then decorate a suitable function + with the constructed object, e.g.:: + + sub_author_name = Substitution(author='Jason') + + @sub_author_name + def some_function(x): + "%(author)s wrote this function" + + # note that some_function.__doc__ is now "Jason wrote this function" + + One can also use positional arguments:: + + sub_first_last_names = Substitution('Edgar Allen', 'Poe') + + @sub_first_last_names + def some_function(x): + "%s %s wrote the Raven" + """ + def __init__(self, *args, **kwargs): + if args and kwargs: + raise TypeError("Only positional or keyword args are allowed") + self.params = args or kwargs + + def __call__(self, func): + if func.__doc__: + func.__doc__ = inspect.cleandoc(func.__doc__) % self.params + return func + + +class _ArtistKwdocLoader(dict): + def __missing__(self, key): + if not key.endswith(":kwdoc"): + raise KeyError(key) + name = key[:-len(":kwdoc")] + from matplotlib.artist import Artist, kwdoc + try: + cls, = (cls for cls in _api.recursive_subclasses(Artist) + if cls.__name__ == name) + except ValueError as e: + raise KeyError(key) from e + return self.setdefault(key, kwdoc(cls)) + + +class _ArtistPropertiesSubstitution: + """ + A class to substitute formatted placeholders in docstrings. + + This is realized in a single instance ``_docstring.interpd``. + + Use `~._ArtistPropertiesSubstition.register` to define placeholders and + their substitution, e.g. ``_docstring.interpd.register(name="some value")``. + + Use this as a decorator to apply the substitution:: + + @_docstring.interpd + def some_func(): + '''Replace %(name)s.''' + + Decorating a class triggers substitution both on the class docstring and + on the class' ``__init__`` docstring (which is a commonly required + pattern for Artist subclasses). + + Substitutions of the form ``%(classname:kwdoc)s`` (ending with the + literal ":kwdoc" suffix) trigger lookup of an Artist subclass with the + given *classname*, and are substituted with the `.kwdoc` of that class. + """ + + def __init__(self): + self.params = _ArtistKwdocLoader() + + def register(self, **kwargs): + """ + Register substitutions. + + ``_docstring.interpd.register(name="some value")`` makes "name" available + as a named parameter that will be replaced by "some value". + """ + self.params.update(**kwargs) + + def __call__(self, obj): + if obj.__doc__: + obj.__doc__ = inspect.cleandoc(obj.__doc__) % self.params + if isinstance(obj, type) and obj.__init__ != object.__init__: + self(obj.__init__) + return obj + + +def copy(source): + """Copy a docstring from another source function (if present).""" + def do_copy(target): + if source.__doc__: + target.__doc__ = source.__doc__ + return target + return do_copy + + +# Create a decorator that will house the various docstring snippets reused +# throughout Matplotlib. +interpd = _ArtistPropertiesSubstitution() diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_docstring.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_docstring.pyi new file mode 100644 index 0000000000000000000000000000000000000000..fb52d084612399f399e6bcd3f07bca85bb010ba0 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_docstring.pyi @@ -0,0 +1,34 @@ +from collections.abc import Callable +from typing import Any, TypeVar, overload + + +_T = TypeVar('_T') + + +def kwarg_doc(text: str) -> Callable[[_T], _T]: ... + + +class Substitution: + @overload + def __init__(self, *args: str): ... + @overload + def __init__(self, **kwargs: str): ... + def __call__(self, func: _T) -> _T: ... + def update(self, *args, **kwargs): ... # type: ignore[no-untyped-def] + + +class _ArtistKwdocLoader(dict[str, str]): + def __missing__(self, key: str) -> str: ... + + +class _ArtistPropertiesSubstitution: + def __init__(self) -> None: ... + def register(self, **kwargs) -> None: ... + def __call__(self, obj: _T) -> _T: ... + + +def copy(source: Any) -> Callable[[_T], _T]: ... + + +dedent_interpd: _ArtistPropertiesSubstitution +interpd: _ArtistPropertiesSubstitution diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_enums.py b/.venv/lib/python3.12/site-packages/matplotlib/_enums.py new file mode 100644 index 0000000000000000000000000000000000000000..75a09b7b5d8c7f9097e965d76fbae7bab2b41a01 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_enums.py @@ -0,0 +1,177 @@ +""" +Enums representing sets of strings that Matplotlib uses as input parameters. + +Matplotlib often uses simple data types like strings or tuples to define a +concept; e.g. the line capstyle can be specified as one of 'butt', 'round', +or 'projecting'. The classes in this module are used internally and serve to +document these concepts formally. + +As an end-user you will not use these classes directly, but only the values +they define. +""" + +from enum import Enum +from matplotlib import _docstring + + +class JoinStyle(str, Enum): + """ + Define how the connection between two line segments is drawn. + + For a visual impression of each *JoinStyle*, `view these docs online + `, or run `JoinStyle.demo`. + + Lines in Matplotlib are typically defined by a 1D `~.path.Path` and a + finite ``linewidth``, where the underlying 1D `~.path.Path` represents the + center of the stroked line. + + By default, `~.backend_bases.GraphicsContextBase` defines the boundaries of + a stroked line to simply be every point within some radius, + ``linewidth/2``, away from any point of the center line. However, this + results in corners appearing "rounded", which may not be the desired + behavior if you are drawing, for example, a polygon or pointed star. + + **Supported values:** + + .. rst-class:: value-list + + 'miter' + the "arrow-tip" style. Each boundary of the filled-in area will + extend in a straight line parallel to the tangent vector of the + centerline at the point it meets the corner, until they meet in a + sharp point. + 'round' + stokes every point within a radius of ``linewidth/2`` of the center + lines. + 'bevel' + the "squared-off" style. It can be thought of as a rounded corner + where the "circular" part of the corner has been cut off. + + .. note:: + + Very long miter tips are cut off (to form a *bevel*) after a + backend-dependent limit called the "miter limit", which specifies the + maximum allowed ratio of miter length to line width. For example, the + PDF backend uses the default value of 10 specified by the PDF standard, + while the SVG backend does not even specify the miter limit, resulting + in a default value of 4 per the SVG specification. Matplotlib does not + currently allow the user to adjust this parameter. + + A more detailed description of the effect of a miter limit can be found + in the `Mozilla Developer Docs + `_ + + .. plot:: + :alt: Demo of possible JoinStyle's + + from matplotlib._enums import JoinStyle + JoinStyle.demo() + + """ + + miter = "miter" + round = "round" + bevel = "bevel" + + @staticmethod + def demo(): + """Demonstrate how each JoinStyle looks for various join angles.""" + import numpy as np + import matplotlib.pyplot as plt + + def plot_angle(ax, x, y, angle, style): + phi = np.radians(angle) + xx = [x + .5, x, x + .5*np.cos(phi)] + yy = [y, y, y + .5*np.sin(phi)] + ax.plot(xx, yy, lw=12, color='tab:blue', solid_joinstyle=style) + ax.plot(xx, yy, lw=1, color='black') + ax.plot(xx[1], yy[1], 'o', color='tab:red', markersize=3) + + fig, ax = plt.subplots(figsize=(5, 4), constrained_layout=True) + ax.set_title('Join style') + for x, style in enumerate(['miter', 'round', 'bevel']): + ax.text(x, 5, style) + for y, angle in enumerate([20, 45, 60, 90, 120]): + plot_angle(ax, x, y, angle, style) + if x == 0: + ax.text(-1.3, y, f'{angle} degrees') + ax.set_xlim(-1.5, 2.75) + ax.set_ylim(-.5, 5.5) + ax.set_axis_off() + fig.show() + + +JoinStyle.input_description = "{" \ + + ", ".join([f"'{js.name}'" for js in JoinStyle]) \ + + "}" + + +class CapStyle(str, Enum): + r""" + Define how the two endpoints (caps) of an unclosed line are drawn. + + How to draw the start and end points of lines that represent a closed curve + (i.e. that end in a `~.path.Path.CLOSEPOLY`) is controlled by the line's + `JoinStyle`. For all other lines, how the start and end points are drawn is + controlled by the *CapStyle*. + + For a visual impression of each *CapStyle*, `view these docs online + ` or run `CapStyle.demo`. + + By default, `~.backend_bases.GraphicsContextBase` draws a stroked line as + squared off at its endpoints. + + **Supported values:** + + .. rst-class:: value-list + + 'butt' + the line is squared off at its endpoint. + 'projecting' + the line is squared off as in *butt*, but the filled in area + extends beyond the endpoint a distance of ``linewidth/2``. + 'round' + like *butt*, but a semicircular cap is added to the end of the + line, of radius ``linewidth/2``. + + .. plot:: + :alt: Demo of possible CapStyle's + + from matplotlib._enums import CapStyle + CapStyle.demo() + + """ + butt = "butt" + projecting = "projecting" + round = "round" + + @staticmethod + def demo(): + """Demonstrate how each CapStyle looks for a thick line segment.""" + import matplotlib.pyplot as plt + + fig = plt.figure(figsize=(4, 1.2)) + ax = fig.add_axes([0, 0, 1, 0.8]) + ax.set_title('Cap style') + + for x, style in enumerate(['butt', 'round', 'projecting']): + ax.text(x+0.25, 0.85, style, ha='center') + xx = [x, x+0.5] + yy = [0, 0] + ax.plot(xx, yy, lw=12, color='tab:blue', solid_capstyle=style) + ax.plot(xx, yy, lw=1, color='black') + ax.plot(xx, yy, 'o', color='tab:red', markersize=3) + + ax.set_ylim(-.5, 1.5) + ax.set_axis_off() + fig.show() + + +CapStyle.input_description = "{" \ + + ", ".join([f"'{cs.name}'" for cs in CapStyle]) \ + + "}" + +_docstring.interpd.register( + JoinStyle=JoinStyle.input_description, + CapStyle=CapStyle.input_description, +) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_enums.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_enums.pyi new file mode 100644 index 0000000000000000000000000000000000000000..714e6cfe03faab19b2a3f939a6be4ac95415e061 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_enums.pyi @@ -0,0 +1,19 @@ +from typing import cast +from enum import Enum + + +class JoinStyle(str, Enum): + miter = "miter" + round = "round" + bevel = "bevel" + @staticmethod + def demo() -> None: ... + + +class CapStyle(str, Enum): + butt = "butt" + projecting = "projecting" + round = "round" + + @staticmethod + def demo() -> None: ... diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_fontconfig_pattern.py b/.venv/lib/python3.12/site-packages/matplotlib/_fontconfig_pattern.py new file mode 100644 index 0000000000000000000000000000000000000000..48bb2956bd7e4cb522e56d6f4d402dc4639bc0f4 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_fontconfig_pattern.py @@ -0,0 +1,111 @@ +""" +A module for parsing and generating `fontconfig patterns`_. + +.. _fontconfig patterns: + https://www.freedesktop.org/software/fontconfig/fontconfig-user.html +""" + +# This class logically belongs in `matplotlib.font_manager`, but placing it +# there would have created cyclical dependency problems, because it also needs +# to be available from `matplotlib.rcsetup` (for parsing matplotlibrc files). + +from functools import lru_cache, partial +import re + +from pyparsing import ( + Group, Optional, ParseException, Regex, StringEnd, Suppress, ZeroOrMore, one_of) + + +_family_punc = r'\\\-:,' +_family_unescape = partial(re.compile(r'\\(?=[%s])' % _family_punc).sub, '') +_family_escape = partial(re.compile(r'(?=[%s])' % _family_punc).sub, r'\\') +_value_punc = r'\\=_:,' +_value_unescape = partial(re.compile(r'\\(?=[%s])' % _value_punc).sub, '') +_value_escape = partial(re.compile(r'(?=[%s])' % _value_punc).sub, r'\\') + + +_CONSTANTS = { + 'thin': ('weight', 'light'), + 'extralight': ('weight', 'light'), + 'ultralight': ('weight', 'light'), + 'light': ('weight', 'light'), + 'book': ('weight', 'book'), + 'regular': ('weight', 'regular'), + 'normal': ('weight', 'normal'), + 'medium': ('weight', 'medium'), + 'demibold': ('weight', 'demibold'), + 'semibold': ('weight', 'semibold'), + 'bold': ('weight', 'bold'), + 'extrabold': ('weight', 'extra bold'), + 'black': ('weight', 'black'), + 'heavy': ('weight', 'heavy'), + 'roman': ('slant', 'normal'), + 'italic': ('slant', 'italic'), + 'oblique': ('slant', 'oblique'), + 'ultracondensed': ('width', 'ultra-condensed'), + 'extracondensed': ('width', 'extra-condensed'), + 'condensed': ('width', 'condensed'), + 'semicondensed': ('width', 'semi-condensed'), + 'expanded': ('width', 'expanded'), + 'extraexpanded': ('width', 'extra-expanded'), + 'ultraexpanded': ('width', 'ultra-expanded'), +} + + +@lru_cache # The parser instance is a singleton. +def _make_fontconfig_parser(): + def comma_separated(elem): + return elem + ZeroOrMore(Suppress(",") + elem) + + family = Regex(fr"([^{_family_punc}]|(\\[{_family_punc}]))*") + size = Regex(r"([0-9]+\.?[0-9]*|\.[0-9]+)") + name = Regex(r"[a-z]+") + value = Regex(fr"([^{_value_punc}]|(\\[{_value_punc}]))*") + prop = Group((name + Suppress("=") + comma_separated(value)) | one_of(_CONSTANTS)) + return ( + Optional(comma_separated(family)("families")) + + Optional("-" + comma_separated(size)("sizes")) + + ZeroOrMore(":" + prop("properties*")) + + StringEnd() + ) + + +# `parse_fontconfig_pattern` is a bottleneck during the tests because it is +# repeatedly called when the rcParams are reset (to validate the default +# fonts). In practice, the cache size doesn't grow beyond a few dozen entries +# during the test suite. +@lru_cache +def parse_fontconfig_pattern(pattern): + """ + Parse a fontconfig *pattern* into a dict that can initialize a + `.font_manager.FontProperties` object. + """ + parser = _make_fontconfig_parser() + try: + parse = parser.parse_string(pattern) + except ParseException as err: + # explain becomes a plain method on pyparsing 3 (err.explain(0)). + raise ValueError("\n" + ParseException.explain(err, 0)) from None + parser.reset_cache() + props = {} + if "families" in parse: + props["family"] = [*map(_family_unescape, parse["families"])] + if "sizes" in parse: + props["size"] = [*parse["sizes"]] + for prop in parse.get("properties", []): + if len(prop) == 1: + prop = _CONSTANTS[prop[0]] + k, *v = prop + props.setdefault(k, []).extend(map(_value_unescape, v)) + return props + + +def generate_fontconfig_pattern(d): + """Convert a `.FontProperties` to a fontconfig pattern string.""" + kvs = [(k, getattr(d, f"get_{k}")()) + for k in ["style", "variant", "weight", "stretch", "file", "size"]] + # Families is given first without a leading keyword. Other entries (which + # are necessarily scalar) are given as key=value, skipping Nones. + return (",".join(_family_escape(f) for f in d.get_family()) + + "".join(f":{k}={_value_escape(str(v))}" + for k, v in kvs if v is not None)) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_image.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_image.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_internal_utils.py b/.venv/lib/python3.12/site-packages/matplotlib/_internal_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0223aa593bb2cb20b58f2b9e41bdc0dfa5ceed35 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_internal_utils.py @@ -0,0 +1,64 @@ +""" +Internal debugging utilities, that are not expected to be used in the rest of +the codebase. + +WARNING: Code in this module may change without prior notice! +""" + +from io import StringIO +from pathlib import Path +import subprocess + +from matplotlib.transforms import TransformNode + + +def graphviz_dump_transform(transform, dest, *, highlight=None): + """ + Generate a graphical representation of the transform tree for *transform* + using the :program:`dot` program (which this function depends on). The + output format (png, dot, etc.) is determined from the suffix of *dest*. + + Parameters + ---------- + transform : `~matplotlib.transform.Transform` + The represented transform. + dest : str + Output filename. The extension must be one of the formats supported + by :program:`dot`, e.g. png, svg, dot, ... + (see https://www.graphviz.org/doc/info/output.html). + highlight : list of `~matplotlib.transform.Transform` or None + The transforms in the tree to be drawn in bold. + If *None*, *transform* is highlighted. + """ + + if highlight is None: + highlight = [transform] + seen = set() + + def recurse(root, buf): + if id(root) in seen: + return + seen.add(id(root)) + props = {} + label = type(root).__name__ + if root._invalid: + label = f'[{label}]' + if root in highlight: + props['style'] = 'bold' + props['shape'] = 'box' + props['label'] = '"%s"' % label + props = ' '.join(map('{0[0]}={0[1]}'.format, props.items())) + buf.write(f'{id(root)} [{props}];\n') + for key, val in vars(root).items(): + if isinstance(val, TransformNode) and id(root) in val._parents: + buf.write(f'"{id(root)}" -> "{id(val)}" ' + f'[label="{key}", fontsize=10];\n') + recurse(val, buf) + + buf = StringIO() + buf.write('digraph G {\n') + recurse(transform, buf) + buf.write('}\n') + subprocess.run( + ['dot', '-T', Path(dest).suffix[1:], '-o', dest], + input=buf.getvalue().encode('utf-8'), check=True) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_layoutgrid.py b/.venv/lib/python3.12/site-packages/matplotlib/_layoutgrid.py new file mode 100644 index 0000000000000000000000000000000000000000..8f81b14765b68fa1f7480c3d34ba74245e39b84f --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_layoutgrid.py @@ -0,0 +1,547 @@ +""" +A layoutgrid is a nrows by ncols set of boxes, meant to be used by +`._constrained_layout`, each box is analogous to a subplotspec element of +a gridspec. + +Each box is defined by left[ncols], right[ncols], bottom[nrows] and top[nrows], +and by two editable margins for each side. The main margin gets its value +set by the size of ticklabels, titles, etc on each Axes that is in the figure. +The outer margin is the padding around the Axes, and space for any +colorbars. + +The "inner" widths and heights of these boxes are then constrained to be the +same (relative the values of `width_ratios[ncols]` and `height_ratios[nrows]`). + +The layoutgrid is then constrained to be contained within a parent layoutgrid, +its column(s) and row(s) specified when it is created. +""" + +import itertools +import kiwisolver as kiwi +import logging +import numpy as np + +import matplotlib as mpl +import matplotlib.patches as mpatches +from matplotlib.transforms import Bbox + +_log = logging.getLogger(__name__) + + +class LayoutGrid: + """ + Analogous to a gridspec, and contained in another LayoutGrid. + """ + + def __init__(self, parent=None, parent_pos=(0, 0), + parent_inner=False, name='', ncols=1, nrows=1, + h_pad=None, w_pad=None, width_ratios=None, + height_ratios=None): + Variable = kiwi.Variable + self.parent_pos = parent_pos + self.parent_inner = parent_inner + self.name = name + seq_id() + if isinstance(parent, LayoutGrid): + self.name = f'{parent.name}.{self.name}' + self.nrows = nrows + self.ncols = ncols + self.height_ratios = np.atleast_1d(height_ratios) + if height_ratios is None: + self.height_ratios = np.ones(nrows) + self.width_ratios = np.atleast_1d(width_ratios) + if width_ratios is None: + self.width_ratios = np.ones(ncols) + + sn = self.name + '_' + if not isinstance(parent, LayoutGrid): + # parent can be a rect if not a LayoutGrid + # allows specifying a rectangle to contain the layout. + self.solver = kiwi.Solver() + else: + parent.add_child(self, *parent_pos) + self.solver = parent.solver + # keep track of artist associated w/ this layout. Can be none + self.artists = np.empty((nrows, ncols), dtype=object) + self.children = np.empty((nrows, ncols), dtype=object) + + self.margins = {} + self.margin_vals = {} + # all the boxes in each column share the same left/right margins: + for todo in ['left', 'right', 'leftcb', 'rightcb']: + # track the value so we can change only if a margin is larger + # than the current value + self.margin_vals[todo] = np.zeros(ncols) + + sol = self.solver + + self.lefts = [Variable(f'{sn}lefts[{i}]') for i in range(ncols)] + self.rights = [Variable(f'{sn}rights[{i}]') for i in range(ncols)] + for todo in ['left', 'right', 'leftcb', 'rightcb']: + self.margins[todo] = [Variable(f'{sn}margins[{todo}][{i}]') + for i in range(ncols)] + for i in range(ncols): + sol.addEditVariable(self.margins[todo][i], 'strong') + + for todo in ['bottom', 'top', 'bottomcb', 'topcb']: + self.margins[todo] = np.empty((nrows), dtype=object) + self.margin_vals[todo] = np.zeros(nrows) + + self.bottoms = [Variable(f'{sn}bottoms[{i}]') for i in range(nrows)] + self.tops = [Variable(f'{sn}tops[{i}]') for i in range(nrows)] + for todo in ['bottom', 'top', 'bottomcb', 'topcb']: + self.margins[todo] = [Variable(f'{sn}margins[{todo}][{i}]') + for i in range(nrows)] + for i in range(nrows): + sol.addEditVariable(self.margins[todo][i], 'strong') + + # set these margins to zero by default. They will be edited as + # children are filled. + self.reset_margins() + self.add_constraints(parent) + + self.h_pad = h_pad + self.w_pad = w_pad + + def __repr__(self): + str = f'LayoutBox: {self.name:25s} {self.nrows}x{self.ncols},\n' + for i in range(self.nrows): + for j in range(self.ncols): + str += f'{i}, {j}: '\ + f'L{self.lefts[j].value():1.3f}, ' \ + f'B{self.bottoms[i].value():1.3f}, ' \ + f'R{self.rights[j].value():1.3f}, ' \ + f'T{self.tops[i].value():1.3f}, ' \ + f'ML{self.margins["left"][j].value():1.3f}, ' \ + f'MR{self.margins["right"][j].value():1.3f}, ' \ + f'MB{self.margins["bottom"][i].value():1.3f}, ' \ + f'MT{self.margins["top"][i].value():1.3f}, \n' + return str + + def reset_margins(self): + """ + Reset all the margins to zero. Must do this after changing + figure size, for instance, because the relative size of the + axes labels etc changes. + """ + for todo in ['left', 'right', 'bottom', 'top', + 'leftcb', 'rightcb', 'bottomcb', 'topcb']: + self.edit_margins(todo, 0.0) + + def add_constraints(self, parent): + # define self-consistent constraints + self.hard_constraints() + # define relationship with parent layoutgrid: + self.parent_constraints(parent) + # define relative widths of the grid cells to each other + # and stack horizontally and vertically. + self.grid_constraints() + + def hard_constraints(self): + """ + These are the redundant constraints, plus ones that make the + rest of the code easier. + """ + for i in range(self.ncols): + hc = [self.rights[i] >= self.lefts[i], + (self.rights[i] - self.margins['right'][i] - + self.margins['rightcb'][i] >= + self.lefts[i] - self.margins['left'][i] - + self.margins['leftcb'][i]) + ] + for c in hc: + self.solver.addConstraint(c | 'required') + + for i in range(self.nrows): + hc = [self.tops[i] >= self.bottoms[i], + (self.tops[i] - self.margins['top'][i] - + self.margins['topcb'][i] >= + self.bottoms[i] - self.margins['bottom'][i] - + self.margins['bottomcb'][i]) + ] + for c in hc: + self.solver.addConstraint(c | 'required') + + def add_child(self, child, i=0, j=0): + # np.ix_ returns the cross product of i and j indices + self.children[np.ix_(np.atleast_1d(i), np.atleast_1d(j))] = child + + def parent_constraints(self, parent): + # constraints that are due to the parent... + # i.e. the first column's left is equal to the + # parent's left, the last column right equal to the + # parent's right... + if not isinstance(parent, LayoutGrid): + # specify a rectangle in figure coordinates + hc = [self.lefts[0] == parent[0], + self.rights[-1] == parent[0] + parent[2], + # top and bottom reversed order... + self.tops[0] == parent[1] + parent[3], + self.bottoms[-1] == parent[1]] + else: + rows, cols = self.parent_pos + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + left = parent.lefts[cols[0]] + right = parent.rights[cols[-1]] + top = parent.tops[rows[0]] + bottom = parent.bottoms[rows[-1]] + if self.parent_inner: + # the layout grid is contained inside the inner + # grid of the parent. + left += parent.margins['left'][cols[0]] + left += parent.margins['leftcb'][cols[0]] + right -= parent.margins['right'][cols[-1]] + right -= parent.margins['rightcb'][cols[-1]] + top -= parent.margins['top'][rows[0]] + top -= parent.margins['topcb'][rows[0]] + bottom += parent.margins['bottom'][rows[-1]] + bottom += parent.margins['bottomcb'][rows[-1]] + hc = [self.lefts[0] == left, + self.rights[-1] == right, + # from top to bottom + self.tops[0] == top, + self.bottoms[-1] == bottom] + for c in hc: + self.solver.addConstraint(c | 'required') + + def grid_constraints(self): + # constrain the ratio of the inner part of the grids + # to be the same (relative to width_ratios) + + # constrain widths: + w = (self.rights[0] - self.margins['right'][0] - + self.margins['rightcb'][0]) + w = (w - self.lefts[0] - self.margins['left'][0] - + self.margins['leftcb'][0]) + w0 = w / self.width_ratios[0] + # from left to right + for i in range(1, self.ncols): + w = (self.rights[i] - self.margins['right'][i] - + self.margins['rightcb'][i]) + w = (w - self.lefts[i] - self.margins['left'][i] - + self.margins['leftcb'][i]) + c = (w == w0 * self.width_ratios[i]) + self.solver.addConstraint(c | 'strong') + # constrain the grid cells to be directly next to each other. + c = (self.rights[i - 1] == self.lefts[i]) + self.solver.addConstraint(c | 'strong') + + # constrain heights: + h = self.tops[0] - self.margins['top'][0] - self.margins['topcb'][0] + h = (h - self.bottoms[0] - self.margins['bottom'][0] - + self.margins['bottomcb'][0]) + h0 = h / self.height_ratios[0] + # from top to bottom: + for i in range(1, self.nrows): + h = (self.tops[i] - self.margins['top'][i] - + self.margins['topcb'][i]) + h = (h - self.bottoms[i] - self.margins['bottom'][i] - + self.margins['bottomcb'][i]) + c = (h == h0 * self.height_ratios[i]) + self.solver.addConstraint(c | 'strong') + # constrain the grid cells to be directly above each other. + c = (self.bottoms[i - 1] == self.tops[i]) + self.solver.addConstraint(c | 'strong') + + # Margin editing: The margins are variable and meant to + # contain things of a fixed size like axes labels, tick labels, titles + # etc + def edit_margin(self, todo, size, cell): + """ + Change the size of the margin for one cell. + + Parameters + ---------- + todo : string (one of 'left', 'right', 'bottom', 'top') + margin to alter. + + size : float + Size of the margin. If it is larger than the existing minimum it + updates the margin size. Fraction of figure size. + + cell : int + Cell column or row to edit. + """ + self.solver.suggestValue(self.margins[todo][cell], size) + self.margin_vals[todo][cell] = size + + def edit_margin_min(self, todo, size, cell=0): + """ + Change the minimum size of the margin for one cell. + + Parameters + ---------- + todo : string (one of 'left', 'right', 'bottom', 'top') + margin to alter. + + size : float + Minimum size of the margin . If it is larger than the + existing minimum it updates the margin size. Fraction of + figure size. + + cell : int + Cell column or row to edit. + """ + + if size > self.margin_vals[todo][cell]: + self.edit_margin(todo, size, cell) + + def edit_margins(self, todo, size): + """ + Change the size of all the margin of all the cells in the layout grid. + + Parameters + ---------- + todo : string (one of 'left', 'right', 'bottom', 'top') + margin to alter. + + size : float + Size to set the margins. Fraction of figure size. + """ + + for i in range(len(self.margin_vals[todo])): + self.edit_margin(todo, size, i) + + def edit_all_margins_min(self, todo, size): + """ + Change the minimum size of all the margin of all + the cells in the layout grid. + + Parameters + ---------- + todo : {'left', 'right', 'bottom', 'top'} + The margin to alter. + + size : float + Minimum size of the margin. If it is larger than the + existing minimum it updates the margin size. Fraction of + figure size. + """ + + for i in range(len(self.margin_vals[todo])): + self.edit_margin_min(todo, size, i) + + def edit_outer_margin_mins(self, margin, ss): + """ + Edit all four margin minimums in one statement. + + Parameters + ---------- + margin : dict + size of margins in a dict with keys 'left', 'right', 'bottom', + 'top' + + ss : SubplotSpec + defines the subplotspec these margins should be applied to + """ + + self.edit_margin_min('left', margin['left'], ss.colspan.start) + self.edit_margin_min('leftcb', margin['leftcb'], ss.colspan.start) + self.edit_margin_min('right', margin['right'], ss.colspan.stop - 1) + self.edit_margin_min('rightcb', margin['rightcb'], ss.colspan.stop - 1) + # rows are from the top down: + self.edit_margin_min('top', margin['top'], ss.rowspan.start) + self.edit_margin_min('topcb', margin['topcb'], ss.rowspan.start) + self.edit_margin_min('bottom', margin['bottom'], ss.rowspan.stop - 1) + self.edit_margin_min('bottomcb', margin['bottomcb'], + ss.rowspan.stop - 1) + + def get_margins(self, todo, col): + """Return the margin at this position""" + return self.margin_vals[todo][col] + + def get_outer_bbox(self, rows=0, cols=0): + """ + Return the outer bounding box of the subplot specs + given by rows and cols. rows and cols can be spans. + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + self.lefts[cols[0]].value(), + self.bottoms[rows[-1]].value(), + self.rights[cols[-1]].value(), + self.tops[rows[0]].value()) + return bbox + + def get_inner_bbox(self, rows=0, cols=0): + """ + Return the inner bounding box of the subplot specs + given by rows and cols. rows and cols can be spans. + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + (self.lefts[cols[0]].value() + + self.margins['left'][cols[0]].value() + + self.margins['leftcb'][cols[0]].value()), + (self.bottoms[rows[-1]].value() + + self.margins['bottom'][rows[-1]].value() + + self.margins['bottomcb'][rows[-1]].value()), + (self.rights[cols[-1]].value() - + self.margins['right'][cols[-1]].value() - + self.margins['rightcb'][cols[-1]].value()), + (self.tops[rows[0]].value() - + self.margins['top'][rows[0]].value() - + self.margins['topcb'][rows[0]].value()) + ) + return bbox + + def get_bbox_for_cb(self, rows=0, cols=0): + """ + Return the bounding box that includes the + decorations but, *not* the colorbar... + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + (self.lefts[cols[0]].value() + + self.margins['leftcb'][cols[0]].value()), + (self.bottoms[rows[-1]].value() + + self.margins['bottomcb'][rows[-1]].value()), + (self.rights[cols[-1]].value() - + self.margins['rightcb'][cols[-1]].value()), + (self.tops[rows[0]].value() - + self.margins['topcb'][rows[0]].value()) + ) + return bbox + + def get_left_margin_bbox(self, rows=0, cols=0): + """ + Return the left margin bounding box of the subplot specs + given by rows and cols. rows and cols can be spans. + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + (self.lefts[cols[0]].value() + + self.margins['leftcb'][cols[0]].value()), + (self.bottoms[rows[-1]].value()), + (self.lefts[cols[0]].value() + + self.margins['leftcb'][cols[0]].value() + + self.margins['left'][cols[0]].value()), + (self.tops[rows[0]].value())) + return bbox + + def get_bottom_margin_bbox(self, rows=0, cols=0): + """ + Return the left margin bounding box of the subplot specs + given by rows and cols. rows and cols can be spans. + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + (self.lefts[cols[0]].value()), + (self.bottoms[rows[-1]].value() + + self.margins['bottomcb'][rows[-1]].value()), + (self.rights[cols[-1]].value()), + (self.bottoms[rows[-1]].value() + + self.margins['bottom'][rows[-1]].value() + + self.margins['bottomcb'][rows[-1]].value() + )) + return bbox + + def get_right_margin_bbox(self, rows=0, cols=0): + """ + Return the left margin bounding box of the subplot specs + given by rows and cols. rows and cols can be spans. + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + (self.rights[cols[-1]].value() - + self.margins['right'][cols[-1]].value() - + self.margins['rightcb'][cols[-1]].value()), + (self.bottoms[rows[-1]].value()), + (self.rights[cols[-1]].value() - + self.margins['rightcb'][cols[-1]].value()), + (self.tops[rows[0]].value())) + return bbox + + def get_top_margin_bbox(self, rows=0, cols=0): + """ + Return the left margin bounding box of the subplot specs + given by rows and cols. rows and cols can be spans. + """ + rows = np.atleast_1d(rows) + cols = np.atleast_1d(cols) + + bbox = Bbox.from_extents( + (self.lefts[cols[0]].value()), + (self.tops[rows[0]].value() - + self.margins['topcb'][rows[0]].value()), + (self.rights[cols[-1]].value()), + (self.tops[rows[0]].value() - + self.margins['topcb'][rows[0]].value() - + self.margins['top'][rows[0]].value())) + return bbox + + def update_variables(self): + """ + Update the variables for the solver attached to this layoutgrid. + """ + self.solver.updateVariables() + +_layoutboxobjnum = itertools.count() + + +def seq_id(): + """Generate a short sequential id for layoutbox objects.""" + return '%06d' % next(_layoutboxobjnum) + + +def plot_children(fig, lg=None, level=0): + """Simple plotting to show where boxes are.""" + if lg is None: + _layoutgrids = fig.get_layout_engine().execute(fig) + lg = _layoutgrids[fig] + colors = mpl.rcParams["axes.prop_cycle"].by_key()["color"] + col = colors[level] + for i in range(lg.nrows): + for j in range(lg.ncols): + bb = lg.get_outer_bbox(rows=i, cols=j) + fig.add_artist( + mpatches.Rectangle(bb.p0, bb.width, bb.height, linewidth=1, + edgecolor='0.7', facecolor='0.7', + alpha=0.2, transform=fig.transFigure, + zorder=-3)) + bbi = lg.get_inner_bbox(rows=i, cols=j) + fig.add_artist( + mpatches.Rectangle(bbi.p0, bbi.width, bbi.height, linewidth=2, + edgecolor=col, facecolor='none', + transform=fig.transFigure, zorder=-2)) + + bbi = lg.get_left_margin_bbox(rows=i, cols=j) + fig.add_artist( + mpatches.Rectangle(bbi.p0, bbi.width, bbi.height, linewidth=0, + edgecolor='none', alpha=0.2, + facecolor=[0.5, 0.7, 0.5], + transform=fig.transFigure, zorder=-2)) + bbi = lg.get_right_margin_bbox(rows=i, cols=j) + fig.add_artist( + mpatches.Rectangle(bbi.p0, bbi.width, bbi.height, linewidth=0, + edgecolor='none', alpha=0.2, + facecolor=[0.7, 0.5, 0.5], + transform=fig.transFigure, zorder=-2)) + bbi = lg.get_bottom_margin_bbox(rows=i, cols=j) + fig.add_artist( + mpatches.Rectangle(bbi.p0, bbi.width, bbi.height, linewidth=0, + edgecolor='none', alpha=0.2, + facecolor=[0.5, 0.5, 0.7], + transform=fig.transFigure, zorder=-2)) + bbi = lg.get_top_margin_bbox(rows=i, cols=j) + fig.add_artist( + mpatches.Rectangle(bbi.p0, bbi.width, bbi.height, linewidth=0, + edgecolor='none', alpha=0.2, + facecolor=[0.7, 0.2, 0.7], + transform=fig.transFigure, zorder=-2)) + for ch in lg.children.flat: + if ch is not None: + plot_children(fig, ch, level=level+1) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_mathtext.py b/.venv/lib/python3.12/site-packages/matplotlib/_mathtext.py new file mode 100644 index 0000000000000000000000000000000000000000..cf35dc1de7db2b8b7ea84c9cc5c2ba9ad6b6c5a0 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_mathtext.py @@ -0,0 +1,2855 @@ +""" +Implementation details for :mod:`.mathtext`. +""" + +from __future__ import annotations + +import abc +import copy +import enum +import functools +import logging +import os +import re +import types +import unicodedata +import string +import typing as T +from typing import NamedTuple + +import numpy as np +from pyparsing import ( + Empty, Forward, Literal, Group, NotAny, OneOrMore, Optional, + ParseBaseException, ParseException, ParseExpression, ParseFatalException, + ParserElement, ParseResults, QuotedString, Regex, StringEnd, ZeroOrMore, + pyparsing_common, nested_expr, one_of) + +import matplotlib as mpl +from . import cbook +from ._mathtext_data import ( + latex_to_bakoma, stix_glyph_fixes, stix_virtual_fonts, tex2uni) +from .font_manager import FontProperties, findfont, get_font +from .ft2font import FT2Font, FT2Image, Kerning, LoadFlags + + +if T.TYPE_CHECKING: + from collections.abc import Iterable + from .ft2font import Glyph + +ParserElement.enable_packrat() +_log = logging.getLogger("matplotlib.mathtext") + + +############################################################################## +# FONTS + + +def get_unicode_index(symbol: str) -> int: # Publicly exported. + r""" + Return the integer index (from the Unicode table) of *symbol*. + + Parameters + ---------- + symbol : str + A single (Unicode) character, a TeX command (e.g. r'\pi') or a Type1 + symbol name (e.g. 'phi'). + """ + try: # This will succeed if symbol is a single Unicode char + return ord(symbol) + except TypeError: + pass + try: # Is symbol a TeX symbol (i.e. \alpha) + return tex2uni[symbol.strip("\\")] + except KeyError as err: + raise ValueError( + f"{symbol!r} is not a valid Unicode character or TeX/Type1 symbol" + ) from err + + +class VectorParse(NamedTuple): + """ + The namedtuple type returned by ``MathTextParser("path").parse(...)``. + + Attributes + ---------- + width, height, depth : float + The global metrics. + glyphs : list + The glyphs including their positions. + rect : list + The list of rectangles. + """ + width: float + height: float + depth: float + glyphs: list[tuple[FT2Font, float, int, float, float]] + rects: list[tuple[float, float, float, float]] + +VectorParse.__module__ = "matplotlib.mathtext" + + +class RasterParse(NamedTuple): + """ + The namedtuple type returned by ``MathTextParser("agg").parse(...)``. + + Attributes + ---------- + ox, oy : float + The offsets are always zero. + width, height, depth : float + The global metrics. + image : FT2Image + A raster image. + """ + ox: float + oy: float + width: float + height: float + depth: float + image: FT2Image + +RasterParse.__module__ = "matplotlib.mathtext" + + +class Output: + r""" + Result of `ship`\ping a box: lists of positioned glyphs and rectangles. + + This class is not exposed to end users, but converted to a `VectorParse` or + a `RasterParse` by `.MathTextParser.parse`. + """ + + def __init__(self, box: Box): + self.box = box + self.glyphs: list[tuple[float, float, FontInfo]] = [] # (ox, oy, info) + self.rects: list[tuple[float, float, float, float]] = [] # (x1, y1, x2, y2) + + def to_vector(self) -> VectorParse: + w, h, d = map( + np.ceil, [self.box.width, self.box.height, self.box.depth]) + gs = [(info.font, info.fontsize, info.num, ox, h - oy + info.offset) + for ox, oy, info in self.glyphs] + rs = [(x1, h - y2, x2 - x1, y2 - y1) + for x1, y1, x2, y2 in self.rects] + return VectorParse(w, h + d, d, gs, rs) + + def to_raster(self, *, antialiased: bool) -> RasterParse: + # Metrics y's and mathtext y's are oriented in opposite directions, + # hence the switch between ymin and ymax. + xmin = min([*[ox + info.metrics.xmin for ox, oy, info in self.glyphs], + *[x1 for x1, y1, x2, y2 in self.rects], 0]) - 1 + ymin = min([*[oy - info.metrics.ymax for ox, oy, info in self.glyphs], + *[y1 for x1, y1, x2, y2 in self.rects], 0]) - 1 + xmax = max([*[ox + info.metrics.xmax for ox, oy, info in self.glyphs], + *[x2 for x1, y1, x2, y2 in self.rects], 0]) + 1 + ymax = max([*[oy - info.metrics.ymin for ox, oy, info in self.glyphs], + *[y2 for x1, y1, x2, y2 in self.rects], 0]) + 1 + w = xmax - xmin + h = ymax - ymin - self.box.depth + d = ymax - ymin - self.box.height + image = FT2Image(int(np.ceil(w)), int(np.ceil(h + max(d, 0)))) + + # Ideally, we could just use self.glyphs and self.rects here, shifting + # their coordinates by (-xmin, -ymin), but this yields slightly + # different results due to floating point slop; shipping twice is the + # old approach and keeps baseline images backcompat. + shifted = ship(self.box, (-xmin, -ymin)) + + for ox, oy, info in shifted.glyphs: + info.font.draw_glyph_to_bitmap( + image, int(ox), int(oy - info.metrics.iceberg), info.glyph, + antialiased=antialiased) + for x1, y1, x2, y2 in shifted.rects: + height = max(int(y2 - y1) - 1, 0) + if height == 0: + center = (y2 + y1) / 2 + y = int(center - (height + 1) / 2) + else: + y = int(y1) + image.draw_rect_filled(int(x1), y, int(np.ceil(x2)), y + height) + return RasterParse(0, 0, w, h + d, d, image) + + +class FontMetrics(NamedTuple): + """ + Metrics of a font. + + Attributes + ---------- + advance : float + The advance distance (in points) of the glyph. + height : float + The height of the glyph in points. + width : float + The width of the glyph in points. + xmin, xmax, ymin, ymax : float + The ink rectangle of the glyph. + iceberg : float + The distance from the baseline to the top of the glyph. (This corresponds to + TeX's definition of "height".) + slanted : bool + Whether the glyph should be considered as "slanted" (currently used for kerning + sub/superscripts). + """ + advance: float + height: float + width: float + xmin: float + xmax: float + ymin: float + ymax: float + iceberg: float + slanted: bool + + +class FontInfo(NamedTuple): + font: FT2Font + fontsize: float + postscript_name: str + metrics: FontMetrics + num: int + glyph: Glyph + offset: float + + +class Fonts(abc.ABC): + """ + An abstract base class for a system of fonts to use for mathtext. + + The class must be able to take symbol keys and font file names and + return the character metrics. It also delegates to a backend class + to do the actual drawing. + """ + + def __init__(self, default_font_prop: FontProperties, load_glyph_flags: LoadFlags): + """ + Parameters + ---------- + default_font_prop : `~.font_manager.FontProperties` + The default non-math font, or the base font for Unicode (generic) + font rendering. + load_glyph_flags : `.ft2font.LoadFlags` + Flags passed to the glyph loader (e.g. ``FT_Load_Glyph`` and + ``FT_Load_Char`` for FreeType-based fonts). + """ + self.default_font_prop = default_font_prop + self.load_glyph_flags = load_glyph_flags + + def get_kern(self, font1: str, fontclass1: str, sym1: str, fontsize1: float, + font2: str, fontclass2: str, sym2: str, fontsize2: float, + dpi: float) -> float: + """ + Get the kerning distance for font between *sym1* and *sym2*. + + See `~.Fonts.get_metrics` for a detailed description of the parameters. + """ + return 0. + + def _get_font(self, font: str) -> FT2Font: + raise NotImplementedError + + def _get_info(self, font: str, font_class: str, sym: str, fontsize: float, + dpi: float) -> FontInfo: + raise NotImplementedError + + def get_metrics(self, font: str, font_class: str, sym: str, fontsize: float, + dpi: float) -> FontMetrics: + r""" + Parameters + ---------- + font : str + One of the TeX font names: "tt", "it", "rm", "cal", "sf", "bf", + "default", "regular", "bb", "frak", "scr". "default" and "regular" + are synonyms and use the non-math font. + font_class : str + One of the TeX font names (as for *font*), but **not** "bb", + "frak", or "scr". This is used to combine two font classes. The + only supported combination currently is ``get_metrics("frak", "bf", + ...)``. + sym : str + A symbol in raw TeX form, e.g., "1", "x", or "\sigma". + fontsize : float + Font size in points. + dpi : float + Rendering dots-per-inch. + + Returns + ------- + FontMetrics + """ + info = self._get_info(font, font_class, sym, fontsize, dpi) + return info.metrics + + def render_glyph(self, output: Output, ox: float, oy: float, font: str, + font_class: str, sym: str, fontsize: float, dpi: float) -> None: + """ + At position (*ox*, *oy*), draw the glyph specified by the remaining + parameters (see `get_metrics` for their detailed description). + """ + info = self._get_info(font, font_class, sym, fontsize, dpi) + output.glyphs.append((ox, oy, info)) + + def render_rect_filled(self, output: Output, + x1: float, y1: float, x2: float, y2: float) -> None: + """ + Draw a filled rectangle from (*x1*, *y1*) to (*x2*, *y2*). + """ + output.rects.append((x1, y1, x2, y2)) + + def get_xheight(self, font: str, fontsize: float, dpi: float) -> float: + """ + Get the xheight for the given *font* and *fontsize*. + """ + raise NotImplementedError() + + def get_underline_thickness(self, font: str, fontsize: float, dpi: float) -> float: + """ + Get the line thickness that matches the given font. Used as a + base unit for drawing lines such as in a fraction or radical. + """ + raise NotImplementedError() + + def get_sized_alternatives_for_symbol(self, fontname: str, + sym: str) -> list[tuple[str, str]]: + """ + Override if your font provides multiple sizes of the same + symbol. Should return a list of symbols matching *sym* in + various sizes. The expression renderer will select the most + appropriate size for a given situation from this list. + """ + return [(fontname, sym)] + + +class TruetypeFonts(Fonts, metaclass=abc.ABCMeta): + """ + A generic base class for all font setups that use Truetype fonts + (through FT2Font). + """ + + def __init__(self, default_font_prop: FontProperties, load_glyph_flags: LoadFlags): + super().__init__(default_font_prop, load_glyph_flags) + # Per-instance cache. + self._get_info = functools.cache(self._get_info) # type: ignore[method-assign] + self._fonts = {} + self.fontmap: dict[str | int, str] = {} + + filename = findfont(self.default_font_prop) + default_font = get_font(filename) + self._fonts['default'] = default_font + self._fonts['regular'] = default_font + + def _get_font(self, font: str | int) -> FT2Font: + if font in self.fontmap: + basename = self.fontmap[font] + else: + # NOTE: An int is only passed by subclasses which have placed int keys into + # `self.fontmap`, so we must cast this to confirm it to typing. + basename = T.cast(str, font) + cached_font = self._fonts.get(basename) + if cached_font is None and os.path.exists(basename): + cached_font = get_font(basename) + self._fonts[basename] = cached_font + self._fonts[cached_font.postscript_name] = cached_font + self._fonts[cached_font.postscript_name.lower()] = cached_font + return T.cast(FT2Font, cached_font) # FIXME: Not sure this is guaranteed. + + def _get_offset(self, font: FT2Font, glyph: Glyph, fontsize: float, + dpi: float) -> float: + if font.postscript_name == 'Cmex10': + return (glyph.height / 64 / 2) + (fontsize/3 * dpi/72) + return 0. + + def _get_glyph(self, fontname: str, font_class: str, + sym: str) -> tuple[FT2Font, int, bool]: + raise NotImplementedError + + # The return value of _get_info is cached per-instance. + def _get_info(self, fontname: str, font_class: str, sym: str, fontsize: float, + dpi: float) -> FontInfo: + font, num, slanted = self._get_glyph(fontname, font_class, sym) + font.set_size(fontsize, dpi) + glyph = font.load_char(num, flags=self.load_glyph_flags) + + xmin, ymin, xmax, ymax = (val / 64 for val in glyph.bbox) + offset = self._get_offset(font, glyph, fontsize, dpi) + metrics = FontMetrics( + advance=glyph.linearHoriAdvance / 65536, + height=glyph.height / 64, + width=glyph.width / 64, + xmin=xmin, + xmax=xmax, + ymin=ymin + offset, + ymax=ymax + offset, + # iceberg is the equivalent of TeX's "height" + iceberg=glyph.horiBearingY / 64 + offset, + slanted=slanted + ) + + return FontInfo( + font=font, + fontsize=fontsize, + postscript_name=font.postscript_name, + metrics=metrics, + num=num, + glyph=glyph, + offset=offset + ) + + def get_xheight(self, fontname: str, fontsize: float, dpi: float) -> float: + font = self._get_font(fontname) + font.set_size(fontsize, dpi) + pclt = font.get_sfnt_table('pclt') + if pclt is None: + # Some fonts don't store the xHeight, so we do a poor man's xHeight + metrics = self.get_metrics( + fontname, mpl.rcParams['mathtext.default'], 'x', fontsize, dpi) + return metrics.iceberg + xHeight = (pclt['xHeight'] / 64.0) * (fontsize / 12.0) * (dpi / 100.0) + return xHeight + + def get_underline_thickness(self, font: str, fontsize: float, dpi: float) -> float: + # This function used to grab underline thickness from the font + # metrics, but that information is just too un-reliable, so it + # is now hardcoded. + return ((0.75 / 12.0) * fontsize * dpi) / 72.0 + + def get_kern(self, font1: str, fontclass1: str, sym1: str, fontsize1: float, + font2: str, fontclass2: str, sym2: str, fontsize2: float, + dpi: float) -> float: + if font1 == font2 and fontsize1 == fontsize2: + info1 = self._get_info(font1, fontclass1, sym1, fontsize1, dpi) + info2 = self._get_info(font2, fontclass2, sym2, fontsize2, dpi) + font = info1.font + return font.get_kerning(info1.num, info2.num, Kerning.DEFAULT) / 64 + return super().get_kern(font1, fontclass1, sym1, fontsize1, + font2, fontclass2, sym2, fontsize2, dpi) + + +class BakomaFonts(TruetypeFonts): + """ + Use the Bakoma TrueType fonts for rendering. + + Symbols are strewn about a number of font files, each of which has + its own proprietary 8-bit encoding. + """ + _fontmap = { + 'cal': 'cmsy10', + 'rm': 'cmr10', + 'tt': 'cmtt10', + 'it': 'cmmi10', + 'bf': 'cmb10', + 'sf': 'cmss10', + 'ex': 'cmex10', + } + + def __init__(self, default_font_prop: FontProperties, load_glyph_flags: LoadFlags): + self._stix_fallback = StixFonts(default_font_prop, load_glyph_flags) + + super().__init__(default_font_prop, load_glyph_flags) + for key, val in self._fontmap.items(): + fullpath = findfont(val) + self.fontmap[key] = fullpath + self.fontmap[val] = fullpath + + _slanted_symbols = set(r"\int \oint".split()) + + def _get_glyph(self, fontname: str, font_class: str, + sym: str) -> tuple[FT2Font, int, bool]: + font = None + if fontname in self.fontmap and sym in latex_to_bakoma: + basename, num = latex_to_bakoma[sym] + slanted = (basename == "cmmi10") or sym in self._slanted_symbols + font = self._get_font(basename) + elif len(sym) == 1: + slanted = (fontname == "it") + font = self._get_font(fontname) + if font is not None: + num = ord(sym) + if font is not None and font.get_char_index(num) != 0: + return font, num, slanted + else: + return self._stix_fallback._get_glyph(fontname, font_class, sym) + + # The Bakoma fonts contain many pre-sized alternatives for the + # delimiters. The AutoSizedChar class will use these alternatives + # and select the best (closest sized) glyph. + _size_alternatives = { + '(': [('rm', '('), ('ex', '\xa1'), ('ex', '\xb3'), + ('ex', '\xb5'), ('ex', '\xc3')], + ')': [('rm', ')'), ('ex', '\xa2'), ('ex', '\xb4'), + ('ex', '\xb6'), ('ex', '\x21')], + '{': [('cal', '{'), ('ex', '\xa9'), ('ex', '\x6e'), + ('ex', '\xbd'), ('ex', '\x28')], + '}': [('cal', '}'), ('ex', '\xaa'), ('ex', '\x6f'), + ('ex', '\xbe'), ('ex', '\x29')], + # The fourth size of '[' is mysteriously missing from the BaKoMa + # font, so I've omitted it for both '[' and ']' + '[': [('rm', '['), ('ex', '\xa3'), ('ex', '\x68'), + ('ex', '\x22')], + ']': [('rm', ']'), ('ex', '\xa4'), ('ex', '\x69'), + ('ex', '\x23')], + r'\lfloor': [('ex', '\xa5'), ('ex', '\x6a'), + ('ex', '\xb9'), ('ex', '\x24')], + r'\rfloor': [('ex', '\xa6'), ('ex', '\x6b'), + ('ex', '\xba'), ('ex', '\x25')], + r'\lceil': [('ex', '\xa7'), ('ex', '\x6c'), + ('ex', '\xbb'), ('ex', '\x26')], + r'\rceil': [('ex', '\xa8'), ('ex', '\x6d'), + ('ex', '\xbc'), ('ex', '\x27')], + r'\langle': [('ex', '\xad'), ('ex', '\x44'), + ('ex', '\xbf'), ('ex', '\x2a')], + r'\rangle': [('ex', '\xae'), ('ex', '\x45'), + ('ex', '\xc0'), ('ex', '\x2b')], + r'\__sqrt__': [('ex', '\x70'), ('ex', '\x71'), + ('ex', '\x72'), ('ex', '\x73')], + r'\backslash': [('ex', '\xb2'), ('ex', '\x2f'), + ('ex', '\xc2'), ('ex', '\x2d')], + r'/': [('rm', '/'), ('ex', '\xb1'), ('ex', '\x2e'), + ('ex', '\xcb'), ('ex', '\x2c')], + r'\widehat': [('rm', '\x5e'), ('ex', '\x62'), ('ex', '\x63'), + ('ex', '\x64')], + r'\widetilde': [('rm', '\x7e'), ('ex', '\x65'), ('ex', '\x66'), + ('ex', '\x67')], + r'<': [('cal', 'h'), ('ex', 'D')], + r'>': [('cal', 'i'), ('ex', 'E')] + } + + for alias, target in [(r'\leftparen', '('), + (r'\rightparen', ')'), + (r'\leftbrace', '{'), + (r'\rightbrace', '}'), + (r'\leftbracket', '['), + (r'\rightbracket', ']'), + (r'\{', '{'), + (r'\}', '}'), + (r'\[', '['), + (r'\]', ']')]: + _size_alternatives[alias] = _size_alternatives[target] + + def get_sized_alternatives_for_symbol(self, fontname: str, + sym: str) -> list[tuple[str, str]]: + return self._size_alternatives.get(sym, [(fontname, sym)]) + + +class UnicodeFonts(TruetypeFonts): + """ + An abstract base class for handling Unicode fonts. + + While some reasonably complete Unicode fonts (such as DejaVu) may + work in some situations, the only Unicode font I'm aware of with a + complete set of math symbols is STIX. + + This class will "fallback" on the Bakoma fonts when a required + symbol cannot be found in the font. + """ + + # Some glyphs are not present in the `cmr10` font, and must be brought in + # from `cmsy10`. Map the Unicode indices of those glyphs to the indices at + # which they are found in `cmsy10`. + _cmr10_substitutions = { + 0x00D7: 0x00A3, # Multiplication sign. + 0x2212: 0x00A1, # Minus sign. + } + + def __init__(self, default_font_prop: FontProperties, load_glyph_flags: LoadFlags): + # This must come first so the backend's owner is set correctly + fallback_rc = mpl.rcParams['mathtext.fallback'] + font_cls: type[TruetypeFonts] | None = { + 'stix': StixFonts, + 'stixsans': StixSansFonts, + 'cm': BakomaFonts + }.get(fallback_rc) + self._fallback_font = (font_cls(default_font_prop, load_glyph_flags) + if font_cls else None) + + super().__init__(default_font_prop, load_glyph_flags) + for texfont in "cal rm tt it bf sf bfit".split(): + prop = mpl.rcParams['mathtext.' + texfont] + font = findfont(prop) + self.fontmap[texfont] = font + prop = FontProperties('cmex10') + font = findfont(prop) + self.fontmap['ex'] = font + + # include STIX sized alternatives for glyphs if fallback is STIX + if isinstance(self._fallback_font, StixFonts): + stixsizedaltfonts = { + 0: 'STIXGeneral', + 1: 'STIXSizeOneSym', + 2: 'STIXSizeTwoSym', + 3: 'STIXSizeThreeSym', + 4: 'STIXSizeFourSym', + 5: 'STIXSizeFiveSym'} + + for size, name in stixsizedaltfonts.items(): + fullpath = findfont(name) + self.fontmap[size] = fullpath + self.fontmap[name] = fullpath + + _slanted_symbols = set(r"\int \oint".split()) + + def _map_virtual_font(self, fontname: str, font_class: str, + uniindex: int) -> tuple[str, int]: + return fontname, uniindex + + def _get_glyph(self, fontname: str, font_class: str, + sym: str) -> tuple[FT2Font, int, bool]: + try: + uniindex = get_unicode_index(sym) + found_symbol = True + except ValueError: + uniindex = ord('?') + found_symbol = False + _log.warning("No TeX to Unicode mapping for %a.", sym) + + fontname, uniindex = self._map_virtual_font( + fontname, font_class, uniindex) + + new_fontname = fontname + + # Only characters in the "Letter" class should be italicized in 'it' + # mode. Greek capital letters should be Roman. + if found_symbol: + if fontname == 'it' and uniindex < 0x10000: + char = chr(uniindex) + if (unicodedata.category(char)[0] != "L" + or unicodedata.name(char).startswith("GREEK CAPITAL")): + new_fontname = 'rm' + + slanted = (new_fontname == 'it') or sym in self._slanted_symbols + found_symbol = False + font = self._get_font(new_fontname) + if font is not None: + if (uniindex in self._cmr10_substitutions + and font.family_name == "cmr10"): + font = get_font( + cbook._get_data_path("fonts/ttf/cmsy10.ttf")) + uniindex = self._cmr10_substitutions[uniindex] + glyphindex = font.get_char_index(uniindex) + if glyphindex != 0: + found_symbol = True + + if not found_symbol: + if self._fallback_font: + if (fontname in ('it', 'regular') + and isinstance(self._fallback_font, StixFonts)): + fontname = 'rm' + + g = self._fallback_font._get_glyph(fontname, font_class, sym) + family = g[0].family_name + if family in list(BakomaFonts._fontmap.values()): + family = "Computer Modern" + _log.info("Substituting symbol %s from %s", sym, family) + return g + + else: + if (fontname in ('it', 'regular') + and isinstance(self, StixFonts)): + return self._get_glyph('rm', font_class, sym) + _log.warning("Font %r does not have a glyph for %a [U+%x], " + "substituting with a dummy symbol.", + new_fontname, sym, uniindex) + font = self._get_font('rm') + uniindex = 0xA4 # currency char, for lack of anything better + slanted = False + + return font, uniindex, slanted + + def get_sized_alternatives_for_symbol(self, fontname: str, + sym: str) -> list[tuple[str, str]]: + if self._fallback_font: + return self._fallback_font.get_sized_alternatives_for_symbol( + fontname, sym) + return [(fontname, sym)] + + +class DejaVuFonts(UnicodeFonts, metaclass=abc.ABCMeta): + _fontmap: dict[str | int, str] = {} + + def __init__(self, default_font_prop: FontProperties, load_glyph_flags: LoadFlags): + # This must come first so the backend's owner is set correctly + if isinstance(self, DejaVuSerifFonts): + self._fallback_font = StixFonts(default_font_prop, load_glyph_flags) + else: + self._fallback_font = StixSansFonts(default_font_prop, load_glyph_flags) + self.bakoma = BakomaFonts(default_font_prop, load_glyph_flags) + TruetypeFonts.__init__(self, default_font_prop, load_glyph_flags) + # Include Stix sized alternatives for glyphs + self._fontmap.update({ + 1: 'STIXSizeOneSym', + 2: 'STIXSizeTwoSym', + 3: 'STIXSizeThreeSym', + 4: 'STIXSizeFourSym', + 5: 'STIXSizeFiveSym', + }) + for key, name in self._fontmap.items(): + fullpath = findfont(name) + self.fontmap[key] = fullpath + self.fontmap[name] = fullpath + + def _get_glyph(self, fontname: str, font_class: str, + sym: str) -> tuple[FT2Font, int, bool]: + # Override prime symbol to use Bakoma. + if sym == r'\prime': + return self.bakoma._get_glyph(fontname, font_class, sym) + else: + # check whether the glyph is available in the display font + uniindex = get_unicode_index(sym) + font = self._get_font('ex') + if font is not None: + glyphindex = font.get_char_index(uniindex) + if glyphindex != 0: + return super()._get_glyph('ex', font_class, sym) + # otherwise return regular glyph + return super()._get_glyph(fontname, font_class, sym) + + +class DejaVuSerifFonts(DejaVuFonts): + """ + A font handling class for the DejaVu Serif fonts + + If a glyph is not found it will fallback to Stix Serif + """ + _fontmap = { + 'rm': 'DejaVu Serif', + 'it': 'DejaVu Serif:italic', + 'bf': 'DejaVu Serif:weight=bold', + 'bfit': 'DejaVu Serif:italic:bold', + 'sf': 'DejaVu Sans', + 'tt': 'DejaVu Sans Mono', + 'ex': 'DejaVu Serif Display', + 0: 'DejaVu Serif', + } + + +class DejaVuSansFonts(DejaVuFonts): + """ + A font handling class for the DejaVu Sans fonts + + If a glyph is not found it will fallback to Stix Sans + """ + _fontmap = { + 'rm': 'DejaVu Sans', + 'it': 'DejaVu Sans:italic', + 'bf': 'DejaVu Sans:weight=bold', + 'bfit': 'DejaVu Sans:italic:bold', + 'sf': 'DejaVu Sans', + 'tt': 'DejaVu Sans Mono', + 'ex': 'DejaVu Sans Display', + 0: 'DejaVu Sans', + } + + +class StixFonts(UnicodeFonts): + """ + A font handling class for the STIX fonts. + + In addition to what UnicodeFonts provides, this class: + + - supports "virtual fonts" which are complete alpha numeric + character sets with different font styles at special Unicode + code points, such as "Blackboard". + + - handles sized alternative characters for the STIXSizeX fonts. + """ + _fontmap: dict[str | int, str] = { + 'rm': 'STIXGeneral', + 'it': 'STIXGeneral:italic', + 'bf': 'STIXGeneral:weight=bold', + 'bfit': 'STIXGeneral:italic:bold', + 'nonunirm': 'STIXNonUnicode', + 'nonuniit': 'STIXNonUnicode:italic', + 'nonunibf': 'STIXNonUnicode:weight=bold', + 0: 'STIXGeneral', + 1: 'STIXSizeOneSym', + 2: 'STIXSizeTwoSym', + 3: 'STIXSizeThreeSym', + 4: 'STIXSizeFourSym', + 5: 'STIXSizeFiveSym', + } + _fallback_font = None + _sans = False + + def __init__(self, default_font_prop: FontProperties, load_glyph_flags: LoadFlags): + TruetypeFonts.__init__(self, default_font_prop, load_glyph_flags) + for key, name in self._fontmap.items(): + fullpath = findfont(name) + self.fontmap[key] = fullpath + self.fontmap[name] = fullpath + + def _map_virtual_font(self, fontname: str, font_class: str, + uniindex: int) -> tuple[str, int]: + # Handle these "fonts" that are actually embedded in + # other fonts. + font_mapping = stix_virtual_fonts.get(fontname) + if (self._sans and font_mapping is None + and fontname not in ('regular', 'default')): + font_mapping = stix_virtual_fonts['sf'] + doing_sans_conversion = True + else: + doing_sans_conversion = False + + if isinstance(font_mapping, dict): + try: + mapping = font_mapping[font_class] + except KeyError: + mapping = font_mapping['rm'] + elif isinstance(font_mapping, list): + mapping = font_mapping + else: + mapping = None + + if mapping is not None: + # Binary search for the source glyph + lo = 0 + hi = len(mapping) + while lo < hi: + mid = (lo+hi)//2 + range = mapping[mid] + if uniindex < range[0]: + hi = mid + elif uniindex <= range[1]: + break + else: + lo = mid + 1 + + if range[0] <= uniindex <= range[1]: + uniindex = uniindex - range[0] + range[3] + fontname = range[2] + elif not doing_sans_conversion: + # This will generate a dummy character + uniindex = 0x1 + fontname = mpl.rcParams['mathtext.default'] + + # Fix some incorrect glyphs. + if fontname in ('rm', 'it'): + uniindex = stix_glyph_fixes.get(uniindex, uniindex) + + # Handle private use area glyphs + if fontname in ('it', 'rm', 'bf', 'bfit') and 0xe000 <= uniindex <= 0xf8ff: + fontname = 'nonuni' + fontname + + return fontname, uniindex + + @functools.cache + def get_sized_alternatives_for_symbol( # type: ignore[override] + self, + fontname: str, + sym: str) -> list[tuple[str, str]] | list[tuple[int, str]]: + fixes = { + '\\{': '{', '\\}': '}', '\\[': '[', '\\]': ']', + '<': '\N{MATHEMATICAL LEFT ANGLE BRACKET}', + '>': '\N{MATHEMATICAL RIGHT ANGLE BRACKET}', + } + sym = fixes.get(sym, sym) + try: + uniindex = get_unicode_index(sym) + except ValueError: + return [(fontname, sym)] + alternatives = [(i, chr(uniindex)) for i in range(6) + if self._get_font(i).get_char_index(uniindex) != 0] + # The largest size of the radical symbol in STIX has incorrect + # metrics that cause it to be disconnected from the stem. + if sym == r'\__sqrt__': + alternatives = alternatives[:-1] + return alternatives + + +class StixSansFonts(StixFonts): + """ + A font handling class for the STIX fonts (that uses sans-serif + characters by default). + """ + _sans = True + + +############################################################################## +# TeX-LIKE BOX MODEL + +# The following is based directly on the document 'woven' from the +# TeX82 source code. This information is also available in printed +# form: +# +# Knuth, Donald E.. 1986. Computers and Typesetting, Volume B: +# TeX: The Program. Addison-Wesley Professional. +# +# The most relevant "chapters" are: +# Data structures for boxes and their friends +# Shipping pages out (ship()) +# Packaging (hpack() and vpack()) +# Data structures for math mode +# Subroutines for math mode +# Typesetting math formulas +# +# Many of the docstrings below refer to a numbered "node" in that +# book, e.g., node123 +# +# Note that (as TeX) y increases downward, unlike many other parts of +# matplotlib. + +# How much text shrinks when going to the next-smallest level. +SHRINK_FACTOR = 0.7 +# The number of different sizes of chars to use, beyond which they will not +# get any smaller +NUM_SIZE_LEVELS = 6 + + +class FontConstantsBase: + """ + A set of constants that controls how certain things, such as sub- + and superscripts are laid out. These are all metrics that can't + be reliably retrieved from the font metrics in the font itself. + """ + # Percentage of x-height of additional horiz. space after sub/superscripts + script_space: T.ClassVar[float] = 0.05 + + # Percentage of x-height that sub/superscripts drop below the baseline + subdrop: T.ClassVar[float] = 0.4 + + # Percentage of x-height that superscripts are raised from the baseline + sup1: T.ClassVar[float] = 0.7 + + # Percentage of x-height that subscripts drop below the baseline + sub1: T.ClassVar[float] = 0.3 + + # Percentage of x-height that subscripts drop below the baseline when a + # superscript is present + sub2: T.ClassVar[float] = 0.5 + + # Percentage of x-height that sub/superscripts are offset relative to the + # nucleus edge for non-slanted nuclei + delta: T.ClassVar[float] = 0.025 + + # Additional percentage of last character height above 2/3 of the + # x-height that superscripts are offset relative to the subscript + # for slanted nuclei + delta_slanted: T.ClassVar[float] = 0.2 + + # Percentage of x-height that superscripts and subscripts are offset for + # integrals + delta_integral: T.ClassVar[float] = 0.1 + + +class ComputerModernFontConstants(FontConstantsBase): + script_space = 0.075 + subdrop = 0.2 + sup1 = 0.45 + sub1 = 0.2 + sub2 = 0.3 + delta = 0.075 + delta_slanted = 0.3 + delta_integral = 0.3 + + +class STIXFontConstants(FontConstantsBase): + script_space = 0.1 + sup1 = 0.8 + sub2 = 0.6 + delta = 0.05 + delta_slanted = 0.3 + delta_integral = 0.3 + + +class STIXSansFontConstants(FontConstantsBase): + script_space = 0.05 + sup1 = 0.8 + delta_slanted = 0.6 + delta_integral = 0.3 + + +class DejaVuSerifFontConstants(FontConstantsBase): + pass + + +class DejaVuSansFontConstants(FontConstantsBase): + pass + + +# Maps font family names to the FontConstantBase subclass to use +_font_constant_mapping = { + 'DejaVu Sans': DejaVuSansFontConstants, + 'DejaVu Sans Mono': DejaVuSansFontConstants, + 'DejaVu Serif': DejaVuSerifFontConstants, + 'cmb10': ComputerModernFontConstants, + 'cmex10': ComputerModernFontConstants, + 'cmmi10': ComputerModernFontConstants, + 'cmr10': ComputerModernFontConstants, + 'cmss10': ComputerModernFontConstants, + 'cmsy10': ComputerModernFontConstants, + 'cmtt10': ComputerModernFontConstants, + 'STIXGeneral': STIXFontConstants, + 'STIXNonUnicode': STIXFontConstants, + 'STIXSizeFiveSym': STIXFontConstants, + 'STIXSizeFourSym': STIXFontConstants, + 'STIXSizeThreeSym': STIXFontConstants, + 'STIXSizeTwoSym': STIXFontConstants, + 'STIXSizeOneSym': STIXFontConstants, + # Map the fonts we used to ship, just for good measure + 'Bitstream Vera Sans': DejaVuSansFontConstants, + 'Bitstream Vera': DejaVuSansFontConstants, + } + + +def _get_font_constant_set(state: ParserState) -> type[FontConstantsBase]: + constants = _font_constant_mapping.get( + state.fontset._get_font(state.font).family_name, FontConstantsBase) + # STIX sans isn't really its own fonts, just different code points + # in the STIX fonts, so we have to detect this one separately. + if (constants is STIXFontConstants and + isinstance(state.fontset, StixSansFonts)): + return STIXSansFontConstants + return constants + + +class Node: + """A node in the TeX box model.""" + + def __init__(self) -> None: + self.size = 0 + + def __repr__(self) -> str: + return type(self).__name__ + + def get_kerning(self, next: Node | None) -> float: + return 0.0 + + def shrink(self) -> None: + """ + Shrinks one level smaller. There are only three levels of + sizes, after which things will no longer get smaller. + """ + self.size += 1 + + def render(self, output: Output, x: float, y: float) -> None: + """Render this node.""" + + +class Box(Node): + """A node with a physical location.""" + + def __init__(self, width: float, height: float, depth: float) -> None: + super().__init__() + self.width = width + self.height = height + self.depth = depth + + def shrink(self) -> None: + super().shrink() + if self.size < NUM_SIZE_LEVELS: + self.width *= SHRINK_FACTOR + self.height *= SHRINK_FACTOR + self.depth *= SHRINK_FACTOR + + def render(self, output: Output, # type: ignore[override] + x1: float, y1: float, x2: float, y2: float) -> None: + pass + + +class Vbox(Box): + """A box with only height (zero width).""" + + def __init__(self, height: float, depth: float): + super().__init__(0., height, depth) + + +class Hbox(Box): + """A box with only width (zero height and depth).""" + + def __init__(self, width: float): + super().__init__(width, 0., 0.) + + +class Char(Node): + """ + A single character. + + Unlike TeX, the font information and metrics are stored with each `Char` + to make it easier to lookup the font metrics when needed. Note that TeX + boxes have a width, height, and depth, unlike Type1 and TrueType which use + a full bounding box and an advance in the x-direction. The metrics must + be converted to the TeX model, and the advance (if different from width) + must be converted into a `Kern` node when the `Char` is added to its parent + `Hlist`. + """ + + def __init__(self, c: str, state: ParserState): + super().__init__() + self.c = c + self.fontset = state.fontset + self.font = state.font + self.font_class = state.font_class + self.fontsize = state.fontsize + self.dpi = state.dpi + # The real width, height and depth will be set during the + # pack phase, after we know the real fontsize + self._update_metrics() + + def __repr__(self) -> str: + return '`%s`' % self.c + + def _update_metrics(self) -> None: + metrics = self._metrics = self.fontset.get_metrics( + self.font, self.font_class, self.c, self.fontsize, self.dpi) + if self.c == ' ': + self.width = metrics.advance + else: + self.width = metrics.width + self.height = metrics.iceberg + self.depth = -(metrics.iceberg - metrics.height) + + def is_slanted(self) -> bool: + return self._metrics.slanted + + def get_kerning(self, next: Node | None) -> float: + """ + Return the amount of kerning between this and the given character. + + This method is called when characters are strung together into `Hlist` + to create `Kern` nodes. + """ + advance = self._metrics.advance - self.width + kern = 0. + if isinstance(next, Char): + kern = self.fontset.get_kern( + self.font, self.font_class, self.c, self.fontsize, + next.font, next.font_class, next.c, next.fontsize, + self.dpi) + return advance + kern + + def render(self, output: Output, x: float, y: float) -> None: + self.fontset.render_glyph( + output, x, y, + self.font, self.font_class, self.c, self.fontsize, self.dpi) + + def shrink(self) -> None: + super().shrink() + if self.size < NUM_SIZE_LEVELS: + self.fontsize *= SHRINK_FACTOR + self.width *= SHRINK_FACTOR + self.height *= SHRINK_FACTOR + self.depth *= SHRINK_FACTOR + + +class Accent(Char): + """ + The font metrics need to be dealt with differently for accents, + since they are already offset correctly from the baseline in + TrueType fonts. + """ + def _update_metrics(self) -> None: + metrics = self._metrics = self.fontset.get_metrics( + self.font, self.font_class, self.c, self.fontsize, self.dpi) + self.width = metrics.xmax - metrics.xmin + self.height = metrics.ymax - metrics.ymin + self.depth = 0 + + def shrink(self) -> None: + super().shrink() + self._update_metrics() + + def render(self, output: Output, x: float, y: float) -> None: + self.fontset.render_glyph( + output, x - self._metrics.xmin, y + self._metrics.ymin, + self.font, self.font_class, self.c, self.fontsize, self.dpi) + + +class List(Box): + """A list of nodes (either horizontal or vertical).""" + + def __init__(self, elements: T.Sequence[Node]): + super().__init__(0., 0., 0.) + self.shift_amount = 0. # An arbitrary offset + self.children = [*elements] # The child nodes of this list + # The following parameters are set in the vpack and hpack functions + self.glue_set = 0. # The glue setting of this list + self.glue_sign = 0 # 0: normal, -1: shrinking, 1: stretching + self.glue_order = 0 # The order of infinity (0 - 3) for the glue + + def __repr__(self) -> str: + return '{}[{}]'.format( + super().__repr__(), + self.width, self.height, + self.depth, self.shift_amount, + ', '.join([repr(x) for x in self.children])) + + def _set_glue(self, x: float, sign: int, totals: list[float], + error_type: str) -> None: + self.glue_order = o = next( + # Highest order of glue used by the members of this list. + (i for i in range(len(totals))[::-1] if totals[i] != 0), 0) + self.glue_sign = sign + if totals[o] != 0.: + self.glue_set = x / totals[o] + else: + self.glue_sign = 0 + self.glue_ratio = 0. + if o == 0: + if len(self.children): + _log.warning("%s %s: %r", + error_type, type(self).__name__, self) + + def shrink(self) -> None: + for child in self.children: + child.shrink() + super().shrink() + if self.size < NUM_SIZE_LEVELS: + self.shift_amount *= SHRINK_FACTOR + self.glue_set *= SHRINK_FACTOR + + +class Hlist(List): + """A horizontal list of boxes.""" + + def __init__(self, elements: T.Sequence[Node], w: float = 0.0, + m: T.Literal['additional', 'exactly'] = 'additional', + do_kern: bool = True): + super().__init__(elements) + if do_kern: + self.kern() + self.hpack(w=w, m=m) + + def kern(self) -> None: + """ + Insert `Kern` nodes between `Char` nodes to set kerning. + + The `Char` nodes themselves determine the amount of kerning they need + (in `~Char.get_kerning`), and this function just creates the correct + linked list. + """ + new_children = [] + num_children = len(self.children) + if num_children: + for i in range(num_children): + elem = self.children[i] + if i < num_children - 1: + next = self.children[i + 1] + else: + next = None + + new_children.append(elem) + kerning_distance = elem.get_kerning(next) + if kerning_distance != 0.: + kern = Kern(kerning_distance) + new_children.append(kern) + self.children = new_children + + def hpack(self, w: float = 0.0, + m: T.Literal['additional', 'exactly'] = 'additional') -> None: + r""" + Compute the dimensions of the resulting boxes, and adjust the glue if + one of those dimensions is pre-specified. The computed sizes normally + enclose all of the material inside the new box; but some items may + stick out if negative glue is used, if the box is overfull, or if a + ``\vbox`` includes other boxes that have been shifted left. + + Parameters + ---------- + w : float, default: 0 + A width. + m : {'exactly', 'additional'}, default: 'additional' + Whether to produce a box whose width is 'exactly' *w*; or a box + with the natural width of the contents, plus *w* ('additional'). + + Notes + ----- + The defaults produce a box with the natural width of the contents. + """ + # I don't know why these get reset in TeX. Shift_amount is pretty + # much useless if we do. + # self.shift_amount = 0. + h = 0. + d = 0. + x = 0. + total_stretch = [0.] * 4 + total_shrink = [0.] * 4 + for p in self.children: + if isinstance(p, Char): + x += p.width + h = max(h, p.height) + d = max(d, p.depth) + elif isinstance(p, Box): + x += p.width + if not np.isinf(p.height) and not np.isinf(p.depth): + s = getattr(p, 'shift_amount', 0.) + h = max(h, p.height - s) + d = max(d, p.depth + s) + elif isinstance(p, Glue): + glue_spec = p.glue_spec + x += glue_spec.width + total_stretch[glue_spec.stretch_order] += glue_spec.stretch + total_shrink[glue_spec.shrink_order] += glue_spec.shrink + elif isinstance(p, Kern): + x += p.width + self.height = h + self.depth = d + + if m == 'additional': + w += x + self.width = w + x = w - x + + if x == 0.: + self.glue_sign = 0 + self.glue_order = 0 + self.glue_ratio = 0. + return + if x > 0.: + self._set_glue(x, 1, total_stretch, "Overful") + else: + self._set_glue(x, -1, total_shrink, "Underful") + + +class Vlist(List): + """A vertical list of boxes.""" + + def __init__(self, elements: T.Sequence[Node], h: float = 0.0, + m: T.Literal['additional', 'exactly'] = 'additional'): + super().__init__(elements) + self.vpack(h=h, m=m) + + def vpack(self, h: float = 0.0, + m: T.Literal['additional', 'exactly'] = 'additional', + l: float = np.inf) -> None: + """ + Compute the dimensions of the resulting boxes, and to adjust the glue + if one of those dimensions is pre-specified. + + Parameters + ---------- + h : float, default: 0 + A height. + m : {'exactly', 'additional'}, default: 'additional' + Whether to produce a box whose height is 'exactly' *h*; or a box + with the natural height of the contents, plus *h* ('additional'). + l : float, default: np.inf + The maximum height. + + Notes + ----- + The defaults produce a box with the natural height of the contents. + """ + # I don't know why these get reset in TeX. Shift_amount is pretty + # much useless if we do. + # self.shift_amount = 0. + w = 0. + d = 0. + x = 0. + total_stretch = [0.] * 4 + total_shrink = [0.] * 4 + for p in self.children: + if isinstance(p, Box): + x += d + p.height + d = p.depth + if not np.isinf(p.width): + s = getattr(p, 'shift_amount', 0.) + w = max(w, p.width + s) + elif isinstance(p, Glue): + x += d + d = 0. + glue_spec = p.glue_spec + x += glue_spec.width + total_stretch[glue_spec.stretch_order] += glue_spec.stretch + total_shrink[glue_spec.shrink_order] += glue_spec.shrink + elif isinstance(p, Kern): + x += d + p.width + d = 0. + elif isinstance(p, Char): + raise RuntimeError( + "Internal mathtext error: Char node found in Vlist") + + self.width = w + if d > l: + x += d - l + self.depth = l + else: + self.depth = d + + if m == 'additional': + h += x + self.height = h + x = h - x + + if x == 0: + self.glue_sign = 0 + self.glue_order = 0 + self.glue_ratio = 0. + return + + if x > 0.: + self._set_glue(x, 1, total_stretch, "Overful") + else: + self._set_glue(x, -1, total_shrink, "Underful") + + +class Rule(Box): + """ + A solid black rectangle. + + It has *width*, *depth*, and *height* fields just as in an `Hlist`. + However, if any of these dimensions is inf, the actual value will be + determined by running the rule up to the boundary of the innermost + enclosing box. This is called a "running dimension". The width is never + running in an `Hlist`; the height and depth are never running in a `Vlist`. + """ + + def __init__(self, width: float, height: float, depth: float, state: ParserState): + super().__init__(width, height, depth) + self.fontset = state.fontset + + def render(self, output: Output, # type: ignore[override] + x: float, y: float, w: float, h: float) -> None: + self.fontset.render_rect_filled(output, x, y, x + w, y + h) + + +class Hrule(Rule): + """Convenience class to create a horizontal rule.""" + + def __init__(self, state: ParserState, thickness: float | None = None): + if thickness is None: + thickness = state.get_current_underline_thickness() + height = depth = thickness * 0.5 + super().__init__(np.inf, height, depth, state) + + +class Vrule(Rule): + """Convenience class to create a vertical rule.""" + + def __init__(self, state: ParserState): + thickness = state.get_current_underline_thickness() + super().__init__(thickness, np.inf, np.inf, state) + + +class _GlueSpec(NamedTuple): + width: float + stretch: float + stretch_order: int + shrink: float + shrink_order: int + + +_GlueSpec._named = { # type: ignore[attr-defined] + 'fil': _GlueSpec(0., 1., 1, 0., 0), + 'fill': _GlueSpec(0., 1., 2, 0., 0), + 'filll': _GlueSpec(0., 1., 3, 0., 0), + 'neg_fil': _GlueSpec(0., 0., 0, 1., 1), + 'neg_fill': _GlueSpec(0., 0., 0, 1., 2), + 'neg_filll': _GlueSpec(0., 0., 0, 1., 3), + 'empty': _GlueSpec(0., 0., 0, 0., 0), + 'ss': _GlueSpec(0., 1., 1, -1., 1), +} + + +class Glue(Node): + """ + Most of the information in this object is stored in the underlying + ``_GlueSpec`` class, which is shared between multiple glue objects. + (This is a memory optimization which probably doesn't matter anymore, but + it's easier to stick to what TeX does.) + """ + + def __init__(self, + glue_type: _GlueSpec | T.Literal["fil", "fill", "filll", + "neg_fil", "neg_fill", "neg_filll", + "empty", "ss"]): + super().__init__() + if isinstance(glue_type, str): + glue_spec = _GlueSpec._named[glue_type] # type: ignore[attr-defined] + elif isinstance(glue_type, _GlueSpec): + glue_spec = glue_type + else: + raise ValueError("glue_type must be a glue spec name or instance") + self.glue_spec = glue_spec + + def shrink(self) -> None: + super().shrink() + if self.size < NUM_SIZE_LEVELS: + g = self.glue_spec + self.glue_spec = g._replace(width=g.width * SHRINK_FACTOR) + + +class HCentered(Hlist): + """ + A convenience class to create an `Hlist` whose contents are + centered within its enclosing box. + """ + + def __init__(self, elements: list[Node]): + super().__init__([Glue('ss'), *elements, Glue('ss')], do_kern=False) + + +class VCentered(Vlist): + """ + A convenience class to create a `Vlist` whose contents are + centered within its enclosing box. + """ + + def __init__(self, elements: list[Node]): + super().__init__([Glue('ss'), *elements, Glue('ss')]) + + +class Kern(Node): + """ + A `Kern` node has a width field to specify a (normally + negative) amount of spacing. This spacing correction appears in + horizontal lists between letters like A and V when the font + designer said that it looks better to move them closer together or + further apart. A kern node can also appear in a vertical list, + when its *width* denotes additional spacing in the vertical + direction. + """ + + height = 0 + depth = 0 + + def __init__(self, width: float): + super().__init__() + self.width = width + + def __repr__(self) -> str: + return "k%.02f" % self.width + + def shrink(self) -> None: + super().shrink() + if self.size < NUM_SIZE_LEVELS: + self.width *= SHRINK_FACTOR + + +class AutoHeightChar(Hlist): + """ + A character as close to the given height and depth as possible. + + When using a font with multiple height versions of some characters (such as + the BaKoMa fonts), the correct glyph will be selected, otherwise this will + always just return a scaled version of the glyph. + """ + + def __init__(self, c: str, height: float, depth: float, state: ParserState, + always: bool = False, factor: float | None = None): + alternatives = state.fontset.get_sized_alternatives_for_symbol( + state.font, c) + + xHeight = state.fontset.get_xheight( + state.font, state.fontsize, state.dpi) + + state = state.copy() + target_total = height + depth + for fontname, sym in alternatives: + state.font = fontname + char = Char(sym, state) + # Ensure that size 0 is chosen when the text is regular sized but + # with descender glyphs by subtracting 0.2 * xHeight + if char.height + char.depth >= target_total - 0.2 * xHeight: + break + + shift = 0.0 + if state.font != 0 or len(alternatives) == 1: + if factor is None: + factor = target_total / (char.height + char.depth) + state.fontsize *= factor + char = Char(sym, state) + + shift = (depth - char.depth) + + super().__init__([char]) + self.shift_amount = shift + + +class AutoWidthChar(Hlist): + """ + A character as close to the given width as possible. + + When using a font with multiple width versions of some characters (such as + the BaKoMa fonts), the correct glyph will be selected, otherwise this will + always just return a scaled version of the glyph. + """ + + def __init__(self, c: str, width: float, state: ParserState, always: bool = False, + char_class: type[Char] = Char): + alternatives = state.fontset.get_sized_alternatives_for_symbol( + state.font, c) + + state = state.copy() + for fontname, sym in alternatives: + state.font = fontname + char = char_class(sym, state) + if char.width >= width: + break + + factor = width / char.width + state.fontsize *= factor + char = char_class(sym, state) + + super().__init__([char]) + self.width = char.width + + +def ship(box: Box, xy: tuple[float, float] = (0, 0)) -> Output: + """ + Ship out *box* at offset *xy*, converting it to an `Output`. + + Since boxes can be inside of boxes inside of boxes, the main work of `ship` + is done by two mutually recursive routines, `hlist_out` and `vlist_out`, + which traverse the `Hlist` nodes and `Vlist` nodes inside of horizontal + and vertical boxes. The global variables used in TeX to store state as it + processes have become local variables here. + """ + ox, oy = xy + cur_v = 0. + cur_h = 0. + off_h = ox + off_v = oy + box.height + output = Output(box) + + def clamp(value: float) -> float: + return -1e9 if value < -1e9 else +1e9 if value > +1e9 else value + + def hlist_out(box: Hlist) -> None: + nonlocal cur_v, cur_h, off_h, off_v + + cur_g = 0 + cur_glue = 0. + glue_order = box.glue_order + glue_sign = box.glue_sign + base_line = cur_v + left_edge = cur_h + + for p in box.children: + if isinstance(p, Char): + p.render(output, cur_h + off_h, cur_v + off_v) + cur_h += p.width + elif isinstance(p, Kern): + cur_h += p.width + elif isinstance(p, List): + # node623 + if len(p.children) == 0: + cur_h += p.width + else: + edge = cur_h + cur_v = base_line + p.shift_amount + if isinstance(p, Hlist): + hlist_out(p) + elif isinstance(p, Vlist): + # p.vpack(box.height + box.depth, 'exactly') + vlist_out(p) + else: + assert False, "unreachable code" + cur_h = edge + p.width + cur_v = base_line + elif isinstance(p, Box): + # node624 + rule_height = p.height + rule_depth = p.depth + rule_width = p.width + if np.isinf(rule_height): + rule_height = box.height + if np.isinf(rule_depth): + rule_depth = box.depth + if rule_height > 0 and rule_width > 0: + cur_v = base_line + rule_depth + p.render(output, + cur_h + off_h, cur_v + off_v, + rule_width, rule_height) + cur_v = base_line + cur_h += rule_width + elif isinstance(p, Glue): + # node625 + glue_spec = p.glue_spec + rule_width = glue_spec.width - cur_g + if glue_sign != 0: # normal + if glue_sign == 1: # stretching + if glue_spec.stretch_order == glue_order: + cur_glue += glue_spec.stretch + cur_g = round(clamp(box.glue_set * cur_glue)) + elif glue_spec.shrink_order == glue_order: + cur_glue += glue_spec.shrink + cur_g = round(clamp(box.glue_set * cur_glue)) + rule_width += cur_g + cur_h += rule_width + + def vlist_out(box: Vlist) -> None: + nonlocal cur_v, cur_h, off_h, off_v + + cur_g = 0 + cur_glue = 0. + glue_order = box.glue_order + glue_sign = box.glue_sign + left_edge = cur_h + cur_v -= box.height + top_edge = cur_v + + for p in box.children: + if isinstance(p, Kern): + cur_v += p.width + elif isinstance(p, List): + if len(p.children) == 0: + cur_v += p.height + p.depth + else: + cur_v += p.height + cur_h = left_edge + p.shift_amount + save_v = cur_v + p.width = box.width + if isinstance(p, Hlist): + hlist_out(p) + elif isinstance(p, Vlist): + vlist_out(p) + else: + assert False, "unreachable code" + cur_v = save_v + p.depth + cur_h = left_edge + elif isinstance(p, Box): + rule_height = p.height + rule_depth = p.depth + rule_width = p.width + if np.isinf(rule_width): + rule_width = box.width + rule_height += rule_depth + if rule_height > 0 and rule_depth > 0: + cur_v += rule_height + p.render(output, + cur_h + off_h, cur_v + off_v, + rule_width, rule_height) + elif isinstance(p, Glue): + glue_spec = p.glue_spec + rule_height = glue_spec.width - cur_g + if glue_sign != 0: # normal + if glue_sign == 1: # stretching + if glue_spec.stretch_order == glue_order: + cur_glue += glue_spec.stretch + cur_g = round(clamp(box.glue_set * cur_glue)) + elif glue_spec.shrink_order == glue_order: # shrinking + cur_glue += glue_spec.shrink + cur_g = round(clamp(box.glue_set * cur_glue)) + rule_height += cur_g + cur_v += rule_height + elif isinstance(p, Char): + raise RuntimeError( + "Internal mathtext error: Char node found in vlist") + + assert isinstance(box, Hlist) + hlist_out(box) + return output + + +############################################################################## +# PARSER + + +def Error(msg: str) -> ParserElement: + """Helper class to raise parser errors.""" + def raise_error(s: str, loc: int, toks: ParseResults) -> T.Any: + raise ParseFatalException(s, loc, msg) + + return Empty().set_parse_action(raise_error) + + +class ParserState: + """ + Parser state. + + States are pushed and popped from a stack as necessary, and the "current" + state is always at the top of the stack. + + Upon entering and leaving a group { } or math/non-math, the stack is pushed + and popped accordingly. + """ + + def __init__(self, fontset: Fonts, font: str, font_class: str, fontsize: float, + dpi: float): + self.fontset = fontset + self._font = font + self.font_class = font_class + self.fontsize = fontsize + self.dpi = dpi + + def copy(self) -> ParserState: + return copy.copy(self) + + @property + def font(self) -> str: + return self._font + + @font.setter + def font(self, name: str) -> None: + if name in ('rm', 'it', 'bf', 'bfit'): + self.font_class = name + self._font = name + + def get_current_underline_thickness(self) -> float: + """Return the underline thickness for this state.""" + return self.fontset.get_underline_thickness( + self.font, self.fontsize, self.dpi) + + +def cmd(expr: str, args: ParserElement) -> ParserElement: + r""" + Helper to define TeX commands. + + ``cmd("\cmd", args)`` is equivalent to + ``"\cmd" - (args | Error("Expected \cmd{arg}{...}"))`` where the names in + the error message are taken from element names in *args*. If *expr* + already includes arguments (e.g. "\cmd{arg}{...}"), then they are stripped + when constructing the parse element, but kept (and *expr* is used as is) in + the error message. + """ + + def names(elt: ParserElement) -> T.Generator[str, None, None]: + if isinstance(elt, ParseExpression): + for expr in elt.exprs: + yield from names(expr) + elif elt.resultsName: + yield elt.resultsName + + csname = expr.split("{", 1)[0] + err = (csname + "".join("{%s}" % name for name in names(args)) + if expr == csname else expr) + return csname - (args | Error(f"Expected {err}")) + + +class Parser: + """ + A pyparsing-based parser for strings containing math expressions. + + Raw text may also appear outside of pairs of ``$``. + + The grammar is based directly on that in TeX, though it cuts a few corners. + """ + + class _MathStyle(enum.Enum): + DISPLAYSTYLE = 0 + TEXTSTYLE = 1 + SCRIPTSTYLE = 2 + SCRIPTSCRIPTSTYLE = 3 + + _binary_operators = set( + '+ * - \N{MINUS SIGN}' + r''' + \pm \sqcap \rhd + \mp \sqcup \unlhd + \times \vee \unrhd + \div \wedge \oplus + \ast \setminus \ominus + \star \wr \otimes + \circ \diamond \oslash + \bullet \bigtriangleup \odot + \cdot \bigtriangledown \bigcirc + \cap \triangleleft \dagger + \cup \triangleright \ddagger + \uplus \lhd \amalg + \dotplus \dotminus \Cap + \Cup \barwedge \boxdot + \boxminus \boxplus \boxtimes + \curlyvee \curlywedge \divideontimes + \doublebarwedge \leftthreetimes \rightthreetimes + \slash \veebar \barvee + \cupdot \intercal \amalg + \circledcirc \circleddash \circledast + \boxbar \obar \merge + \minuscolon \dotsminusdots + '''.split()) + + _relation_symbols = set(r''' + = < > : + \leq \geq \equiv \models + \prec \succ \sim \perp + \preceq \succeq \simeq \mid + \ll \gg \asymp \parallel + \subset \supset \approx \bowtie + \subseteq \supseteq \cong \Join + \sqsubset \sqsupset \neq \smile + \sqsubseteq \sqsupseteq \doteq \frown + \in \ni \propto \vdash + \dashv \dots \doteqdot \leqq + \geqq \lneqq \gneqq \lessgtr + \leqslant \geqslant \eqgtr \eqless + \eqslantless \eqslantgtr \lesseqgtr \backsim + \backsimeq \lesssim \gtrsim \precsim + \precnsim \gnsim \lnsim \succsim + \succnsim \nsim \lesseqqgtr \gtreqqless + \gtreqless \subseteqq \supseteqq \subsetneqq + \supsetneqq \lessapprox \approxeq \gtrapprox + \precapprox \succapprox \precnapprox \succnapprox + \npreccurlyeq \nsucccurlyeq \nsqsubseteq \nsqsupseteq + \sqsubsetneq \sqsupsetneq \nlesssim \ngtrsim + \nlessgtr \ngtrless \lnapprox \gnapprox + \napprox \approxeq \approxident \lll + \ggg \nparallel \Vdash \Vvdash + \nVdash \nvdash \vDash \nvDash + \nVDash \oequal \simneqq \triangle + \triangleq \triangleeq \triangleleft + \triangleright \ntriangleleft \ntriangleright + \trianglelefteq \ntrianglelefteq \trianglerighteq + \ntrianglerighteq \blacktriangleleft \blacktriangleright + \equalparallel \measuredrightangle \varlrtriangle + \Doteq \Bumpeq \Subset \Supset + \backepsilon \because \therefore \bot + \top \bumpeq \circeq \coloneq + \curlyeqprec \curlyeqsucc \eqcirc \eqcolon + \eqsim \fallingdotseq \gtrdot \gtrless + \ltimes \rtimes \lessdot \ne + \ncong \nequiv \ngeq \ngtr + \nleq \nless \nmid \notin + \nprec \nsubset \nsubseteq \nsucc + \nsupset \nsupseteq \pitchfork \preccurlyeq + \risingdotseq \subsetneq \succcurlyeq \supsetneq + \varpropto \vartriangleleft \scurel + \vartriangleright \rightangle \equal \backcong + \eqdef \wedgeq \questeq \between + \veeeq \disin \varisins \isins + \isindot \varisinobar \isinobar \isinvb + \isinE \nisd \varnis \nis + \varniobar \niobar \bagmember \ratio + \Equiv \stareq \measeq \arceq + \rightassert \rightModels \smallin \smallowns + \notsmallowns \nsimeq'''.split()) + + _arrow_symbols = set(r""" + \leftarrow \longleftarrow \uparrow \Leftarrow \Longleftarrow + \Uparrow \rightarrow \longrightarrow \downarrow \Rightarrow + \Longrightarrow \Downarrow \leftrightarrow \updownarrow + \longleftrightarrow \updownarrow \Leftrightarrow + \Longleftrightarrow \Updownarrow \mapsto \longmapsto \nearrow + \hookleftarrow \hookrightarrow \searrow \leftharpoonup + \rightharpoonup \swarrow \leftharpoondown \rightharpoondown + \nwarrow \rightleftharpoons \leadsto \dashrightarrow + \dashleftarrow \leftleftarrows \leftrightarrows \Lleftarrow + \Rrightarrow \twoheadleftarrow \leftarrowtail \looparrowleft + \leftrightharpoons \curvearrowleft \circlearrowleft \Lsh + \upuparrows \upharpoonleft \downharpoonleft \multimap + \leftrightsquigarrow \rightrightarrows \rightleftarrows + \rightrightarrows \rightleftarrows \twoheadrightarrow + \rightarrowtail \looparrowright \rightleftharpoons + \curvearrowright \circlearrowright \Rsh \downdownarrows + \upharpoonright \downharpoonright \rightsquigarrow \nleftarrow + \nrightarrow \nLeftarrow \nRightarrow \nleftrightarrow + \nLeftrightarrow \to \Swarrow \Searrow \Nwarrow \Nearrow + \leftsquigarrow \overleftarrow \overleftrightarrow \cwopencirclearrow + \downzigzagarrow \cupleftarrow \rightzigzagarrow \twoheaddownarrow + \updownarrowbar \twoheaduparrow \rightarrowbar \updownarrows + \barleftarrow \mapsfrom \mapsdown \mapsup \Ldsh \Rdsh + """.split()) + + _spaced_symbols = _binary_operators | _relation_symbols | _arrow_symbols + + _punctuation_symbols = set(r', ; . ! \ldotp \cdotp'.split()) + + _overunder_symbols = set(r''' + \sum \prod \coprod \bigcap \bigcup \bigsqcup \bigvee + \bigwedge \bigodot \bigotimes \bigoplus \biguplus + '''.split()) + + _overunder_functions = set("lim liminf limsup sup max min".split()) + + _dropsub_symbols = set(r'\int \oint \iint \oiint \iiint \oiiint \iiiint'.split()) + + _fontnames = set("rm cal it tt sf bf bfit " + "default bb frak scr regular".split()) + + _function_names = set(""" + arccos csc ker min arcsin deg lg Pr arctan det lim sec arg dim + liminf sin cos exp limsup sinh cosh gcd ln sup cot hom log tan + coth inf max tanh""".split()) + + _ambi_delims = set(r""" + | \| / \backslash \uparrow \downarrow \updownarrow \Uparrow + \Downarrow \Updownarrow . \vert \Vert""".split()) + _left_delims = set(r""" + ( [ \{ < \lfloor \langle \lceil \lbrace \leftbrace \lbrack \leftparen \lgroup + """.split()) + _right_delims = set(r""" + ) ] \} > \rfloor \rangle \rceil \rbrace \rightbrace \rbrack \rightparen \rgroup + """.split()) + _delims = _left_delims | _right_delims | _ambi_delims + + _small_greek = set([unicodedata.name(chr(i)).split()[-1].lower() for i in + range(ord('\N{GREEK SMALL LETTER ALPHA}'), + ord('\N{GREEK SMALL LETTER OMEGA}') + 1)]) + _latin_alphabets = set(string.ascii_letters) + + def __init__(self) -> None: + p = types.SimpleNamespace() + + def set_names_and_parse_actions() -> None: + for key, val in vars(p).items(): + if not key.startswith('_'): + # Set names on (almost) everything -- very useful for debugging + # token, placeable, and auto_delim are forward references which + # are left without names to ensure useful error messages + if key not in ("token", "placeable", "auto_delim"): + val.set_name(key) + # Set actions + if hasattr(self, key): + val.set_parse_action(getattr(self, key)) + + # Root definitions. + + # In TeX parlance, a csname is a control sequence name (a "\foo"). + def csnames(group: str, names: Iterable[str]) -> Regex: + ends_with_alpha = [] + ends_with_nonalpha = [] + for name in names: + if name[-1].isalpha(): + ends_with_alpha.append(name) + else: + ends_with_nonalpha.append(name) + return Regex( + r"\\(?P<{group}>(?:{alpha})(?![A-Za-z]){additional}{nonalpha})".format( + group=group, + alpha="|".join(map(re.escape, ends_with_alpha)), + additional="|" if ends_with_nonalpha else "", + nonalpha="|".join(map(re.escape, ends_with_nonalpha)), + ) + ) + + p.float_literal = Regex(r"[-+]?([0-9]+\.?[0-9]*|\.[0-9]+)") + p.space = one_of(self._space_widths)("space") + + p.style_literal = one_of( + [str(e.value) for e in self._MathStyle])("style_literal") + + p.symbol = Regex( + r"[a-zA-Z0-9 +\-*/<>=:,.;!\?&'@()\[\]|\U00000080-\U0001ffff]" + r"|\\[%${}\[\]_|]" + + r"|\\(?:{})(?![A-Za-z])".format( + "|".join(map(re.escape, tex2uni))) + )("sym").leave_whitespace() + p.unknown_symbol = Regex(r"\\[A-Za-z]+")("name") + + p.font = csnames("font", self._fontnames) + p.start_group = Optional(r"\math" + one_of(self._fontnames)("font")) + "{" + p.end_group = Literal("}") + + p.delim = one_of(self._delims) + + # Mutually recursive definitions. (Minimizing the number of Forward + # elements is important for speed.) + p.auto_delim = Forward() + p.placeable = Forward() + p.named_placeable = Forward() + p.required_group = Forward() + p.optional_group = Forward() + p.token = Forward() + + # Workaround for placable being part of a cycle of definitions + # calling `p.placeable("name")` results in a copy, so not guaranteed + # to get the definition added after it is used. + # ref https://github.com/matplotlib/matplotlib/issues/25204 + # xref https://github.com/pyparsing/pyparsing/issues/95 + p.named_placeable <<= p.placeable + + set_names_and_parse_actions() # for mutually recursive definitions. + + p.optional_group <<= "{" + ZeroOrMore(p.token)("group") + "}" + p.required_group <<= "{" + OneOrMore(p.token)("group") + "}" + + p.customspace = cmd(r"\hspace", "{" + p.float_literal("space") + "}") + + p.accent = ( + csnames("accent", [*self._accent_map, *self._wide_accents]) + - p.named_placeable("sym")) + + p.function = csnames("name", self._function_names) + + p.group = p.start_group + ZeroOrMore(p.token)("group") + p.end_group + p.unclosed_group = (p.start_group + ZeroOrMore(p.token)("group") + StringEnd()) + + p.frac = cmd(r"\frac", p.required_group("num") + p.required_group("den")) + p.dfrac = cmd(r"\dfrac", p.required_group("num") + p.required_group("den")) + p.binom = cmd(r"\binom", p.required_group("num") + p.required_group("den")) + + p.genfrac = cmd( + r"\genfrac", + "{" + Optional(p.delim)("ldelim") + "}" + + "{" + Optional(p.delim)("rdelim") + "}" + + "{" + p.float_literal("rulesize") + "}" + + "{" + Optional(p.style_literal)("style") + "}" + + p.required_group("num") + + p.required_group("den")) + + p.sqrt = cmd( + r"\sqrt{value}", + Optional("[" + OneOrMore(NotAny("]") + p.token)("root") + "]") + + p.required_group("value")) + + p.overline = cmd(r"\overline", p.required_group("body")) + + p.overset = cmd( + r"\overset", + p.optional_group("annotation") + p.optional_group("body")) + p.underset = cmd( + r"\underset", + p.optional_group("annotation") + p.optional_group("body")) + + p.text = cmd(r"\text", QuotedString('{', '\\', end_quote_char="}")) + + p.substack = cmd(r"\substack", + nested_expr(opener="{", closer="}", + content=Group(OneOrMore(p.token)) + + ZeroOrMore(Literal("\\\\").suppress()))("parts")) + + p.subsuper = ( + (Optional(p.placeable)("nucleus") + + OneOrMore(one_of(["_", "^"]) - p.placeable)("subsuper") + + Regex("'*")("apostrophes")) + | Regex("'+")("apostrophes") + | (p.named_placeable("nucleus") + Regex("'*")("apostrophes")) + ) + + p.simple = p.space | p.customspace | p.font | p.subsuper + + p.token <<= ( + p.simple + | p.auto_delim + | p.unclosed_group + | p.unknown_symbol # Must be last + ) + + p.operatorname = cmd(r"\operatorname", "{" + ZeroOrMore(p.simple)("name") + "}") + + p.boldsymbol = cmd( + r"\boldsymbol", "{" + ZeroOrMore(p.simple)("value") + "}") + + p.placeable <<= ( + p.accent # Must be before symbol as all accents are symbols + | p.symbol # Must be second to catch all named symbols and single + # chars not in a group + | p.function + | p.operatorname + | p.group + | p.frac + | p.dfrac + | p.binom + | p.genfrac + | p.overset + | p.underset + | p.sqrt + | p.overline + | p.text + | p.boldsymbol + | p.substack + ) + + mdelim = r"\middle" - (p.delim("mdelim") | Error("Expected a delimiter")) + p.auto_delim <<= ( + r"\left" - (p.delim("left") | Error("Expected a delimiter")) + + ZeroOrMore(p.simple | p.auto_delim | mdelim)("mid") + + r"\right" - (p.delim("right") | Error("Expected a delimiter")) + ) + + # Leaf definitions. + p.math = OneOrMore(p.token) + p.math_string = QuotedString('$', '\\', unquote_results=False) + p.non_math = Regex(r"(?:(?:\\[$])|[^$])*").leave_whitespace() + p.main = ( + p.non_math + ZeroOrMore(p.math_string + p.non_math) + StringEnd() + ) + set_names_and_parse_actions() # for leaf definitions. + + self._expression = p.main + self._math_expression = p.math + + # To add space to nucleus operators after sub/superscripts + self._in_subscript_or_superscript = False + + def parse(self, s: str, fonts_object: Fonts, fontsize: float, dpi: float) -> Hlist: + """ + Parse expression *s* using the given *fonts_object* for + output, at the given *fontsize* and *dpi*. + + Returns the parse tree of `Node` instances. + """ + self._state_stack = [ + ParserState(fonts_object, 'default', 'rm', fontsize, dpi)] + self._em_width_cache: dict[tuple[str, float, float], float] = {} + try: + result = self._expression.parse_string(s) + except ParseBaseException as err: + # explain becomes a plain method on pyparsing 3 (err.explain(0)). + raise ValueError("\n" + ParseException.explain(err, 0)) from None + self._state_stack = [] + self._in_subscript_or_superscript = False + # prevent operator spacing from leaking into a new expression + self._em_width_cache = {} + ParserElement.reset_cache() + return T.cast(Hlist, result[0]) # Known return type from main. + + def get_state(self) -> ParserState: + """Get the current `State` of the parser.""" + return self._state_stack[-1] + + def pop_state(self) -> None: + """Pop a `State` off of the stack.""" + self._state_stack.pop() + + def push_state(self) -> None: + """Push a new `State` onto the stack, copying the current state.""" + self._state_stack.append(self.get_state().copy()) + + def main(self, toks: ParseResults) -> list[Hlist]: + return [Hlist(toks.as_list())] + + def math_string(self, toks: ParseResults) -> ParseResults: + return self._math_expression.parse_string(toks[0][1:-1], parse_all=True) + + def math(self, toks: ParseResults) -> T.Any: + hlist = Hlist(toks.as_list()) + self.pop_state() + return [hlist] + + def non_math(self, toks: ParseResults) -> T.Any: + s = toks[0].replace(r'\$', '$') + symbols = [Char(c, self.get_state()) for c in s] + hlist = Hlist(symbols) + # We're going into math now, so set font to 'it' + self.push_state() + self.get_state().font = mpl.rcParams['mathtext.default'] + return [hlist] + + float_literal = staticmethod(pyparsing_common.convert_to_float) + + def text(self, toks: ParseResults) -> T.Any: + self.push_state() + state = self.get_state() + state.font = 'rm' + hlist = Hlist([Char(c, state) for c in toks[1]]) + self.pop_state() + return [hlist] + + def _make_space(self, percentage: float) -> Kern: + # In TeX, an em (the unit usually used to measure horizontal lengths) + # is not the width of the character 'm'; it is the same in different + # font styles (e.g. roman or italic). Mathtext, however, uses 'm' in + # the italic style so that horizontal spaces don't depend on the + # current font style. + state = self.get_state() + key = (state.font, state.fontsize, state.dpi) + width = self._em_width_cache.get(key) + if width is None: + metrics = state.fontset.get_metrics( + 'it', mpl.rcParams['mathtext.default'], 'm', + state.fontsize, state.dpi) + width = metrics.advance + self._em_width_cache[key] = width + return Kern(width * percentage) + + _space_widths = { + r'\,': 0.16667, # 3/18 em = 3 mu + r'\thinspace': 0.16667, # 3/18 em = 3 mu + r'\/': 0.16667, # 3/18 em = 3 mu + r'\>': 0.22222, # 4/18 em = 4 mu + r'\:': 0.22222, # 4/18 em = 4 mu + r'\;': 0.27778, # 5/18 em = 5 mu + r'\ ': 0.33333, # 6/18 em = 6 mu + r'~': 0.33333, # 6/18 em = 6 mu, nonbreakable + r'\enspace': 0.5, # 9/18 em = 9 mu + r'\quad': 1, # 1 em = 18 mu + r'\qquad': 2, # 2 em = 36 mu + r'\!': -0.16667, # -3/18 em = -3 mu + } + + def space(self, toks: ParseResults) -> T.Any: + num = self._space_widths[toks["space"]] + box = self._make_space(num) + return [box] + + def customspace(self, toks: ParseResults) -> T.Any: + return [self._make_space(toks["space"])] + + def symbol(self, s: str, loc: int, + toks: ParseResults | dict[str, str]) -> T.Any: + c = toks["sym"] + if c == "-": + # "U+2212 minus sign is the preferred representation of the unary + # and binary minus sign rather than the ASCII-derived U+002D + # hyphen-minus, because minus sign is unambiguous and because it + # is rendered with a more desirable length, usually longer than a + # hyphen." (https://www.unicode.org/reports/tr25/) + c = "\N{MINUS SIGN}" + try: + char = Char(c, self.get_state()) + except ValueError as err: + raise ParseFatalException(s, loc, + "Unknown symbol: %s" % c) from err + + if c in self._spaced_symbols: + # iterate until we find previous character, needed for cases + # such as $=-2$, ${ -2}$, $ -2$, or $ -2$. + prev_char = next((c for c in s[:loc][::-1] if c != ' '), '') + # Binary operators at start of string should not be spaced + # Also, operators in sub- or superscripts should not be spaced + if (self._in_subscript_or_superscript or ( + c in self._binary_operators and ( + len(s[:loc].split()) == 0 or prev_char in { + '{', *self._left_delims, *self._relation_symbols}))): + return [char] + else: + return [Hlist([self._make_space(0.2), + char, + self._make_space(0.2)], + do_kern=True)] + elif c in self._punctuation_symbols: + prev_char = next((c for c in s[:loc][::-1] if c != ' '), '') + next_char = next((c for c in s[loc + 1:] if c != ' '), '') + + # Do not space commas between brackets + if c == ',': + if prev_char == '{' and next_char == '}': + return [char] + + # Do not space dots as decimal separators + if c == '.' and prev_char.isdigit() and next_char.isdigit(): + return [char] + else: + return [Hlist([char, self._make_space(0.2)], do_kern=True)] + return [char] + + def unknown_symbol(self, s: str, loc: int, toks: ParseResults) -> T.Any: + raise ParseFatalException(s, loc, f"Unknown symbol: {toks['name']}") + + _accent_map = { + r'hat': r'\circumflexaccent', + r'breve': r'\combiningbreve', + r'bar': r'\combiningoverline', + r'grave': r'\combininggraveaccent', + r'acute': r'\combiningacuteaccent', + r'tilde': r'\combiningtilde', + r'dot': r'\combiningdotabove', + r'ddot': r'\combiningdiaeresis', + r'dddot': r'\combiningthreedotsabove', + r'ddddot': r'\combiningfourdotsabove', + r'vec': r'\combiningrightarrowabove', + r'"': r'\combiningdiaeresis', + r"`": r'\combininggraveaccent', + r"'": r'\combiningacuteaccent', + r'~': r'\combiningtilde', + r'.': r'\combiningdotabove', + r'^': r'\circumflexaccent', + r'overrightarrow': r'\rightarrow', + r'overleftarrow': r'\leftarrow', + r'mathring': r'\circ', + } + + _wide_accents = set(r"widehat widetilde widebar".split()) + + def accent(self, toks: ParseResults) -> T.Any: + state = self.get_state() + thickness = state.get_current_underline_thickness() + accent = toks["accent"] + sym = toks["sym"] + accent_box: Node + if accent in self._wide_accents: + accent_box = AutoWidthChar( + '\\' + accent, sym.width, state, char_class=Accent) + else: + accent_box = Accent(self._accent_map[accent], state) + if accent == 'mathring': + accent_box.shrink() + accent_box.shrink() + centered = HCentered([Hbox(sym.width / 4.0), accent_box]) + centered.hpack(sym.width, 'exactly') + return Vlist([ + centered, + Vbox(0., thickness * 2.0), + Hlist([sym]) + ]) + + def function(self, s: str, loc: int, toks: ParseResults) -> T.Any: + hlist = self.operatorname(s, loc, toks) + hlist.function_name = toks["name"] + return hlist + + def operatorname(self, s: str, loc: int, toks: ParseResults) -> T.Any: + self.push_state() + state = self.get_state() + state.font = 'rm' + hlist_list: list[Node] = [] + # Change the font of Chars, but leave Kerns alone + name = toks["name"] + for c in name: + if isinstance(c, Char): + c.font = 'rm' + c._update_metrics() + hlist_list.append(c) + elif isinstance(c, str): + hlist_list.append(Char(c, state)) + else: + hlist_list.append(c) + next_char_loc = loc + len(name) + 1 + if isinstance(name, ParseResults): + next_char_loc += len('operatorname{}') + next_char = next((c for c in s[next_char_loc:] if c != ' '), '') + delimiters = self._delims | {'^', '_'} + if (next_char not in delimiters and + name not in self._overunder_functions): + # Add thin space except when followed by parenthesis, bracket, etc. + hlist_list += [self._make_space(self._space_widths[r'\,'])] + self.pop_state() + # if followed by a super/subscript, set flag to true + # This flag tells subsuper to add space after this operator + if next_char in {'^', '_'}: + self._in_subscript_or_superscript = True + else: + self._in_subscript_or_superscript = False + + return Hlist(hlist_list) + + def start_group(self, toks: ParseResults) -> T.Any: + self.push_state() + # Deal with LaTeX-style font tokens + if toks.get("font"): + self.get_state().font = toks.get("font") + return [] + + def group(self, toks: ParseResults) -> T.Any: + grp = Hlist(toks.get("group", [])) + return [grp] + + def required_group(self, toks: ParseResults) -> T.Any: + return Hlist(toks.get("group", [])) + + optional_group = required_group + + def end_group(self) -> T.Any: + self.pop_state() + return [] + + def unclosed_group(self, s: str, loc: int, toks: ParseResults) -> T.Any: + raise ParseFatalException(s, len(s), "Expected '}'") + + def font(self, toks: ParseResults) -> T.Any: + self.get_state().font = toks["font"] + return [] + + def is_overunder(self, nucleus: Node) -> bool: + if isinstance(nucleus, Char): + return nucleus.c in self._overunder_symbols + elif isinstance(nucleus, Hlist) and hasattr(nucleus, 'function_name'): + return nucleus.function_name in self._overunder_functions + return False + + def is_dropsub(self, nucleus: Node) -> bool: + if isinstance(nucleus, Char): + return nucleus.c in self._dropsub_symbols + return False + + def is_slanted(self, nucleus: Node) -> bool: + if isinstance(nucleus, Char): + return nucleus.is_slanted() + return False + + def subsuper(self, s: str, loc: int, toks: ParseResults) -> T.Any: + nucleus = toks.get("nucleus", Hbox(0)) + subsuper = toks.get("subsuper", []) + napostrophes = len(toks.get("apostrophes", [])) + + if not subsuper and not napostrophes: + return nucleus + + sub = super = None + while subsuper: + op, arg, *subsuper = subsuper + if op == '_': + if sub is not None: + raise ParseFatalException("Double subscript") + sub = arg + else: + if super is not None: + raise ParseFatalException("Double superscript") + super = arg + + state = self.get_state() + rule_thickness = state.fontset.get_underline_thickness( + state.font, state.fontsize, state.dpi) + xHeight = state.fontset.get_xheight( + state.font, state.fontsize, state.dpi) + + if napostrophes: + if super is None: + super = Hlist([]) + for i in range(napostrophes): + super.children.extend(self.symbol(s, loc, {"sym": "\\prime"})) + # kern() and hpack() needed to get the metrics right after + # extending + super.kern() + super.hpack() + + # Handle over/under symbols, such as sum or prod + if self.is_overunder(nucleus): + vlist = [] + shift = 0. + width = nucleus.width + if super is not None: + super.shrink() + width = max(width, super.width) + if sub is not None: + sub.shrink() + width = max(width, sub.width) + + vgap = rule_thickness * 3.0 + if super is not None: + hlist = HCentered([super]) + hlist.hpack(width, 'exactly') + vlist.extend([hlist, Vbox(0, vgap)]) + hlist = HCentered([nucleus]) + hlist.hpack(width, 'exactly') + vlist.append(hlist) + if sub is not None: + hlist = HCentered([sub]) + hlist.hpack(width, 'exactly') + vlist.extend([Vbox(0, vgap), hlist]) + shift = hlist.height + vgap + nucleus.depth + vlt = Vlist(vlist) + vlt.shift_amount = shift + result = Hlist([vlt]) + return [result] + + # We remove kerning on the last character for consistency (otherwise + # it will compute kerning based on non-shrunk characters and may put + # them too close together when superscripted) + # We change the width of the last character to match the advance to + # consider some fonts with weird metrics: e.g. stix's f has a width of + # 7.75 and a kerning of -4.0 for an advance of 3.72, and we want to put + # the superscript at the advance + last_char = nucleus + if isinstance(nucleus, Hlist): + new_children = nucleus.children + if len(new_children): + # remove last kern + if (isinstance(new_children[-1], Kern) and + isinstance(new_children[-2], Char)): + new_children = new_children[:-1] + last_char = new_children[-1] + if isinstance(last_char, Char): + last_char.width = last_char._metrics.advance + # create new Hlist without kerning + nucleus = Hlist(new_children, do_kern=False) + else: + if isinstance(nucleus, Char): + last_char.width = last_char._metrics.advance + nucleus = Hlist([nucleus]) + + # Handle regular sub/superscripts + constants = _get_font_constant_set(state) + lc_height = last_char.height + lc_baseline = 0 + if self.is_dropsub(last_char): + lc_baseline = last_char.depth + + # Compute kerning for sub and super + superkern = constants.delta * xHeight + subkern = constants.delta * xHeight + if self.is_slanted(last_char): + superkern += constants.delta * xHeight + superkern += (constants.delta_slanted * + (lc_height - xHeight * 2. / 3.)) + if self.is_dropsub(last_char): + subkern = (3 * constants.delta - + constants.delta_integral) * lc_height + superkern = (3 * constants.delta + + constants.delta_integral) * lc_height + else: + subkern = 0 + + x: List + if super is None: + # node757 + # Note: One of super or sub must be a Node if we're in this function, but + # mypy can't know this, since it can't interpret pyparsing expressions, + # hence the cast. + x = Hlist([Kern(subkern), T.cast(Node, sub)]) + x.shrink() + if self.is_dropsub(last_char): + shift_down = lc_baseline + constants.subdrop * xHeight + else: + shift_down = constants.sub1 * xHeight + x.shift_amount = shift_down + else: + x = Hlist([Kern(superkern), super]) + x.shrink() + if self.is_dropsub(last_char): + shift_up = lc_height - constants.subdrop * xHeight + else: + shift_up = constants.sup1 * xHeight + if sub is None: + x.shift_amount = -shift_up + else: # Both sub and superscript + y = Hlist([Kern(subkern), sub]) + y.shrink() + if self.is_dropsub(last_char): + shift_down = lc_baseline + constants.subdrop * xHeight + else: + shift_down = constants.sub2 * xHeight + # If sub and superscript collide, move super up + clr = (2.0 * rule_thickness - + ((shift_up - x.depth) - (y.height - shift_down))) + if clr > 0.: + shift_up += clr + x = Vlist([ + x, + Kern((shift_up - x.depth) - (y.height - shift_down)), + y]) + x.shift_amount = shift_down + + if not self.is_dropsub(last_char): + x.width += constants.script_space * xHeight + + # Do we need to add a space after the nucleus? + # To find out, check the flag set by operatorname + spaced_nucleus: list[Node] = [nucleus, x] + if self._in_subscript_or_superscript: + spaced_nucleus += [self._make_space(self._space_widths[r'\,'])] + self._in_subscript_or_superscript = False + + result = Hlist(spaced_nucleus) + return [result] + + def _genfrac(self, ldelim: str, rdelim: str, rule: float | None, style: _MathStyle, + num: Hlist, den: Hlist) -> T.Any: + state = self.get_state() + thickness = state.get_current_underline_thickness() + + for _ in range(style.value): + num.shrink() + den.shrink() + cnum = HCentered([num]) + cden = HCentered([den]) + width = max(num.width, den.width) + cnum.hpack(width, 'exactly') + cden.hpack(width, 'exactly') + vlist = Vlist([cnum, # numerator + Vbox(0, thickness * 2.0), # space + Hrule(state, rule), # rule + Vbox(0, thickness * 2.0), # space + cden # denominator + ]) + + # Shift so the fraction line sits in the middle of the + # equals sign + metrics = state.fontset.get_metrics( + state.font, mpl.rcParams['mathtext.default'], + '=', state.fontsize, state.dpi) + shift = (cden.height - + ((metrics.ymax + metrics.ymin) / 2 - + thickness * 3.0)) + vlist.shift_amount = shift + + result = [Hlist([vlist, Hbox(thickness * 2.)])] + if ldelim or rdelim: + if ldelim == '': + ldelim = '.' + if rdelim == '': + rdelim = '.' + return self._auto_sized_delimiter(ldelim, + T.cast(list[Box | Char | str], + result), + rdelim) + return result + + def style_literal(self, toks: ParseResults) -> T.Any: + return self._MathStyle(int(toks["style_literal"])) + + def genfrac(self, toks: ParseResults) -> T.Any: + return self._genfrac( + toks.get("ldelim", ""), toks.get("rdelim", ""), + toks["rulesize"], toks.get("style", self._MathStyle.TEXTSTYLE), + toks["num"], toks["den"]) + + def frac(self, toks: ParseResults) -> T.Any: + return self._genfrac( + "", "", self.get_state().get_current_underline_thickness(), + self._MathStyle.TEXTSTYLE, toks["num"], toks["den"]) + + def dfrac(self, toks: ParseResults) -> T.Any: + return self._genfrac( + "", "", self.get_state().get_current_underline_thickness(), + self._MathStyle.DISPLAYSTYLE, toks["num"], toks["den"]) + + def binom(self, toks: ParseResults) -> T.Any: + return self._genfrac( + "(", ")", 0, + self._MathStyle.TEXTSTYLE, toks["num"], toks["den"]) + + def _genset(self, s: str, loc: int, toks: ParseResults) -> T.Any: + annotation = toks["annotation"] + body = toks["body"] + thickness = self.get_state().get_current_underline_thickness() + + annotation.shrink() + centered_annotation = HCentered([annotation]) + centered_body = HCentered([body]) + width = max(centered_annotation.width, centered_body.width) + centered_annotation.hpack(width, 'exactly') + centered_body.hpack(width, 'exactly') + + vgap = thickness * 3 + if s[loc + 1] == "u": # \underset + vlist = Vlist([ + centered_body, # body + Vbox(0, vgap), # space + centered_annotation # annotation + ]) + # Shift so the body sits in the same vertical position + vlist.shift_amount = centered_body.depth + centered_annotation.height + vgap + else: # \overset + vlist = Vlist([ + centered_annotation, # annotation + Vbox(0, vgap), # space + centered_body # body + ]) + + # To add horizontal gap between symbols: wrap the Vlist into + # an Hlist and extend it with an Hbox(0, horizontal_gap) + return vlist + + overset = underset = _genset + + def sqrt(self, toks: ParseResults) -> T.Any: + root = toks.get("root") + body = toks["value"] + state = self.get_state() + thickness = state.get_current_underline_thickness() + + # Determine the height of the body, and add a little extra to + # the height so it doesn't seem cramped + height = body.height - body.shift_amount + thickness * 5.0 + depth = body.depth + body.shift_amount + check = AutoHeightChar(r'\__sqrt__', height, depth, state, always=True) + height = check.height - check.shift_amount + depth = check.depth + check.shift_amount + + # Put a little extra space to the left and right of the body + padded_body = Hlist([Hbox(2 * thickness), body, Hbox(2 * thickness)]) + rightside = Vlist([Hrule(state), Glue('fill'), padded_body]) + # Stretch the glue between the hrule and the body + rightside.vpack(height + (state.fontsize * state.dpi) / (100.0 * 12.0), + 'exactly', depth) + + # Add the root and shift it upward so it is above the tick. + # The value of 0.6 is a hard-coded hack ;) + if not root: + root = Box(check.width * 0.5, 0., 0.) + else: + root = Hlist(root) + root.shrink() + root.shrink() + + root_vlist = Vlist([Hlist([root])]) + root_vlist.shift_amount = -height * 0.6 + + hlist = Hlist([root_vlist, # Root + # Negative kerning to put root over tick + Kern(-check.width * 0.5), + check, # Check + rightside]) # Body + return [hlist] + + def overline(self, toks: ParseResults) -> T.Any: + body = toks["body"] + + state = self.get_state() + thickness = state.get_current_underline_thickness() + + height = body.height - body.shift_amount + thickness * 3.0 + depth = body.depth + body.shift_amount + + # Place overline above body + rightside = Vlist([Hrule(state), Glue('fill'), Hlist([body])]) + + # Stretch the glue between the hrule and the body + rightside.vpack(height + (state.fontsize * state.dpi) / (100.0 * 12.0), + 'exactly', depth) + + hlist = Hlist([rightside]) + return [hlist] + + def _auto_sized_delimiter(self, front: str, + middle: list[Box | Char | str], + back: str) -> T.Any: + state = self.get_state() + if len(middle): + height = max([x.height for x in middle if not isinstance(x, str)]) + depth = max([x.depth for x in middle if not isinstance(x, str)]) + factor = None + for idx, el in enumerate(middle): + if el == r'\middle': + c = T.cast(str, middle[idx + 1]) # Should be one of p.delims. + if c != '.': + middle[idx + 1] = AutoHeightChar( + c, height, depth, state, factor=factor) + else: + middle.remove(c) + del middle[idx] + # There should only be \middle and its delimiter as str, which have + # just been removed. + middle_part = T.cast(list[Box | Char], middle) + else: + height = 0 + depth = 0 + factor = 1.0 + middle_part = [] + + parts: list[Node] = [] + # \left. and \right. aren't supposed to produce any symbols + if front != '.': + parts.append( + AutoHeightChar(front, height, depth, state, factor=factor)) + parts.extend(middle_part) + if back != '.': + parts.append( + AutoHeightChar(back, height, depth, state, factor=factor)) + hlist = Hlist(parts) + return hlist + + def auto_delim(self, toks: ParseResults) -> T.Any: + return self._auto_sized_delimiter( + toks["left"], + # if "mid" in toks ... can be removed when requiring pyparsing 3. + toks["mid"].as_list() if "mid" in toks else [], + toks["right"]) + + def boldsymbol(self, toks: ParseResults) -> T.Any: + self.push_state() + state = self.get_state() + hlist: list[Node] = [] + name = toks["value"] + for c in name: + if isinstance(c, Hlist): + k = c.children[1] + if isinstance(k, Char): + k.font = "bf" + k._update_metrics() + hlist.append(c) + elif isinstance(c, Char): + c.font = "bf" + if (c.c in self._latin_alphabets or + c.c[1:] in self._small_greek): + c.font = "bfit" + c._update_metrics() + c._update_metrics() + hlist.append(c) + else: + hlist.append(c) + self.pop_state() + + return Hlist(hlist) + + def substack(self, toks: ParseResults) -> T.Any: + parts = toks["parts"] + state = self.get_state() + thickness = state.get_current_underline_thickness() + + hlist = [Hlist(k) for k in parts[0]] + max_width = max(map(lambda c: c.width, hlist)) + + vlist = [] + for sub in hlist: + cp = HCentered([sub]) + cp.hpack(max_width, 'exactly') + vlist.append(cp) + + stack = [val + for pair in zip(vlist, [Vbox(0, thickness * 2)] * len(vlist)) + for val in pair] + del stack[-1] + vlt = Vlist(stack) + result = [Hlist([vlt])] + return result diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_mathtext_data.py b/.venv/lib/python3.12/site-packages/matplotlib/_mathtext_data.py new file mode 100644 index 0000000000000000000000000000000000000000..5819ee7430447f9ef5f6e760b65cdd5933ef9395 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_mathtext_data.py @@ -0,0 +1,1742 @@ +""" +font data tables for truetype and afm computer modern fonts +""" + +from __future__ import annotations +from typing import overload + +latex_to_bakoma = { + '\\__sqrt__' : ('cmex10', 0x70), + '\\bigcap' : ('cmex10', 0x5c), + '\\bigcup' : ('cmex10', 0x5b), + '\\bigodot' : ('cmex10', 0x4b), + '\\bigoplus' : ('cmex10', 0x4d), + '\\bigotimes' : ('cmex10', 0x4f), + '\\biguplus' : ('cmex10', 0x5d), + '\\bigvee' : ('cmex10', 0x5f), + '\\bigwedge' : ('cmex10', 0x5e), + '\\coprod' : ('cmex10', 0x61), + '\\int' : ('cmex10', 0x5a), + '\\langle' : ('cmex10', 0xad), + '\\leftangle' : ('cmex10', 0xad), + '\\leftbrace' : ('cmex10', 0xa9), + '\\oint' : ('cmex10', 0x49), + '\\prod' : ('cmex10', 0x59), + '\\rangle' : ('cmex10', 0xae), + '\\rightangle' : ('cmex10', 0xae), + '\\rightbrace' : ('cmex10', 0xaa), + '\\sum' : ('cmex10', 0x58), + '\\widehat' : ('cmex10', 0x62), + '\\widetilde' : ('cmex10', 0x65), + '\\{' : ('cmex10', 0xa9), + '\\}' : ('cmex10', 0xaa), + '{' : ('cmex10', 0xa9), + '}' : ('cmex10', 0xaa), + + ',' : ('cmmi10', 0x3b), + '.' : ('cmmi10', 0x3a), + '/' : ('cmmi10', 0x3d), + '<' : ('cmmi10', 0x3c), + '>' : ('cmmi10', 0x3e), + '\\alpha' : ('cmmi10', 0xae), + '\\beta' : ('cmmi10', 0xaf), + '\\chi' : ('cmmi10', 0xc2), + '\\combiningrightarrowabove' : ('cmmi10', 0x7e), + '\\delta' : ('cmmi10', 0xb1), + '\\ell' : ('cmmi10', 0x60), + '\\epsilon' : ('cmmi10', 0xb2), + '\\eta' : ('cmmi10', 0xb4), + '\\flat' : ('cmmi10', 0x5b), + '\\frown' : ('cmmi10', 0x5f), + '\\gamma' : ('cmmi10', 0xb0), + '\\imath' : ('cmmi10', 0x7b), + '\\iota' : ('cmmi10', 0xb6), + '\\jmath' : ('cmmi10', 0x7c), + '\\kappa' : ('cmmi10', 0x2219), + '\\lambda' : ('cmmi10', 0xb8), + '\\leftharpoondown' : ('cmmi10', 0x29), + '\\leftharpoonup' : ('cmmi10', 0x28), + '\\mu' : ('cmmi10', 0xb9), + '\\natural' : ('cmmi10', 0x5c), + '\\nu' : ('cmmi10', 0xba), + '\\omega' : ('cmmi10', 0x21), + '\\phi' : ('cmmi10', 0xc1), + '\\pi' : ('cmmi10', 0xbc), + '\\psi' : ('cmmi10', 0xc3), + '\\rho' : ('cmmi10', 0xbd), + '\\rightharpoondown' : ('cmmi10', 0x2b), + '\\rightharpoonup' : ('cmmi10', 0x2a), + '\\sharp' : ('cmmi10', 0x5d), + '\\sigma' : ('cmmi10', 0xbe), + '\\smile' : ('cmmi10', 0x5e), + '\\tau' : ('cmmi10', 0xbf), + '\\theta' : ('cmmi10', 0xb5), + '\\triangleleft' : ('cmmi10', 0x2f), + '\\triangleright' : ('cmmi10', 0x2e), + '\\upsilon' : ('cmmi10', 0xc0), + '\\varepsilon' : ('cmmi10', 0x22), + '\\varphi' : ('cmmi10', 0x27), + '\\varrho' : ('cmmi10', 0x25), + '\\varsigma' : ('cmmi10', 0x26), + '\\vartheta' : ('cmmi10', 0x23), + '\\wp' : ('cmmi10', 0x7d), + '\\xi' : ('cmmi10', 0xbb), + '\\zeta' : ('cmmi10', 0xb3), + + '!' : ('cmr10', 0x21), + '%' : ('cmr10', 0x25), + '&' : ('cmr10', 0x26), + '(' : ('cmr10', 0x28), + ')' : ('cmr10', 0x29), + '+' : ('cmr10', 0x2b), + '0' : ('cmr10', 0x30), + '1' : ('cmr10', 0x31), + '2' : ('cmr10', 0x32), + '3' : ('cmr10', 0x33), + '4' : ('cmr10', 0x34), + '5' : ('cmr10', 0x35), + '6' : ('cmr10', 0x36), + '7' : ('cmr10', 0x37), + '8' : ('cmr10', 0x38), + '9' : ('cmr10', 0x39), + ':' : ('cmr10', 0x3a), + ';' : ('cmr10', 0x3b), + '=' : ('cmr10', 0x3d), + '?' : ('cmr10', 0x3f), + '@' : ('cmr10', 0x40), + '[' : ('cmr10', 0x5b), + '\\#' : ('cmr10', 0x23), + '\\$' : ('cmr10', 0x24), + '\\%' : ('cmr10', 0x25), + '\\Delta' : ('cmr10', 0xa2), + '\\Gamma' : ('cmr10', 0xa1), + '\\Lambda' : ('cmr10', 0xa4), + '\\Omega' : ('cmr10', 0xad), + '\\Phi' : ('cmr10', 0xa9), + '\\Pi' : ('cmr10', 0xa6), + '\\Psi' : ('cmr10', 0xaa), + '\\Sigma' : ('cmr10', 0xa7), + '\\Theta' : ('cmr10', 0xa3), + '\\Upsilon' : ('cmr10', 0xa8), + '\\Xi' : ('cmr10', 0xa5), + '\\circumflexaccent' : ('cmr10', 0x5e), + '\\combiningacuteaccent' : ('cmr10', 0xb6), + '\\combiningbreve' : ('cmr10', 0xb8), + '\\combiningdiaeresis' : ('cmr10', 0xc4), + '\\combiningdotabove' : ('cmr10', 0x5f), + '\\combininggraveaccent' : ('cmr10', 0xb5), + '\\combiningoverline' : ('cmr10', 0xb9), + '\\combiningtilde' : ('cmr10', 0x7e), + '\\leftbracket' : ('cmr10', 0x5b), + '\\leftparen' : ('cmr10', 0x28), + '\\rightbracket' : ('cmr10', 0x5d), + '\\rightparen' : ('cmr10', 0x29), + '\\widebar' : ('cmr10', 0xb9), + ']' : ('cmr10', 0x5d), + + '*' : ('cmsy10', 0xa4), + '\N{MINUS SIGN}' : ('cmsy10', 0xa1), + '\\Downarrow' : ('cmsy10', 0x2b), + '\\Im' : ('cmsy10', 0x3d), + '\\Leftarrow' : ('cmsy10', 0x28), + '\\Leftrightarrow' : ('cmsy10', 0x2c), + '\\P' : ('cmsy10', 0x7b), + '\\Re' : ('cmsy10', 0x3c), + '\\Rightarrow' : ('cmsy10', 0x29), + '\\S' : ('cmsy10', 0x78), + '\\Uparrow' : ('cmsy10', 0x2a), + '\\Updownarrow' : ('cmsy10', 0x6d), + '\\Vert' : ('cmsy10', 0x6b), + '\\aleph' : ('cmsy10', 0x40), + '\\approx' : ('cmsy10', 0xbc), + '\\ast' : ('cmsy10', 0xa4), + '\\asymp' : ('cmsy10', 0xb3), + '\\backslash' : ('cmsy10', 0x6e), + '\\bigcirc' : ('cmsy10', 0xb0), + '\\bigtriangledown' : ('cmsy10', 0x35), + '\\bigtriangleup' : ('cmsy10', 0x34), + '\\bot' : ('cmsy10', 0x3f), + '\\bullet' : ('cmsy10', 0xb2), + '\\cap' : ('cmsy10', 0x5c), + '\\cdot' : ('cmsy10', 0xa2), + '\\circ' : ('cmsy10', 0xb1), + '\\clubsuit' : ('cmsy10', 0x7c), + '\\cup' : ('cmsy10', 0x5b), + '\\dag' : ('cmsy10', 0x79), + '\\dashv' : ('cmsy10', 0x61), + '\\ddag' : ('cmsy10', 0x7a), + '\\diamond' : ('cmsy10', 0xa6), + '\\diamondsuit' : ('cmsy10', 0x7d), + '\\div' : ('cmsy10', 0xa5), + '\\downarrow' : ('cmsy10', 0x23), + '\\emptyset' : ('cmsy10', 0x3b), + '\\equiv' : ('cmsy10', 0xb4), + '\\exists' : ('cmsy10', 0x39), + '\\forall' : ('cmsy10', 0x38), + '\\geq' : ('cmsy10', 0xb8), + '\\gg' : ('cmsy10', 0xc0), + '\\heartsuit' : ('cmsy10', 0x7e), + '\\in' : ('cmsy10', 0x32), + '\\infty' : ('cmsy10', 0x31), + '\\lbrace' : ('cmsy10', 0x66), + '\\lceil' : ('cmsy10', 0x64), + '\\leftarrow' : ('cmsy10', 0xc3), + '\\leftrightarrow' : ('cmsy10', 0x24), + '\\leq' : ('cmsy10', 0x2219), + '\\lfloor' : ('cmsy10', 0x62), + '\\ll' : ('cmsy10', 0xbf), + '\\mid' : ('cmsy10', 0x6a), + '\\mp' : ('cmsy10', 0xa8), + '\\nabla' : ('cmsy10', 0x72), + '\\nearrow' : ('cmsy10', 0x25), + '\\neg' : ('cmsy10', 0x3a), + '\\ni' : ('cmsy10', 0x33), + '\\nwarrow' : ('cmsy10', 0x2d), + '\\odot' : ('cmsy10', 0xaf), + '\\ominus' : ('cmsy10', 0xaa), + '\\oplus' : ('cmsy10', 0xa9), + '\\oslash' : ('cmsy10', 0xae), + '\\otimes' : ('cmsy10', 0xad), + '\\pm' : ('cmsy10', 0xa7), + '\\prec' : ('cmsy10', 0xc1), + '\\preceq' : ('cmsy10', 0xb9), + '\\prime' : ('cmsy10', 0x30), + '\\propto' : ('cmsy10', 0x2f), + '\\rbrace' : ('cmsy10', 0x67), + '\\rceil' : ('cmsy10', 0x65), + '\\rfloor' : ('cmsy10', 0x63), + '\\rightarrow' : ('cmsy10', 0x21), + '\\searrow' : ('cmsy10', 0x26), + '\\sim' : ('cmsy10', 0xbb), + '\\simeq' : ('cmsy10', 0x27), + '\\slash' : ('cmsy10', 0x36), + '\\spadesuit' : ('cmsy10', 0xc4), + '\\sqcap' : ('cmsy10', 0x75), + '\\sqcup' : ('cmsy10', 0x74), + '\\sqsubseteq' : ('cmsy10', 0x76), + '\\sqsupseteq' : ('cmsy10', 0x77), + '\\subset' : ('cmsy10', 0xbd), + '\\subseteq' : ('cmsy10', 0xb5), + '\\succ' : ('cmsy10', 0xc2), + '\\succeq' : ('cmsy10', 0xba), + '\\supset' : ('cmsy10', 0xbe), + '\\supseteq' : ('cmsy10', 0xb6), + '\\swarrow' : ('cmsy10', 0x2e), + '\\times' : ('cmsy10', 0xa3), + '\\to' : ('cmsy10', 0x21), + '\\top' : ('cmsy10', 0x3e), + '\\uparrow' : ('cmsy10', 0x22), + '\\updownarrow' : ('cmsy10', 0x6c), + '\\uplus' : ('cmsy10', 0x5d), + '\\vdash' : ('cmsy10', 0x60), + '\\vee' : ('cmsy10', 0x5f), + '\\vert' : ('cmsy10', 0x6a), + '\\wedge' : ('cmsy10', 0x5e), + '\\wr' : ('cmsy10', 0x6f), + '\\|' : ('cmsy10', 0x6b), + '|' : ('cmsy10', 0x6a), + + '\\_' : ('cmtt10', 0x5f) +} + +# Automatically generated. + +type12uni = { + 'aring' : 229, + 'quotedblright' : 8221, + 'V' : 86, + 'dollar' : 36, + 'four' : 52, + 'Yacute' : 221, + 'P' : 80, + 'underscore' : 95, + 'p' : 112, + 'Otilde' : 213, + 'perthousand' : 8240, + 'zero' : 48, + 'dotlessi' : 305, + 'Scaron' : 352, + 'zcaron' : 382, + 'egrave' : 232, + 'section' : 167, + 'Icircumflex' : 206, + 'ntilde' : 241, + 'ampersand' : 38, + 'dotaccent' : 729, + 'degree' : 176, + 'K' : 75, + 'acircumflex' : 226, + 'Aring' : 197, + 'k' : 107, + 'smalltilde' : 732, + 'Agrave' : 192, + 'divide' : 247, + 'ocircumflex' : 244, + 'asciitilde' : 126, + 'two' : 50, + 'E' : 69, + 'scaron' : 353, + 'F' : 70, + 'bracketleft' : 91, + 'asciicircum' : 94, + 'f' : 102, + 'ordmasculine' : 186, + 'mu' : 181, + 'paragraph' : 182, + 'nine' : 57, + 'v' : 118, + 'guilsinglleft' : 8249, + 'backslash' : 92, + 'six' : 54, + 'A' : 65, + 'icircumflex' : 238, + 'a' : 97, + 'ogonek' : 731, + 'q' : 113, + 'oacute' : 243, + 'ograve' : 242, + 'edieresis' : 235, + 'comma' : 44, + 'otilde' : 245, + 'guillemotright' : 187, + 'ecircumflex' : 234, + 'greater' : 62, + 'uacute' : 250, + 'L' : 76, + 'bullet' : 8226, + 'cedilla' : 184, + 'ydieresis' : 255, + 'l' : 108, + 'logicalnot' : 172, + 'exclamdown' : 161, + 'endash' : 8211, + 'agrave' : 224, + 'Adieresis' : 196, + 'germandbls' : 223, + 'Odieresis' : 214, + 'space' : 32, + 'quoteright' : 8217, + 'ucircumflex' : 251, + 'G' : 71, + 'quoteleft' : 8216, + 'W' : 87, + 'Q' : 81, + 'g' : 103, + 'w' : 119, + 'question' : 63, + 'one' : 49, + 'ring' : 730, + 'figuredash' : 8210, + 'B' : 66, + 'iacute' : 237, + 'Ydieresis' : 376, + 'R' : 82, + 'b' : 98, + 'r' : 114, + 'Ccedilla' : 199, + 'minus' : 8722, + 'Lslash' : 321, + 'Uacute' : 218, + 'yacute' : 253, + 'Ucircumflex' : 219, + 'quotedbl' : 34, + 'onehalf' : 189, + 'Thorn' : 222, + 'M' : 77, + 'eight' : 56, + 'multiply' : 215, + 'grave' : 96, + 'Ocircumflex' : 212, + 'm' : 109, + 'Ugrave' : 217, + 'guilsinglright' : 8250, + 'Ntilde' : 209, + 'questiondown' : 191, + 'Atilde' : 195, + 'ccedilla' : 231, + 'Z' : 90, + 'copyright' : 169, + 'yen' : 165, + 'Eacute' : 201, + 'H' : 72, + 'X' : 88, + 'Idieresis' : 207, + 'bar' : 124, + 'h' : 104, + 'x' : 120, + 'udieresis' : 252, + 'ordfeminine' : 170, + 'braceleft' : 123, + 'macron' : 175, + 'atilde' : 227, + 'Acircumflex' : 194, + 'Oslash' : 216, + 'C' : 67, + 'quotedblleft' : 8220, + 'S' : 83, + 'exclam' : 33, + 'Zcaron' : 381, + 'equal' : 61, + 's' : 115, + 'eth' : 240, + 'Egrave' : 200, + 'hyphen' : 45, + 'period' : 46, + 'igrave' : 236, + 'colon' : 58, + 'Ecircumflex' : 202, + 'trademark' : 8482, + 'Aacute' : 193, + 'cent' : 162, + 'lslash' : 322, + 'c' : 99, + 'N' : 78, + 'breve' : 728, + 'Oacute' : 211, + 'guillemotleft' : 171, + 'n' : 110, + 'idieresis' : 239, + 'braceright' : 125, + 'seven' : 55, + 'brokenbar' : 166, + 'ugrave' : 249, + 'periodcentered' : 183, + 'sterling' : 163, + 'I' : 73, + 'Y' : 89, + 'Eth' : 208, + 'emdash' : 8212, + 'i' : 105, + 'daggerdbl' : 8225, + 'y' : 121, + 'plusminus' : 177, + 'less' : 60, + 'Udieresis' : 220, + 'D' : 68, + 'five' : 53, + 'T' : 84, + 'oslash' : 248, + 'acute' : 180, + 'd' : 100, + 'OE' : 338, + 'Igrave' : 204, + 't' : 116, + 'parenright' : 41, + 'adieresis' : 228, + 'quotesingle' : 39, + 'twodotenleader' : 8229, + 'slash' : 47, + 'ellipsis' : 8230, + 'numbersign' : 35, + 'odieresis' : 246, + 'O' : 79, + 'oe' : 339, + 'o' : 111, + 'Edieresis' : 203, + 'plus' : 43, + 'dagger' : 8224, + 'three' : 51, + 'hungarumlaut' : 733, + 'parenleft' : 40, + 'fraction' : 8260, + 'registered' : 174, + 'J' : 74, + 'dieresis' : 168, + 'Ograve' : 210, + 'j' : 106, + 'z' : 122, + 'ae' : 230, + 'semicolon' : 59, + 'at' : 64, + 'Iacute' : 205, + 'percent' : 37, + 'bracketright' : 93, + 'AE' : 198, + 'asterisk' : 42, + 'aacute' : 225, + 'U' : 85, + 'eacute' : 233, + 'e' : 101, + 'thorn' : 254, + 'u' : 117, +} + +uni2type1 = {v: k for k, v in type12uni.items()} + +# The script below is to sort and format the tex2uni dict + +## For decimal values: int(hex(v), 16) +# newtex = {k: hex(v) for k, v in tex2uni.items()} +# sd = dict(sorted(newtex.items(), key=lambda item: item[0])) +# +## For formatting the sorted dictionary with proper spacing +## the value '24' comes from finding the longest string in +## the newtex keys with len(max(newtex, key=len)) +# for key in sd: +# print("{0:24} : {1: _EntryTypeOut: ... + + +@overload +def _normalize_stix_fontcodes(d: list[_EntryTypeIn]) -> list[_EntryTypeOut]: ... + + +@overload +def _normalize_stix_fontcodes(d: dict[str, list[_EntryTypeIn] | + dict[str, list[_EntryTypeIn]]] + ) -> dict[str, list[_EntryTypeOut] | + dict[str, list[_EntryTypeOut]]]: ... + + +def _normalize_stix_fontcodes(d): + if isinstance(d, tuple): + return tuple(ord(x) if isinstance(x, str) and len(x) == 1 else x for x in d) + elif isinstance(d, list): + return [_normalize_stix_fontcodes(x) for x in d] + elif isinstance(d, dict): + return {k: _normalize_stix_fontcodes(v) for k, v in d.items()} + + +stix_virtual_fonts: dict[str, dict[str, list[_EntryTypeOut]] | list[_EntryTypeOut]] +stix_virtual_fonts = _normalize_stix_fontcodes(_stix_virtual_fonts) + +# Free redundant list now that it has been normalized +del _stix_virtual_fonts + +# Fix some incorrect glyphs. +stix_glyph_fixes = { + # Cap and Cup glyphs are swapped. + 0x22d2: 0x22d3, + 0x22d3: 0x22d2, +} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_path.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_path.pyi new file mode 100644 index 0000000000000000000000000000000000000000..456905528b28e0f4eea31ec9d86433fb43767b85 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_path.pyi @@ -0,0 +1,9 @@ +from collections.abc import Sequence + +import numpy as np + +from .transforms import BboxBase + +def affine_transform(points: np.ndarray, trans: np.ndarray) -> np.ndarray: ... +def count_bboxes_overlapping_bbox(bbox: BboxBase, bboxes: Sequence[BboxBase]) -> int: ... +def update_path_extents(path, trans, rect, minpos, ignore): ... diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_pylab_helpers.py b/.venv/lib/python3.12/site-packages/matplotlib/_pylab_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..a3861aef592008a84ffddadb7a0c76c8833a30af --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_pylab_helpers.py @@ -0,0 +1,134 @@ +""" +Manage figures for the pyplot interface. +""" + +import atexit +from collections import OrderedDict + + +class Gcf: + """ + Singleton to maintain the relation between figures and their managers, and + keep track of and "active" figure and manager. + + The canvas of a figure created through pyplot is associated with a figure + manager, which handles the interaction between the figure and the backend. + pyplot keeps track of figure managers using an identifier, the "figure + number" or "manager number" (which can actually be any hashable value); + this number is available as the :attr:`number` attribute of the manager. + + This class is never instantiated; it consists of an `OrderedDict` mapping + figure/manager numbers to managers, and a set of class methods that + manipulate this `OrderedDict`. + + Attributes + ---------- + figs : OrderedDict + `OrderedDict` mapping numbers to managers; the active manager is at the + end. + """ + + figs = OrderedDict() + + @classmethod + def get_fig_manager(cls, num): + """ + If manager number *num* exists, make it the active one and return it; + otherwise return *None*. + """ + manager = cls.figs.get(num, None) + if manager is not None: + cls.set_active(manager) + return manager + + @classmethod + def destroy(cls, num): + """ + Destroy manager *num* -- either a manager instance or a manager number. + + In the interactive backends, this is bound to the window "destroy" and + "delete" events. + + It is recommended to pass a manager instance, to avoid confusion when + two managers share the same number. + """ + if all(hasattr(num, attr) for attr in ["num", "destroy"]): + manager = num + if cls.figs.get(manager.num) is manager: + cls.figs.pop(manager.num) + else: + try: + manager = cls.figs.pop(num) + except KeyError: + return + if hasattr(manager, "_cidgcf"): + manager.canvas.mpl_disconnect(manager._cidgcf) + manager.destroy() + + @classmethod + def destroy_fig(cls, fig): + """Destroy figure *fig*.""" + num = next((manager.num for manager in cls.figs.values() + if manager.canvas.figure == fig), None) + if num is not None: + cls.destroy(num) + + @classmethod + def destroy_all(cls): + """Destroy all figures.""" + for manager in list(cls.figs.values()): + manager.canvas.mpl_disconnect(manager._cidgcf) + manager.destroy() + cls.figs.clear() + + @classmethod + def has_fignum(cls, num): + """Return whether figure number *num* exists.""" + return num in cls.figs + + @classmethod + def get_all_fig_managers(cls): + """Return a list of figure managers.""" + return list(cls.figs.values()) + + @classmethod + def get_num_fig_managers(cls): + """Return the number of figures being managed.""" + return len(cls.figs) + + @classmethod + def get_active(cls): + """Return the active manager, or *None* if there is no manager.""" + return next(reversed(cls.figs.values())) if cls.figs else None + + @classmethod + def _set_new_active_manager(cls, manager): + """Adopt *manager* into pyplot and make it the active manager.""" + if not hasattr(manager, "_cidgcf"): + manager._cidgcf = manager.canvas.mpl_connect( + "button_press_event", lambda event: cls.set_active(manager)) + fig = manager.canvas.figure + fig._number = manager.num + label = fig.get_label() + if label: + manager.set_window_title(label) + cls.set_active(manager) + + @classmethod + def set_active(cls, manager): + """Make *manager* the active manager.""" + cls.figs[manager.num] = manager + cls.figs.move_to_end(manager.num) + + @classmethod + def draw_all(cls, force=False): + """ + Redraw all stale managed figures, or, if *force* is True, all managed + figures. + """ + for manager in cls.get_all_fig_managers(): + if force or manager.canvas.figure.stale: + manager.canvas.draw_idle() + + +atexit.register(Gcf.destroy_all) diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_pylab_helpers.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_pylab_helpers.pyi new file mode 100644 index 0000000000000000000000000000000000000000..bdd8cfba3173030b37b24af412970cbb62fee3ef --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_pylab_helpers.pyi @@ -0,0 +1,29 @@ +from collections import OrderedDict + +from matplotlib.backend_bases import FigureManagerBase +from matplotlib.figure import Figure + +class Gcf: + figs: OrderedDict[int, FigureManagerBase] + @classmethod + def get_fig_manager(cls, num: int) -> FigureManagerBase | None: ... + @classmethod + def destroy(cls, num: int | FigureManagerBase) -> None: ... + @classmethod + def destroy_fig(cls, fig: Figure) -> None: ... + @classmethod + def destroy_all(cls) -> None: ... + @classmethod + def has_fignum(cls, num: int) -> bool: ... + @classmethod + def get_all_fig_managers(cls) -> list[FigureManagerBase]: ... + @classmethod + def get_num_fig_managers(cls) -> int: ... + @classmethod + def get_active(cls) -> FigureManagerBase | None: ... + @classmethod + def _set_new_active_manager(cls, manager: FigureManagerBase) -> None: ... + @classmethod + def set_active(cls, manager: FigureManagerBase) -> None: ... + @classmethod + def draw_all(cls, force: bool = ...) -> None: ... diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_qhull.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_qhull.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_text_helpers.py b/.venv/lib/python3.12/site-packages/matplotlib/_text_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..b9603b114bc245ecf32da926573ddb4157210c7a --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_text_helpers.py @@ -0,0 +1,82 @@ +""" +Low-level text helper utilities. +""" + +from __future__ import annotations + +import dataclasses + +from . import _api +from .ft2font import FT2Font, Kerning, LoadFlags + + +@dataclasses.dataclass(frozen=True) +class LayoutItem: + ft_object: FT2Font + char: str + glyph_idx: int + x: float + prev_kern: float + + +def warn_on_missing_glyph(codepoint, fontnames): + _api.warn_external( + f"Glyph {codepoint} " + f"({chr(codepoint).encode('ascii', 'namereplace').decode('ascii')}) " + f"missing from font(s) {fontnames}.") + + block = ("Hebrew" if 0x0590 <= codepoint <= 0x05ff else + "Arabic" if 0x0600 <= codepoint <= 0x06ff else + "Devanagari" if 0x0900 <= codepoint <= 0x097f else + "Bengali" if 0x0980 <= codepoint <= 0x09ff else + "Gurmukhi" if 0x0a00 <= codepoint <= 0x0a7f else + "Gujarati" if 0x0a80 <= codepoint <= 0x0aff else + "Oriya" if 0x0b00 <= codepoint <= 0x0b7f else + "Tamil" if 0x0b80 <= codepoint <= 0x0bff else + "Telugu" if 0x0c00 <= codepoint <= 0x0c7f else + "Kannada" if 0x0c80 <= codepoint <= 0x0cff else + "Malayalam" if 0x0d00 <= codepoint <= 0x0d7f else + "Sinhala" if 0x0d80 <= codepoint <= 0x0dff else + None) + if block: + _api.warn_external( + f"Matplotlib currently does not support {block} natively.") + + +def layout(string, font, *, kern_mode=Kerning.DEFAULT): + """ + Render *string* with *font*. + + For each character in *string*, yield a LayoutItem instance. When such an instance + is yielded, the font's glyph is set to the corresponding character. + + Parameters + ---------- + string : str + The string to be rendered. + font : FT2Font + The font. + kern_mode : Kerning + A FreeType kerning mode. + + Yields + ------ + LayoutItem + """ + x = 0 + prev_glyph_idx = None + char_to_font = font._get_fontmap(string) + base_font = font + for char in string: + # This has done the fallback logic + font = char_to_font.get(char, base_font) + glyph_idx = font.get_char_index(ord(char)) + kern = ( + base_font.get_kerning(prev_glyph_idx, glyph_idx, kern_mode) / 64 + if prev_glyph_idx is not None else 0. + ) + x += kern + glyph = font.load_glyph(glyph_idx, flags=LoadFlags.NO_HINTING) + yield LayoutItem(font, char, glyph_idx, x, kern) + x += glyph.linearHoriAdvance / 65536 + prev_glyph_idx = glyph_idx diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_tight_bbox.py b/.venv/lib/python3.12/site-packages/matplotlib/_tight_bbox.py new file mode 100644 index 0000000000000000000000000000000000000000..db72bbdff020680dfd833229cac41e9b33e428ee --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_tight_bbox.py @@ -0,0 +1,84 @@ +""" +Helper module for the *bbox_inches* parameter in `.Figure.savefig`. +""" + +from matplotlib.transforms import Bbox, TransformedBbox, Affine2D + + +def adjust_bbox(fig, bbox_inches, fixed_dpi=None): + """ + Temporarily adjust the figure so that only the specified area + (bbox_inches) is saved. + + It modifies fig.bbox, fig.bbox_inches, + fig.transFigure._boxout, and fig.patch. While the figure size + changes, the scale of the original figure is conserved. A + function which restores the original values are returned. + """ + origBbox = fig.bbox + origBboxInches = fig.bbox_inches + _boxout = fig.transFigure._boxout + + old_aspect = [] + locator_list = [] + sentinel = object() + for ax in fig.axes: + locator = ax.get_axes_locator() + if locator is not None: + ax.apply_aspect(locator(ax, None)) + locator_list.append(locator) + current_pos = ax.get_position(original=False).frozen() + ax.set_axes_locator(lambda a, r, _pos=current_pos: _pos) + # override the method that enforces the aspect ratio on the Axes + if 'apply_aspect' in ax.__dict__: + old_aspect.append(ax.apply_aspect) + else: + old_aspect.append(sentinel) + ax.apply_aspect = lambda pos=None: None + + def restore_bbox(): + for ax, loc, aspect in zip(fig.axes, locator_list, old_aspect): + ax.set_axes_locator(loc) + if aspect is sentinel: + # delete our no-op function which un-hides the original method + del ax.apply_aspect + else: + ax.apply_aspect = aspect + + fig.bbox = origBbox + fig.bbox_inches = origBboxInches + fig.transFigure._boxout = _boxout + fig.transFigure.invalidate() + fig.patch.set_bounds(0, 0, 1, 1) + + if fixed_dpi is None: + fixed_dpi = fig.dpi + tr = Affine2D().scale(fixed_dpi) + dpi_scale = fixed_dpi / fig.dpi + + fig.bbox_inches = Bbox.from_bounds(0, 0, *bbox_inches.size) + x0, y0 = tr.transform(bbox_inches.p0) + w1, h1 = fig.bbox.size * dpi_scale + fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1) + fig.transFigure.invalidate() + + fig.bbox = TransformedBbox(fig.bbox_inches, tr) + + fig.patch.set_bounds(x0 / w1, y0 / h1, + fig.bbox.width / w1, fig.bbox.height / h1) + + return restore_bbox + + +def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None): + """ + A function that needs to be called when figure dpi changes during the + drawing (e.g., rasterizing). It recovers the bbox and re-adjust it with + the new dpi. + """ + + bbox_inches, restore_bbox = bbox_inches_restore + restore_bbox() + r = adjust_bbox(fig, bbox_inches, fixed_dpi) + + return bbox_inches, r diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_tight_layout.py b/.venv/lib/python3.12/site-packages/matplotlib/_tight_layout.py new file mode 100644 index 0000000000000000000000000000000000000000..548da79fff04320193a7170d5cf0f9b9fa743c0a --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_tight_layout.py @@ -0,0 +1,301 @@ +""" +Routines to adjust subplot params so that subplots are +nicely fit in the figure. In doing so, only axis labels, tick labels, Axes +titles and offsetboxes that are anchored to Axes are currently considered. + +Internally, this module assumes that the margins (left margin, etc.) which are +differences between ``Axes.get_tightbbox`` and ``Axes.bbox`` are independent of +Axes position. This may fail if ``Axes.adjustable`` is ``datalim`` as well as +such cases as when left or right margin are affected by xlabel. +""" + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, artist as martist +from matplotlib.font_manager import FontProperties +from matplotlib.transforms import Bbox + + +def _auto_adjust_subplotpars( + fig, renderer, shape, span_pairs, subplot_list, + ax_bbox_list=None, pad=1.08, h_pad=None, w_pad=None, rect=None): + """ + Return a dict of subplot parameters to adjust spacing between subplots + or ``None`` if resulting Axes would have zero height or width. + + Note that this function ignores geometry information of subplot itself, but + uses what is given by the *shape* and *subplot_list* parameters. Also, the + results could be incorrect if some subplots have ``adjustable=datalim``. + + Parameters + ---------- + shape : tuple[int, int] + Number of rows and columns of the grid. + span_pairs : list[tuple[slice, slice]] + List of rowspans and colspans occupied by each subplot. + subplot_list : list of subplots + List of subplots that will be used to calculate optimal subplot_params. + pad : float + Padding between the figure edge and the edges of subplots, as a + fraction of the font size. + h_pad, w_pad : float + Padding (height/width) between edges of adjacent subplots, as a + fraction of the font size. Defaults to *pad*. + rect : tuple + (left, bottom, right, top), default: None. + """ + rows, cols = shape + + font_size_inch = (FontProperties( + size=mpl.rcParams["font.size"]).get_size_in_points() / 72) + pad_inch = pad * font_size_inch + vpad_inch = h_pad * font_size_inch if h_pad is not None else pad_inch + hpad_inch = w_pad * font_size_inch if w_pad is not None else pad_inch + + if len(span_pairs) != len(subplot_list) or len(subplot_list) == 0: + raise ValueError + + if rect is None: + margin_left = margin_bottom = margin_right = margin_top = None + else: + margin_left, margin_bottom, _right, _top = rect + margin_right = 1 - _right if _right else None + margin_top = 1 - _top if _top else None + + vspaces = np.zeros((rows + 1, cols)) + hspaces = np.zeros((rows, cols + 1)) + + if ax_bbox_list is None: + ax_bbox_list = [ + Bbox.union([ax.get_position(original=True) for ax in subplots]) + for subplots in subplot_list] + + for subplots, ax_bbox, (rowspan, colspan) in zip( + subplot_list, ax_bbox_list, span_pairs): + if all(not ax.get_visible() for ax in subplots): + continue + + bb = [] + for ax in subplots: + if ax.get_visible(): + bb += [martist._get_tightbbox_for_layout_only(ax, renderer)] + + tight_bbox_raw = Bbox.union(bb) + tight_bbox = fig.transFigure.inverted().transform_bbox(tight_bbox_raw) + + hspaces[rowspan, colspan.start] += ax_bbox.xmin - tight_bbox.xmin # l + hspaces[rowspan, colspan.stop] += tight_bbox.xmax - ax_bbox.xmax # r + vspaces[rowspan.start, colspan] += tight_bbox.ymax - ax_bbox.ymax # t + vspaces[rowspan.stop, colspan] += ax_bbox.ymin - tight_bbox.ymin # b + + fig_width_inch, fig_height_inch = fig.get_size_inches() + + # margins can be negative for Axes with aspect applied, so use max(, 0) to + # make them nonnegative. + if not margin_left: + margin_left = max(hspaces[:, 0].max(), 0) + pad_inch/fig_width_inch + suplabel = fig._supylabel + if suplabel and suplabel.get_in_layout(): + rel_width = fig.transFigure.inverted().transform_bbox( + suplabel.get_window_extent(renderer)).width + margin_left += rel_width + pad_inch/fig_width_inch + if not margin_right: + margin_right = max(hspaces[:, -1].max(), 0) + pad_inch/fig_width_inch + if not margin_top: + margin_top = max(vspaces[0, :].max(), 0) + pad_inch/fig_height_inch + if fig._suptitle and fig._suptitle.get_in_layout(): + rel_height = fig.transFigure.inverted().transform_bbox( + fig._suptitle.get_window_extent(renderer)).height + margin_top += rel_height + pad_inch/fig_height_inch + if not margin_bottom: + margin_bottom = max(vspaces[-1, :].max(), 0) + pad_inch/fig_height_inch + suplabel = fig._supxlabel + if suplabel and suplabel.get_in_layout(): + rel_height = fig.transFigure.inverted().transform_bbox( + suplabel.get_window_extent(renderer)).height + margin_bottom += rel_height + pad_inch/fig_height_inch + + if margin_left + margin_right >= 1: + _api.warn_external('Tight layout not applied. The left and right ' + 'margins cannot be made large enough to ' + 'accommodate all Axes decorations.') + return None + if margin_bottom + margin_top >= 1: + _api.warn_external('Tight layout not applied. The bottom and top ' + 'margins cannot be made large enough to ' + 'accommodate all Axes decorations.') + return None + + kwargs = dict(left=margin_left, + right=1 - margin_right, + bottom=margin_bottom, + top=1 - margin_top) + + if cols > 1: + hspace = hspaces[:, 1:-1].max() + hpad_inch / fig_width_inch + # axes widths: + h_axes = (1 - margin_right - margin_left - hspace * (cols - 1)) / cols + if h_axes < 0: + _api.warn_external('Tight layout not applied. tight_layout ' + 'cannot make Axes width small enough to ' + 'accommodate all Axes decorations') + return None + else: + kwargs["wspace"] = hspace / h_axes + if rows > 1: + vspace = vspaces[1:-1, :].max() + vpad_inch / fig_height_inch + v_axes = (1 - margin_top - margin_bottom - vspace * (rows - 1)) / rows + if v_axes < 0: + _api.warn_external('Tight layout not applied. tight_layout ' + 'cannot make Axes height small enough to ' + 'accommodate all Axes decorations.') + return None + else: + kwargs["hspace"] = vspace / v_axes + + return kwargs + + +def get_subplotspec_list(axes_list, grid_spec=None): + """ + Return a list of subplotspec from the given list of Axes. + + For an instance of Axes that does not support subplotspec, None is inserted + in the list. + + If grid_spec is given, None is inserted for those not from the given + grid_spec. + """ + subplotspec_list = [] + for ax in axes_list: + axes_or_locator = ax.get_axes_locator() + if axes_or_locator is None: + axes_or_locator = ax + + if hasattr(axes_or_locator, "get_subplotspec"): + subplotspec = axes_or_locator.get_subplotspec() + if subplotspec is not None: + subplotspec = subplotspec.get_topmost_subplotspec() + gs = subplotspec.get_gridspec() + if grid_spec is not None: + if gs != grid_spec: + subplotspec = None + elif gs.locally_modified_subplot_params(): + subplotspec = None + else: + subplotspec = None + + subplotspec_list.append(subplotspec) + + return subplotspec_list + + +def get_tight_layout_figure(fig, axes_list, subplotspec_list, renderer, + pad=1.08, h_pad=None, w_pad=None, rect=None): + """ + Return subplot parameters for tight-layouted-figure with specified padding. + + Parameters + ---------- + fig : Figure + axes_list : list of Axes + subplotspec_list : list of `.SubplotSpec` + The subplotspecs of each Axes. + renderer : renderer + pad : float + Padding between the figure edge and the edges of subplots, as a + fraction of the font size. + h_pad, w_pad : float + Padding (height/width) between edges of adjacent subplots. Defaults to + *pad*. + rect : tuple (left, bottom, right, top), default: None. + rectangle in normalized figure coordinates + that the whole subplots area (including labels) will fit into. + Defaults to using the entire figure. + + Returns + ------- + subplotspec or None + subplotspec kwargs to be passed to `.Figure.subplots_adjust` or + None if tight_layout could not be accomplished. + """ + + # Multiple Axes can share same subplotspec (e.g., if using axes_grid1); + # we need to group them together. + ss_to_subplots = {ss: [] for ss in subplotspec_list} + for ax, ss in zip(axes_list, subplotspec_list): + ss_to_subplots[ss].append(ax) + if ss_to_subplots.pop(None, None): + _api.warn_external( + "This figure includes Axes that are not compatible with " + "tight_layout, so results might be incorrect.") + if not ss_to_subplots: + return {} + subplot_list = list(ss_to_subplots.values()) + ax_bbox_list = [ss.get_position(fig) for ss in ss_to_subplots] + + max_nrows = max(ss.get_gridspec().nrows for ss in ss_to_subplots) + max_ncols = max(ss.get_gridspec().ncols for ss in ss_to_subplots) + + span_pairs = [] + for ss in ss_to_subplots: + # The intent here is to support Axes from different gridspecs where + # one's nrows (or ncols) is a multiple of the other (e.g. 2 and 4), + # but this doesn't actually work because the computed wspace, in + # relative-axes-height, corresponds to different physical spacings for + # the 2-row grid and the 4-row grid. Still, this code is left, mostly + # for backcompat. + rows, cols = ss.get_gridspec().get_geometry() + div_row, mod_row = divmod(max_nrows, rows) + div_col, mod_col = divmod(max_ncols, cols) + if mod_row != 0: + _api.warn_external('tight_layout not applied: number of rows ' + 'in subplot specifications must be ' + 'multiples of one another.') + return {} + if mod_col != 0: + _api.warn_external('tight_layout not applied: number of ' + 'columns in subplot specifications must be ' + 'multiples of one another.') + return {} + span_pairs.append(( + slice(ss.rowspan.start * div_row, ss.rowspan.stop * div_row), + slice(ss.colspan.start * div_col, ss.colspan.stop * div_col))) + + kwargs = _auto_adjust_subplotpars(fig, renderer, + shape=(max_nrows, max_ncols), + span_pairs=span_pairs, + subplot_list=subplot_list, + ax_bbox_list=ax_bbox_list, + pad=pad, h_pad=h_pad, w_pad=w_pad) + + # kwargs can be none if tight_layout fails... + if rect is not None and kwargs is not None: + # if rect is given, the whole subplots area (including + # labels) will fit into the rect instead of the + # figure. Note that the rect argument of + # *auto_adjust_subplotpars* specify the area that will be + # covered by the total area of axes.bbox. Thus we call + # auto_adjust_subplotpars twice, where the second run + # with adjusted rect parameters. + + left, bottom, right, top = rect + if left is not None: + left += kwargs["left"] + if bottom is not None: + bottom += kwargs["bottom"] + if right is not None: + right -= (1 - kwargs["right"]) + if top is not None: + top -= (1 - kwargs["top"]) + + kwargs = _auto_adjust_subplotpars(fig, renderer, + shape=(max_nrows, max_ncols), + span_pairs=span_pairs, + subplot_list=subplot_list, + ax_bbox_list=ax_bbox_list, + pad=pad, h_pad=h_pad, w_pad=w_pad, + rect=(left, bottom, right, top)) + + return kwargs diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_tri.pyi b/.venv/lib/python3.12/site-packages/matplotlib/_tri.pyi new file mode 100644 index 0000000000000000000000000000000000000000..a0c710fc2309fbfac3f37297eda919d86de32c76 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_tri.pyi @@ -0,0 +1,36 @@ +# This is a private module implemented in C++ +from typing import final + +import numpy as np +import numpy.typing as npt + +@final +class TrapezoidMapTriFinder: + def __init__(self, triangulation: Triangulation): ... + def find_many(self, x: npt.NDArray[np.float64], y: npt.NDArray[np.float64]) -> npt.NDArray[np.int_]: ... + def get_tree_stats(self) -> list[int | float]: ... + def initialize(self) -> None: ... + def print_tree(self) -> None: ... + +@final +class TriContourGenerator: + def __init__(self, triangulation: Triangulation, z: npt.NDArray[np.float64]): ... + def create_contour(self, level: float) -> tuple[list[float], list[int]]: ... + def create_filled_contour(self, lower_level: float, upper_level: float) -> tuple[list[float], list[int]]: ... + +@final +class Triangulation: + def __init__( + self, + x: npt.NDArray[np.float64], + y: npt.NDArray[np.float64], + triangles: npt.NDArray[np.int_], + mask: npt.NDArray[np.bool_] | tuple[()], + edges: npt.NDArray[np.int_] | tuple[()], + neighbors: npt.NDArray[np.int_] | tuple[()], + correct_triangle_orientation: bool, + ): ... + def calculate_plane_coefficients(self, z: npt.ArrayLike) -> npt.NDArray[np.float64]: ... + def get_edges(self) -> npt.NDArray[np.int_]: ... + def get_neighbors(self) -> npt.NDArray[np.int_]: ... + def set_mask(self, mask: npt.NDArray[np.bool_] | tuple[()]) -> None: ... diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_type1font.py b/.venv/lib/python3.12/site-packages/matplotlib/_type1font.py new file mode 100644 index 0000000000000000000000000000000000000000..b3e08f52c035e65837e489bf79da60d28e3ac0a8 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_type1font.py @@ -0,0 +1,879 @@ +""" +A class representing a Type 1 font. + +This version reads pfa and pfb files and splits them for embedding in +pdf files. It also supports SlantFont and ExtendFont transformations, +similarly to pdfTeX and friends. There is no support yet for subsetting. + +Usage:: + + font = Type1Font(filename) + clear_part, encrypted_part, finale = font.parts + slanted_font = font.transform({'slant': 0.167}) + extended_font = font.transform({'extend': 1.2}) + +Sources: + +* Adobe Technical Note #5040, Supporting Downloadable PostScript + Language Fonts. + +* Adobe Type 1 Font Format, Adobe Systems Incorporated, third printing, + v1.1, 1993. ISBN 0-201-57044-0. +""" + +from __future__ import annotations + +import binascii +import functools +import logging +import re +import string +import struct +import typing as T + +import numpy as np + +from matplotlib.cbook import _format_approx +from . import _api + +_log = logging.getLogger(__name__) + + +class _Token: + """ + A token in a PostScript stream. + + Attributes + ---------- + pos : int + Position, i.e. offset from the beginning of the data. + raw : str + Raw text of the token. + kind : str + Description of the token (for debugging or testing). + """ + __slots__ = ('pos', 'raw') + kind = '?' + + def __init__(self, pos, raw): + _log.debug('type1font._Token %s at %d: %r', self.kind, pos, raw) + self.pos = pos + self.raw = raw + + def __str__(self): + return f"<{self.kind} {self.raw} @{self.pos}>" + + def endpos(self): + """Position one past the end of the token""" + return self.pos + len(self.raw) + + def is_keyword(self, *names): + """Is this a name token with one of the names?""" + return False + + def is_slash_name(self): + """Is this a name token that starts with a slash?""" + return False + + def is_delim(self): + """Is this a delimiter token?""" + return False + + def is_number(self): + """Is this a number token?""" + return False + + def value(self): + return self.raw + + +class _NameToken(_Token): + kind = 'name' + + def is_slash_name(self): + return self.raw.startswith('/') + + def value(self): + return self.raw[1:] + + +class _BooleanToken(_Token): + kind = 'boolean' + + def value(self): + return self.raw == 'true' + + +class _KeywordToken(_Token): + kind = 'keyword' + + def is_keyword(self, *names): + return self.raw in names + + +class _DelimiterToken(_Token): + kind = 'delimiter' + + def is_delim(self): + return True + + def opposite(self): + return {'[': ']', ']': '[', + '{': '}', '}': '{', + '<<': '>>', '>>': '<<' + }[self.raw] + + +class _WhitespaceToken(_Token): + kind = 'whitespace' + + +class _StringToken(_Token): + kind = 'string' + _escapes_re = re.compile(r'\\([\\()nrtbf]|[0-7]{1,3})') + _replacements = {'\\': '\\', '(': '(', ')': ')', 'n': '\n', + 'r': '\r', 't': '\t', 'b': '\b', 'f': '\f'} + _ws_re = re.compile('[\0\t\r\f\n ]') + + @classmethod + def _escape(cls, match): + group = match.group(1) + try: + return cls._replacements[group] + except KeyError: + return chr(int(group, 8)) + + @functools.lru_cache + def value(self): + if self.raw[0] == '(': + return self._escapes_re.sub(self._escape, self.raw[1:-1]) + else: + data = self._ws_re.sub('', self.raw[1:-1]) + if len(data) % 2 == 1: + data += '0' + return binascii.unhexlify(data) + + +class _BinaryToken(_Token): + kind = 'binary' + + def value(self): + return self.raw[1:] + + +class _NumberToken(_Token): + kind = 'number' + + def is_number(self): + return True + + def value(self): + if '.' not in self.raw: + return int(self.raw) + else: + return float(self.raw) + + +def _tokenize(data: bytes, skip_ws: bool) -> T.Generator[_Token, int, None]: + """ + A generator that produces _Token instances from Type-1 font code. + + The consumer of the generator may send an integer to the tokenizer to + indicate that the next token should be _BinaryToken of the given length. + + Parameters + ---------- + data : bytes + The data of the font to tokenize. + + skip_ws : bool + If true, the generator will drop any _WhitespaceTokens from the output. + """ + + text = data.decode('ascii', 'replace') + whitespace_or_comment_re = re.compile(r'[\0\t\r\f\n ]+|%[^\r\n]*') + token_re = re.compile(r'/{0,2}[^]\0\t\r\f\n ()<>{}/%[]+') + instring_re = re.compile(r'[()\\]') + hex_re = re.compile(r'^<[0-9a-fA-F\0\t\r\f\n ]*>$') + oct_re = re.compile(r'[0-7]{1,3}') + pos = 0 + next_binary: int | None = None + + while pos < len(text): + if next_binary is not None: + n = next_binary + next_binary = (yield _BinaryToken(pos, data[pos:pos+n])) + pos += n + continue + match = whitespace_or_comment_re.match(text, pos) + if match: + if not skip_ws: + next_binary = (yield _WhitespaceToken(pos, match.group())) + pos = match.end() + elif text[pos] == '(': + # PostScript string rules: + # - parentheses must be balanced + # - backslashes escape backslashes and parens + # - also codes \n\r\t\b\f and octal escapes are recognized + # - other backslashes do not escape anything + start = pos + pos += 1 + depth = 1 + while depth: + match = instring_re.search(text, pos) + if match is None: + raise ValueError( + f'Unterminated string starting at {start}') + pos = match.end() + if match.group() == '(': + depth += 1 + elif match.group() == ')': + depth -= 1 + else: # a backslash + char = text[pos] + if char in r'\()nrtbf': + pos += 1 + else: + octal = oct_re.match(text, pos) + if octal: + pos = octal.end() + else: + pass # non-escaping backslash + next_binary = (yield _StringToken(start, text[start:pos])) + elif text[pos:pos + 2] in ('<<', '>>'): + next_binary = (yield _DelimiterToken(pos, text[pos:pos + 2])) + pos += 2 + elif text[pos] == '<': + start = pos + try: + pos = text.index('>', pos) + 1 + except ValueError as e: + raise ValueError(f'Unterminated hex string starting at {start}' + ) from e + if not hex_re.match(text[start:pos]): + raise ValueError(f'Malformed hex string starting at {start}') + next_binary = (yield _StringToken(pos, text[start:pos])) + else: + match = token_re.match(text, pos) + if match: + raw = match.group() + if raw.startswith('/'): + next_binary = (yield _NameToken(pos, raw)) + elif match.group() in ('true', 'false'): + next_binary = (yield _BooleanToken(pos, raw)) + else: + try: + float(raw) + next_binary = (yield _NumberToken(pos, raw)) + except ValueError: + next_binary = (yield _KeywordToken(pos, raw)) + pos = match.end() + else: + next_binary = (yield _DelimiterToken(pos, text[pos])) + pos += 1 + + +class _BalancedExpression(_Token): + pass + + +def _expression(initial, tokens, data): + """ + Consume some number of tokens and return a balanced PostScript expression. + + Parameters + ---------- + initial : _Token + The token that triggered parsing a balanced expression. + tokens : iterator of _Token + Following tokens. + data : bytes + Underlying data that the token positions point to. + + Returns + ------- + _BalancedExpression + """ + delim_stack = [] + token = initial + while True: + if token.is_delim(): + if token.raw in ('[', '{'): + delim_stack.append(token) + elif token.raw in (']', '}'): + if not delim_stack: + raise RuntimeError(f"unmatched closing token {token}") + match = delim_stack.pop() + if match.raw != token.opposite(): + raise RuntimeError( + f"opening token {match} closed by {token}" + ) + if not delim_stack: + break + else: + raise RuntimeError(f'unknown delimiter {token}') + elif not delim_stack: + break + token = next(tokens) + return _BalancedExpression( + initial.pos, + data[initial.pos:token.endpos()].decode('ascii', 'replace') + ) + + +class Type1Font: + """ + A class representing a Type-1 font, for use by backends. + + Attributes + ---------- + parts : tuple + A 3-tuple of the cleartext part, the encrypted part, and the finale of + zeros. + + decrypted : bytes + The decrypted form of ``parts[1]``. + + prop : dict[str, Any] + A dictionary of font properties. Noteworthy keys include: + + - FontName: PostScript name of the font + - Encoding: dict from numeric codes to glyph names + - FontMatrix: bytes object encoding a matrix + - UniqueID: optional font identifier, dropped when modifying the font + - CharStrings: dict from glyph names to byte code + - Subrs: array of byte code subroutines + - OtherSubrs: bytes object encoding some PostScript code + """ + __slots__ = ('parts', 'decrypted', 'prop', '_pos', '_abbr') + # the _pos dict contains (begin, end) indices to parts[0] + decrypted + # so that they can be replaced when transforming the font; + # but since sometimes a definition appears in both parts[0] and decrypted, + # _pos[name] is an array of such pairs + # + # _abbr maps three standard abbreviations to their particular names in + # this font (e.g. 'RD' is named '-|' in some fonts) + + def __init__(self, input): + """ + Initialize a Type-1 font. + + Parameters + ---------- + input : str or 3-tuple + Either a pfb file name, or a 3-tuple of already-decoded Type-1 + font `~.Type1Font.parts`. + """ + if isinstance(input, tuple) and len(input) == 3: + self.parts = input + else: + with open(input, 'rb') as file: + data = self._read(file) + self.parts = self._split(data) + + self.decrypted = self._decrypt(self.parts[1], 'eexec') + self._abbr = {'RD': 'RD', 'ND': 'ND', 'NP': 'NP'} + self._parse() + + def _read(self, file): + """Read the font from a file, decoding into usable parts.""" + rawdata = file.read() + if not rawdata.startswith(b'\x80'): + return rawdata + + data = b'' + while rawdata: + if not rawdata.startswith(b'\x80'): + raise RuntimeError('Broken pfb file (expected byte 128, ' + 'got %d)' % rawdata[0]) + type = rawdata[1] + if type in (1, 2): + length, = struct.unpack('> 8)) + key = ((key+byte) * 52845 + 22719) & 0xffff + + return bytes(plaintext[ndiscard:]) + + @staticmethod + def _encrypt(plaintext, key, ndiscard=4): + """ + Encrypt plaintext using the Type-1 font algorithm. + + The algorithm is described in Adobe's "Adobe Type 1 Font Format". + The key argument can be an integer, or one of the strings + 'eexec' and 'charstring', which map to the key specified for the + corresponding part of Type-1 fonts. + + The ndiscard argument should be an integer, usually 4. That + number of bytes is prepended to the plaintext before encryption. + This function prepends NUL bytes for reproducibility, even though + the original algorithm uses random bytes, presumably to avoid + cryptanalysis. + """ + + key = _api.check_getitem({'eexec': 55665, 'charstring': 4330}, key=key) + ciphertext = [] + for byte in b'\0' * ndiscard + plaintext: + c = byte ^ (key >> 8) + ciphertext.append(c) + key = ((key + c) * 52845 + 22719) & 0xffff + + return bytes(ciphertext) + + def _parse(self): + """ + Find the values of various font properties. This limited kind + of parsing is described in Chapter 10 "Adobe Type Manager + Compatibility" of the Type-1 spec. + """ + # Start with reasonable defaults + prop = {'Weight': 'Regular', 'ItalicAngle': 0.0, 'isFixedPitch': False, + 'UnderlinePosition': -100, 'UnderlineThickness': 50} + pos = {} + data = self.parts[0] + self.decrypted + + source = _tokenize(data, True) + while True: + # See if there is a key to be assigned a value + # e.g. /FontName in /FontName /Helvetica def + try: + token = next(source) + except StopIteration: + break + if token.is_delim(): + # skip over this - we want top-level keys only + _expression(token, source, data) + if token.is_slash_name(): + key = token.value() + keypos = token.pos + else: + continue + + # Some values need special parsing + if key in ('Subrs', 'CharStrings', 'Encoding', 'OtherSubrs'): + prop[key], endpos = { + 'Subrs': self._parse_subrs, + 'CharStrings': self._parse_charstrings, + 'Encoding': self._parse_encoding, + 'OtherSubrs': self._parse_othersubrs + }[key](source, data) + pos.setdefault(key, []).append((keypos, endpos)) + continue + + try: + token = next(source) + except StopIteration: + break + + if isinstance(token, _KeywordToken): + # constructs like + # FontDirectory /Helvetica known {...} {...} ifelse + # mean the key was not really a key + continue + + if token.is_delim(): + value = _expression(token, source, data).raw + else: + value = token.value() + + # look for a 'def' possibly preceded by access modifiers + try: + kw = next( + kw for kw in source + if not kw.is_keyword('readonly', 'noaccess', 'executeonly') + ) + except StopIteration: + break + + # sometimes noaccess def and readonly def are abbreviated + if kw.is_keyword('def', self._abbr['ND'], self._abbr['NP']): + prop[key] = value + pos.setdefault(key, []).append((keypos, kw.endpos())) + + # detect the standard abbreviations + if value == '{noaccess def}': + self._abbr['ND'] = key + elif value == '{noaccess put}': + self._abbr['NP'] = key + elif value == '{string currentfile exch readstring pop}': + self._abbr['RD'] = key + + # Fill in the various *Name properties + if 'FontName' not in prop: + prop['FontName'] = (prop.get('FullName') or + prop.get('FamilyName') or + 'Unknown') + if 'FullName' not in prop: + prop['FullName'] = prop['FontName'] + if 'FamilyName' not in prop: + extras = ('(?i)([ -](regular|plain|italic|oblique|(semi)?bold|' + '(ultra)?light|extra|condensed))+$') + prop['FamilyName'] = re.sub(extras, '', prop['FullName']) + # Decrypt the encrypted parts + ndiscard = prop.get('lenIV', 4) + cs = prop['CharStrings'] + for key, value in cs.items(): + cs[key] = self._decrypt(value, 'charstring', ndiscard) + if 'Subrs' in prop: + prop['Subrs'] = [ + self._decrypt(value, 'charstring', ndiscard) + for value in prop['Subrs'] + ] + + self.prop = prop + self._pos = pos + + def _parse_subrs(self, tokens, _data): + count_token = next(tokens) + if not count_token.is_number(): + raise RuntimeError( + f"Token following /Subrs must be a number, was {count_token}" + ) + count = count_token.value() + array = [None] * count + next(t for t in tokens if t.is_keyword('array')) + for _ in range(count): + next(t for t in tokens if t.is_keyword('dup')) + index_token = next(tokens) + if not index_token.is_number(): + raise RuntimeError( + "Token following dup in Subrs definition must be a " + f"number, was {index_token}" + ) + nbytes_token = next(tokens) + if not nbytes_token.is_number(): + raise RuntimeError( + "Second token following dup in Subrs definition must " + f"be a number, was {nbytes_token}" + ) + token = next(tokens) + if not token.is_keyword(self._abbr['RD']): + raise RuntimeError( + f"Token preceding subr must be {self._abbr['RD']}, " + f"was {token}" + ) + binary_token = tokens.send(1+nbytes_token.value()) + array[index_token.value()] = binary_token.value() + + return array, next(tokens).endpos() + + @staticmethod + def _parse_charstrings(tokens, _data): + count_token = next(tokens) + if not count_token.is_number(): + raise RuntimeError( + "Token following /CharStrings must be a number, " + f"was {count_token}" + ) + count = count_token.value() + charstrings = {} + next(t for t in tokens if t.is_keyword('begin')) + while True: + token = next(t for t in tokens + if t.is_keyword('end') or t.is_slash_name()) + if token.raw == 'end': + return charstrings, token.endpos() + glyphname = token.value() + nbytes_token = next(tokens) + if not nbytes_token.is_number(): + raise RuntimeError( + f"Token following /{glyphname} in CharStrings definition " + f"must be a number, was {nbytes_token}" + ) + next(tokens) # usually RD or |- + binary_token = tokens.send(1+nbytes_token.value()) + charstrings[glyphname] = binary_token.value() + + @staticmethod + def _parse_encoding(tokens, _data): + # this only works for encodings that follow the Adobe manual + # but some old fonts include non-compliant data - we log a warning + # and return a possibly incomplete encoding + encoding = {} + while True: + token = next(t for t in tokens + if t.is_keyword('StandardEncoding', 'dup', 'def')) + if token.is_keyword('StandardEncoding'): + return _StandardEncoding, token.endpos() + if token.is_keyword('def'): + return encoding, token.endpos() + index_token = next(tokens) + if not index_token.is_number(): + _log.warning( + f"Parsing encoding: expected number, got {index_token}" + ) + continue + name_token = next(tokens) + if not name_token.is_slash_name(): + _log.warning( + f"Parsing encoding: expected slash-name, got {name_token}" + ) + continue + encoding[index_token.value()] = name_token.value() + + @staticmethod + def _parse_othersubrs(tokens, data): + init_pos = None + while True: + token = next(tokens) + if init_pos is None: + init_pos = token.pos + if token.is_delim(): + _expression(token, tokens, data) + elif token.is_keyword('def', 'ND', '|-'): + return data[init_pos:token.endpos()], token.endpos() + + def transform(self, effects): + """ + Return a new font that is slanted and/or extended. + + Parameters + ---------- + effects : dict + A dict with optional entries: + + - 'slant' : float, default: 0 + Tangent of the angle that the font is to be slanted to the + right. Negative values slant to the left. + - 'extend' : float, default: 1 + Scaling factor for the font width. Values less than 1 condense + the glyphs. + + Returns + ------- + `Type1Font` + """ + fontname = self.prop['FontName'] + italicangle = self.prop['ItalicAngle'] + + array = [ + float(x) for x in (self.prop['FontMatrix'] + .lstrip('[').rstrip(']').split()) + ] + oldmatrix = np.eye(3, 3) + oldmatrix[0:3, 0] = array[::2] + oldmatrix[0:3, 1] = array[1::2] + modifier = np.eye(3, 3) + + if 'slant' in effects: + slant = effects['slant'] + fontname += f'_Slant_{int(1000 * slant)}' + italicangle = round( + float(italicangle) - np.arctan(slant) / np.pi * 180, + 5 + ) + modifier[1, 0] = slant + + if 'extend' in effects: + extend = effects['extend'] + fontname += f'_Extend_{int(1000 * extend)}' + modifier[0, 0] = extend + + newmatrix = np.dot(modifier, oldmatrix) + array[::2] = newmatrix[0:3, 0] + array[1::2] = newmatrix[0:3, 1] + fontmatrix = ( + f"[{' '.join(_format_approx(x, 6) for x in array)}]" + ) + replacements = ( + [(x, f'/FontName/{fontname} def') + for x in self._pos['FontName']] + + [(x, f'/ItalicAngle {italicangle} def') + for x in self._pos['ItalicAngle']] + + [(x, f'/FontMatrix {fontmatrix} readonly def') + for x in self._pos['FontMatrix']] + + [(x, '') for x in self._pos.get('UniqueID', [])] + ) + + data = bytearray(self.parts[0]) + data.extend(self.decrypted) + len0 = len(self.parts[0]) + for (pos0, pos1), value in sorted(replacements, reverse=True): + data[pos0:pos1] = value.encode('ascii', 'replace') + if pos0 < len(self.parts[0]): + if pos1 >= len(self.parts[0]): + raise RuntimeError( + f"text to be replaced with {value} spans " + "the eexec boundary" + ) + len0 += len(value) - pos1 + pos0 + + data = bytes(data) + return Type1Font(( + data[:len0], + self._encrypt(data[len0:], 'eexec'), + self.parts[2] + )) + + +_StandardEncoding = { + **{ord(letter): letter for letter in string.ascii_letters}, + 0: '.notdef', + 32: 'space', + 33: 'exclam', + 34: 'quotedbl', + 35: 'numbersign', + 36: 'dollar', + 37: 'percent', + 38: 'ampersand', + 39: 'quoteright', + 40: 'parenleft', + 41: 'parenright', + 42: 'asterisk', + 43: 'plus', + 44: 'comma', + 45: 'hyphen', + 46: 'period', + 47: 'slash', + 48: 'zero', + 49: 'one', + 50: 'two', + 51: 'three', + 52: 'four', + 53: 'five', + 54: 'six', + 55: 'seven', + 56: 'eight', + 57: 'nine', + 58: 'colon', + 59: 'semicolon', + 60: 'less', + 61: 'equal', + 62: 'greater', + 63: 'question', + 64: 'at', + 91: 'bracketleft', + 92: 'backslash', + 93: 'bracketright', + 94: 'asciicircum', + 95: 'underscore', + 96: 'quoteleft', + 123: 'braceleft', + 124: 'bar', + 125: 'braceright', + 126: 'asciitilde', + 161: 'exclamdown', + 162: 'cent', + 163: 'sterling', + 164: 'fraction', + 165: 'yen', + 166: 'florin', + 167: 'section', + 168: 'currency', + 169: 'quotesingle', + 170: 'quotedblleft', + 171: 'guillemotleft', + 172: 'guilsinglleft', + 173: 'guilsinglright', + 174: 'fi', + 175: 'fl', + 177: 'endash', + 178: 'dagger', + 179: 'daggerdbl', + 180: 'periodcentered', + 182: 'paragraph', + 183: 'bullet', + 184: 'quotesinglbase', + 185: 'quotedblbase', + 186: 'quotedblright', + 187: 'guillemotright', + 188: 'ellipsis', + 189: 'perthousand', + 191: 'questiondown', + 193: 'grave', + 194: 'acute', + 195: 'circumflex', + 196: 'tilde', + 197: 'macron', + 198: 'breve', + 199: 'dotaccent', + 200: 'dieresis', + 202: 'ring', + 203: 'cedilla', + 205: 'hungarumlaut', + 206: 'ogonek', + 207: 'caron', + 208: 'emdash', + 225: 'AE', + 227: 'ordfeminine', + 232: 'Lslash', + 233: 'Oslash', + 234: 'OE', + 235: 'ordmasculine', + 241: 'ae', + 245: 'dotlessi', + 248: 'lslash', + 249: 'oslash', + 250: 'oe', + 251: 'germandbls', +} diff --git a/.venv/lib/python3.12/site-packages/matplotlib/_version.py b/.venv/lib/python3.12/site-packages/matplotlib/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..45a224751d77b73a7aee86ffe5f0fd9b9e40e6d5 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/_version.py @@ -0,0 +1 @@ +version = "3.10.8" diff --git a/.venv/lib/python3.12/site-packages/matplotlib/animation.py b/.venv/lib/python3.12/site-packages/matplotlib/animation.py new file mode 100644 index 0000000000000000000000000000000000000000..a87f002011249f353bd740363e55a0d32c62bc38 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/matplotlib/animation.py @@ -0,0 +1,1823 @@ +import abc +import base64 +import contextlib +from io import BytesIO, TextIOWrapper +import itertools +import logging +from pathlib import Path +import shutil +import subprocess +import sys +from tempfile import TemporaryDirectory +import uuid +import warnings + +import numpy as np +from PIL import Image + +import matplotlib as mpl +from matplotlib._animation_data import ( + DISPLAY_TEMPLATE, INCLUDED_FRAMES, JS_INCLUDE, STYLE_INCLUDE) +from matplotlib import _api, cbook +import matplotlib.colors as mcolors + +_log = logging.getLogger(__name__) + +# Process creation flag for subprocess to prevent it raising a terminal +# window. See for example https://stackoverflow.com/q/24130623/ +subprocess_creation_flags = ( + subprocess.CREATE_NO_WINDOW if sys.platform == 'win32' else 0) + + +def adjusted_figsize(w, h, dpi, n): + """ + Compute figure size so that pixels are a multiple of n. + + Parameters + ---------- + w, h : float + Size in inches. + + dpi : float + The dpi. + + n : int + The target multiple. + + Returns + ------- + wnew, hnew : float + The new figure size in inches. + """ + + # this maybe simplified if / when we adopt consistent rounding for + # pixel size across the whole library + def correct_roundoff(x, dpi, n): + if int(x*dpi) % n != 0: + if int(np.nextafter(x, np.inf)*dpi) % n == 0: + x = np.nextafter(x, np.inf) + elif int(np.nextafter(x, -np.inf)*dpi) % n == 0: + x = np.nextafter(x, -np.inf) + return x + + wnew = int(w * dpi / n) * n / dpi + hnew = int(h * dpi / n) * n / dpi + return correct_roundoff(wnew, dpi, n), correct_roundoff(hnew, dpi, n) + + +class MovieWriterRegistry: + """Registry of available writer classes by human readable name.""" + + def __init__(self): + self._registered = dict() + + def register(self, name): + """ + Decorator for registering a class under a name. + + Example use:: + + @registry.register(name) + class Foo: + pass + """ + def wrapper(writer_cls): + self._registered[name] = writer_cls + return writer_cls + return wrapper + + def is_available(self, name): + """ + Check if given writer is available by name. + + Parameters + ---------- + name : str + + Returns + ------- + bool + """ + try: + cls = self._registered[name] + except KeyError: + return False + return cls.isAvailable() + + def __iter__(self): + """Iterate over names of available writer class.""" + for name in self._registered: + if self.is_available(name): + yield name + + def list(self): + """Get a list of available MovieWriters.""" + return [*self] + + def __getitem__(self, name): + """Get an available writer class from its name.""" + if self.is_available(name): + return self._registered[name] + raise RuntimeError(f"Requested MovieWriter ({name}) not available") + + +writers = MovieWriterRegistry() + + +class AbstractMovieWriter(abc.ABC): + """ + Abstract base class for writing movies, providing a way to grab frames by + calling `~AbstractMovieWriter.grab_frame`. + + `setup` is called to start the process and `finish` is called afterwards. + `saving` is provided as a context manager to facilitate this process as :: + + with moviewriter.saving(fig, outfile='myfile.mp4', dpi=100): + # Iterate over frames + moviewriter.grab_frame(**savefig_kwargs) + + The use of the context manager ensures that `setup` and `finish` are + performed as necessary. + + An instance of a concrete subclass of this class can be given as the + ``writer`` argument of `Animation.save()`. + """ + + def __init__(self, fps=5, metadata=None, codec=None, bitrate=None): + self.fps = fps + self.metadata = metadata if metadata is not None else {} + self.codec = mpl._val_or_rc(codec, 'animation.codec') + self.bitrate = mpl._val_or_rc(bitrate, 'animation.bitrate') + + @abc.abstractmethod + def setup(self, fig, outfile, dpi=None): + """ + Setup for writing the movie file. + + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + The figure object that contains the information for frames. + outfile : str + The filename of the resulting movie file. + dpi : float, default: ``fig.dpi`` + The DPI (or resolution) for the file. This controls the size + in pixels of the resulting movie file. + """ + # Check that path is valid + Path(outfile).parent.resolve(strict=True) + self.outfile = outfile + self.fig = fig + if dpi is None: + dpi = self.fig.dpi + self.dpi = dpi + + @property + def frame_size(self): + """A tuple ``(width, height)`` in pixels of a movie frame.""" + w, h = self.fig.get_size_inches() + return int(w * self.dpi), int(h * self.dpi) + + def _supports_transparency(self): + """ + Whether this writer supports transparency. + + Writers may consult output file type and codec to determine this at runtime. + """ + return False + + @abc.abstractmethod + def grab_frame(self, **savefig_kwargs): + """ + Grab the image information from the figure and save as a movie frame. + + All keyword arguments in *savefig_kwargs* are passed on to the + `~.Figure.savefig` call that saves the figure. However, several + keyword arguments that are supported by `~.Figure.savefig` may not be + passed as they are controlled by the MovieWriter: + + - *dpi*, *bbox_inches*: These may not be passed because each frame of the + animation much be exactly the same size in pixels. + - *format*: This is controlled by the MovieWriter. + """ + + @abc.abstractmethod + def finish(self): + """Finish any processing for writing the movie.""" + + @contextlib.contextmanager + def saving(self, fig, outfile, dpi, *args, **kwargs): + """ + Context manager to facilitate writing the movie file. + + ``*args, **kw`` are any parameters that should be passed to `setup`. + """ + if mpl.rcParams['savefig.bbox'] == 'tight': + _log.info("Disabling savefig.bbox = 'tight', as it may cause " + "frame size to vary, which is inappropriate for " + "animation.") + + # This particular sequence is what contextlib.contextmanager wants + self.setup(fig, outfile, dpi, *args, **kwargs) + with mpl.rc_context({'savefig.bbox': None}): + try: + yield self + finally: + self.finish() + + +class MovieWriter(AbstractMovieWriter): + """ + Base class for writing movies. + + This is a base class for MovieWriter subclasses that write a movie frame + data to a pipe. You cannot instantiate this class directly. + See examples for how to use its subclasses. + + Attributes + ---------- + frame_format : str + The format used in writing frame data, defaults to 'rgba'. + fig : `~matplotlib.figure.Figure` + The figure to capture data from. + This must be provided by the subclasses. + """ + + # Builtin writer subclasses additionally define the _exec_key and _args_key + # attributes, which indicate the rcParams entries where the path to the + # executable and additional command-line arguments to the executable are + # stored. Third-party writers cannot meaningfully set these as they cannot + # extend rcParams with new keys. + + # Pipe-based writers only support RGBA, but file-based ones support more + # formats. + supported_formats = ["rgba"] + + def __init__(self, fps=5, codec=None, bitrate=None, extra_args=None, + metadata=None): + """ + Parameters + ---------- + fps : int, default: 5 + Movie frame rate (per second). + codec : str or None, default: :rc:`animation.codec` + The codec to use. + bitrate : int, default: :rc:`animation.bitrate` + The bitrate of the movie, in kilobits per second. Higher values + means higher quality movies, but increase the file size. A value + of -1 lets the underlying movie encoder select the bitrate. + extra_args : list of str or None, optional + Extra command-line arguments passed to the underlying movie encoder. These + arguments are passed last to the encoder, just before the filename. The + default, None, means to use :rc:`animation.[name-of-encoder]_args` for the + builtin writers. + metadata : dict[str, str], default: {} + A dictionary of keys and values for metadata to include in the + output file. Some keys that may be of use include: + title, artist, genre, subject, copyright, srcform, comment. + """ + if type(self) is MovieWriter: + # TODO MovieWriter is still an abstract class and needs to be + # extended with a mixin. This should be clearer in naming + # and description. For now, just give a reasonable error + # message to users. + raise TypeError( + 'MovieWriter cannot be instantiated directly. Please use one ' + 'of its subclasses.') + + super().__init__(fps=fps, metadata=metadata, codec=codec, + bitrate=bitrate) + self.frame_format = self.supported_formats[0] + self.extra_args = extra_args + + def _adjust_frame_size(self): + if self.codec == 'h264': + wo, ho = self.fig.get_size_inches() + w, h = adjusted_figsize(wo, ho, self.dpi, 2) + if (wo, ho) != (w, h): + self.fig.set_size_inches(w, h, forward=True) + _log.info('figure size in inches has been adjusted ' + 'from %s x %s to %s x %s', wo, ho, w, h) + else: + w, h = self.fig.get_size_inches() + _log.debug('frame size in pixels is %s x %s', *self.frame_size) + return w, h + + def setup(self, fig, outfile, dpi=None): + # docstring inherited + super().setup(fig, outfile, dpi=dpi) + self._w, self._h = self._adjust_frame_size() + # Run here so that grab_frame() can write the data to a pipe. This + # eliminates the need for temp files. + self._run() + + def _run(self): + # Uses subprocess to call the program for assembling frames into a + # movie file. *args* returns the sequence of command line arguments + # from a few configuration options. + command = self._args() + _log.info('MovieWriter._run: running command: %s', + cbook._pformat_subprocess(command)) + PIPE = subprocess.PIPE + self._proc = subprocess.Popen( + command, stdin=PIPE, stdout=PIPE, stderr=PIPE, + creationflags=subprocess_creation_flags) + + def finish(self): + """Finish any processing for writing the movie.""" + out, err = self._proc.communicate() + # Use the encoding/errors that universal_newlines would use. + out = TextIOWrapper(BytesIO(out)).read() + err = TextIOWrapper(BytesIO(err)).read() + if out: + _log.log( + logging.WARNING if self._proc.returncode else logging.DEBUG, + "MovieWriter stdout:\n%s", out) + if err: + _log.log( + logging.WARNING if self._proc.returncode else logging.DEBUG, + "MovieWriter stderr:\n%s", err) + if self._proc.returncode: + raise subprocess.CalledProcessError( + self._proc.returncode, self._proc.args, out, err) + + def grab_frame(self, **savefig_kwargs): + # docstring inherited + _validate_grabframe_kwargs(savefig_kwargs) + _log.debug('MovieWriter.grab_frame: Grabbing frame.') + # Readjust the figure size in case it has been changed by the user. + # All frames must have the same size to save the movie correctly. + self.fig.set_size_inches(self._w, self._h) + # Save the figure data to the sink, using the frame format and dpi. + self.fig.savefig(self._proc.stdin, format=self.frame_format, + dpi=self.dpi, **savefig_kwargs) + + def _args(self): + """Assemble list of encoder-specific command-line arguments.""" + return NotImplementedError("args needs to be implemented by subclass.") + + @classmethod + def bin_path(cls): + """ + Return the binary path to the commandline tool used by a specific + subclass. This is a class method so that the tool can be looked for + before making a particular MovieWriter subclass available. + """ + return str(mpl.rcParams[cls._exec_key]) + + @classmethod + def isAvailable(cls): + """Return whether a MovieWriter subclass is actually available.""" + return shutil.which(cls.bin_path()) is not None + + +class FileMovieWriter(MovieWriter): + """ + `MovieWriter` for writing to individual files and stitching at the end. + + This must be sub-classed to be useful. + """ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.frame_format = mpl.rcParams['animation.frame_format'] + + def setup(self, fig, outfile, dpi=None, frame_prefix=None): + """ + Setup for writing the movie file. + + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + The figure to grab the rendered frames from. + outfile : str + The filename of the resulting movie file. + dpi : float, default: ``fig.dpi`` + The dpi of the output file. This, with the figure size, + controls the size in pixels of the resulting movie file. + frame_prefix : str, optional + The filename prefix to use for temporary files. If *None* (the + default), files are written to a temporary directory which is + deleted by `finish`; if not *None*, no temporary files are + deleted. + """ + # Check that path is valid + Path(outfile).parent.resolve(strict=True) + self.fig = fig + self.outfile = outfile + if dpi is None: + dpi = self.fig.dpi + self.dpi = dpi + self._adjust_frame_size() + + if frame_prefix is None: + self._tmpdir = TemporaryDirectory() + self.temp_prefix = str(Path(self._tmpdir.name, 'tmp')) + else: + self._tmpdir = None + self.temp_prefix = frame_prefix + self._frame_counter = 0 # used for generating sequential file names + self._temp_paths = list() + self.fname_format_str = '%s%%07d.%s' + + def __del__(self): + if hasattr(self, '_tmpdir') and self._tmpdir: + self._tmpdir.cleanup() + + @property + def frame_format(self): + """ + Format (png, jpeg, etc.) to use for saving the frames, which can be + decided by the individual subclasses. + """ + return self._frame_format + + @frame_format.setter + def frame_format(self, frame_format): + if frame_format in self.supported_formats: + self._frame_format = frame_format + else: + _api.warn_external( + f"Ignoring file format {frame_format!r} which is not " + f"supported by {type(self).__name__}; using " + f"{self.supported_formats[0]} instead.") + self._frame_format = self.supported_formats[0] + + def _base_temp_name(self): + # Generates a template name (without number) given the frame format + # for extension and the prefix. + return self.fname_format_str % (self.temp_prefix, self.frame_format) + + def grab_frame(self, **savefig_kwargs): + # docstring inherited + # Creates a filename for saving using basename and counter. + _validate_grabframe_kwargs(savefig_kwargs) + path = Path(self._base_temp_name() % self._frame_counter) + self._temp_paths.append(path) # Record the filename for later use. + self._frame_counter += 1 # Ensures each created name is unique. + _log.debug('FileMovieWriter.grab_frame: Grabbing frame %d to path=%s', + self._frame_counter, path) + with open(path, 'wb') as sink: # Save figure to the sink. + self.fig.savefig(sink, format=self.frame_format, dpi=self.dpi, + **savefig_kwargs) + + def finish(self): + # Call run here now that all frame grabbing is done. All temp files + # are available to be assembled. + try: + self._run() + super().finish() + finally: + if self._tmpdir: + _log.debug( + 'MovieWriter: clearing temporary path=%s', self._tmpdir + ) + self._tmpdir.cleanup() + + +@writers.register('pillow') +class PillowWriter(AbstractMovieWriter): + def _supports_transparency(self): + return True + + @classmethod + def isAvailable(cls): + return True + + def setup(self, fig, outfile, dpi=None): + super().setup(fig, outfile, dpi=dpi) + self._frames = [] + + def grab_frame(self, **savefig_kwargs): + _validate_grabframe_kwargs(savefig_kwargs) + buf = BytesIO() + self.fig.savefig( + buf, **{**savefig_kwargs, "format": "rgba", "dpi": self.dpi}) + im = Image.frombuffer( + "RGBA", self.frame_size, buf.getbuffer(), "raw", "RGBA", 0, 1) + if im.getextrema()[3][0] < 255: + # This frame has transparency, so we'll just add it as is. + self._frames.append(im) + else: + # Without transparency, we switch to RGB mode, which converts to P mode a + # little better if needed (specifically, this helps with GIF output.) + self._frames.append(im.convert("RGB")) + + def finish(self): + self._frames[0].save( + self.outfile, save_all=True, append_images=self._frames[1:], + duration=int(1000 / self.fps), loop=0) + + +# Base class of ffmpeg information. Has the config keys and the common set +# of arguments that controls the *output* side of things. +class FFMpegBase: + """ + Mixin class for FFMpeg output. + + This is a base class for the concrete `FFMpegWriter` and `FFMpegFileWriter` + classes. + """ + + _exec_key = 'animation.ffmpeg_path' + _args_key = 'animation.ffmpeg_args' + + def _supports_transparency(self): + suffix = Path(self.outfile).suffix + if suffix in {'.apng', '.avif', '.gif', '.webm', '.webp'}: + return True + # This list was found by going through `ffmpeg -codecs` for video encoders, + # running them with _support_transparency() forced to True, and checking that + # the "Pixel format" in Kdenlive included alpha. Note this is not a guarantee + # that transparency will work; you may also need to pass `-pix_fmt`, but we + # trust the user has done so if they are asking for these formats. + return self.codec in { + 'apng', 'avrp', 'bmp', 'cfhd', 'dpx', 'ffv1', 'ffvhuff', 'gif', 'huffyuv', + 'jpeg2000', 'ljpeg', 'png', 'prores', 'prores_aw', 'prores_ks', 'qtrle', + 'rawvideo', 'targa', 'tiff', 'utvideo', 'v408', } + + @property + def output_args(self): + args = [] + suffix = Path(self.outfile).suffix + if suffix in {'.apng', '.avif', '.gif', '.webm', '.webp'}: + self.codec = suffix[1:] + else: + args.extend(['-vcodec', self.codec]) + extra_args = (self.extra_args if self.extra_args is not None + else mpl.rcParams[self._args_key]) + # For h264, the default format is yuv444p, which is not compatible + # with quicktime (and others). Specifying yuv420p fixes playback on + # iOS, as well as HTML5 video in firefox and safari (on both Windows and + # macOS). Also fixes internet explorer. This is as of 2015/10/29. + if self.codec == 'h264' and '-pix_fmt' not in extra_args: + args.extend(['-pix_fmt', 'yuv420p']) + # For GIF, we're telling FFmpeg to split the video stream, to generate + # a palette, and then use it for encoding. + elif self.codec == 'gif' and '-filter_complex' not in extra_args: + args.extend(['-filter_complex', + 'split [a][b];[a] palettegen [p];[b][p] paletteuse']) + # For AVIF, we're telling FFmpeg to split the video stream, extract the alpha, + # in order to place it in a secondary stream, as needed by AVIF-in-FFmpeg. + elif self.codec == 'avif' and '-filter_complex' not in extra_args: + args.extend(['-filter_complex', + 'split [rgb][rgba]; [rgba] alphaextract [alpha]', + '-map', '[rgb]', '-map', '[alpha]']) + if self.bitrate > 0: + args.extend(['-b', '%dk' % self.bitrate]) # %dk: bitrate in kbps. + for k, v in self.metadata.items(): + args.extend(['-metadata', f'{k}={v}']) + args.extend(extra_args) + + return args + ['-y', self.outfile] + + +# Combine FFMpeg options with pipe-based writing +@writers.register('ffmpeg') +class FFMpegWriter(FFMpegBase, MovieWriter): + """ + Pipe-based ffmpeg writer. + + Frames are streamed directly to ffmpeg via a pipe and written in a single pass. + + This effectively works as a slideshow input to ffmpeg with the fps passed as + ``-framerate``, so see also `their notes on frame rates`_ for further details. + + .. _their notes on frame rates: https://trac.ffmpeg.org/wiki/Slideshow#Framerates + """ + def _args(self): + # Returns the command line parameters for subprocess to use + # ffmpeg to create a movie using a pipe. + args = [self.bin_path(), '-f', 'rawvideo', '-vcodec', 'rawvideo', + '-s', '%dx%d' % self.frame_size, '-pix_fmt', self.frame_format, + '-framerate', str(self.fps)] + # Logging is quieted because subprocess.PIPE has limited buffer size. + # If you have a lot of frames in your animation and set logging to + # DEBUG, you will have a buffer overrun. + if _log.getEffectiveLevel() > logging.DEBUG: + args += ['-loglevel', 'error'] + args += ['-i', 'pipe:'] + self.output_args + return args + + +# Combine FFMpeg options with temp file-based writing +@writers.register('ffmpeg_file') +class FFMpegFileWriter(FFMpegBase, FileMovieWriter): + """ + File-based ffmpeg writer. + + Frames are written to temporary files on disk and then stitched together at the end. + + This effectively works as a slideshow input to ffmpeg with the fps passed as + ``-framerate``, so see also `their notes on frame rates`_ for further details. + + .. _their notes on frame rates: https://trac.ffmpeg.org/wiki/Slideshow#Framerates + """ + supported_formats = ['png', 'jpeg', 'tiff', 'raw', 'rgba'] + + def _args(self): + # Returns the command line parameters for subprocess to use + # ffmpeg to create a movie using a collection of temp images + args = [] + # For raw frames, we need to explicitly tell ffmpeg the metadata. + if self.frame_format in {'raw', 'rgba'}: + args += [ + '-f', 'image2', '-vcodec', 'rawvideo', + '-video_size', '%dx%d' % self.frame_size, + '-pixel_format', 'rgba', + ] + args += ['-framerate', str(self.fps), '-i', self._base_temp_name()] + if not self._tmpdir: + args += ['-frames:v', str(self._frame_counter)] + # Logging is quieted because subprocess.PIPE has limited buffer size. + # If you have a lot of frames in your animation and set logging to + # DEBUG, you will have a buffer overrun. + if _log.getEffectiveLevel() > logging.DEBUG: + args += ['-loglevel', 'error'] + return [self.bin_path(), *args, *self.output_args] + + +# Base class for animated GIFs with ImageMagick +class ImageMagickBase: + """ + Mixin class for ImageMagick output. + + This is a base class for the concrete `ImageMagickWriter` and + `ImageMagickFileWriter` classes, which define an ``input_names`` attribute + (or property) specifying the input names passed to ImageMagick. + """ + + _exec_key = 'animation.convert_path' + _args_key = 'animation.convert_args' + + def _supports_transparency(self): + suffix = Path(self.outfile).suffix + return suffix in {'.apng', '.avif', '.gif', '.webm', '.webp'} + + def _args(self): + # ImageMagick does not recognize "raw". + fmt = "rgba" if self.frame_format == "raw" else self.frame_format + extra_args = (self.extra_args if self.extra_args is not None + else mpl.rcParams[self._args_key]) + return [ + self.bin_path(), + "-size", "%ix%i" % self.frame_size, + "-depth", "8", + "-delay", str(100 / self.fps), + "-loop", "0", + f"{fmt}:{self.input_names}", + *extra_args, + self.outfile, + ] + + @classmethod + def bin_path(cls): + binpath = super().bin_path() + if binpath == 'convert': + binpath = mpl._get_executable_info('magick').executable + return binpath + + @classmethod + def isAvailable(cls): + try: + return super().isAvailable() + except mpl.ExecutableNotFoundError as _enf: + # May be raised by get_executable_info. + _log.debug('ImageMagick unavailable due to: %s', _enf) + return False + + +# Combine ImageMagick options with pipe-based writing +@writers.register('imagemagick') +class ImageMagickWriter(ImageMagickBase, MovieWriter): + """ + Pipe-based animated gif writer. + + Frames are streamed directly to ImageMagick via a pipe and written + in a single pass. + """ + + input_names = "-" # stdin + + +# Combine ImageMagick options with temp file-based writing +@writers.register('imagemagick_file') +class ImageMagickFileWriter(ImageMagickBase, FileMovieWriter): + """ + File-based animated gif writer. + + Frames are written to temporary files on disk and then stitched + together at the end. + """ + + supported_formats = ['png', 'jpeg', 'tiff', 'raw', 'rgba'] + input_names = property( + lambda self: f'{self.temp_prefix}*.{self.frame_format}') + + +# Taken directly from jakevdp's JSAnimation package at +# http://github.com/jakevdp/JSAnimation +def _included_frames(frame_count, frame_format, frame_dir): + return INCLUDED_FRAMES.format(Nframes=frame_count, + frame_dir=frame_dir, + frame_format=frame_format) + + +def _embedded_frames(frame_list, frame_format): + """frame_list should be a list of base64-encoded png files""" + if frame_format == 'svg': + # Fix MIME type for svg + frame_format = 'svg+xml' + template = ' frames[{0}] = "data:image/{1};base64,{2}"\n' + return "\n" + "".join( + template.format(i, frame_format, frame_data.replace('\n', '\\\n')) + for i, frame_data in enumerate(frame_list)) + + +@writers.register('html') +class HTMLWriter(FileMovieWriter): + """Writer for JavaScript-based HTML movies.""" + + supported_formats = ['png', 'jpeg', 'tiff', 'svg'] + + @classmethod + def isAvailable(cls): + return True + + def __init__(self, fps=30, codec=None, bitrate=None, extra_args=None, + metadata=None, embed_frames=False, default_mode='loop', + embed_limit=None): + + if extra_args: + _log.warning("HTMLWriter ignores 'extra_args'") + extra_args = () # Don't lookup nonexistent rcParam[args_key]. + self.embed_frames = embed_frames + self.default_mode = default_mode.lower() + _api.check_in_list(['loop', 'once', 'reflect'], + default_mode=self.default_mode) + + # Save embed limit, which is given in MB + self._bytes_limit = mpl._val_or_rc(embed_limit, 'animation.embed_limit') + # Convert from MB to bytes + self._bytes_limit *= 1024 * 1024 + + super().__init__(fps, codec, bitrate, extra_args, metadata) + + def setup(self, fig, outfile, dpi=None, frame_dir=None): + outfile = Path(outfile) + _api.check_in_list(['.html', '.htm'], outfile_extension=outfile.suffix) + + self._saved_frames = [] + self._total_bytes = 0 + self._hit_limit = False + + if not self.embed_frames: + if frame_dir is None: + frame_dir = outfile.with_name(outfile.stem + '_frames') + frame_dir.mkdir(parents=True, exist_ok=True) + frame_prefix = frame_dir / 'frame' + else: + frame_prefix = None + + super().setup(fig, outfile, dpi, frame_prefix) + self._clear_temp = False + + def grab_frame(self, **savefig_kwargs): + _validate_grabframe_kwargs(savefig_kwargs) + if self.embed_frames: + # Just stop processing if we hit the limit + if self._hit_limit: + return + f = BytesIO() + self.fig.savefig(f, format=self.frame_format, + dpi=self.dpi, **savefig_kwargs) + imgdata64 = base64.encodebytes(f.getvalue()).decode('ascii') + self._total_bytes += len(imgdata64) + if self._total_bytes >= self._bytes_limit: + _log.warning( + "Animation size has reached %s bytes, exceeding the limit " + "of %s. If you're sure you want a larger animation " + "embedded, set the animation.embed_limit rc parameter to " + "a larger value (in MB). This and further frames will be " + "dropped.", self._total_bytes, self._bytes_limit) + self._hit_limit = True + else: + self._saved_frames.append(imgdata64) + else: + return super().grab_frame(**savefig_kwargs) + + def finish(self): + # save the frames to an html file + if self.embed_frames: + fill_frames = _embedded_frames(self._saved_frames, + self.frame_format) + frame_count = len(self._saved_frames) + else: + # temp names is filled by FileMovieWriter + frame_count = len(self._temp_paths) + fill_frames = _included_frames( + frame_count, self.frame_format, + self._temp_paths[0].parent.relative_to(self.outfile.parent)) + mode_dict = dict(once_checked='', + loop_checked='', + reflect_checked='') + mode_dict[self.default_mode + '_checked'] = 'checked' + + interval = 1000 // self.fps + + with open(self.outfile, 'w') as of: + of.write(JS_INCLUDE + STYLE_INCLUDE) + of.write(DISPLAY_TEMPLATE.format(id=uuid.uuid4().hex, + Nframes=frame_count, + fill_frames=fill_frames, + interval=interval, + **mode_dict)) + + # Duplicate the temporary file clean up logic from + # FileMovieWriter.finish. We cannot call the inherited version of + # finish because it assumes that there is a subprocess that we either + # need to call to merge many frames together or that there is a + # subprocess call that we need to clean up. + if self._tmpdir: + _log.debug('MovieWriter: clearing temporary path=%s', self._tmpdir) + self._tmpdir.cleanup() + + +class Animation: + """ + A base class for Animations. + + This class is not usable as is, and should be subclassed to provide needed + behavior. + + .. note:: + + You must store the created Animation in a variable that lives as long + as the animation should run. Otherwise, the Animation object will be + garbage-collected and the animation stops. + + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + The figure object used to get needed events, such as draw or resize. + + event_source : object, optional + A class that can run a callback when desired events + are generated, as well as be stopped and started. + + Examples include timers (see `TimedAnimation`) and file + system notifications. + + blit : bool, default: False + Whether blitting is used to optimize drawing. If the backend does not + support blitting, then this parameter has no effect. + + See Also + -------- + FuncAnimation, ArtistAnimation + """ + + def __init__(self, fig, event_source=None, blit=False): + self._draw_was_started = False + + self._fig = fig + # Disables blitting for backends that don't support it. This + # allows users to request it if available, but still have a + # fallback that works if it is not. + self._blit = blit and fig.canvas.supports_blit + + # These are the basics of the animation. The frame sequence represents + # information for each frame of the animation and depends on how the + # drawing is handled by the subclasses. The event source fires events + # that cause the frame sequence to be iterated. + self.frame_seq = self.new_frame_seq() + self.event_source = event_source + + # Instead of starting the event source now, we connect to the figure's + # draw_event, so that we only start once the figure has been drawn. + self._first_draw_id = fig.canvas.mpl_connect('draw_event', self._start) + + # Connect to the figure's close_event so that we don't continue to + # fire events and try to draw to a deleted figure. + self._close_id = self._fig.canvas.mpl_connect('close_event', + self._stop) + if self._blit: + self._setup_blit() + + def __del__(self): + if not getattr(self, '_draw_was_started', True): + warnings.warn( + 'Animation was deleted without rendering anything. This is ' + 'most likely not intended. To prevent deletion, assign the ' + 'Animation to a variable, e.g. `anim`, that exists until you ' + 'output the Animation using `plt.show()` or ' + '`anim.save()`.' + ) + + def _start(self, *args): + """ + Starts interactive animation. Adds the draw frame command to the GUI + handler, calls show to start the event loop. + """ + # Do not start the event source if saving() it. + if self._fig.canvas.is_saving(): + return + # First disconnect our draw event handler + self._fig.canvas.mpl_disconnect(self._first_draw_id) + + # Now do any initial draw + self._init_draw() + + # Add our callback for stepping the animation and + # actually start the event_source. + self.event_source.add_callback(self._step) + self.event_source.start() + + def _stop(self, *args): + # On stop we disconnect all of our events. + if self._blit: + self._fig.canvas.mpl_disconnect(self._resize_id) + self._fig.canvas.mpl_disconnect(self._close_id) + self.event_source.remove_callback(self._step) + self.event_source = None + + def save(self, filename, writer=None, fps=None, dpi=None, codec=None, + bitrate=None, extra_args=None, metadata=None, extra_anim=None, + savefig_kwargs=None, *, progress_callback=None): + """ + Save the animation as a movie file by drawing every frame. + + Parameters + ---------- + filename : str + The output filename, e.g., :file:`mymovie.mp4`. + + writer : `MovieWriter` or str, default: :rc:`animation.writer` + A `MovieWriter` instance to use or a key that identifies a + class to use, such as 'ffmpeg'. + + fps : int, optional + Movie frame rate (per second). If not set, the frame rate from the + animation's frame interval. + + dpi : float, default: :rc:`savefig.dpi` + Controls the dots per inch for the movie frames. Together with + the figure's size in inches, this controls the size of the movie. + + codec : str, default: :rc:`animation.codec`. + The video codec to use. Not all codecs are supported by a given + `MovieWriter`. + + bitrate : int, default: :rc:`animation.bitrate` + The bitrate of the movie, in kilobits per second. Higher values + means higher quality movies, but increase the file size. A value + of -1 lets the underlying movie encoder select the bitrate. + + extra_args : list of str or None, optional + Extra command-line arguments passed to the underlying movie encoder. These + arguments are passed last to the encoder, just before the output filename. + The default, None, means to use :rc:`animation.[name-of-encoder]_args` for + the builtin writers. + + metadata : dict[str, str], default: {} + Dictionary of keys and values for metadata to include in + the output file. Some keys that may be of use include: + title, artist, genre, subject, copyright, srcform, comment. + + extra_anim : list, default: [] + Additional `Animation` objects that should be included + in the saved movie file. These need to be from the same + `.Figure` instance. Also, animation frames will + just be simply combined, so there should be a 1:1 correspondence + between the frames from the different animations. + + savefig_kwargs : dict, default: {} + Keyword arguments passed to each `~.Figure.savefig` call used to + save the individual frames. + + progress_callback : function, optional + A callback function that will be called for every frame to notify + the saving progress. It must have the signature :: + + def func(current_frame: int, total_frames: int) -> Any + + where *current_frame* is the current frame number and *total_frames* is the + total number of frames to be saved. *total_frames* is set to None, if the + total number of frames cannot be determined. Return values may exist but are + ignored. + + Example code to write the progress to stdout:: + + progress_callback = lambda i, n: print(f'Saving frame {i}/{n}') + + Notes + ----- + *fps*, *codec*, *bitrate*, *extra_args* and *metadata* are used to + construct a `.MovieWriter` instance and can only be passed if + *writer* is a string. If they are passed as non-*None* and *writer* + is a `.MovieWriter`, a `RuntimeError` will be raised. + """ + + all_anim = [self] + if extra_anim is not None: + all_anim.extend(anim for anim in extra_anim + if anim._fig is self._fig) + + # Disable "Animation was deleted without rendering" warning. + for anim in all_anim: + anim._draw_was_started = True + + if writer is None: + writer = mpl.rcParams['animation.writer'] + elif (not isinstance(writer, str) and + any(arg is not None + for arg in (fps, codec, bitrate, extra_args, metadata))): + raise RuntimeError('Passing in values for arguments ' + 'fps, codec, bitrate, extra_args, or metadata ' + 'is not supported when writer is an existing ' + 'MovieWriter instance. These should instead be ' + 'passed as arguments when creating the ' + 'MovieWriter instance.') + + if savefig_kwargs is None: + savefig_kwargs = {} + else: + # we are going to mutate this below + savefig_kwargs = dict(savefig_kwargs) + + if fps is None and hasattr(self, '_interval'): + # Convert interval in ms to frames per second + fps = 1000. / self._interval + + # Reuse the savefig DPI for ours if none is given. + dpi = mpl._val_or_rc(dpi, 'savefig.dpi') + if dpi == 'figure': + dpi = self._fig.dpi + + writer_kwargs = {} + if codec is not None: + writer_kwargs['codec'] = codec + if bitrate is not None: + writer_kwargs['bitrate'] = bitrate + if extra_args is not None: + writer_kwargs['extra_args'] = extra_args + if metadata is not None: + writer_kwargs['metadata'] = metadata + + # If we have the name of a writer, instantiate an instance of the + # registered class. + if isinstance(writer, str): + try: + writer_cls = writers[writer] + except RuntimeError: # Raised if not available. + writer_cls = PillowWriter # Always available. + _log.warning("MovieWriter %s unavailable; using Pillow " + "instead.", writer) + writer = writer_cls(fps, **writer_kwargs) + _log.info('Animation.save using %s', type(writer)) + + if 'bbox_inches' in savefig_kwargs: + _log.warning("Warning: discarding the 'bbox_inches' argument in " + "'savefig_kwargs' as it may cause frame size " + "to vary, which is inappropriate for animation.") + savefig_kwargs.pop('bbox_inches') + + # Create a new sequence of frames for saved data. This is different + # from new_frame_seq() to give the ability to save 'live' generated + # frame information to be saved later. + # TODO: Right now, after closing the figure, saving a movie won't work + # since GUI widgets are gone. Either need to remove extra code to + # allow for this non-existent use case or find a way to make it work. + + def _pre_composite_to_white(color): + r, g, b, a = mcolors.to_rgba(color) + return a * np.array([r, g, b]) + 1 - a + + # canvas._is_saving = True makes the draw_event animation-starting + # callback a no-op; canvas.manager = None prevents resizing the GUI + # widget (both are likewise done in savefig()). + with (writer.saving(self._fig, filename, dpi), + cbook._setattr_cm(self._fig.canvas, _is_saving=True, manager=None)): + if not writer._supports_transparency(): + facecolor = savefig_kwargs.get('facecolor', + mpl.rcParams['savefig.facecolor']) + if facecolor == 'auto': + facecolor = self._fig.get_facecolor() + savefig_kwargs['facecolor'] = _pre_composite_to_white(facecolor) + savefig_kwargs['transparent'] = False # just to be safe! + + for anim in all_anim: + anim._init_draw() # Clear the initial frame + frame_number = 0 + # TODO: Currently only FuncAnimation has a save_count + # attribute. Can we generalize this to all Animations? + save_count_list = [getattr(a, '_save_count', None) + for a in all_anim] + if None in save_count_list: + total_frames = None + else: + total_frames = sum(save_count_list) + for data in zip(*[a.new_saved_frame_seq() for a in all_anim]): + for anim, d in zip(all_anim, data): + # TODO: See if turning off blit is really necessary + anim._draw_next_frame(d, blit=False) + if progress_callback is not None: + progress_callback(frame_number, total_frames) + frame_number += 1 + writer.grab_frame(**savefig_kwargs) + + def _step(self, *args): + """ + Handler for getting events. By default, gets the next frame in the + sequence and hands the data off to be drawn. + """ + # Returns True to indicate that the event source should continue to + # call _step, until the frame sequence reaches the end of iteration, + # at which point False will be returned. + try: + framedata = next(self.frame_seq) + self._draw_next_frame(framedata, self._blit) + return True + except StopIteration: + return False + + def new_frame_seq(self): + """Return a new sequence of frame information.""" + # Default implementation is just an iterator over self._framedata + return iter(self._framedata) + + def new_saved_frame_seq(self): + """Return a new sequence of saved/cached frame information.""" + # Default is the same as the regular frame sequence + return self.new_frame_seq() + + def _draw_next_frame(self, framedata, blit): + # Breaks down the drawing of the next frame into steps of pre- and + # post- draw, as well as the drawing of the frame itself. + self._pre_draw(framedata, blit) + self._draw_frame(framedata) + self._post_draw(framedata, blit) + + def _init_draw(self): + # Initial draw to clear the frame. Also used by the blitting code + # when a clean base is required. + self._draw_was_started = True + + def _pre_draw(self, framedata, blit): + # Perform any cleaning or whatnot before the drawing of the frame. + # This default implementation allows blit to clear the frame. + if blit: + self._blit_clear(self._drawn_artists) + + def _draw_frame(self, framedata): + # Performs actual drawing of the frame. + raise NotImplementedError('Needs to be implemented by subclasses to' + ' actually make an animation.') + + def _post_draw(self, framedata, blit): + # After the frame is rendered, this handles the actual flushing of + # the draw, which can be a direct draw_idle() or make use of the + # blitting. + if blit and self._drawn_artists: + self._blit_draw(self._drawn_artists) + else: + self._fig.canvas.draw_idle() + + # The rest of the code in this class is to facilitate easy blitting + def _blit_draw(self, artists): + # Handles blitted drawing, which renders only the artists given instead + # of the entire figure. + updated_ax = {a.axes for a in artists} + # Enumerate artists to cache Axes backgrounds. We do not draw + # artists yet to not cache foreground from plots with shared Axes + for ax in updated_ax: + # If we haven't cached the background for the current view of this + # Axes object, do so now. This might not always be reliable, but + # it's an attempt to automate the process. + cur_view = ax._get_view() + view, bg = self._blit_cache.get(ax, (object(), None)) + if cur_view != view: + self._blit_cache[ax] = ( + cur_view, ax.figure.canvas.copy_from_bbox(ax.bbox)) + # Make a separate pass to draw foreground. + for a in artists: + a.axes.draw_artist(a) + # After rendering all the needed artists, blit each Axes individually. + for ax in updated_ax: + ax.figure.canvas.blit(ax.bbox) + + def _blit_clear(self, artists): + # Get a list of the Axes that need clearing from the artists that + # have been drawn. Grab the appropriate saved background from the + # cache and restore. + axes = {a.axes for a in artists} + for ax in axes: + try: + view, bg = self._blit_cache[ax] + except KeyError: + continue + if ax._get_view() == view: + ax.figure.canvas.restore_region(bg) + else: + self._blit_cache.pop(ax) + + def _setup_blit(self): + # Setting up the blit requires: a cache of the background for the Axes + self._blit_cache = dict() + self._drawn_artists = [] + # _post_draw needs to be called first to initialize the renderer + self._post_draw(None, self._blit) + # Then we need to clear the Frame for the initial draw + # This is typically handled in _on_resize because QT and Tk + # emit a resize event on launch, but the macosx backend does not, + # thus we force it here for everyone for consistency + self._init_draw() + # Connect to future resize events + self._resize_id = self._fig.canvas.mpl_connect('resize_event', + self._on_resize) + + def _on_resize(self, event): + # On resize, we need to disable the resize event handling so we don't + # get too many events. Also stop the animation events, so that + # we're paused. Reset the cache and re-init. Set up an event handler + # to catch once the draw has actually taken place. + self._fig.canvas.mpl_disconnect(self._resize_id) + self.event_source.stop() + self._blit_cache.clear() + self._init_draw() + self._resize_id = self._fig.canvas.mpl_connect('draw_event', + self._end_redraw) + + def _end_redraw(self, event): + # Now that the redraw has happened, do the post draw flushing and + # blit handling. Then re-enable all of the original events. + self._post_draw(None, False) + self.event_source.start() + self._fig.canvas.mpl_disconnect(self._resize_id) + self._resize_id = self._fig.canvas.mpl_connect('resize_event', + self._on_resize) + + def to_html5_video(self, embed_limit=None): + """ + Convert the animation to an HTML5 ``