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import os |
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import torch |
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import torch.nn as nn |
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from torch.nn import functional as F |
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def get_fp_maxval(bits=8, mantissa_bit=3, sign_bits=1): |
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_bits = torch.tensor(bits) |
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_mantissa_bit = torch.tensor(mantissa_bit) |
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_sign_bits = torch.tensor(sign_bits) |
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M = torch.clamp(torch.round(_mantissa_bit), 1, _bits - _sign_bits) |
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E = _bits - _sign_bits - M |
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bias = 2 ** (E - 1) - 1 |
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mantissa = 1 |
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for i in range(mantissa_bit - 1): |
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mantissa += 1 / (2 ** (i+1)) |
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maxval = mantissa * 2 ** (2**E - 1 - bias) |
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return maxval |
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def quantize_to_fp8(x, bits=8, mantissa_bit=3, sign_bits=1): |
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""" |
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Default is E4M3. |
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""" |
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bits = torch.tensor(bits) |
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mantissa_bit = torch.tensor(mantissa_bit) |
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sign_bits = torch.tensor(sign_bits) |
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M = torch.clamp(torch.round(mantissa_bit), 1, bits - sign_bits) |
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E = bits - sign_bits - M |
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bias = 2 ** (E - 1) - 1 |
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mantissa = 1 |
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for i in range(mantissa_bit - 1): |
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mantissa += 1 / (2 ** (i+1)) |
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maxval = mantissa * 2 ** (2**E - 1 - bias) |
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minval = - maxval |
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minval = - maxval if sign_bits == 1 else torch.zeros_like(maxval) |
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input_clamp = torch.min(torch.max(x, minval), maxval) |
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log_scales = torch.clamp((torch.floor(torch.log2(torch.abs(input_clamp)) + bias)).detach(), 1.0) |
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log_scales = 2.0 ** (log_scales - M - bias.type(x.dtype)) |
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qdq_out = torch.round(input_clamp / log_scales) * log_scales |
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return qdq_out, log_scales |
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def fp8_tensor_quant(x, scale, bits=8, mantissa_bit=3, sign_bits=1): |
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for i in range(len(x.shape) - 1): |
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scale = scale.unsqueeze(-1) |
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new_x = x / scale |
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quant_dequant_x, log_scales = quantize_to_fp8(new_x, bits=bits, mantissa_bit=mantissa_bit, sign_bits=sign_bits) |
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return quant_dequant_x, scale, log_scales |
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def fp8_activation_dequant(qdq_out, scale, dtype): |
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qdq_out = qdq_out.type(dtype) |
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quant_dequant_x = qdq_out * scale.to(dtype) |
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return quant_dequant_x |
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def fp8_linear_forward(cls, original_dtype, input): |
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weight_dtype = cls.weight.dtype |
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if cls.weight.dtype != torch.float8_e4m3fn: |
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maxval = get_fp_maxval() |
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scale = torch.max(torch.abs(cls.weight.flatten())) / maxval |
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linear_weight, scale, log_scales = fp8_tensor_quant(cls.weight, scale) |
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linear_weight = linear_weight.to(torch.float8_e4m3fn) |
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weight_dtype = linear_weight.dtype |
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else: |
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scale = cls.fp8_scale.to(cls.weight.device) |
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linear_weight = cls.weight |
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if weight_dtype == torch.float8_e4m3fn and cls.weight.sum() != 0: |
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if True or len(input.shape) == 3: |
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cls_dequant = fp8_activation_dequant(linear_weight, scale, original_dtype) |
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if cls.bias != None: |
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output = F.linear(input, cls_dequant, cls.bias) |
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else: |
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output = F.linear(input, cls_dequant) |
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return output |
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else: |
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return cls.original_forward(input.to(original_dtype)) |
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else: |
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return cls.original_forward(input) |
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def convert_fp8_linear(module, dit_weight_path, original_dtype, params_to_keep={}): |
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setattr(module, "fp8_matmul_enabled", True) |
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fp8_map_path = dit_weight_path.replace('.pt', '_map.pt') |
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if os.path.exists(fp8_map_path): |
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fp8_map = torch.load(fp8_map_path, map_location=lambda storage, loc: storage) |
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else: |
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raise ValueError(f"Invalid fp8_map path: {fp8_map_path}.") |
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fp8_layers = [] |
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for key, layer in module.named_modules(): |
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if isinstance(layer, nn.Linear) and ('double_blocks' in key or 'single_blocks' in key): |
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fp8_layers.append(key) |
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original_forward = layer.forward |
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layer.weight = torch.nn.Parameter(layer.weight.to(torch.float8_e4m3fn)) |
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setattr(layer, "fp8_scale", fp8_map[key].to(dtype=original_dtype)) |
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setattr(layer, "original_forward", original_forward) |
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setattr(layer, "forward", lambda input, m=layer: fp8_linear_forward(m, original_dtype, input)) |
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