fb4959b2c5
Co-authored-by: YanbingJiang <yanbing.jiang@intel.com> Co-authored-by: mingfeima <mingfei.ma@intel.com>
97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import math
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import torch
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precision = {
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torch.bfloat16: 1e-2,
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torch.float16: 1e-3,
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torch.float32: 1e-5,
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}
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def per_token_quant_int8(x):
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x = x.float()
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absmax = x.abs().max(dim=-1).values
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absmax = absmax.clamp_min(1e-10).unsqueeze(-1)
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scale_x = absmax / 127
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x_q = x.mul(127 / absmax)
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x_q = torch.round(x_q).to(torch.int8)
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return x_q, scale_x
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def convert_weight(weight, scale_block_size, A_dtype):
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N, K = weight.size()
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fp8_max = 448.0
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scale_block_size_N, scale_block_size_K = scale_block_size # (128, 128)
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pad_N = (scale_block_size_N - (N % scale_block_size_N)) % scale_block_size_N
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pad_K = (scale_block_size_K - (K % scale_block_size_K)) % scale_block_size_K
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if pad_N > 0 or pad_K > 0:
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weight = torch.nn.functional.pad(weight, (0, pad_K, 0, pad_N))
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weight_blocks = weight.view(
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math.ceil(N / scale_block_size_N),
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scale_block_size_N,
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math.ceil(K / scale_block_size_K),
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scale_block_size_K,
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) # (8, 128, 8, 128)
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weight_blocks = weight_blocks.permute(0, 2, 1, 3).contiguous() # (8, 8, 128, 128)
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# Step 2: compute per-block max abs values → scale
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abs_max = weight_blocks.abs().amax(dim=(-2, -1), keepdim=True) # (8, 8, 1, 1)
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scales = abs_max / fp8_max
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scales = torch.where(
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scales == 0, torch.ones_like(scales), scales
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) # avoid division by zero
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q_fp8 = (weight_blocks / scales).to(torch.float8_e4m3fn)
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q_fp8_reshape = q_fp8.permute(0, 2, 1, 3).contiguous()
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if pad_N > 0 or pad_K > 0:
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q_fp8_reshape = q_fp8_reshape.view(N + pad_N, K + pad_K)
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q_fp8_reshape = q_fp8_reshape[:N, :K].contiguous()
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else:
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q_fp8_reshape = q_fp8_reshape.view(N, K)
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dq_weight = q_fp8.float() * scales
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dq_weight = dq_weight.permute(0, 2, 1, 3).contiguous() # (8, 128, 8, 128)
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if pad_N > 0 or pad_K > 0:
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w_dq = dq_weight.view(N + pad_N, K + pad_K).to(A_dtype)
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w_dq = w_dq[:N, :K].contiguous()
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else:
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w_dq = dq_weight.view(N, K).to(A_dtype)
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scales = scales.view(
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math.ceil(N / scale_block_size_N), math.ceil(K / scale_block_size_K)
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)
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return q_fp8_reshape, scales, w_dq
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def native_w8a8_per_token_matmul(A, B, As, Bs, bias, output_dtype=torch.bfloat16):
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"""Matrix multiplication function that supports per-token input quantization and per-column weight quantization"""
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A = A.to(torch.float32)
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B = B.to(torch.float32)
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assert A.shape[-1] == B.shape[-1], "Dimension mismatch"
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assert B.ndim == 2 and B.is_contiguous(), "B must be a 2D contiguous tensor"
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# Reshape input
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M = A.numel() // A.shape[-1]
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B = B.t() # Transpose weight matrix
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N, K = B.shape
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origin_C_shape = A.shape[:-1] + (K,)
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A = A.reshape(M, N)
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# As is per-token [M, 1], Bs is per-column [1, K]
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C = torch.matmul(A, B) # [M, K]
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C = As * C * Bs.view(1, -1) # Broadcast per-column scale
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if bias is not None:
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C.add_(bias.view(1, -1))
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return C.reshape(origin_C_shape).to(output_dtype)
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