import math import pytest import torch import torch.nn.functional as F from einops import rearrange, repeat from flash_attn.ops.layer_norm import ( DropoutAddLayerNorm, dropout_add_layer_norm, dropout_add_layer_norm_parallel_residual, dropout_add_layer_norm_subset, ) from flash_attn.ops.rms_norm import ( DropoutAddRMSNorm, dropout_add_rms_norm, dropout_add_rms_norm_parallel_residual, dropout_add_rms_norm_subset, ) try: from apex.normalization import FusedRMSNorm from apex.normalization.fused_layer_norm import fused_rms_norm_affine except: FusedRMSNorm, fused_rms_norm_affine = None, None is_sm8x = torch.cuda.get_device_capability("cuda")[0] >= 8 @pytest.mark.parametrize("is_rms_norm", [False, True]) @pytest.mark.parametrize("has_colscale", [True, False]) # @pytest.mark.parametrize('has_colscale', [False]) @pytest.mark.parametrize("has_rowscale", [True, False]) # @pytest.mark.parametrize('has_rowscale', [True]) @pytest.mark.parametrize("has_residual", [True, False]) # @pytest.mark.parametrize('has_residual', [False]) @pytest.mark.parametrize("dropout_p", [0.37, 0.0]) # @pytest.mark.parametrize('dropout_p', [0.0]) @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) # @pytest.mark.parametrize('weight_dtype', [torch.float32]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) # @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float16, torch.float32)]) @pytest.mark.parametrize( "hidden_size", [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144], ) # @pytest.mark.parametrize('hidden_size', [256]) def test_dropout_layer_norm_training( hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p, has_residual, has_rowscale, has_colscale, is_rms_norm, ): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported if is_rms_norm and FusedRMSNorm is None: pytest.skip() # We need Apex's FusedRMSNorm to test layer_norm_cls = torch.nn.LayerNorm if not is_rms_norm else FusedRMSNorm our_layer_norm_cls = DropoutAddLayerNorm if not is_rms_norm else DropoutAddRMSNorm our_layer_norm_func = dropout_add_layer_norm if not is_rms_norm else dropout_add_rms_norm device = "cuda" # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 1e-4) # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone().requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() if has_colscale: colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) colscale_pt = colscale.detach().clone().requires_grad_() colscale_ref = colscale.detach().clone().float().requires_grad_() else: colscale = None if has_residual: res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() else: res = None if has_rowscale: rowscale = torch.empty(batch_size, seqlen, device=device, dtype=input_dtype) survival_rate = 0.87 rowscale = rowscale.bernoulli_(survival_rate) / survival_rate x0_scaled_pt = x0_pt * rearrange(rowscale, "... -> ... 1") x0_scaled_ref = x0_ref * rearrange(rowscale, "... -> ... 1") else: rowscale = None x0_scaled_pt = x0_pt x0_scaled_ref = x0_ref if has_colscale: x0_scaled_pt = x0_scaled_pt * colscale_pt x0_scaled_ref = x0_scaled_ref * colscale_ref model_pt = layer_norm_cls(hidden_size).to(device=device, dtype=weight_dtype) torch.nn.init.normal_(model_pt.weight) if not is_rms_norm: torch.nn.init.normal_(model_pt.bias) model_ref = layer_norm_cls(hidden_size).to(device=device, dtype=torch.float32) model = our_layer_norm_cls(hidden_size, p=dropout_p, device=device, dtype=weight_dtype) with torch.no_grad(): model.weight.copy_(model_pt.weight) model_ref.weight.copy_(model_pt.weight) if not is_rms_norm: model.bias.copy_(model_pt.bias) model_ref.bias.copy_(model_pt.bias) residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32 out, dmask = our_layer_norm_func( x0, res, model.weight, model.bias, model.p, model.eps, rowscale=rowscale, layerscale=colscale, residual_in_fp32=residual_in_fp32, return_dropout_mask=True, ) assert out.dtype == input_dtype print(f"Actual dropout fraction: {1 - dmask.float().mean().item()}") if has_residual: residual_pt = ( (x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p) + res_pt.float() ).to(dtype=residual_dtype) residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p) + res_ref else: residual_pt = ((x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p)).to( dtype=residual_dtype ) residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p) out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype) out_ref = model_ref(residual_ref) assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4 g = torch.randn_like(out) / batch_size out_pt.backward(g) out.backward(g) out_ref.backward(g) assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4 if has_residual: assert (res.grad - res_ref.grad).abs().max() <= 4 * ( res_pt.grad - res_ref.grad ).abs().max() + 1e-4 assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 3 * ( model_pt.weight.grad - model_ref.weight.grad ).abs().max() + 3e-5 if not is_rms_norm: assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * ( model_pt.bias.grad - model_ref.bias.grad ).abs().max() + 3e-5 if has_colscale: assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * ( colscale_pt.grad - colscale_ref.grad ).abs().max() + 2e-4 @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) @pytest.mark.parametrize("hidden_size", [768, 1024, 1280, 1536, 1600, 2048, 2560, 3072, 4096, 5120]) def test_dropout_layer_norm_eval(hidden_size, input_dtype, residual_dtype, weight_dtype): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported device = "cuda" # rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 1e-4) dropout_p = 0.37 # set seed torch.random.manual_seed(0) batch_size = 32 seqlen = 512 x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone().requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype) torch.nn.init.normal_(model_pt.weight) torch.nn.init.normal_(model_pt.bias) model = DropoutAddLayerNorm(hidden_size, p=dropout_p, device=device, dtype=weight_dtype) model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32) with torch.no_grad(): model.weight.copy_(model_pt.weight) model.bias.copy_(model_pt.bias) model_ref.weight.copy_(model_pt.weight) model_ref.bias.copy_(model_pt.bias) model_pt.eval() model.eval() model_ref.eval() out = model(x0, res) residual_pt = (x0_pt.float() + res_pt.float()).to(dtype=residual_dtype) residual_ref = x0_ref + res_ref out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(input_dtype) out_ref = model_ref(residual_ref) assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4 @pytest.mark.parametrize("is_rms_norm", [False, True]) @pytest.mark.parametrize("has_colscale", [True, False]) @pytest.mark.parametrize("has_rowscale", [True, False]) @pytest.mark.parametrize("has_residual", [True, False]) @pytest.mark.parametrize("dropout_p", [0.37, 0.0]) @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) # @pytest.mark.parametrize('has_colscale', [True]) # @pytest.mark.parametrize('has_rowscale', [False]) # @pytest.mark.parametrize('has_residual', [True]) # @pytest.mark.parametrize('dropout_p', [0.0]) # @pytest.mark.parametrize('weight_dtype', [torch.float32]) # @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float32, torch.float32)]) @pytest.mark.parametrize( "hidden_size", [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144], ) # @pytest.mark.parametrize('hidden_size', [256]) def test_dropout_layer_norm_prenorm_training( hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p, has_residual, has_rowscale, has_colscale, is_rms_norm, ): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported if is_rms_norm and FusedRMSNorm is None: pytest.skip() # We need Apex's FusedRMSNorm to test layer_norm_cls = torch.nn.LayerNorm if not is_rms_norm else FusedRMSNorm our_layer_norm_cls = DropoutAddLayerNorm if not is_rms_norm else DropoutAddRMSNorm our_layer_norm_func = dropout_add_layer_norm if not is_rms_norm else dropout_add_rms_norm device = "cuda" # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 2e-4) # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone().requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() if has_colscale: colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) colscale_pt = colscale.detach().clone().requires_grad_() colscale_ref = colscale.detach().clone().float().requires_grad_() else: colscale = None if has_residual: res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() else: res = None if has_rowscale: rowscale = torch.empty(batch_size, seqlen, device=device, dtype=input_dtype) survival_rate = 0.87 rowscale = rowscale.bernoulli_(survival_rate) / survival_rate x0_scaled_pt = x0_pt * rearrange(rowscale, "... -> ... 1") x0_scaled_ref = x0_ref * rearrange(rowscale, "... -> ... 1") else: rowscale = None x0_scaled_pt = x0_pt x0_scaled_ref = x0_ref if has_colscale: x0_scaled_pt = x0_scaled_pt * colscale_pt x0_scaled_ref = x0_scaled_ref * colscale_ref model_pt = layer_norm_cls(hidden_size).to(device=device, dtype=weight_dtype) torch.nn.init.normal_(model_pt.weight) if not is_rms_norm: torch.nn.init.normal_(model_pt.bias) model_ref = layer_norm_cls(hidden_size).to(device=device, dtype=torch.float32) model = our_layer_norm_cls( hidden_size, prenorm=True, p=dropout_p, device=device, dtype=weight_dtype ) with torch.no_grad(): model.weight.copy_(model_pt.weight) model_ref.weight.copy_(model_pt.weight) if not is_rms_norm: model.bias.copy_(model_pt.bias) model_ref.bias.copy_(model_pt.bias) residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32 out, residual, dmask = our_layer_norm_func( x0, res, model.weight, model.bias, model.p, model.eps, rowscale=rowscale, layerscale=colscale, prenorm=True, residual_in_fp32=residual_in_fp32, return_dropout_mask=True, ) print(f"Actual dropout fraction: {1 - dmask.float().mean().item()}") if has_residual: residual_pt = ( (x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p) + res_pt.float() ).to(dtype=residual_dtype) residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p) + res_ref else: residual_pt = ((x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p)).to( dtype=residual_dtype ) residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p) out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype) out_ref = model_ref(residual_ref) assert out.dtype == input_dtype assert residual.dtype == residual_dtype assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4 assert (residual - residual_ref).abs().max() <= 4 * ( residual_pt - residual_ref ).abs().max() + 1e-4 g = torch.randn_like(out) / batch_size (out_pt * F.sigmoid(residual_pt)).backward(g) (out * F.sigmoid(residual)).backward(g) (out_ref * F.sigmoid(residual_ref.to(dtype=residual_dtype))).backward(g) assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4 if has_residual: assert (res.grad - res_ref.grad).abs().max() <= 4 * ( res_pt.grad - res_ref.grad ).abs().max() + 1e-4 assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 2 * ( model_pt.weight.grad - model_ref.weight.grad ).abs().max() + 2e-4 if not is_rms_norm: assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * ( model_pt.bias.grad - model_ref.bias.grad ).abs().max() + 2e-4 if has_colscale: assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * ( colscale_pt.grad - colscale_ref.grad ).abs().max() + 2e-4 @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) @pytest.mark.parametrize("hidden_size", [768, 1024, 1280, 1536, 1600, 2048, 2560, 3072, 4096, 5120]) def test_dropout_layer_norm_prenorm_eval(hidden_size, input_dtype, residual_dtype, weight_dtype): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported device = "cuda" # rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 1e-4) dropout_p = 0.37 # set seed torch.random.manual_seed(0) batch_size = 32 seqlen = 512 x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone().requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype) torch.nn.init.normal_(model_pt.weight) torch.nn.init.normal_(model_pt.bias) model = DropoutAddLayerNorm( hidden_size, prenorm=True, p=dropout_p, device=device, dtype=weight_dtype ) model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32) with torch.no_grad(): model.weight.copy_(model_pt.weight) model.bias.copy_(model_pt.bias) model_ref.weight.copy_(model_pt.weight) model_ref.bias.copy_(model_pt.bias) model_pt.eval() model.eval() model_ref.eval() out, residual = model(x0, res) residual_pt = (x0_pt.float() + res_pt.float()).to(dtype=residual_dtype) residual_ref = x0_ref + res_ref out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(input_dtype) out_ref = model_ref(residual_ref) assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4 assert (residual - residual_ref).abs().max() <= 4 * ( residual_pt - residual_ref ).abs().max() + 1e-4 @pytest.mark.parametrize("has_colscale", [True, False]) @pytest.mark.parametrize("has_residual", [True, False]) @pytest.mark.parametrize("dropout_p", [0.37, 0.0]) @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) # @pytest.mark.parametrize('has_colscale', [True]) # @pytest.mark.parametrize('has_residual', [True]) # @pytest.mark.parametrize('dropout_p', [0.0]) # @pytest.mark.parametrize('weight_dtype', [torch.float32]) # @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float32, torch.float32)]) @pytest.mark.parametrize( "hidden_size", [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144], ) # @pytest.mark.parametrize('hidden_size', [256]) def test_dropout_layer_norm_subset_training( hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p, has_residual, has_colscale ): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported device = "cuda" # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 2e-4) # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 drop_path_rate = 0.4 drop_path_scale = 1 / (1 - drop_path_rate) def generate_droppath_masks(batch_size, seqlen, drop_path_rate, device): # Do it on CPU so we can get the numrows (with .item()) without GPU-CPU sync mask_batch = torch.rand(batch_size) < 1 - drop_path_rate numrows = (mask_batch).sum().item() * seqlen mask_batch = mask_batch.to(device=device, non_blocking=True) mask_batch_seqlen = repeat(mask_batch, "b -> (b s)", s=seqlen) subset = torch.cumsum(mask_batch_seqlen, dim=0, dtype=torch.int32).masked_fill_( ~mask_batch_seqlen, 0 ) return mask_batch, numrows, rearrange(subset, "(b s) -> b s", b=batch_size) x0_mask_batch, x0_numrows, x0_subset = generate_droppath_masks( batch_size, seqlen, drop_path_rate, device ) out_mask_batch, out_numrows, out_subset = generate_droppath_masks( batch_size, seqlen, drop_path_rate, device ) x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone()[x0_mask_batch].requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() if has_colscale: colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) colscale_pt = colscale.detach().clone().requires_grad_() colscale_ref = colscale.detach().clone().float().requires_grad_() else: colscale = None if has_residual: res_pt = torch.randn_like(x0_pt, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() else: res = None if has_colscale: x0_scaled_pt = x0_pt * colscale_pt x0_scaled_ref = x0_ref * colscale_ref else: x0_scaled_pt = x0_pt x0_scaled_ref = x0_ref model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype) torch.nn.init.normal_(model_pt.weight) torch.nn.init.normal_(model_pt.bias) model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32) model = DropoutAddLayerNorm( hidden_size, prenorm=False, p=dropout_p, device=device, dtype=weight_dtype ) with torch.no_grad(): model.weight.copy_(model_pt.weight) model.bias.copy_(model_pt.bias) model_ref.weight.copy_(model_pt.weight) model_ref.bias.copy_(model_pt.bias) residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32 out, dmask = dropout_add_layer_norm_subset( x0, res, model.weight, model.bias, model.p, model.eps, layerscale=colscale, x0_subset=x0_subset, out_subset=out_subset, rowscale_const=drop_path_scale, out_numrows=out_numrows, prenorm=False, residual_in_fp32=residual_in_fp32, return_dropout_mask=True, ) print(f"Actual dropout fraction: {1 - dmask.float().mean().item()}") x0_scaled_pt = ( x0_scaled_pt.masked_fill(repeat(~x0_mask_batch, "b -> b s d", s=seqlen, d=hidden_size), 0) * drop_path_scale ) x0_scaled_ref = ( x0_scaled_ref.masked_fill(repeat(~x0_mask_batch, "b -> b s d", s=seqlen, d=hidden_size), 0) * drop_path_scale ) dmask_expanded = torch.zeros_like(x0_pt, dtype=torch.uint8) dmask_expanded[x0_mask_batch] = dmask if has_residual: residual_pt = ( (x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p) + res_pt.float() ).to(dtype=residual_dtype) residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p) + res_ref else: residual_pt = ((x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p)).to( dtype=residual_dtype ) residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p) out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype)[out_mask_batch] out_ref = model_ref(residual_ref)[out_mask_batch] assert out.dtype == input_dtype assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4 g = torch.randn_like(out) / batch_size out_pt.backward(g) out.backward(g) out_ref.backward(g) assert (x0.grad - x0_ref.grad[x0_mask_batch]).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad)[ x0_mask_batch ].abs().max() + 1e-4 if has_residual: assert (res.grad - res_ref.grad).abs().max() <= 4 * ( res_pt.grad - res_ref.grad ).abs().max() + 1e-4 assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 2 * ( model_pt.weight.grad - model_ref.weight.grad ).abs().max() + 2e-4 assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * ( model_pt.bias.grad - model_ref.bias.grad ).abs().max() + 2e-4 if has_colscale: assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * ( colscale_pt.grad - colscale_ref.grad ).abs().max() + 2e-4 @pytest.mark.parametrize("has_colscale", [True, False]) @pytest.mark.parametrize("has_residual", [True, False]) @pytest.mark.parametrize("dropout_p", [0.37, 0.0]) @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) # @pytest.mark.parametrize('has_colscale', [True]) # @pytest.mark.parametrize('has_residual', [True]) # @pytest.mark.parametrize('dropout_p', [0.0]) # @pytest.mark.parametrize('weight_dtype', [torch.float32]) # @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float32, torch.float32)]) @pytest.mark.parametrize( "hidden_size", [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144], ) # @pytest.mark.parametrize('hidden_size', [256]) def test_dropout_layer_norm_subset_prenorm_training( hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p, has_residual, has_colscale ): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported device = "cuda" # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 2e-4) # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 drop_path_rate = 0.4 drop_path_scale = 1 / (1 - drop_path_rate) def generate_droppath_masks(batch_size, seqlen, drop_path_rate, device): # Do it on CPU so we can get the numrows (with .item()) without GPU-CPU sync mask_batch = torch.rand(batch_size) < 1 - drop_path_rate numrows = (mask_batch).sum().item() * seqlen mask_batch = mask_batch.to(device=device, non_blocking=True) mask_batch_seqlen = repeat(mask_batch, "b -> (b s)", s=seqlen) subset = torch.cumsum(mask_batch_seqlen, dim=0, dtype=torch.int32).masked_fill_( ~mask_batch_seqlen, 0 ) return mask_batch, numrows, rearrange(subset, "(b s) -> b s", b=batch_size) x0_mask_batch, x0_numrows, x0_subset = generate_droppath_masks( batch_size, seqlen, drop_path_rate, device ) out_mask_batch, out_numrows, out_subset = generate_droppath_masks( batch_size, seqlen, drop_path_rate, device ) x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone()[x0_mask_batch].requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() if has_colscale: colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) colscale_pt = colscale.detach().clone().requires_grad_() colscale_ref = colscale.detach().clone().float().requires_grad_() else: colscale = None if has_residual: res_pt = torch.randn_like(x0_pt, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() else: res = None if has_colscale: x0_scaled_pt = x0_pt * colscale_pt x0_scaled_ref = x0_ref * colscale_ref else: x0_scaled_pt = x0_pt x0_scaled_ref = x0_ref model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype) torch.nn.init.normal_(model_pt.weight) torch.nn.init.normal_(model_pt.bias) model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32) model = DropoutAddLayerNorm( hidden_size, prenorm=True, p=dropout_p, device=device, dtype=weight_dtype ) with torch.no_grad(): model.weight.copy_(model_pt.weight) model.bias.copy_(model_pt.bias) model_ref.weight.copy_(model_pt.weight) model_ref.bias.copy_(model_pt.bias) residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32 out, residual, dmask = dropout_add_layer_norm_subset( x0, res, model.weight, model.bias, model.p, model.eps, layerscale=colscale, x0_subset=x0_subset, out_subset=out_subset, rowscale_const=drop_path_scale, out_numrows=out_numrows, prenorm=True, residual_in_fp32=residual_in_fp32, return_dropout_mask=True, ) print(f"Actual dropout fraction: {1 - dmask.float().mean().item()}") x0_scaled_pt = ( x0_scaled_pt.masked_fill(repeat(~x0_mask_batch, "b -> b s d", s=seqlen, d=hidden_size), 0) * drop_path_scale ) x0_scaled_ref = ( x0_scaled_ref.masked_fill(repeat(~x0_mask_batch, "b -> b s d", s=seqlen, d=hidden_size), 0) * drop_path_scale ) dmask_expanded = torch.zeros_like(x0_pt, dtype=torch.uint8) dmask_expanded[x0_mask_batch] = dmask if has_residual: residual_pt = ( (x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p) + res_pt.float() ).to(dtype=residual_dtype) residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p) + res_ref else: residual_pt = ((x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p)).to( dtype=residual_dtype ) residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p) out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype)[out_mask_batch] out_ref = model_ref(residual_ref)[out_mask_batch] assert out.dtype == input_dtype assert residual.dtype == residual_dtype assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4 assert (residual - residual_ref).abs().max() <= 4 * ( residual_pt - residual_ref ).abs().max() + 1e-4 g = torch.randn_like(out) / batch_size (out_pt * F.sigmoid(residual_pt[out_mask_batch]) + residual_pt.mean(0, keepdim=True)).backward( g ) (out * F.sigmoid(residual[out_mask_batch]) + residual.mean(0, keepdim=True)).backward(g) ( out_ref * F.sigmoid(residual_ref[out_mask_batch].to(dtype=residual_dtype)) + residual_ref.mean(0, keepdim=True) ).backward(g) assert (x0.grad - x0_ref.grad[x0_mask_batch]).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad)[ x0_mask_batch ].abs().max() + 1e-4 if has_residual: assert (res.grad - res_ref.grad).abs().max() <= 4 * ( res_pt.grad - res_ref.grad ).abs().max() + 1e-4 assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 2 * ( model_pt.weight.grad - model_ref.weight.grad ).abs().max() + 2e-4 assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * ( model_pt.bias.grad - model_ref.bias.grad ).abs().max() + 2e-4 if has_colscale: assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * ( colscale_pt.grad - colscale_ref.grad ).abs().max() + 2e-4 @pytest.mark.parametrize("is_rms_norm", [False, True]) # @pytest.mark.parametrize('is_rms_norm', [False]) @pytest.mark.parametrize("tied_norm", [False, True]) # @pytest.mark.parametrize('tied_norm', [False]) @pytest.mark.parametrize("has_residual", [True, False]) # @pytest.mark.parametrize('has_residual', [False]) @pytest.mark.parametrize("has_x1", [True, False]) # @pytest.mark.parametrize('has_x1', [True]) @pytest.mark.parametrize("dropout_p", [0.37, 0.0]) # @pytest.mark.parametrize('dropout_p', [0.0]) @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) # @pytest.mark.parametrize('weight_dtype', [torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) # @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float16, torch.float32)]) @pytest.mark.parametrize( "hidden_size", [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144], ) # @pytest.mark.parametrize('hidden_size', [256]) def test_dropout_layer_norm_parallel_residual_training( hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p, has_x1, has_residual, tied_norm, is_rms_norm, ): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported if is_rms_norm and fused_rms_norm_affine is None: pytest.skip() # We need Apex's FusedRMSNorm to test our_layer_norm_func = ( dropout_add_layer_norm_parallel_residual if not is_rms_norm else dropout_add_rms_norm_parallel_residual ) device = "cuda" # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 1e-4) # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone().requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() if has_x1: x1_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x1 = x1_pt.detach().clone().requires_grad_() x1_ref = x1_pt.detach().clone().float().requires_grad_() else: x1 = None if has_residual: res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() else: res = None weight0 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) bias0 = ( torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) if not is_rms_norm else None ) weight0_pt = weight0.detach().clone().requires_grad_() weight0_ref = weight0.detach().clone().float().requires_grad_() bias0_pt = bias0.detach().clone().requires_grad_() if bias0 is not None else None bias0_ref = bias0.detach().clone().float().requires_grad_() if bias0 is not None else None if not tied_norm: weight1 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) bias1 = ( torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) if not is_rms_norm else None ) weight1_pt = weight1.detach().clone().requires_grad_() weight1_ref = weight1.detach().clone().float().requires_grad_() bias1_pt = bias1.detach().clone().requires_grad_() if bias1 is not None else None bias1_ref = bias1.detach().clone().float().requires_grad_() if bias1 is not None else None else: weight1, bias1 = None, None epsilon = 1e-5 residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32 out0, out1, dmask0, dmask1 = our_layer_norm_func( x0, x1, res, weight0, bias0, weight1, bias1, dropout_p, epsilon, residual_in_fp32=residual_in_fp32, return_dropout_mask=True, ) assert out0.dtype == input_dtype if not tied_norm: assert out1.dtype == input_dtype print(f"Actual dropout fraction: {1 - dmask0.float().mean().item()}") if has_residual: if has_x1: residual_pt = ( (x0_pt.float() * dmask0.float()) / (1 - dropout_p) + (x1_pt.float() * dmask1.float()) / (1 - dropout_p) + res_pt.float() ).to(dtype=residual_dtype) residual_ref = ( (x0_ref * dmask0.float()) / (1 - dropout_p) + (x1_ref * dmask1.float()) / (1 - dropout_p) ) + res_ref else: residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p) + res_pt.float()).to( dtype=residual_dtype ) residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) + res_ref else: if has_x1: residual_pt = ( (x0_pt.float() * dmask0.float()) / (1 - dropout_p) + (x1_pt.float() * dmask1.float()) / (1 - dropout_p) ).to(dtype=residual_dtype) residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) + ( x1_ref * dmask1.float() ) / (1 - dropout_p) else: residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)).to( dtype=residual_dtype ) residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) if not is_rms_norm: out0_pt = F.layer_norm( residual_pt.to(dtype=weight_dtype), (hidden_size,), weight0_pt, bias0_pt, eps=epsilon ).to(dtype=input_dtype) out0_ref = F.layer_norm(residual_ref, (hidden_size,), weight0_ref, bias0_ref, eps=epsilon) if not tied_norm: out1_pt = F.layer_norm( residual_pt.to(dtype=weight_dtype), (hidden_size,), weight1_pt, bias1_pt, eps=epsilon, ).to(dtype=input_dtype) out1_ref = F.layer_norm( residual_ref, (hidden_size,), weight1_ref, bias1_ref, eps=epsilon ) else: out0_pt = fused_rms_norm_affine( residual_pt.to(dtype=weight_dtype), weight0_pt, (hidden_size,), eps=epsilon ).to(dtype=input_dtype) out0_ref = fused_rms_norm_affine(residual_ref, weight0_ref, (hidden_size,), eps=epsilon) if not tied_norm: out1_pt = fused_rms_norm_affine( residual_pt.to(dtype=weight_dtype), weight1_pt, (hidden_size,), eps=epsilon ).to(dtype=input_dtype) out1_ref = fused_rms_norm_affine(residual_ref, weight1_ref, (hidden_size,), eps=epsilon) assert (out0 - out0_ref).abs().max() <= 4 * (out0_pt - out0_ref).abs().max() + 1e-4 if not tied_norm: assert (out1 - out1_ref).abs().max() <= 4 * (out1_pt - out1_ref).abs().max() + 1e-4 g0 = torch.randn_like(out0) / batch_size if tied_norm: out0.backward(g0) out0_pt.backward(g0) out0_ref.backward(g0) else: g1 = torch.randn_like(out1) / batch_size (out0 * g0 + out1 * g1).sum().backward() (out0_pt * g0 + out1_pt * g1).sum().backward() (out0_ref * g0 + out1_ref * g1).sum().backward() assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4 if has_x1: assert (x1.grad - x1_ref.grad).abs().max() <= 4 * ( x1_pt.grad - x1_ref.grad ).abs().max() + 1e-4 if has_residual: assert (res.grad - res_ref.grad).abs().max() <= 4 * ( res_pt.grad - res_ref.grad ).abs().max() + 1e-4 assert (weight0.grad - weight0_ref.grad).abs().max() <= 3 * ( weight0_pt.grad - weight0_ref.grad ).abs().max() + 3e-5 if not is_rms_norm: assert (bias0.grad - bias0_ref.grad).abs().max() <= 2 * ( bias0_pt.grad - bias0_ref.grad ).abs().max() + 3e-5 if not tied_norm: assert (weight1.grad - weight1_ref.grad).abs().max() <= 3 * ( weight1_pt.grad - weight1_ref.grad ).abs().max() + 3e-5 if not is_rms_norm: assert (bias1.grad - bias1_ref.grad).abs().max() <= 2 * ( bias1_pt.grad - bias1_ref.grad ).abs().max() + 3e-5 @pytest.mark.parametrize("is_rms_norm", [False, True]) # @pytest.mark.parametrize('is_rms_norm', [False]) @pytest.mark.parametrize("tied_norm", [False, True]) # @pytest.mark.parametrize('tied_norm', [False]) @pytest.mark.parametrize("has_residual", [True, False]) # @pytest.mark.parametrize('has_residual', [False]) @pytest.mark.parametrize("has_x1", [True, False]) # @pytest.mark.parametrize('has_x1', [True]) @pytest.mark.parametrize("dropout_p", [0.37, 0.0]) # @pytest.mark.parametrize('dropout_p', [0.0]) @pytest.mark.parametrize("weight_dtype", [torch.float32, torch.float16]) # @pytest.mark.parametrize('weight_dtype', [torch.float16]) @pytest.mark.parametrize( "input_dtype,residual_dtype", [(torch.float16, torch.float16), (torch.float16, torch.float32), (torch.float32, torch.float32)] + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []), ) # @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float16, torch.float32)]) @pytest.mark.parametrize( "hidden_size", [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144], ) # @pytest.mark.parametrize('hidden_size', [256]) def test_dropout_layer_norm_parallel_residual_prenorm_training( hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p, has_x1, has_residual, tied_norm, is_rms_norm, ): if weight_dtype == torch.float16 and input_dtype == torch.bfloat16: pytest.skip() # Not supported if is_rms_norm and fused_rms_norm_affine is None: pytest.skip() # We need Apex's FusedRMSNorm to test our_layer_norm_func = ( dropout_add_layer_norm_parallel_residual if not is_rms_norm else dropout_add_rms_norm_parallel_residual ) device = "cuda" # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4) rtol, atol = (1e-3, 1e-4) # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 x0_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x0 = x0_pt.detach().clone().requires_grad_() x0_ref = x0_pt.detach().clone().float().requires_grad_() if has_x1: x1_pt = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=input_dtype, requires_grad=True ) x1 = x1_pt.detach().clone().requires_grad_() x1_ref = x1_pt.detach().clone().float().requires_grad_() else: x1 = None if has_residual: res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True) res = res_pt.detach().clone().requires_grad_() res_ref = res_pt.detach().clone().float().requires_grad_() else: res = None weight0 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) bias0 = ( torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) if not is_rms_norm else None ) weight0_pt = weight0.detach().clone().requires_grad_() weight0_ref = weight0.detach().clone().float().requires_grad_() bias0_pt = bias0.detach().clone().requires_grad_() if bias0 is not None else None bias0_ref = bias0.detach().clone().float().requires_grad_() if bias0 is not None else None if not tied_norm: weight1 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) bias1 = ( torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True) if not is_rms_norm else None ) weight1_pt = weight1.detach().clone().requires_grad_() weight1_ref = weight1.detach().clone().float().requires_grad_() bias1_pt = bias1.detach().clone().requires_grad_() if bias1 is not None else None bias1_ref = bias1.detach().clone().float().requires_grad_() if bias1 is not None else None else: weight1, bias1 = None, None epsilon = 1e-5 residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32 out0, out1, residual, dmask0, dmask1 = our_layer_norm_func( x0, x1, res, weight0, bias0, weight1, bias1, dropout_p, epsilon, prenorm=True, residual_in_fp32=residual_in_fp32, return_dropout_mask=True, ) assert out0.dtype == input_dtype if not tied_norm: assert out1.dtype == input_dtype print(f"Actual dropout fraction: {1 - dmask0.float().mean().item()}") if has_residual: if has_x1: residual_pt = ( (x0_pt.float() * dmask0.float()) / (1 - dropout_p) + (x1_pt.float() * dmask1.float()) / (1 - dropout_p) + res_pt.float() ).to(dtype=residual_dtype) residual_ref = ( (x0_ref * dmask0.float()) / (1 - dropout_p) + (x1_ref * dmask1.float()) / (1 - dropout_p) ) + res_ref else: residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p) + res_pt.float()).to( dtype=residual_dtype ) residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) + res_ref else: if has_x1: residual_pt = ( (x0_pt.float() * dmask0.float()) / (1 - dropout_p) + (x1_pt.float() * dmask1.float()) / (1 - dropout_p) ).to(dtype=residual_dtype) residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) + ( x1_ref * dmask1.float() ) / (1 - dropout_p) else: residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)).to( dtype=residual_dtype ) residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) if not is_rms_norm: out0_pt = F.layer_norm( residual_pt.to(dtype=weight_dtype), (hidden_size,), weight0_pt, bias0_pt, eps=epsilon ).to(dtype=input_dtype) out0_ref = F.layer_norm(residual_ref, (hidden_size,), weight0_ref, bias0_ref, eps=epsilon) if not tied_norm: out1_pt = F.layer_norm( residual_pt.to(dtype=weight_dtype), (hidden_size,), weight1_pt, bias1_pt, eps=epsilon, ).to(dtype=input_dtype) out1_ref = F.layer_norm( residual_ref, (hidden_size,), weight1_ref, bias1_ref, eps=epsilon ) else: out0_pt = fused_rms_norm_affine( residual_pt.to(dtype=weight_dtype), weight0_pt, (hidden_size,), eps=epsilon ).to(dtype=input_dtype) out0_ref = fused_rms_norm_affine(residual_ref, weight0_ref, (hidden_size,), eps=epsilon) if not tied_norm: out1_pt = fused_rms_norm_affine( residual_pt.to(dtype=weight_dtype), weight1_pt, (hidden_size,), eps=epsilon ).to(dtype=input_dtype) out1_ref = fused_rms_norm_affine(residual_ref, weight1_ref, (hidden_size,), eps=epsilon) assert (out0 - out0_ref).abs().max() <= 4 * (out0_pt - out0_ref).abs().max() + 1e-4 if not tied_norm: assert (out1 - out1_ref).abs().max() <= 4 * (out1_pt - out1_ref).abs().max() + 1e-4 assert (residual - residual_ref).abs().max() <= 4 * ( residual_pt - residual_ref ).abs().max() + 1e-4 g0 = torch.randn_like(out0) / batch_size if tied_norm: (out0 * F.sigmoid(residual)).backward(g0) (out0_pt * F.sigmoid(residual_pt)).backward(g0) (out0_ref * F.sigmoid(residual_ref)).backward(g0) else: g1 = torch.randn_like(out1) / batch_size (out0 * F.sigmoid(residual) * g0 + out1 * g1).sum().backward() (out0_pt * F.sigmoid(residual_pt) * g0 + out1_pt * g1).sum().backward() (out0_ref * F.sigmoid(residual_ref) * g0 + out1_ref * g1).sum().backward() assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4 if has_x1: assert (x1.grad - x1_ref.grad).abs().max() <= 4 * ( x1_pt.grad - x1_ref.grad ).abs().max() + 1e-4 if has_residual: assert (res.grad - res_ref.grad).abs().max() <= 4 * ( res_pt.grad - res_ref.grad ).abs().max() + 1e-4 assert (weight0.grad - weight0_ref.grad).abs().max() <= 3 * ( weight0_pt.grad - weight0_ref.grad ).abs().max() + 3e-5 if not is_rms_norm: assert (bias0.grad - bias0_ref.grad).abs().max() <= 2 * ( bias0_pt.grad - bias0_ref.grad ).abs().max() + 3e-5 if not tied_norm: assert (weight1.grad - weight1_ref.grad).abs().max() <= 3 * ( weight1_pt.grad - weight1_ref.grad ).abs().max() + 3e-5 if not is_rms_norm: assert (bias1.grad - bias1_ref.grad).abs().max() <= 2 * ( bias1_pt.grad - bias1_ref.grad ).abs().max() + 3e-5 def test_dropout_layer_norm_randomness(): hidden_size = 256 dtype = torch.float32 dropout_p = 0.1 device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 8 seqlen = 512 x0 = torch.randn( batch_size, seqlen, hidden_size, device=device, dtype=dtype, requires_grad=True ) res = torch.randn_like(x0, dtype=dtype, requires_grad=True) model = DropoutAddLayerNorm(hidden_size, p=dropout_p, device=device, dtype=dtype) torch.random.manual_seed(42) _, dmask0 = dropout_add_layer_norm( x0, res, model.weight, model.bias, model.p, model.eps, return_dropout_mask=True ) # Subsequent call should have a different dropout mask _, dmask1 = dropout_add_layer_norm( x0, res, model.weight, model.bias, model.p, model.eps, return_dropout_mask=True ) torch.random.manual_seed(42) # Resetting the seed, should get the same dropout mask _, dmask2 = dropout_add_layer_norm( x0, res, model.weight, model.bias, model.p, model.eps, return_dropout_mask=True ) assert not torch.equal(dmask0, dmask1) assert torch.equal(dmask0, dmask2)